Applied Sciences

13 pages, 2406 KiB

Open AccessArticle

Factors Affecting Radial Increment Dynamics in Lithuanian Populations of Common Juniper (Juniperus communis L.)

by Rasa Vaitkevičiūtė, Ekaterina Makrickiene and Edgaras Linkevičius

Appl. Sci. 2024, 14(8), 3536; https://doi.org/10.3390/app14083536 - 22 Apr 2024

Viewed by 1462

Although common juniper (Juniperus communis L.) is a widely spread species and important for the forest biodiversity and economy in many European countries, it remains one of the least studied coniferous species. This research is the first attempt to evaluate the factors [...] Read more.

Although common juniper (Juniperus communis L.) is a widely spread species and important for the forest biodiversity and economy in many European countries, it remains one of the least studied coniferous species. This research is the first attempt to evaluate the factors affecting the increment of Juniperus communis in Lithuanian populations. The aim of this article is to evaluate the patterns of radial increment in Juniperus communis and to identify the key factors influencing the increment. We collected stem discs from 160 junipers in 8 stands distributed in the different regions of Lithuania and performed the tree-ring analysis. All studied junipers expressed a pronounced eccentricity of the stem. The results of our study revealed four patterns of Juniperus communis’ radial increment, which are strongly dependent on the granulometric properties of the soil and hydrologic conditions. The effect of climatic conditions on the Juniperus communis increment was strongly dependent on the terrain; however, most of the junipers had a positive reaction to the temperatures in April, July, and August and to the precipitation in February. Full article

► Show Figures

Figure 1

10 pages, 494 KiB

Open AccessArticle

Acceleration Capacity and Vertical Jump Performance Relationship in Prepubertal Children

by Baptiste Chanel, Nicolas Babault and Carole Cometti

Appl. Sci. 2024, 14(8), 3535; https://doi.org/10.3390/app14083535 - 22 Apr 2024

Viewed by 1937

Abstract

Sprint and jump abilities are considered basic skills that are regularly evaluated in training and school contexts. The correlations between these two skills have previously been established in adults and adolescents, but they have not been fully assessed in children. The present study [...] Read more.

Sprint and jump abilities are considered basic skills that are regularly evaluated in training and school contexts. The correlations between these two skills have previously been established in adults and adolescents, but they have not been fully assessed in children. The present study aimed to explore sprinting and jumping ability in prepubertal boys and girls. Thirty-one prepubertal individuals (aged 8–11 years) were assessed during sprinting for different distances (5, 10, and 20 m) and using different vertical and horizontal jump modalities (squat jump, countermovement jump, broad jump, and hop test). Correlations between the different results were tested. Strong correlations were found between vertical jump and sprint performances, especially over short distances. These results suggested that vertical jump tests are more sensitive than horizontal jumps to reveal acceleration capacity in children. Full article

(This article belongs to the Special Issue Advances in Sports Training and Biomechanics)

► Show Figures

Figure 1

19 pages, 3440 KiB

Open AccessArticle

AOSMA-MLP: A Novel Method for Hybrid Metaheuristics Artificial Neural Networks and a New Approach for Prediction of Geothermal Reservoir Temperature

by Ezgi Gurgenc, Osman Altay and Elif Varol Altay

Appl. Sci. 2024, 14(8), 3534; https://doi.org/10.3390/app14083534 - 22 Apr 2024

Cited by 2 | Viewed by 1222

Abstract

To ascertain the optimal and most efficient reservoir temperature of a geothermal source, long-term field studies and analyses utilizing specialized devices are essential. Although these requirements increase project costs and induce delays, utilizing machine learning techniques based on hydrogeochemical data can minimize losses [...] Read more.

To ascertain the optimal and most efficient reservoir temperature of a geothermal source, long-term field studies and analyses utilizing specialized devices are essential. Although these requirements increase project costs and induce delays, utilizing machine learning techniques based on hydrogeochemical data can minimize losses by accurately predicting reservoir temperatures. In recent years, applying hybrid methods to real-world challenges has become increasingly prevalent over traditional machine learning methodologies. This study introduces a novel machine learning approach, named AOSMA-MLP, integrating the adaptive opposition slime mould algorithm (AOSMA) and multilayer perceptron (MLP) techniques, specifically designed for predicting the reservoir temperature of geothermal resources. Additionally, this work compares the basic artificial neural network and widely recognized algorithms in the literature, such as the whale optimization algorithm, ant lion algorithm, and SMA, under equal conditions using various evaluation regression metrics. The results demonstrated that AOSMA-MLP outperforms basic MLP and other metaheuristic-based MLPