Applied Sciences

13 pages, 1477 KiB

Open AccessEditor’s ChoiceArticle

Assessment of Natural Radioactivity and Radiological Risks in River Sediments from Calabria (Southern Italy)

by Francesco Caridi, Marcella Di Bella, Giuseppe Sabatino, Giovanna Belmusto, Maria Rita Fede, Davide Romano, Francesco Italiano and Antonio Francesco Mottese

Appl. Sci. 2021, 11(4), 1729; https://doi.org/10.3390/app11041729 - 15 Feb 2021

Cited by 28 | Viewed by 3017

Abstract

This study was developed to carry out a comprehensive radiological assessment of natural radioactivity for river sediment samples from Calabria, southern Italy, and to define a baseline background for the area on a radiation map. In the studied area, elevated levels of natural [...] Read more.

This study was developed to carry out a comprehensive radiological assessment of natural radioactivity for river sediment samples from Calabria, southern Italy, and to define a baseline background for the area on a radiation map. In the studied area, elevated levels of natural radionuclides are expected, due to the outcropping acidic intrusive and metamorphic rocks from which the radioactive elements derive. To identify and quantify the natural radioisotopes, ninety river sediment samples from nine selected coastal sampling points (ten samples for each point) were collected as representative of the Ionian and the Tyrrhenian coastline of Calabria. The samples were analyzed using a gamma ray spectrometer equipped with a high-purity germanium (HPGe) detector. The values of mean activity concentrations of ²²⁶Ra, ²³²Th and ⁴⁰K measured for the studied samples are (21.3 ± 6.3) Bq kg⁻¹, (30.3 ± 4.5) Bq kg⁻¹ and (849 ± 79) Bq kg⁻¹, respectively. The calculated radiological hazard indices showed average values of 63 nGy h⁻¹ (absorbed dose rate), 0.078 mSv y⁻¹ (effective dose outdoors), 0.111 mSv y⁻¹ (effective dose indoors), 63 Bq kg⁻¹ (radium equivalent), 0.35 (H_ex), 0.41 (H_in), 0.50 (activity concentration index) and 458 µSv y⁻¹ (Annual Gonadal Equivalent Dose, AGED). In order to delineate the spatial distribution of natural radionuclides on the radiological map and to identify the areas with low, medium and high radioactivity values, the Surfer 10 software was employed. Finally, the multivariate statistical analysis was performed to deduce the interdependency and any existing relationships between the radiological indices and the concentrations of the radionuclides. The results of this study, also compared with values of other locations of the Italian Peninsula characterized by similar local geological conditions, can be used as a baseline for future investigations about radioactivity background in the investigated area. Full article

(This article belongs to the Special Issue Advances in Environmental Applied Physics)

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17 pages, 1454 KiB

Open AccessEditor’s ChoiceReview

Nano-Elicitation as an Effective and Emerging Strategy for In Vitro Production of Industrially Important Flavonoids

by Amna Komal Khan, Sidra Kousar, Duangjai Tungmunnithum, Christophe Hano, Bilal Haider Abbasi and Sumaira Anjum

Appl. Sci. 2021, 11(4), 1694; https://doi.org/10.3390/app11041694 - 14 Feb 2021

Cited by 36 | Viewed by 5131

Abstract

Flavonoids represent a popular class of industrially important bioactive compounds. They possess valuable health-benefiting and disease preventing properties, and therefore they are an important component of the pharmaceutical, nutraceutical, cosmetical and medicinal industries. Moreover, flavonoids possess significant antiallergic, antihepatotoxic, anti-inflammatory, antioxidant, antitumor, antiviral, [...] Read more.

Flavonoids represent a popular class of industrially important bioactive compounds. They possess valuable health-benefiting and disease preventing properties, and therefore they are an important component of the pharmaceutical, nutraceutical, cosmetical and medicinal industries. Moreover, flavonoids possess significant antiallergic, antihepatotoxic, anti-inflammatory, antioxidant, antitumor, antiviral, and antibacterial as well as cardio-protective activities. Due to these properties, there is a rise in global demand for flavonoids, forming a significant part of the world market. However, obtaining flavonoids directly from plants has some limitations, such as low quantity, poor extraction, over-exploitation, time consuming process and loss of flora. Henceforth, there is a shift towards the in vitro production of flavonoids using the plant tissue culture technique to achieve better yields in less time. In order to achieve the productivity of flavonoids at an industrially competitive level, elicitation is a useful tool. The elicitation of in vitro cultures induces stressful conditions to plants, activates the plant defense system and enhances the accumulation of secondary metabolites in higher quantities. In this regard, nanoparticles (NPs) have emerged as novel and effective elicitors for enhancing the in vitro production of industrially important flavonoids. Different classes of NPs, including metallic NPs (silver and copper), metallic oxide NPs (copper oxide, iron oxide, zinc oxide, silicon dioxide) and carbon nanotubes, are widely reported as nano-elicitors of flavonoids discussed herein. Lastly, the mechanisms of NPs as well as knowledge gaps in the area of the nano-elicitation of flavonoids have been highlighted in this review. Full article

(This article belongs to the Special Issue Industrial Applications of Flavonoids: Current Uses and Future Trends)

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21 pages, 595 KiB

Open AccessEditor’s ChoiceArticle

Intelligent Cyber Attack Detection and Classification for Network-Based Intrusion Detection Systems

by Nuno Oliveira, Isabel Praça, Eva Maia and Orlando Sousa

Appl. Sci. 2021, 11(4), 1674; https://doi.org/10.3390/app11041674 - 13 Feb 2021

Cited by 68 | Viewed by 8571

Abstract

With the latest advances in information and communication technologies, greater amounts of sensitive user and corporate information are shared continuously across the network, making it susceptible to an attack that can compromise data confidentiality, integrity, and availability. Intrusion Detection Systems (IDS) are important [...] Read more.

With the latest advances in information and communication technologies, greater amounts of sensitive user and corporate information are shared continuously across the network, making it susceptible to an attack that can compromise data confidentiality, integrity, and availability. Intrusion Detection Systems (IDS) are important security mechanisms that can perform the timely detection of malicious events through the inspection of network traffic or host-based logs. Many machine learning techniques have proven to be successful at conducting anomaly detection throughout the years, but only a few considered the sequential nature of data. This work proposes a sequential approach and evaluates the performance of a Random Forest (RF), a Multi-Layer Perceptron (MLP), and a Long-Short Term Memory (LSTM) on the CIDDS-001 dataset. The resulting performance measures of this particular approach are compared with the ones obtained from a more traditional one, which only considers individual flow information, in order to determine which methodology best suits the concerned scenario. The experimental outcomes suggest that anomaly detection can be better addressed from a sequential perspective. The LSTM is a highly reliable model for acquiring sequential patterns in network traffic data, achieving an accuracy of 99.94% and an f1-score of 91.66%. Full article

(This article belongs to the Special Issue Machine Learning for Cybersecurity Threats, Challenges, and Opportunities)

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24 pages, 1868 KiB

Open AccessEditor’s ChoiceReview

Wind-Induced Phenomena in Long-Span Cable-Supported Bridges: A Comparative Review of Wind Tunnel Tests and Computational Fluid Dynamics Modelling

by Yuxiang Zhang, Philip Cardiff and Jennifer Keenahan

Appl. Sci. 2021, 11(4), 1642; https://doi.org/10.3390/app11041642 - 11 Feb 2021

Cited by 23 | Viewed by 5271

Abstract

Engineers, architects, planners and designers must carefully consider the effects of wind in their work. Due to their slender and flexible nature, long-span bridges can often experience vibrations due to the wind, and so the careful analysis of wind effects is paramount. Traditionally, [...] Read more.

Engineers, architects, planners and designers must carefully consider the effects of wind in their work. Due to their slender and flexible nature, long-span bridges can often experience vibrations due to the wind, and so the careful analysis of wind effects is paramount. Traditionally, wind tunnel tests have been the preferred method of conducting bridge wind analysis. In recent times, owing to improved computational power, computational fluid dynamics simulations are coming to the fore as viable means of analysing wind effects on bridges. The focus of this paper is on long-span cable-supported bridges. Wind issues in long-span cable-supported bridges can include flutter, vortex-induced vibrations and rain–wind-induced vibrations. This paper presents a state-of-the-art review of research on the use of wind tunnel tests and computational fluid dynamics modelling of these wind issues on long-span bridges. Full article

(This article belongs to the Special Issue Applications of Computational Fluid Dynamics to the Built Environment)

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14 pages, 5613 KiB

Open AccessEditor’s ChoiceArticle

Hybrid Metal/Polymer Filaments for Fused Filament Fabrication (FFF) to Print Metal Parts

by Claudio Tosto, Jacopo Tirillò, Fabrizio Sarasini and Gianluca Cicala

Appl. Sci. 2021, 11(4), 1444; https://doi.org/10.3390/app11041444 - 5 Feb 2021

Cited by 81 | Viewed by 6717

Abstract

The exploitation of mechanical properties and customization possibilities of 3D printed metal parts usually come at the cost of complex and expensive equipment. To address this issue, hybrid metal/polymer composite filaments have been studied allowing the printing of metal parts by using the [...] Read more.

The exploitation of mechanical properties and customization possibilities of 3D printed metal parts usually come at the cost of complex and expensive equipment. To address this issue, hybrid metal/polymer composite filaments have been studied allowing the printing of metal parts by using the standard Fused Filament Fabrication (FFF) approach. The resulting hybrid metal/polymer part, the so called “green”, can then be transformed into a dense metal part using debinding and sintering cycles. In this work, we investigated the manufacturing and characterization of green and sintered parts obtained by FFF of two commercial hybrid metal/polymer filaments, i.e., the Ultrafuse 316L by BASF and the 17-4 PH by Markforged. The Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray Spectrometry (EDS) analyses of the mesostructure highlighted incomplete raster bonding and voids like those observed in conventional FFF-printed polymeric structures despite the sintering cycle. A significant role in the tensile properties was played by the building orientation, with samples printed flatwise featuring the highest mechanical properties, though lower than those achievable with standard metal additive manufacturing techniques. Full article

(This article belongs to the Special Issue Design, Synthesis and Characterization of Hybrid Composite Materials)

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Graphical abstract

32 pages, 6280 KiB

Open AccessEditor’s ChoiceArticle

A Serious Gaming Approach for Crowdsensing in Urban Water Infrastructure with Blockchain Support

by Alexandru Predescu, Diana Arsene, Bogdan Pahonțu, Mariana Mocanu and Costin Chiru

Appl. Sci. 2021, 11(4), 1449; https://doi.org/10.3390/app11041449 - 5 Feb 2021

Cited by 24 | Viewed by 4034

Abstract

This paper presents the current state of the gaming industry, which provides an important background for an effective serious game implementation in mobile crowdsensing. An overview of existing solutions, scientific studies and market research highlights the current trends and the potential applications for [...] Read more.

This paper presents the current state of the gaming industry, which provides an important background for an effective serious game implementation in mobile crowdsensing. An overview of existing solutions, scientific studies and market research highlights the current trends and the potential applications for citizen-centric platforms in the context of Cyber–Physical–Social systems. The proposed solution focuses on serious games applied in urban water management from the perspective of mobile crowdsensing, with a reward-driven mechanism defined for the crowdsensing tasks. The serious game is designed to provide entertainment value by means of gamified interaction with the environment, while the crowdsensing component involves a set of roles for finding, solving and validating water-related issues. The mathematical model of distance-constrained multi-depot vehicle routing problem with heterogeneous fleet capacity is evaluated in the context of the proposed scenario, with random initial conditions given by the location of players, while the Vickrey–Clarke–Groves auction model provides an alternative to the centralized task allocation strategy, subject to the same evaluation method. A blockchain component based on the Hyperledger Fabric architecture provides the level of trust required for achieving overall platform utility for different stakeholders in mobile crowdsensing. Full article

(This article belongs to the Special Issue Secure and Intelligent Mobile Systems)

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22 pages, 3477 KiB

Open AccessEditor’s ChoiceReview

Current Applications of Ultrasound in Fruit and Vegetables Osmotic Dehydration Processes

by Małgorzata Nowacka, Magdalena Dadan and Urszula Tylewicz

Appl. Sci. 2021, 11(3), 1269; https://doi.org/10.3390/app11031269 - 30 Jan 2021

Cited by 36 | Viewed by 8732

Abstract

Ultrasound (US) is a promising technology, which can be used to improve the efficacy of the processes in food technology and the quality of final product. US technique is used, e.g., to support mass and heat transfer processes, such as osmotic dehydration, drying [...] Read more.

Ultrasound (US) is a promising technology, which can be used to improve the efficacy of the processes in food technology and the quality of final product. US technique is used, e.g., to support mass and heat transfer processes, such as osmotic dehydration, drying and freezing, as well as extraction, crystallization, emulsification, filtration, etc. Osmotic dehydration (OD) is a well-known process applied in food processing; however, improvements are required due to the long duration of the process. Therefore, many recent studies focus on the development of OD combined with sonication as a pretreatment method and support during the OD process. The article describes the mechanism of the OD process as well as those of US and changes in microstructure caused by sonication. Furthermore, it focuses on current applications of US in fruits and vegetables OD processes, comparison of ultrasound-assisted osmotic dehydration to sonication treatment and synergic effect of US and other innovative technics/treatments in OD (such as innovative osmotic solutions, blanching, pulsed electric field, reduced pressure and edible coatings). Additionally, the physical and functional properties of tissue subjected to ultrasound pretreatment before OD as well as ultrasound-assisted osmotic dehydration are described. Full article

(This article belongs to the Special Issue Drying Technologies in Food Processing)

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22 pages, 2690 KiB

Open AccessEditor’s ChoiceArticle

Deep Learning Method for Fault Detection of Wind Turbine Converter

by Cheng Xiao, Zuojun Liu, Tieling Zhang and Xu Zhang

Appl. Sci. 2021, 11(3), 1280; https://doi.org/10.3390/app11031280 - 30 Jan 2021

Cited by 62 | Viewed by 5745

Abstract

The converter is an important component in wind turbine power drive-train systems, and usually, it has a higher failure rate. Therefore, detecting the potential faults for prediction of its failure has become indispensable for condition-based maintenance and operation of wind turbines. This paper [...] Read more.

The converter is an important component in wind turbine power drive-train systems, and usually, it has a higher failure rate. Therefore, detecting the potential faults for prediction of its failure has become indispensable for condition-based maintenance and operation of wind turbines. This paper presents an approach to wind turbine converter fault detection using convolutional neural network models which are developed by using wind turbine Supervisory Control and Data Acquisition (SCADA) system data. The approach starts with the selection of fault indicator variables, and then the fault indicator variables data are extracted from a wind turbine SCADA system. Using the data, radar charts are generated, and the convolutional neural network models are applied to feature extraction from the radar charts and characteristic analysis of the feature for fault detection. Based on the analysis of the Octave Convolution (OctConv) network structure, an improved AOctConv (Attention Octave Convolution) structure is proposed in this paper, and it is applied to the ResNet50 backbone network (named as AOC–ResNet50). It is found that the algorithm based on AOC–ResNet50 overcomes the issues of information asymmetry caused by the asymmetry of the sampling method and the damage to the original features in the high and low frequency domains by the OctConv structure. Finally, the AOC–ResNet50 network is employed for fault detection of the wind turbine converter using 10 min SCADA system data. It is verified that the fault detection accuracy using the AOC–ResNet50 network is up to 98.0%, which is higher than the fault detection accuracy using the ResNet50 and Oct–ResNet50 networks. Therefore, the effectiveness of the AOC–ResNet50 network model in wind turbine converter fault detection is identified. The novelty of this paper lies in a novel AOC–ResNet50 network proposed and its effectiveness in wind turbine fault detection. This was verified through a comparative study on wind turbine power converter fault detection with other competitive convolutional neural network models for deep learning. Full article

(This article belongs to the Section Mechanical Engineering)

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20 pages, 1162 KiB

Open AccessEditor’s ChoiceReview

Polyphenols: A Promising Avenue in Therapeutic Solutions for Wound Care

by Inês Guimarães, Sara Baptista-Silva, Manuela Pintado and Ana L. Oliveira

Appl. Sci. 2021, 11(3), 1230; https://doi.org/10.3390/app11031230 - 29 Jan 2021

Cited by 53 | Viewed by 6806

Abstract

In chronic wounds, the regeneration process is compromised, which brings complexity to the therapeutic approaches that need to be adopted, while representing an enormous loss in the patients’ quality of life with consequent economical costs. Chronic wounds are highly prone to infection, which [...] Read more.

In chronic wounds, the regeneration process is compromised, which brings complexity to the therapeutic approaches that need to be adopted, while representing an enormous loss in the patients’ quality of life with consequent economical costs. Chronic wounds are highly prone to infection, which can ultimately lead to septicemia and morbidity. Classic therapies are increasing antibiotic resistance, which is becoming a critical problem beyond complex wounds. Therefore, it is essential to study new antimicrobial polymeric systems and compounds that can be effective alternatives to reduce infection, even at lower concentrations. The biological potential of polyphenols allows them to be an efficient alternative to commercial antibiotics, responding to the need to find new options for chronic wound care. Nonetheless, phenolic compounds may have some drawbacks when targeting wound applications, such as low stability and consequent decreased biological performance at the wound site. To overcome these limitations, polymeric-based systems have been developed as carriers of polyphenols for wound healing, improving its stability, controlling the release kinetics, and therefore increasing the performance and effectiveness. This review aims to highlight possible smart and bio-based wound dressings, providing an overview of the biological potential of polyphenolic agents as natural antimicrobial agents and strategies to stabilize and deliver them in the treatment of complex wounds. Polymer-based particulate systems are highlighted here due to their impact as carriers to increase polyphenols bioavailability at the wound site in different types of formulations. Full article

(This article belongs to the Special Issue Advances in Antimicrobial Sustainable Polymers)

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15 pages, 2338 KiB

Open AccessEditor’s ChoiceArticle

Attention-Based Transfer Learning for Efficient Pneumonia Detection in Chest X-ray Images

by So-Mi Cha, Seung-Seok Lee and Bonggyun Ko

Appl. Sci. 2021, 11(3), 1242; https://doi.org/10.3390/app11031242 - 29 Jan 2021

Cited by 25 | Viewed by 5217

Abstract

Pneumonia is a form of acute respiratory infection commonly caused by germs, viruses, and fungi, and can prove fatal at any age. Chest X-rays is the most common technique for diagnosing pneumonia. There have been several attempts to apply transfer learning based on [...] Read more.

Pneumonia is a form of acute respiratory infection commonly caused by germs, viruses, and fungi, and can prove fatal at any age. Chest X-rays is the most common technique for diagnosing pneumonia. There have been several attempts to apply transfer learning based on a Convolutional Neural Network to build a stable model in computer-aided diagnosis. Recently, with the appearance of an attention mechanism that automatically focuses on the critical part of the image that is crucial for the diagnosis of disease, it is possible to increase the performance of previous models. The goal of this study is to improve the accuracy of a computer-aided diagnostic approach that medical professionals can easily use as an auxiliary tool. In this paper, we proposed the attention-based transfer learning framework for efficient pneumonia detection in chest X-ray images. We collected features from three-types of pre-trained models, ResNet152, DenseNet121, ResNet18 as a role of feature extractor. We redefined the classifier for a new task and applied the attention mechanism as a feature selector. As a result, the proposed approach achieved accuracy, F-score, Area Under the Curve(AUC), precision and recall of 96.63%, 0.973, 96.03%, 96.23% and 98.46%, respectively. Full article

(This article belongs to the Special Issue Medical Artificial Intelligence)

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31 pages, 29073 KiB

Open AccessEditor’s ChoiceReview

Advances in Metal Additive Manufacturing: A Review of Common Processes, Industrial Applications, and Current Challenges

by Ana Vafadar, Ferdinando Guzzomi, Alexander Rassau and Kevin Hayward

Appl. Sci. 2021, 11(3), 1213; https://doi.org/10.3390/app11031213 - 28 Jan 2021

Cited by 357 | Viewed by 37855

Abstract

In recent years, Additive Manufacturing (AM), also called 3D printing, has been expanding into several industrial sectors due to the technology providing opportunities in terms of improved functionality, productivity, and competitiveness. While metal AM technologies have almost unlimited potential, and the range of [...] Read more.

In recent years, Additive Manufacturing (AM), also called 3D printing, has been expanding into several industrial sectors due to the technology providing opportunities in terms of improved functionality, productivity, and competitiveness. While metal AM technologies have almost unlimited potential, and the range of applications has increased in recent years, industries have faced challenges in the adoption of these technologies and coping with a turbulent market. Despite the extensive work that has been completed on the properties of metal AM materials, there is still a need of a robust understanding of processes, challenges, application-specific needs, and considerations associated with these technologies. Therefore, the goal of this study is to present a comprehensive review of the most common metal AM technologies, an exploration of metal AM advancements, and industrial applications for the different AM technologies across various industry sectors. This study also outlines current limitations and challenges, which prevent industries to fully benefit from the metal AM opportunities, including production volume, standards compliance, post processing, product quality, maintenance, and materials range. Overall, this paper provides a survey as the benchmark for future industrial applications and research and development projects, in order to assist industries in selecting a suitable AM technology for their application. Full article

(This article belongs to the Special Issue Additive Manufacturing and System: From Methods to Applications)

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16 pages, 2357 KiB

Open AccessEditor’s ChoiceArticle

Arundo donax L. Biomass Production in a Polluted Area: Effects of Two Harvest Timings on Heavy Metals Uptake

by Tommaso Danelli, Alessio Sepulcri, Giacomo Masetti, Federico Colombo, Stefano Sangiorgio, Elena Cassani, Simone Anelli, Fabrizio Adani and Roberto Pilu

Appl. Sci. 2021, 11(3), 1147; https://doi.org/10.3390/app11031147 - 27 Jan 2021

Cited by 28 | Viewed by 3474

Abstract

Within the framework of energy biomass production, Arundo donax L. is very promising for its capability to grow on marginal lands with high yields. This potential can be realized in unused polluted areas where the energy production can be coupled with phytoremediation, and [...] Read more.

Within the framework of energy biomass production, Arundo donax L. is very promising for its capability to grow on marginal lands with high yields. This potential can be realized in unused polluted areas where the energy production can be coupled with phytoremediation, and harvested biomass represents a resource and a means to remove contaminants from the soil. Two main processes are considered to evaluate A. donax L. biomass as an energy crop, determined by the timing of harvest: anaerobic digestion with fresh biomass before winter and combustion (e.g., pyrolysis and gasification) of dry canes in late winter. The aim of this work was to evaluate the use of A. donax L. in an area polluted by heavy metals for phytoextraction and energy production at two different harvest times (October and February). For that purpose, we established in polluted area in northern Italy (Caffaro area, Brescia) an experimental field of A. donax, and included switchgrass (Panicum virgatum L.) and mixed meadow species as controls. The results obtained by ICP-MS analysis performed on harvested biomasses highlighted a differential uptake of heavy metals depending on harvest time. In particular, considering the yield in the third year, A. donax was able to remove from the soil 3.87 kg ha⁻¹ of Zn, 2.09 kg ha⁻¹ of Cu and 0.007 kg ha⁻¹ of Cd when harvested in October. Production of A. donax L. for anaerobic digestion or combustion in polluted areas represents a potential solution for both energy production and phytoextraction of heavy metals, in particular Cu, Zn and Cd. Full article

(This article belongs to the Special Issue Heavy Metals in the Environment – Causes and Consequences)

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12 pages, 2681 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Novel Derivatives of 4-Methyl-1,2,3-Thiadiazole-5-Carboxylic Acid Hydrazide: Synthesis, Lipophilicity, and In Vitro Antimicrobial Activity Screening

by Kinga Paruch, Łukasz Popiołek, Anna Biernasiuk, Anna Berecka-Rycerz, Anna Malm, Anna Gumieniczek and Monika Wujec

Appl. Sci. 2021, 11(3), 1180; https://doi.org/10.3390/app11031180 - 27 Jan 2021

Cited by 16 | Viewed by 2970

Abstract

Bacterial infections, especially those caused by strains resistant to commonly used antibiotics and chemotherapeutics, are still a current threat to public health. Therefore, the search for new molecules with potential antimicrobial activity is an important research goal. In this article, we present the [...] Read more.

Bacterial infections, especially those caused by strains resistant to commonly used antibiotics and chemotherapeutics, are still a current threat to public health. Therefore, the search for new molecules with potential antimicrobial activity is an important research goal. In this article, we present the synthesis and evaluation of the in vitro antimicrobial activity of a series of 15 new derivatives of 4-methyl-1,2,3-thiadiazole-5-carboxylic acid. The potential antimicrobial effect of the new compounds was observed mainly against Gram-positive bacteria. Compound 15, with the 5-nitro-2-furoyl moiety, showed the highest bioactivity: minimum inhibitory concentration (MIC) = 1.95–15.62 µg/mL and minimum bactericidal concentration (MBC)/MIC = 1–4 µg/mL. Full article

(This article belongs to the Special Issue Synthesis of Novel Heterocyclic Compounds and Evaluation of Their Antimicrobial Activity)

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25 pages, 13006 KiB

Open AccessEditor’s ChoiceReview

Recent Advances in Acoustics of Transitional Airfoils with Feedback-Loop Interactions: A Review

by Vladimir Golubev

Appl. Sci. 2021, 11(3), 1057; https://doi.org/10.3390/app11031057 - 25 Jan 2021

Cited by 16 | Viewed by 3502

Abstract

We discuss herein recent experimental and numerical studies examining resonant flow-acoustic feedback–loop interactions in transitional airfoils (i.e., possessing a notable area of laminar-to-turbulent boundary-layer transition) characteristic of low-to-medium Reynolds number flow regimes. Such interactions are commonly attributed to the viscous dynamics of the [...] Read more.

We discuss herein recent experimental and numerical studies examining resonant flow-acoustic feedback–loop interactions in transitional airfoils (i.e., possessing a notable area of laminar-to-turbulent boundary-layer transition) characteristic of low-to-medium Reynolds number flow regimes. Such interactions are commonly attributed to the viscous dynamics of the convected boundary-layer structures scattering into acoustic waves at the trailing edge which propagate upstream and re-excite the convected vortical structures. While it has been long suspected that the acoustic feedback mechanism is responsible for the highly pronounced, often multi-tonal response, the exact reason of how the boundary-layer instability structures could reach a sufficient degree of amplification to sustain the feedback-loop process and exhibit specific tonal signature remained unclear. This review thus pays particular attention to the critical role of the separation bubble in the feedback process and emphasizes the complementary roles of the experimental and numerical works in elucidating an intricate connection between the airfoil radiated tonal acoustic signature and the properties of the separation zones as determined by airfoil geometry and flow regimes. Full article

(This article belongs to the Special Issue Airframe Noise and Airframe/Propulsion Integration)

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42 pages, 15316 KiB

Open AccessEditor’s ChoiceArticle

Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview

by Davide Bombarda, Giorgio Matteo Vitetta and Giovanni Ferrante

Appl. Sci. 2021, 11(3), 1071; https://doi.org/10.3390/app11031071 - 25 Jan 2021

Cited by 53 | Viewed by 11104

Abstract

Rail tracks undergo massive stresses that can affect their structural integrity and produce rail breakage. The last phenomenon represents a serious concern for railway management authorities, since it may cause derailments and, consequently, losses of rolling stock material and lives. Therefore, the activities [...] Read more.

Rail tracks undergo massive stresses that can affect their structural integrity and produce rail breakage. The last phenomenon represents a serious concern for railway management authorities, since it may cause derailments and, consequently, losses of rolling stock material and lives. Therefore, the activities of track maintenance and inspection are of paramount importance. In recent years, the use of various technologies for monitoring rails and the detection of their defects has been investigated; however, despite the important progresses in this field, substantial research efforts are still required to achieve higher scanning speeds and improve the reliability of diagnostic procedures. It is expected that, in the near future, an important role in track maintenance and inspection will be played by the ultrasonic guided wave technology. In this manuscript, its use in rail track monitoring is investigated in detail; moreover, both of the main strategies investigated in the technical literature are taken into consideration. The first strategy consists of the installation of the monitoring instrumentation on board a moving test vehicle that scans the track below while running. The second strategy, instead, is based on distributing the instrumentation throughout the entire rail network, so that continuous monitoring in quasi-real-time can be obtained. In our analysis of the proposed solutions, the prototypes and the employed methods are described. Full article

(This article belongs to the Special Issue Novel Approaches for Structural Health Monitoring)

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26 pages, 5623 KiB

Open AccessEditor’s ChoiceArticle

Mitigation of NaCl Stress in Wheat by Rhizosphere Engineering Using Salt Habitat Adapted PGPR Halotolerant Bacteria

by Souhila Kerbab, Allaoua Silini, Ali Chenari Bouket, Hafsa Cherif-Silini, Manal Eshelli, Nour El Houda Rabhi and Lassaad Belbahri

Appl. Sci. 2021, 11(3), 1034; https://doi.org/10.3390/app11031034 - 24 Jan 2021

Cited by 64 | Viewed by 6158

Abstract

There is a great interest in mitigating soil salinity that limits plant growth and productivity. In this study, eighty-nine strains were isolated from the rhizosphere and endosphere of two halophyte species (Suaeda mollis and Salsola tetrandra) collected from three chotts in [...] Read more.

There is a great interest in mitigating soil salinity that limits plant growth and productivity. In this study, eighty-nine strains were isolated from the rhizosphere and endosphere of two halophyte species (Suaeda mollis and Salsola tetrandra) collected from three chotts in Algeria. They were screened for diverse plant growth-promoting traits, antifungal activity and tolerance to different physico-chemical conditions (pH, PEG, and NaCl) to evaluate their efficiency in mitigating salt stress and enhancing the growth of Arabidopsis thaliana and durum wheat under NaCl–stress conditions. Three bacterial strains BR5, OR15, and RB13 were finally selected and identified as Bacillus atropheus. The Bacterial strains (separately and combined) were then used for inoculating Arabidopsis thaliana and durum wheat during the seed germination stage under NaCl stress conditions. Results indicated that inoculation of both plant spp. with the bacterial strains separately or combined considerably improved the growth parameters. Three soils with different salinity levels (S1 = 0.48, S2 = 3.81, and S3 = 2.80 mS/cm) were used to investigate the effects of selected strains (BR5, OR15, and RB13; separately and combined) on several growth parameters of wheat plants. The inoculation (notably the multi-strain consortium) proved a better approach to increase the chlorophyll and carotenoid contents as compared to control plants. However, proline content, lipid peroxidation, and activities of antioxidant enzymes decreased after inoculation with the plant growth-promoting rhizobacteria (PGPR) that can attenuate the adverse effects of salt stress by reducing the reactive oxygen species (ROS) production. These results indicated that under saline soil conditions, halotolerant PGPR strains are promising candidates as biofertilizers under salt stress conditions. Full article

(This article belongs to the Special Issue Plant Growth Promoting Microorganisms Useful for Soil Desalinization)

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22 pages, 9125 KiB

Open AccessEditor’s ChoiceArticle

A Tool for the Rapid Seismic Assessment of Historic Masonry Structures Based on Limit Analysis Optimisation and Rocking Dynamics

by Marco Francesco Funari, Anjali Mehrotra and Paulo B. Lourenço

Appl. Sci. 2021, 11(3), 942; https://doi.org/10.3390/app11030942 - 21 Jan 2021

Cited by 55 | Viewed by 4835

Abstract

This paper presents a user-friendly, CAD-interfaced methodology for the rapid seismic assessment of historic masonry structures. The proposed multi-level procedure consists of a two-step analysis that combines upper bound limit analysis with non-linear dynamic (rocking) analysis to solve for seismic collapse in a [...] Read more.

This paper presents a user-friendly, CAD-interfaced methodology for the rapid seismic assessment of historic masonry structures. The proposed multi-level procedure consists of a two-step analysis that combines upper bound limit analysis with non-linear dynamic (rocking) analysis to solve for seismic collapse in a computationally-efficient manner. In the first step, the failure mechanisms are defined by means of parameterization of the failure surfaces. Hence, the upper bound limit theorem of the limit analysis, coupled with a heuristic solver, is subsequently adopted to search for the load multiplier’s minimum value and the macro-block geometry. In the second step, the kinematic constants defining the rocking equation of motion are automatically computed for the refined macro-block model, which can be solved for representative time-histories. The proposed methodology has been entirely integrated in the user-friendly visual programming environment offered by Rhinoceros3D + Grasshopper, allowing it to be used by students, researchers and practicing structural engineers. Unlike time-consuming advanced methods of analysis, the proposed method allows users to perform a seismic assessment of masonry buildings in a rapid and computationally-efficient manner. Such an approach is particularly useful for territorial scale vulnerability analysis (e.g., for risk assessment and mitigation historic city centres) or as post-seismic event response (when the safety and stability of a large number of buildings need to be assessed with limited resources). The capabilities of the tool are demonstrated by comparing its predictions with those arising from the literature as well as from code-based assessment methods for three case studies. Full article

(This article belongs to the Special Issue Experimental and Numerical Analysis in Building Rehabilitation and Strengthening)

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20 pages, 3296 KiB

Open AccessEditor’s ChoiceArticle

A Multiobjective Decision-Making Model for Risk-Based Maintenance Scheduling of Railway Earthworks

by Irina Stipanovic, Zaharah Allah Bukhsh, Cormac Reale and Kenneth Gavin

Appl. Sci. 2021, 11(3), 965; https://doi.org/10.3390/app11030965 - 21 Jan 2021

Cited by 12 | Viewed by 5012

Abstract

Aged earthworks constitute a major proportion of European rail infrastructures, the replacement and remediation of which poses a serious problem. Considering the scale of the networks involved, it is infeasible both in terms of track downtime and money to replace all of these [...] Read more.

Aged earthworks constitute a major proportion of European rail infrastructures, the replacement and remediation of which poses a serious problem. Considering the scale of the networks involved, it is infeasible both in terms of track downtime and money to replace all of these assets. It is, therefore, imperative to develop a rational means of managing slope infrastructure to determine the best use of available resources and plan maintenance in order of criticality. To do so, it is necessary to not just consider the structural performance of the asset but also to consider the safety and security of its users, the socioeconomic impact of remediation/failure and the relative importance of the asset to the network. This paper addresses this by looking at maintenance planning on a network level using multi-attribute utility theory (MAUT). MAUT is a methodology that allows one to balance the priorities of different objectives in a harmonious fashion allowing for a holistic means of ranking assets and, subsequently, a rational means of investing in maintenance. In this situation, three different attributes are considered when examining the utility of different maintenance options, namely availability (the user cost), economy (the financial implications) and structural reliability (the structural performance and subsequent safety of the structure). The main impact of this paper is to showcase that network maintenance planning can be carried out proactively in a manner that is balanced against the needs of the organization. Full article

(This article belongs to the Special Issue 10th Anniversary of Applied Sciences Invited Papers in Civil Engineering Section)

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30 pages, 2557 KiB

Open AccessEditor’s ChoiceArticle

Intrinsically Motivated Open-Ended Multi-Task Learning Using Transfer Learning to Discover Task Hierarchy

by Nicolas Duminy, Sao Mai Nguyen, Junshuai Zhu, Dominique Duhaut and Jerome Kerdreux

Appl. Sci. 2021, 11(3), 975; https://doi.org/10.3390/app11030975 - 21 Jan 2021

Cited by 8 | Viewed by 3256

Abstract

In open-ended continuous environments, robots need to learn multiple parameterised control tasks in hierarchical reinforcement learning. We hypothesise that the most complex tasks can be learned more easily by transferring knowledge from simpler tasks, and faster by adapting the complexity of the actions [...] Read more.

In open-ended continuous environments, robots need to learn multiple parameterised control tasks in hierarchical reinforcement learning. We hypothesise that the most complex tasks can be learned more easily by transferring knowledge from simpler tasks, and faster by adapting the complexity of the actions to the task. We propose a task-oriented representation of complex actions, called procedures, to learn online task relationships and unbounded sequences of action primitives to control the different observables of the environment. Combining both goal-babbling with imitation learning, and active learning with transfer of knowledge based on intrinsic motivation, our algorithm self-organises its learning process. It chooses at any given time a task to focus on; and what, how, when and from whom to transfer knowledge. We show with a simulation and a real industrial robot arm, in cross-task and cross-learner transfer settings, that task composition is key to tackle highly complex tasks. Task decomposition is also efficiently transferred across different embodied learners and by active imitation, where the robot requests just a small amount of demonstrations and the adequate type of information. The robot learns and exploits task dependencies so as to learn tasks of every complexity. Full article

(This article belongs to the Special Issue Cognitive Robotics)

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18 pages, 23003 KiB

Open AccessFeature PaperEditor’s ChoiceReview

Review of Dew Point Evaporative Cooling Technology for Air Conditioning Applications

by Anna Pacak and William Worek

Appl. Sci. 2021, 11(3), 934; https://doi.org/10.3390/app11030934 - 20 Jan 2021

Cited by 29 | Viewed by 6226

Abstract

Indirect evaporative cooling has the potential to significantly improve the natural environment. It follows from a significant reduction in electricity consumption in the hot period, and hence lower operating costs for cooling systems. This paper presents the current state of knowledge and research [...] Read more.

Indirect evaporative cooling has the potential to significantly improve the natural environment. It follows from a significant reduction in electricity consumption in the hot period, and hence lower operating costs for cooling systems. This paper presents the current state of knowledge and research directions on dew point indirect evaporative cooling. It was found that researchers focus on the development of dew point indirect evaporative coolers (DPIEC) by improving its design, geometry, water distribution, and new porous materials implementation. To evaluate the performance of new types of DPIEC, different methods are used by the scientists. Finally, optimized devices are studied in terms of their performance in different systems, like hybrid and desiccant systems, considering different climate conditions. Potential directions of development of evaporative technologies were indicated, such as increasing the coefficient of performance of solid desiccant evaporative cooling systems, developing novel geometry, and efficient water distribution, including development of porous materials. Full article

(This article belongs to the Special Issue Fundamentals and Recent Advances in Heating, Ventilation, and Air-Conditioning Systems (HVAC))

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30 pages, 4762 KiB

Open AccessEditor’s ChoiceArticle

Fundamentals of Building Deconstruction as a Circular Economy Strategy for the Reuse of Construction Materials

by Gaetano Bertino, Johannes Kisser, Julia Zeilinger, Guenter Langergraber, Tatjana Fischer and Doris Österreicher

Appl. Sci. 2021, 11(3), 939; https://doi.org/10.3390/app11030939 - 20 Jan 2021

Cited by 85 | Viewed by 22349

Abstract

The construction industry is one of the most environmentally detrimental industries in the world, impacting directly the use of raw materials, their determination of use involving the whole lifecycle, as well as all their surrounding environment. However, within the building sector, the transition [...] Read more.

The construction industry is one of the most environmentally detrimental industries in the world, impacting directly the use of raw materials, their determination of use involving the whole lifecycle, as well as all their surrounding environment. However, within the building sector, the transition from a linear to a circular economy is still at an early stage. Business models need to be reconsidered to include new and improved methods and innovative services that could lead to a net reduction in the use of resources and minimizing the waste disposed on landfills. In this context, an important role in buildings’ circularity is “deconstruction”, which is understood as a well-considered selective dismantlement of building components, in prevision of a future reuse, repurposing, or recycling. It represents a sustainable alternative to common demolition, which tends to be an arbitrary and destructive process, and although faster and cheaper, it typically creates a substantial amount of waste. The purpose of this article is to analyze the deconstruction potential of buildings and the strategies to apply in order to keep the impacts on the urban environment low. The article aims to facilitate the implementation of circular economy strategies for buildings by proposing common principles for deconstruction as a sustainable alternative to demolition and defining the key points to be applied during the design and planning process regardless of the type of construction system or material used. Full article

(This article belongs to the Special Issue Sustainable and Durable Building Materials)

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30 pages, 602 KiB

Open AccessEditor’s ChoiceArticle

Human-Centered Artificial Intelligence for Designing Accessible Cultural Heritage

by Galena Pisoni, Natalia Díaz-Rodríguez, Hannie Gijlers and Linda Tonolli

Appl. Sci. 2021, 11(2), 870; https://doi.org/10.3390/app11020870 - 19 Jan 2021

Cited by 59 | Viewed by 15036

Abstract

This paper reviews the literature concerning technology used for creating and delivering accessible museum and cultural heritage sites experiences. It highlights the importance of the delivery suited for everyone from different areas of expertise, namely interaction design, pedagogical and participatory design, and it [...] Read more.

This paper reviews the literature concerning technology used for creating and delivering accessible museum and cultural heritage sites experiences. It highlights the importance of the delivery suited for everyone from different areas of expertise, namely interaction design, pedagogical and participatory design, and it presents how recent and future artificial intelligence (AI) developments can be used for this aim, i.e.,improving and widening online and in situ accessibility. From the literature review analysis, we articulate a conceptual framework that incorporates key elements that constitute museum and cultural heritage online experiences and how these elements are related to each other. Concrete opportunities for future directions empirical research for accessibility of cultural heritage contents are suggested and further discussed. Full article

(This article belongs to the Special Issue AI and HCI Methods and Techniques for Cultural Heritage Curation, Exploration and Fruition)

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18 pages, 4527 KiB

Open AccessEditor’s ChoiceReview

Meta-Analysis and Systematic Review of the Application of Machine Learning Classifiers in Biomedical Applications of Infrared Thermography

by Carolina Magalhaes, Joaquim Mendes and Ricardo Vardasca

Appl. Sci. 2021, 11(2), 842; https://doi.org/10.3390/app11020842 - 18 Jan 2021

Cited by 30 | Viewed by 4691

Abstract

Atypical body temperature values can be an indication of abnormal physiological processes associated with several health conditions. Infrared thermal (IRT) imaging is an innocuous imaging modality capable of capturing the natural thermal radiation emitted by the skin surface, which is connected to physiology-related [...] Read more.

Atypical body temperature values can be an indication of abnormal physiological processes associated with several health conditions. Infrared thermal (IRT) imaging is an innocuous imaging modality capable of capturing the natural thermal radiation emitted by the skin surface, which is connected to physiology-related pathological states. The implementation of artificial intelligence (AI) methods for interpretation of thermal data can be an interesting solution to supply a second opinion to physicians in a diagnostic/therapeutic assessment scenario. The aim of this work was to perform a systematic review and meta-analysis concerning different biomedical thermal applications in conjunction with machine learning strategies. The bibliographic search yielded 68 records for a qualitative synthesis and 34 for quantitative analysis. The results show potential for the implementation of IRT imaging with AI, but more work is needed to retrieve significant features and improve classification metrics. Full article

(This article belongs to the Section Applied Biosciences and Bioengineering)

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21 pages, 7971 KiB

Open AccessEditor’s ChoiceArticle

Geostatistical Analysis of the Spatial Correlation between Territorial Anthropization and Flooding Vulnerability: Application to the DANA Phenomenon in a Mediterranean Watershed

by Salvador Garcia-Ayllon and John Radke

Appl. Sci. 2021, 11(2), 809; https://doi.org/10.3390/app11020809 - 16 Jan 2021

Cited by 21 | Viewed by 4653

Abstract

Climate change is making intense DANA (depresión aislada en niveles altos) type rains a more frequent phenomenon in Mediterranean basins. This trend, combined with the transformation of the territory derived from diffuse anthropization processes, has created an explosive cocktail for many [...] Read more.

Climate change is making intense DANA (depresión aislada en niveles altos) type rains a more frequent phenomenon in Mediterranean basins. This trend, combined with the transformation of the territory derived from diffuse anthropization processes, has created an explosive cocktail for many coastal towns due to flooding events. To evaluate this problem and the impact of its main guiding parameters, a geostatistical analysis of the territory based on GIS indicators and an NDVI (Normalized Difference Vegetation Index) analysis is developed. The assessment of the validity of a proposed methodology is applied to the case study of the Campo de Cartagena watershed located around the Mar Menor, a Mediterranean coastal lagoon in Southeastern Spain. This area has suffered three catastrophic floods derived from the DANA phenomenon between 2016 and 2019. The results show that apart from the effects derived from climate change, the real issue that amplifies the damage caused by floods is the diffuse anthropization process in the area, which has caused the loss of the natural hydrographic network that traditionally existed in the basin. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and GIS for Natural Hazards Assessment)

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13 pages, 2306 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Deep Learning-Based Methods for Prostate Segmentation in Magnetic Resonance Imaging

by Albert Comelli, Navdeep Dahiya, Alessandro Stefano, Federica Vernuccio, Marzia Portoghese, Giuseppe Cutaia, Alberto Bruno, Giuseppe Salvaggio and Anthony Yezzi

Appl. Sci. 2021, 11(2), 782; https://doi.org/10.3390/app11020782 - 15 Jan 2021

Cited by 58 | Viewed by 6645

Abstract

Magnetic Resonance Imaging-based prostate segmentation is an essential task for adaptive radiotherapy and for radiomics studies whose purpose is to identify associations between imaging features and patient outcomes. Because manual delineation is a time-consuming task, we present three deep-learning (DL) approaches, namely UNet, [...] Read more.

Magnetic Resonance Imaging-based prostate segmentation is an essential task for adaptive radiotherapy and for radiomics studies whose purpose is to identify associations between imaging features and patient outcomes. Because manual delineation is a time-consuming task, we present three deep-learning (DL) approaches, namely UNet, efficient neural network (ENet), and efficient residual factorized convNet (ERFNet), whose aim is to tackle the fully-automated, real-time, and 3D delineation process of the prostate gland on T2-weighted MRI. While UNet is used in many biomedical image delineation applications, ENet and ERFNet are mainly applied in self-driving cars to compensate for limited hardware availability while still achieving accurate segmentation. We apply these models to a limited set of 85 manual prostate segmentations using the k-fold validation strategy and the Tversky loss function and we compare their results. We find that ENet and UNet are more accurate than ERFNet, with ENet much faster than UNet. Specifically, ENet obtains a dice similarity coefficient of 90.89% and a segmentation time of about 6 s using central processing unit (CPU) hardware to simulate real clinical conditions where graphics processing unit (GPU) is not always available. In conclusion, ENet could be efficiently applied for prostate delineation even in small image training datasets with potential benefit for patient management personalization. Full article

(This article belongs to the Special Issue Image Processing and Analysis for Preclinical and Clinical Applications)

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16 pages, 4616 KiB

Open AccessEditor’s ChoiceArticle

Effect of Hydrogen Addition on the Energetic and Ecologic Parameters of an SI Engine Fueled by Biogas

by Saugirdas Pukalskas, Donatas Kriaučiūnas, Alfredas Rimkus, Grzegorz Przybyła, Paweł Droździel and Dalibor Barta

Appl. Sci. 2021, 11(2), 742; https://doi.org/10.3390/app11020742 - 14 Jan 2021

Cited by 33 | Viewed by 3441

Abstract

The global policy solution seeks to reduce the usage of fossil fuels and greenhouse gas (GHG) emissions, and biogas (BG) represents a solutions to these problems. The use of biogas could help cope with increased amounts of waste and reduce usage of fossil [...] Read more.

The global policy solution seeks to reduce the usage of fossil fuels and greenhouse gas (GHG) emissions, and biogas (BG) represents a solutions to these problems. The use of biogas could help cope with increased amounts of waste and reduce usage of fossil fuels. Biogas could be used in compressed natural gas (CNG) engines, but the engine electronic control unit (ECU) needs to be modified. In this research, a spark ignition (SI) engine was tested for mixtures of biogas and hydrogen (volumetric hydrogen concentration of 0, 14, 24, 33, and 43%). In all experiments, two cases of spark timing (ST) were used: the first for an optimal mixture and the second for CNG. The results show that hydrogen increases combustion quality and reduces incomplete combustion products. Because of BG’s lower burning speed, the advanced ST increased brake thermal efficiency (BTE) by 4.3% when the engine was running on biogas. Adding 14 vol% of hydrogen (H₂) increases the burning speed of the mixture and enhances BTE by 2.6% at spark timing optimal for CNG (CNG ST) and 0.6% at the optimal mixture ST (mixture ST). Analyses of the rate of heat release (ROHR), temperature, and pressure increase in the cylinder were carried out using utility BURN in AVL BOOST software. Full article

(This article belongs to the Special Issue Advanced Engine Technologies and Innovative Vehicle Driving Systems)

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12 pages, 2091 KiB

Open AccessEditor’s ChoiceArticle

Genetic Algorithm Based Deep Learning Neural Network Structure and Hyperparameter Optimization

by Sanghyeop Lee, Junyeob Kim, Hyeon Kang, Do-Young Kang and Jangsik Park

Appl. Sci. 2021, 11(2), 744; https://doi.org/10.3390/app11020744 - 14 Jan 2021

Cited by 57 | Viewed by 7956

Abstract

Alzheimer’s disease is one of the major challenges of population ageing, and diagnosis and prediction of the disease through various biomarkers is the key. While the application of deep learning as imaging technologies has recently expanded across the medical industry, empirical design of [...] Read more.

Alzheimer’s disease is one of the major challenges of population ageing, and diagnosis and prediction of the disease through various biomarkers is the key. While the application of deep learning as imaging technologies has recently expanded across the medical industry, empirical design of these technologies is very difficult. The main reason for this problem is that the performance of the Convolutional Neural Networks (CNN) differ greatly depending on the statistical distribution of the input dataset. Different hyperparameters also greatly affect the convergence of the CNN models. With this amount of information, selecting appropriate parameters for the network structure has became a large research area. Genetic Algorithm (GA), is a very popular technique to automatically select a high-performance network architecture. In this paper, we show the possibility of optimising the network architecture using GA, where its search space includes both network structure configuration and hyperparameters. To verify the performance of our Algorithm, we used an amyloid brain image dataset that is used for Alzheimer’s disease diagnosis. As a result, our algorithm outperforms Genetic CNN by

11.73 %

on a given classification task. Full article

(This article belongs to the Section Computing and Artificial Intelligence)

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14 pages, 2303 KiB

Open AccessEditor’s ChoiceArticle

The Effect of Chairside Verbal Instructions Matched with Instagram Social Media on Oral Hygiene of Young Orthodontic Patients: A Randomized Clinical Trial

by Andrea Scribante, Simone Gallo, Karin Bertino, Stefania Meles, Paola Gandini and Maria Francesca Sfondrini

Appl. Sci. 2021, 11(2), 706; https://doi.org/10.3390/app11020706 - 13 Jan 2021

Cited by 29 | Viewed by 4091

Abstract

Objective: To investigate the effectiveness of Instagram in improving oral hygiene compliance and knowledge in young orthodontic patients compared to traditional chairside verbal instructions. Design: Single-center, parallel, randomized controlled trial. Setting: Section of Dentistry of University of Pavia. Participants: 40 patients having fixed [...] Read more.

Objective: To investigate the effectiveness of Instagram in improving oral hygiene compliance and knowledge in young orthodontic patients compared to traditional chairside verbal instructions. Design: Single-center, parallel, randomized controlled trial. Setting: Section of Dentistry of University of Pavia. Participants: 40 patients having fixed appliances in both arches were recruited and randomly divided into an intervention (n = 20) and a control group (n = 20). Intervention: At a first appointment, both groups were given verbal instructions and motivated to oral hygiene. In addition, multimedia contents on Instagram were sent weekly to trial participants for six months. Main outcome measures: For all participants, the bleeding index (BI), modified gingival index (MGI), and plaque index (PI) were assessed at baseline (T0), after one (T1), three (T2), and six months (T3). A questionnaire was administered at the beginning (T0) and at the end of the study (T3) to assess participants’ knowledge. Results: In both groups, BI, MGI, and PI significantly decreased (p < 0.05) at T1 (means control group: BI 0.26 ± 0.22, MGI 0.77 ± 0.36, PI 0.53 ± 0.20; means test group: BI 0.24 ± 0.22, MGI 0.65 ± 0.46, PI 0.49 ± 0.21) compared to baseline (means control group: BI 0.56 ± 0.27, MGI 1.23 ± 0.41, PI 0.87 ± 0.23; means test group: BI 0.54 ± 0.26, MGI 1.18 ± 0.39, PI 0.93 ± 0.20) but no significant differences in clinical measures were showed between T1, T2, and T3 (p > 0.05) (intragroup differences). Trial patients demonstrated significant improvements in knowledge with respect to controls comparing scores at T0 and T3 (p < 0.05) but despite this result in the test group clinical outcomes did not report significant intergroup differences at any time (p > 0.05). Conclusions: Presenting multimedia information through Instagram resulted in a significant improvement in knowledge. Therefore, this social media represents an aid to the standard verbal motivation performed by orthodontists towards young patients under an orthodontic treatment. Full article

(This article belongs to the Special Issue Clinical Applications for Dentistry and Oral Health)

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14 pages, 1238 KiB

Open AccessEditor’s ChoiceArticle

Evaluation of the Reaction Time and Accuracy Rate in Normal Subjects, MCI, and Dementia Using Serious Games

by Yen-Ting Chen, Chun-Ju Hou, Natan Derek, Shuo-Bin Huang, Min-Wei Huang and You-Yu Wang

Appl. Sci. 2021, 11(2), 628; https://doi.org/10.3390/app11020628 - 11 Jan 2021

Cited by 18 | Viewed by 4448

Abstract

The main purpose of this research is to evaluate the differences in the reaction time and accuracy rate of three categories of subjects using our serious games. Thirty-seven subjects were divided into three groups: normal (n₁ = 16), MCI (Mild Cognitive [...] Read more.

The main purpose of this research is to evaluate the differences in the reaction time and accuracy rate of three categories of subjects using our serious games. Thirty-seven subjects were divided into three groups: normal (n₁ = 16), MCI (Mild Cognitive Impairment) (n₂ = 10), and dementia—moderate-to-severe (n₃ = 11) groups based on the MMSE (Mini Mental State Examination). Two serious games were designed: (1) whack-a-mole and (2) hit-the-ball. Two dependent variables, reaction time and accuracy rate, were statistically analyzed to compare elders’ performances in the games among the three groups for three levels of speed: slow, medium, and fast. There were significance differences between the normal group, the MCI group, and the moderate-to-severe dementia group in both the reaction-time and accuracy-rate analyses. We determined that the reaction times of the MCI and dementia groups were shorter compared to those of the normal group, with poorer results also observed in accuracy rate. Therefore, we conclude that our serious games have the feasibility to evaluate reaction performance and could be used in the daily lives of elders followed by clinical treatment in the future. Full article

(This article belongs to the Special Issue Serious Games and Mixed Reality Applications for Healthcare)

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21 pages, 1523 KiB

Open AccessEditor’s ChoiceReview

A Review on Recent Advancements of Graphene and Graphene-Related Materials in Biological Applications

by Federica Catania, Elena Marras, Mauro Giorcelli, Pravin Jagdale, Luca Lavagna, Alberto Tagliaferro and Mattia Bartoli

Appl. Sci. 2021, 11(2), 614; https://doi.org/10.3390/app11020614 - 10 Jan 2021

Cited by 84 | Viewed by 8367

Abstract

Graphene is the most outstanding material among the new nanostructured carbonaceous species discovered and produced. Graphene’s astonishing properties (i.e., electronic conductivity, mechanical robustness, large surface area) have led to a deep change in the material science field. In this review, after a brief [...] Read more.

Graphene is the most outstanding material among the new nanostructured carbonaceous species discovered and produced. Graphene’s astonishing properties (i.e., electronic conductivity, mechanical robustness, large surface area) have led to a deep change in the material science field. In this review, after a brief overview of the main characteristics of graphene and related materials, we present an extensive overview of the most recent achievements in biological uses of graphene and related materials. Full article

(This article belongs to the Special Issue Graphene Growth and Its Nanostructuring)

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22 pages, 6553 KiB

Open AccessEditor’s ChoiceArticle

Nonlinear Dynamic Response of a Precast Concrete Building to Sudden Column Removal

by Simone Ravasini, Beatrice Belletti, Emanuele Brunesi, Roberto Nascimbene and Fulvio Parisi

Appl. Sci. 2021, 11(2), 599; https://doi.org/10.3390/app11020599 - 9 Jan 2021

Cited by 24 | Viewed by 3824

Abstract

Robustness of reinforced concrete (RC) structures is an ongoing challenging research topic in the engineering community. During an extreme event, the loss of vertical load-bearing elements can activate large-deformation resisting mechanisms such as membrane and catenary actions in beams and floor slabs of [...] Read more.

Robustness of reinforced concrete (RC) structures is an ongoing challenging research topic in the engineering community. During an extreme event, the loss of vertical load-bearing elements can activate large-deformation resisting mechanisms such as membrane and catenary actions in beams and floor slabs of cast-in-situ RC buildings to resist gravity loads. However, few studies have been conducted for precast concrete (PC) buildings, especially focused on the capacity of such structures to withstand column loss scenarios, which mainly relies on connection strength. Additional resistance resource and alternate load paths could be reached via tying systems. In this paper, the progressive collapse resistance of a PC frame building is analyzed by means of nonlinear dynamic finite element analyses focusing on the fundamental roles played by beam-to-column connection strength and tying reinforcement. A simplified modelling approach is illustrated in order to investigate the response of such a structural typology to a number of sudden column-removal scenarios. The relative simplicity of the modelling technique is considered useful for engineering practice, providing new input for further research in this field. Full article

(This article belongs to the Special Issue Structural Reliability of RC Frame Buildings)

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19 pages, 989 KiB

Open AccessEditor’s ChoiceArticle

Predicting Compressive Strength of Concrete Containing Recycled Aggregate Using Modified ANN with Different Optimization Algorithms

by Amirreza Kandiri, Farid Sartipi and Mahdi Kioumarsi

Appl. Sci. 2021, 11(2), 485; https://doi.org/10.3390/app11020485 - 6 Jan 2021

Cited by 72 | Viewed by 4278

Abstract

Using recycled aggregate in concrete is one of the best ways to reduce construction pollution and prevent the exploitation of natural resources to provide the needed aggregate. However, recycled aggregates affect the mechanical properties of concrete, but the existing information on the subject [...] Read more.

Using recycled aggregate in concrete is one of the best ways to reduce construction pollution and prevent the exploitation of natural resources to provide the needed aggregate. However, recycled aggregates affect the mechanical properties of concrete, but the existing information on the subject is less than what the industry needs. Compressive strength, on the other hand, is the most important mechanical property of concrete. Therefore, having predictive models to provide the required information can be helpful to convince the industry to increase the use of recycled aggregate in concrete. In this research, three different optimization algorithms including genetic algorithm (GA), salp swarm algorithm (SSA), and grasshopper optimization algorithm (GOA) are employed to be hybridized with artificial neural network (ANN) separately to predict the compressive strength of concrete containing recycled aggregate, and a M5P tree model is used to test the efficiency of the ANNs. The results of this study show the superior efficiency of the modified ANN with SSA when compared to other models. However, the statistical indicators of the hybrid ANNs with SSA, GA, and GOA are so close to each other. Full article

(This article belongs to the Section Civil Engineering)

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12 pages, 3543 KiB

Open AccessEditor’s ChoiceArticle

Detection of Cardiac Structural Abnormalities in Fetal Ultrasound Videos Using Deep Learning

by Masaaki Komatsu, Akira Sakai, Reina Komatsu, Ryu Matsuoka, Suguru Yasutomi, Kanto Shozu, Ai Dozen, Hidenori Machino, Hirokazu Hidaka, Tatsuya Arakaki, Ken Asada, Syuzo Kaneko, Akihiko Sekizawa and Ryuji Hamamoto

Appl. Sci. 2021, 11(1), 371; https://doi.org/10.3390/app11010371 - 2 Jan 2021

Cited by 80 | Viewed by 11077

Abstract

Artificial Intelligence (AI) technologies have recently been applied to medical imaging for diagnostic support. With respect to fetal ultrasound screening of congenital heart disease (CHD), it is still challenging to achieve consistently accurate diagnoses owing to its manual operation and the technical differences [...] Read more.

Artificial Intelligence (AI) technologies have recently been applied to medical imaging for diagnostic support. With respect to fetal ultrasound screening of congenital heart disease (CHD), it is still challenging to achieve consistently accurate diagnoses owing to its manual operation and the technical differences among examiners. Hence, we proposed an architecture of Supervised Object detection with Normal data Only (SONO), based on a convolutional neural network (CNN), to detect cardiac substructures and structural abnormalities in fetal ultrasound videos. We used a barcode-like timeline to visualize the probability of detection and calculated an abnormality score of each video. Performance evaluations of detecting cardiac structural abnormalities utilized videos of sequential cross-sections around a four-chamber view (Heart) and three-vessel trachea view (Vessels). The mean value of abnormality scores in CHD cases was significantly higher than normal cases (p < 0.001). The areas under the receiver operating characteristic curve in Heart and Vessels produced by SONO were 0.787 and 0.891, respectively, higher than the other conventional algorithms. SONO achieves an automatic detection of each cardiac substructure in fetal ultrasound videos, and shows an applicability to detect cardiac structural abnormalities. The barcode-like timeline is informative for examiners to capture the clinical characteristic of each case, and it is also expected to acquire one of the important features in the field of medical AI: the development of “explainable AI.” Full article

(This article belongs to the Special Issue Machine Learning in Medical Applications)

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18 pages, 1173 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

A Survey on Robotic Technologies for Forest Firefighting: Applying Drone Swarms to Improve Firefighters’ Efficiency and Safety

by Juan Jesús Roldán-Gómez, Eduardo González-Gironda and Antonio Barrientos

Appl. Sci. 2021, 11(1), 363; https://doi.org/10.3390/app11010363 - 1 Jan 2021

Cited by 78 | Viewed by 13894

Abstract

Forest firefighting missions encompass multiple tasks related to prevention, surveillance, and extinguishing. This work presents a complete survey of firefighters on the current problems in their work and the potential technological solutions. Additionally, it reviews the efforts performed by the academy and industry [...] Read more.

Forest firefighting missions encompass multiple tasks related to prevention, surveillance, and extinguishing. This work presents a complete survey of firefighters on the current problems in their work and the potential technological solutions. Additionally, it reviews the efforts performed by the academy and industry to apply different types of robots in the context of firefighting missions. Finally, all this information is used to propose a concept of operation for the comprehensive application of drone swarms in firefighting. The proposed system is a fleet of quadcopters that individually are only able to visit waypoints and use payloads, but collectively can perform tasks of surveillance, mapping, monitoring, etc. Three operator roles are defined, each one with different access to information and functions in the mission: mission commander, team leaders, and team members. These operators take advantage of virtual and augmented reality interfaces to intuitively get the information of the scenario and, in the case of the mission commander, control the drone swarm. Full article

(This article belongs to the Special Issue Multi-Robot Systems: Challenges, Trends and Applications)

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15 pages, 6685 KiB

Open AccessEditor’s ChoiceArticle

Development of 340-GHz Transceiver Front End Based on GaAs Monolithic Integration Technology for THz Active Imaging Array

by Yang Liu, Bo Zhang, Yinian Feng, Xiaolin Lv, Dongfeng Ji, Zhongqian Niu, Yilin Yang, Xiangyang Zhao and Yong Fan

Appl. Sci. 2020, 10(21), 7924; https://doi.org/10.3390/app10217924 - 9 Nov 2020

Cited by 71 | Viewed by 4604

Abstract

Frequency multipliers and mixers based on Schottky barrier diodes (SBDs) are widely used in terahertz (THz) imaging applications. However, they still face obstacles, such as poor performance consistency caused by discrete flip-chip diodes, as well as low efficiency and large receiving noise temperature. [...] Read more.

Frequency multipliers and mixers based on Schottky barrier diodes (SBDs) are widely used in terahertz (THz) imaging applications. However, they still face obstacles, such as poor performance consistency caused by discrete flip-chip diodes, as well as low efficiency and large receiving noise temperature. It is very hard to meet the requirement of multiple channels in THz imaging array. In order to solve this problem, 12-μm-thick gallium arsenide (GaAs) monolithic integrated technology was adopted. In the process, the diode chip shared the same GaAs substrate with the transmission line, and the diode’s pads were seamlessly connected to the transmission line without using silver glue. A three-dimensional (3D) electromagnetic (EM) model of the diode chip was established in Ansys High Frequency Structure Simulator (HFSS) to accurately characterize the parasitic parameters. Based on the model, by quantitatively analyzing the influence of the surface channel width and the diode anode junction area on the best efficiency, the final parameters and dimensions of the diode were further optimized and determined. Finally, three 0.34 THz triplers and subharmonic mixers (SHMs) were manufactured, assembled, and measured for demonstration, all of which comprised a waveguide housing, a GaAs circuit integrated with diodes, and other external connectors. Experimental results show that all the triplers and SHMs had great performance consistency. Typically, when the input power was 100 mW, the output power of the THz tripler was greater than 1 mW in the frequency range of 324 GHz to 352 GHz, and a peak efficiency of 6.8% was achieved at 338 GHz. The THz SHM exhibited quite a low double sideband (DSB) noise temperature of 900~1500 K and a DSB conversion loss of 6.9~9 dB over the frequency range of 325~352 GHz. It is indicated that the GaAs monolithic integrated process, diodes modeling, and circuits simulation method in this paper provide an effective way to design THz frequency multiplier and mixer circuits. Full article

(This article belongs to the Special Issue Terahertz Sensing and Imaging)

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19 pages, 1993 KiB

Open AccessEditor’s ChoiceReview

The Impact of Drought in Plant Metabolism: How to Exploit Tolerance Mechanisms to Increase Crop Production

by Dhriti Kapoor, Savita Bhardwaj, Marco Landi, Arti Sharma, Muthusamy Ramakrishnan and Anket Sharma

Appl. Sci. 2020, 10(16), 5692; https://doi.org/10.3390/app10165692 - 17 Aug 2020

Cited by 354 | Viewed by 19516

Abstract

Plants are often exposed to unfavorable environmental conditions, for instance abiotic stresses, which dramatically alter distribution of plant species among ecological niches and limit the yields of crop species. Among these, drought stress is one of the most impacting factors which alter seriously [...] Read more.

Plants are often exposed to unfavorable environmental conditions, for instance abiotic stresses, which dramatically alter distribution of plant species among ecological niches and limit the yields of crop species. Among these, drought stress is one of the most impacting factors which alter seriously the plant physiology, finally leading to the decline of the crop productivity. Drought stress causes in plants a set of morpho-anatomical, physiological and biochemical changes, mainly addressed to limit the loss of water by transpiration with the attempt to increase the plant water use efficiency. The stomata closure, one of the first consistent reactions observed under drought, results in a series of consequent physiological/biochemical adjustments aimed at balancing the photosynthetic process as well as at enhancing the plant defense barriers against drought-promoted stress (e.g., stimulation of antioxidant systems, accumulation of osmolytes and stimulation of aquaporin synthesis), all representing an attempt by the plant to overcome the unfavorable period of limited water availability. In view of the severe changes in water availability imposed by climate change factors and considering the increasing human population, it is therefore of outmost importance to highlight: (i) how plants react to drought; (ii) the mechanisms of tolerance exhibited by some species/cultivars; and (iii) the techniques aimed at increasing the tolerance of crop species against limited water availability. All these aspects are necessary to respond to the continuously increasing demand for food, which unfortunately parallels the loss of arable land due to changes in rainfall dynamics and prolonged period of drought provoked by climate change factors. This review summarizes the most updated findings on the impact of drought stress on plant morphological, biochemical and physiological features and highlights plant mechanisms of tolerance which could be exploited to increase the plant capability to survive under limited water availability. In addition, possible applicative strategies to help the plant in counteracting unfavorable drought periods are also discussed. Full article

(This article belongs to the Special Issue Plant Response to Arid Environment)

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18 pages, 7008 KiB

Open AccessEditor’s ChoiceArticle

Incorporation of Bioactive Glasses Containing Mg, Sr, and Zn in Electrospun PCL Fibers by Using Benign Solvents

by Rachele Sergi, Valeria Cannillo, Aldo R. Boccaccini and Liliana Liverani

Appl. Sci. 2020, 10(16), 5530; https://doi.org/10.3390/app10165530 - 10 Aug 2020

Cited by 26 | Viewed by 3777

Abstract

Poly(ε-caprolactone) (PCL) and PCL/bioactive glass composite fiber mats were produced by electrospinning technique. To improve cell adhesion and proliferation (i) 45S5, (ii) a bioactive glass containing strontium and magnesium oxides, and (iii) a bioactive glass containing zinc oxide were separately added to the [...] Read more.

Poly(ε-caprolactone) (PCL) and PCL/bioactive glass composite fiber mats were produced by electrospinning technique. To improve cell adhesion and proliferation (i) 45S5, (ii) a bioactive glass containing strontium and magnesium oxides, and (iii) a bioactive glass containing zinc oxide were separately added to the starting PCL solution before electrospinning. A good incorporation of bioactive glass particles in PCL electrospun mats was confirmed by SEM and FTIR analyses. Bioactivity was evaluated by immersion of PCL mats and PCL/bioactive glass electrospun fiber mats in simulated body fluid (SBF). Bone murine stromal cells (ST-2) were employed in WST-8 assay to assess cell viability, cell morphology, and proliferation. The results showed that the presence of bioactive glass particles in the fibers enhances cell adhesion and proliferation compared to neat PCL mats. Furthermore, PCL/bioactive glass electrospun mats showed higher wound-healing rate (measured as cell migration rate) in vitro compared to neat PCL electrospun mats. Therefore, the characteristics of the PCL matrix combined with biological properties of bioactive glasses make PCL/bioactive glass composite ideal candidate for biomedical application. Full article

(This article belongs to the Special Issue Novel Insights in Fabrication of Scaffolds Using Electrospinning and Electrochemical / Electrophoretic Depositions)

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31 pages, 3546 KiB

Open AccessEditor’s ChoiceArticle

A New Concept of Digital Twin Supporting Optimization and Resilience of Factories of the Future

by Adrien Bécue, Eva Maia, Linda Feeken, Philipp Borchers and Isabel Praça

Appl. Sci. 2020, 10(13), 4482; https://doi.org/10.3390/app10134482 - 28 Jun 2020

Cited by 103 | Viewed by 18363

Abstract

In the context of Industry 4.0, a growing use is being made of simulation-based decision-support tools commonly named Digital Twins. Digital Twins are replicas of the physical manufacturing assets, providing means for the monitoring and control of individual assets. Although extensive research on [...] Read more.

In the context of Industry 4.0, a growing use is being made of simulation-based decision-support tools commonly named Digital Twins. Digital Twins are replicas of the physical manufacturing assets, providing means for the monitoring and control of individual assets. Although extensive research on Digital Twins and their applications has been carried out, the majority of existing approaches are asset specific. Little consideration is made of human factors and interdependencies between different production assets are commonly ignored. In this paper, we address those limitations and propose innovations for cognitive modeling and co-simulation which may unleash novel uses of Digital Twins in Factories of the Future. We introduce a holistic Digital Twin approach, in which the factory is not represented by a set of separated Digital Twins but by a comprehensive modeling and simulation capacity embracing the full manufacturing process including external network dependencies. Furthermore, we introduce novel approaches for integrating models of human behavior and capacities for security testing with Digital Twins and show how the holistic Digital Twin can enable new services for the optimization and resilience of Factories of the Future. To illustrate this approach, we introduce a specific use-case implemented in field of Aerospace System Manufacturing. Full article

(This article belongs to the Special Issue Cyber Factories – Intelligent and Secure Factories of the Future)

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14 pages, 970 KiB

Open AccessEditor’s ChoiceArticle

Levels and Changes of Physical Activity in Adolescents during the COVID-19 Pandemic: Contextualizing Urban vs. Rural Living Environment

by Natasa Zenic, Redha Taiar, Barbara Gilic, Mateo Blazevic, Dora Maric, Haris Pojskic and Damir Sekulic

Appl. Sci. 2020, 10(11), 3997; https://doi.org/10.3390/app10113997 - 9 Jun 2020

Cited by 135 | Viewed by 14997

Abstract

The COVID-19 pandemic and the social distancing implemented shortly after influence physical activity levels (PALs). The purpose of this investigation was to evaluate the changes in PAL and factors associated with PALs among Croatian adolescents while considering the impact of community (urban vs. [...] Read more.

The COVID-19 pandemic and the social distancing implemented shortly after influence physical activity levels (PALs). The purpose of this investigation was to evaluate the changes in PAL and factors associated with PALs among Croatian adolescents while considering the impact of community (urban vs. rural living environment). The sample included 823 adolescents (mean age: 16.5 ± 2.1 years) who were tested on baseline (from October 2019 to March 2020; before COVID-19 pandemic in Croatia) and follow-up (in April 2020; during the COVID-19 pandemic and imposed rules of social distancing). Baseline testing included anthropometrics, physical fitness status, and evaluation of PALs, while follow-up included only PALs (evaluated by a standardized questionnaire through an internet application). The results showed a significant influence of the living environment on the decrease of PAL, with a larger decrease in urban adolescents. Logistic regression showed a higher likelihood for normal PALs at baseline in adolescents who had better fitness status, with no strong confounding effect of the urban/rural environment. The fitness status of urban adolescents predicted their PALs at follow-up. The differences between urban and rural adolescents with regard to the established changes in PALs and relationships between the predictors and PALs are explained by the characteristics of the living communities (lack of organized sports in rural areas), and the level of social distancing in the studied period and region/country. Full article

(This article belongs to the Special Issue COVID-19: Impact on Human Health and Behavior)

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15 pages, 688 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

COVID-19: A Comparison of Time Series Methods to Forecast Percentage of Active Cases per Population

by Vasilis Papastefanopoulos, Pantelis Linardatos and Sotiris Kotsiantis

Appl. Sci. 2020, 10(11), 3880; https://doi.org/10.3390/app10113880 - 3 Jun 2020

Cited by 119 | Viewed by 16896

Abstract

The ongoing COVID-19 pandemic has caused worldwide socioeconomic unrest, forcing governments to introduce extreme measures to reduce its spread. Being able to accurately forecast when the outbreak will hit its peak would significantly diminish the impact of the disease, as it would allow [...] Read more.

The ongoing COVID-19 pandemic has caused worldwide socioeconomic unrest, forcing governments to introduce extreme measures to reduce its spread. Being able to accurately forecast when the outbreak will hit its peak would significantly diminish the impact of the disease, as it would allow governments to alter their policy accordingly and plan ahead for the preventive steps needed such as public health messaging, raising awareness of citizens and increasing the capacity of the health system. This study investigated the accuracy of a variety of time series modeling approaches for coronavirus outbreak detection in ten different countries with the highest number of confirmed cases as of 4 May 2020. For each of these countries, six different time series approaches were developed and compared using two publicly available datasets regarding the progression of the virus in each country and the population of each country, respectively. The results demonstrate that, given data produced using actual testing for a small portion of the population, machine learning time series methods can learn and scale to accurately estimate the percentage of the total population that will become affected in the future. Full article

(This article belongs to the Special Issue 10th Anniversary of Applied Sciences: Invited Papers in Computing and Artificial Intelligence Section)

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12 pages, 3068 KiB

Open AccessEditor’s ChoiceArticle

Performance Evaluation of Two Machine Learning Techniques in Heating and Cooling Loads Forecasting of Residential Buildings

by Arash Moradzadeh, Amin Mansour-Saatloo, Behnam Mohammadi-Ivatloo and Amjad Anvari-Moghaddam

Appl. Sci. 2020, 10(11), 3829; https://doi.org/10.3390/app10113829 - 31 May 2020

Cited by 109 | Viewed by 4708

Abstract

Nowadays, since energy management of buildings contributes to the operation cost, many efforts are made to optimize the energy consumption of buildings. In addition, the most consumed energy in the buildings is assigned to the indoor heating and cooling comforts. In this regard, [...] Read more.

Nowadays, since energy management of buildings contributes to the operation cost, many efforts are made to optimize the energy consumption of buildings. In addition, the most consumed energy in the buildings is assigned to the indoor heating and cooling comforts. In this regard, this paper proposes a heating and cooling load forecasting methodology, which by taking this methodology into the account energy consumption of the buildings can be optimized. Multilayer perceptron (MLP) and support vector regression (SVR) for the heating and cooling load forecasting of residential buildings are employed. MLP and SVR are the applications of artificial neural networks and machine learning, respectively. These methods commonly are used for modeling and regression and produce a linear mapping between input and output variables. Proposed methods are taught using training data pertaining to the characteristics of each sample in the dataset. To apply the proposed methods, a simulated dataset will be used, in which the technical parameters of the building are used as input variables and heating and cooling loads are selected as output variables for each network. Finally, the simulation and numerical results illustrates the effectiveness of the proposed methodologies. Full article

(This article belongs to the Special Issue Renewable Energy Systems 2020)

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17 pages, 499 KiB

Open AccessEditor’s ChoiceReview

Trends in Biodiesel Production from Animal Fat Waste

by Fidel Toldrá-Reig, Leticia Mora and Fidel Toldrá

Appl. Sci. 2020, 10(10), 3644; https://doi.org/10.3390/app10103644 - 25 May 2020

Cited by 125 | Viewed by 15346

Abstract

The agro-food industry generates large amounts of waste that contribute to environmental contamination. Animal fat waste constitutes some of the most relevant waste and the treatment of such waste is quite costly because environmental regulations are quite strict. Part of such costs might [...] Read more.

The agro-food industry generates large amounts of waste that contribute to environmental contamination. Animal fat waste constitutes some of the most relevant waste and the treatment of such waste is quite costly because environmental regulations are quite strict. Part of such costs might be reduced through the generation of bioenergy. Biodiesel constitutes a valid renewable source of energy because it is biodegradable, non-toxic and has a good combustion emission profile and can be blended up to 20% with fossil diesel for its use in many countries. Furthermore, up to 70% of the total cost of biodiesel majorly depends on the cost of the raw materials used, which can be reduced using animal fat waste because they are cheaper than vegetable oil waste. In fact, 6% of total feedstock corresponded to animal fat in 2019. Transesterification with alkaline catalysis is still preferred at industrial plants producing biodiesel. Recent developments in heterogeneous catalysts that can be easily recovered, regenerated and reused, as well as immobilized lipases with increased stability and resistance to alcohol denaturation, are promising for future industrial use. This manuscript reviews the available processes and recent advances for biodiesel generation from animal fat waste. Full article

(This article belongs to the Special Issue New Trends in Environmental Engineering, Agriculture, Food Production, and Analysis)

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29 pages, 2044 KiB

Open AccessFeature PaperEditor’s ChoiceReview

Biochar for Wastewater Treatment—Conversion Technologies and Applications

by Ghizlane Enaime, Abdelaziz Baçaoui, Abdelrani Yaacoubi and Manfred Lübken

Appl. Sci. 2020, 10(10), 3492; https://doi.org/10.3390/app10103492 - 18 May 2020

Cited by 270 | Viewed by 17357

Abstract

Biochar as a stable carbon-rich material shows incredible potential to handle water/wastewater contaminants. Its application is gaining increasing interest due to the availability of feedstock, the simplicity of the preparation methods, and their enhanced physico-chemical properties. The efficacy of biochar to remove organic [...] Read more.

Biochar as a stable carbon-rich material shows incredible potential to handle water/wastewater contaminants. Its application is gaining increasing interest due to the availability of feedstock, the simplicity of the preparation methods, and their enhanced physico-chemical properties. The efficacy of biochar to remove organic and inorganic pollutants depends on its surface area, pore size distribution, surface functional groups, and the size of the molecules to be removed, while the physical architecture and surface properties of biochar depend on the nature of feedstock and the preparation method/conditions. For instance, pyrolysis at high temperatures generally produces hydrophobic biochars with higher surface area and micropore volume, allowing it to be more suitable for organic contaminants sorption, whereas biochars produced at low temperatures own smaller pore size, lower surface area, and higher oxygen-containing functional groups and are more suitable to remove inorganic contaminants. In the field of water/wastewater treatment, biochar can have extensive application prospects. Biochar have been widely used as an additive/support media during anaerobic digestion and as filter media for the removal of suspended matter, heavy metals and pathogens. Biochar was also tested for its efficiency as a support-based catalyst for the degradation of dyes and recalcitrant contaminants. The current review discusses on the different methods for biochar production and provides an overview of current applications of biochar in wastewater treatment. Full article

(This article belongs to the Special Issue Biochar for the Environmental Wastewater Treatment)

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22 pages, 4406 KiB

Open AccessEditor’s ChoiceReview

Smart Manufacturing Systems and Applied Industrial Technologies for a Sustainable Industry: A Systematic Literature Review

by Raffaele Cioffi, Marta Travaglioni, Giuseppina Piscitelli, Antonella Petrillo and Adele Parmentola

Appl. Sci. 2020, 10(8), 2897; https://doi.org/10.3390/app10082897 - 22 Apr 2020

Cited by 75 | Viewed by 10408

Abstract

Smart manufacturing is considered as a new paradigm that makes work smarter and more connected, bringing speed and flexibility through the introduction of digital innovation. Today, digital innovation is closely linked to the “sustainability” of companies. Digital innovation and sustainability are two inseparable [...] Read more.

Smart manufacturing is considered as a new paradigm that makes work smarter and more connected, bringing speed and flexibility through the introduction of digital innovation. Today, digital innovation is closely linked to the “sustainability” of companies. Digital innovation and sustainability are two inseparable principles that are based on the concept of circular economy. Digital innovation enables a circular economy model, promoting the use of solutions like digital platforms, smart devices, and artificial intelligence that help to optimize resources. Thus, the purpose of the research is to present a systematic literature review on what enabling technologies can promote new circular business models. A total of 31 articles were included in the study. Our results showed that realization of the circular economy involved two main changes: (i) managerial changes and (ii) legislative changes. Furthermore, the creation of the circular economy can certainly be facilitated by innovation, especially through the introduction of new technologies and through the introduction of digital innovations. Full article

(This article belongs to the Special Issue Smart Manufacturing Systems for Industry 5.0: Challenges and Opportunities)

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19 pages, 5683 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Automatically Processing IFC Clipping Representation for BIM and GIS Integration at the Process Level

by Junxiang Zhu, Peng Wu, Mengcheng Chen, Mi Jeong Kim, Xiangyu Wang and Tingchen Fang

Appl. Sci. 2020, 10(6), 2009; https://doi.org/10.3390/app10062009 - 15 Mar 2020

Cited by 89 | Viewed by 8945

Abstract

The integration of building information modeling (BIM) and geographic information system (GIS) is attracting more attention than ever due to its potential benefits for both the architecture, engineering, and construction (AEC) domain and the geospatial industry. The main challenge in BIM and GIS [...] Read more.

The integration of building information modeling (BIM) and geographic information system (GIS) is attracting more attention than ever due to its potential benefits for both the architecture, engineering, and construction (AEC) domain and the geospatial industry. The main challenge in BIM and GIS integrated application comes from the fundamental data conversion, especially for the geometric information. BIM and GIS use different modeling paradigms to represent objects. The BIM dataset takes, for example, Industry Foundation Classes (IFC) that use solid models, such as boundary representation (B-Rep), swept solid, constructive solid geometry (CSG), and clipping, while the GIS dataset mainly uses surface models or B-Rep. The fundamental data conversion between BIM and GIS is the foundation of BIM and GIS integrated application. However, the efficiency of data conversion has been greatly impaired by the human intervention needed, especially for the conversion of the clipping geometry. The goal of this study is to automate the conversion of IFC clipping representation into the shapefile format. A process-level approach was developed with an algorithm for instantiating unbounded half spaces using B-Rep. Four IFC models were used to validate the proposed method. The results show that (1) the proposed approach can successfully automate the conversion of IFC clipping representation into the shapefile format; and (2) increasing boundary size has no effect on the file size of unbounded half spaces, but slightly increases the producing time of half spaces and processing time of building components. The efficiency of this study can be further improved by using an open-source package, instead of using the low-efficiency packages provided by ArcGIS. Full article

(This article belongs to the Special Issue BIM and GIS Integration for Driving Smarter Decisions)

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16 pages, 4404 KiB

Open AccessEditor’s ChoiceArticle

Real-Time Remote Maintenance Support Based on Augmented Reality (AR)

by Dimitris Mourtzis, Vasileios Siatras and John Angelopoulos

Appl. Sci. 2020, 10(5), 1855; https://doi.org/10.3390/app10051855 - 8 Mar 2020

Cited by 109 | Viewed by 11018

Abstract

In the realm of the current industrial revolution, interesting innovations as well as new techniques are constantly being introduced by offering fertile ground for further investigation and improvement in the industrial engineering domain. More specifically, cutting-edge digital technologies in the field of Extended [...] Read more.

In the realm of the current industrial revolution, interesting innovations as well as new techniques are constantly being introduced by offering fertile ground for further investigation and improvement in the industrial engineering domain. More specifically, cutting-edge digital technologies in the field of Extended Reality (XR) have become mainstream including Augmented Reality (AR). Furthermore, Cloud Computing has enabled the provision of high-quality services, especially in the controversial field of maintenance. However, since modern machines are becoming more complex, maintenance must be carried out from experienced and well-trained personnel, while overseas support is timely and financially costly. Although AR is a back-bone technology facilitating the development of robust maintenance support tools, they are limited to the provision of predefined scenarios, covering only a limited number of scenarios. This research work aims to address this emerging challenge with the design and development of a framework, for the support of remote maintenance and repair operation based on AR, by creating suitable communication channels between the shop-floor technicians and the expert engineers who are utilizing real-time feedback from the operator’s field of view. The applicability of the developed framework is tested in vitro in a lab-based machine shop and in a real-life industrial scenario. Full article

(This article belongs to the Special Issue Novel Industry 4.0 Technologies and Applications)

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14 pages, 3183 KiB

Open AccessEditor’s ChoiceReview

Artifactual Lung Ultrasonography: It Is a Matter of Traps, Order, and Disorder

by Gino Soldati, Andrea Smargiassi, Libertario Demi and Riccardo Inchingolo

Appl. Sci. 2020, 10(5), 1570; https://doi.org/10.3390/app10051570 - 25 Feb 2020

Cited by 59 | Viewed by 7369

Abstract

When inspecting the lung with standard ultrasound B-mode imaging, numerous artifacts can be visualized. These artifacts are useful to recognize and evaluate several pathological conditions in Emergency and Intensive Care Medicine. More recently, the interest of the Pulmonologists has turned to the echographic [...] Read more.

When inspecting the lung with standard ultrasound B-mode imaging, numerous artifacts can be visualized. These artifacts are useful to recognize and evaluate several pathological conditions in Emergency and Intensive Care Medicine. More recently, the interest of the Pulmonologists has turned to the echographic study of the interstitial pathology of the lung. In fact, all lung pathologies which increase the density of the tissue, and do not consolidate the organ, are characterized by the presence of ultrasound artifacts. Many studies of the past have only assessed the number of vertical artifacts (generally known as B-Lines) as a sign of disease severity. However, recent observations suggest that the appearance of the individual artifacts, their variability, and their internal structure, may play a role for a non-invasive characterization of the surface of the lungs, directing the diagnoses and identifying groups of diseases. In this review, we discuss the meaning of lung ultrasound artifacts, and introduce hypothesis on the correlation between their presence and the structural variation of the sub-pleural tissue in light of current knowledge of the acoustic properties of the pleural plane. Full article

(This article belongs to the Special Issue Signal Processing and Image Analysis Techniques for Lung Ultrasound Imaging)

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29 pages, 4614 KiB

Open AccessEditor’s ChoiceReview

Edge Couplers in Silicon Photonic Integrated Circuits: A Review

by Xin Mu, Sailong Wu, Lirong Cheng and H.Y. Fu

Appl. Sci. 2020, 10(4), 1538; https://doi.org/10.3390/app10041538 - 24 Feb 2020

Cited by 149 | Viewed by 30993

Abstract

Silicon photonics has drawn increasing attention in the past few decades and is a promising key technology for future daily applications due to its various merits including ultra-low cost, high integration density owing to the high refractive index of silicon, and compatibility with [...] Read more.

Silicon photonics has drawn increasing attention in the past few decades and is a promising key technology for future daily applications due to its various merits including ultra-low cost, high integration density owing to the high refractive index of silicon, and compatibility with current semiconductor fabrication process. Optical interconnects is an important issue in silicon photonic integrated circuits for transmitting light, and fiber-to-chip optical interconnects is vital in application scenarios such as data centers and optical transmission systems. There are mainly two categories of fiber-to-chip optical coupling: off-plane coupling and in-plane coupling. Grating couplers work under the former category, while edge couplers function as in-plane coupling. In this paper, we mainly focus on edge couplers in silicon photonic integrated circuits. We deliver an introduction to the research background, operation mechanisms, and design principles of silicon photonic edge couplers. The state-of-the-art of edge couplers is reviewed according to the different structural configurations of the device, while identifying the performance, fabrication feasibility, and applications. In addition, a brief comparison between edge couplers and grating couplers is conducted. Packaging issues are also discussed, and several prospective techniques for further improvements of edge couplers are proposed. Full article

(This article belongs to the Special Issue Advances on New Silicon-Integrating Devices: From the Technology of Silicon to the Technology with Silicon)

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29 pages, 6865 KiB

Open AccessEditor’s ChoiceArticle

Sehaa: A Big Data Analytics Tool for Healthcare Symptoms and Diseases Detection Using Twitter, Apache Spark, and Machine Learning

by Shoayee Alotaibi, Rashid Mehmood, Iyad Katib, Omer Rana and Aiiad Albeshri

Appl. Sci. 2020, 10(4), 1398; https://doi.org/10.3390/app10041398 - 19 Feb 2020

Cited by 81 | Viewed by 11883

Abstract

Smartness, which underpins smart cities and societies, is defined by our ability to engage with our environments, analyze them, and make decisions, all in a timely manner. Healthcare is the prime candidate needing the transformative capability of this smartness. Social media could enable [...] Read more.

Smartness, which underpins smart cities and societies, is defined by our ability to engage with our environments, analyze them, and make decisions, all in a timely manner. Healthcare is the prime candidate needing the transformative capability of this smartness. Social media could enable a ubiquitous and continuous engagement between healthcare stakeholders, leading to better public health. Current works are limited in their scope, functionality, and scalability. This paper proposes Sehaa, a big data analytics tool for healthcare in the Kingdom of Saudi Arabia (KSA) using Twitter data in Arabic. Sehaa uses Naive Bayes, Logistic Regression, and multiple feature extraction methods to detect various diseases in the KSA. Sehaa found that the top five diseases in Saudi Arabia in terms of the actual afflicted cases are dermal diseases, heart diseases, hypertension, cancer, and diabetes. Riyadh and Jeddah need to do more in creating awareness about the top diseases. Taif is the healthiest city in the KSA in terms of the detected diseases and awareness activities. Sehaa is developed over Apache Spark allowing true scalability. The dataset used comprises 18.9 million tweets collected from November 2018 to September 2019. The results are evaluated using well-known numerical criteria (Accuracy and F1-Score) and are validated against externally available statistics. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications to Smart City and Smart Enterprise)

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37 pages, 3851 KiB

Open AccessEditor’s ChoiceReview

Autonomous Underwater Vehicles: Localization, Navigation, and Communication for Collaborative Missions

by Josué González-García, Alfonso Gómez-Espinosa, Enrique Cuan-Urquizo, Luis Govinda García-Valdovinos, Tomás Salgado-Jiménez and Jesús Arturo Escobedo Cabello

Appl. Sci. 2020, 10(4), 1256; https://doi.org/10.3390/app10041256 - 13 Feb 2020

Cited by 176 | Viewed by 26278

Abstract

Development of Autonomous Underwater Vehicles (AUVs) has permitted the automatization of many tasks originally achieved with manned vehicles in underwater environments. Teams of AUVs designed to work within a common mission are opening the possibilities for new and more complex applications. In underwater [...] Read more.

Development of Autonomous Underwater Vehicles (AUVs) has permitted the automatization of many tasks originally achieved with manned vehicles in underwater environments. Teams of AUVs designed to work within a common mission are opening the possibilities for new and more complex applications. In underwater environments, communication, localization, and navigation of AUVs are considered challenges due to the impossibility of relying on radio communications and global positioning systems. For a long time, acoustic systems have been the main approach for solving these challenges. However, they present their own shortcomings, which are more relevant for AUV teams. As a result, researchers have explored different alternatives. To summarize and analyze these alternatives, a review of the literature is presented in this paper. Finally, a summary of collaborative AUV teams and missions is also included, with the aim of analyzing their applicability, advantages, and limitations. Full article

(This article belongs to the Special Issue Underwater Robots in Ocean and Coastal Applications)

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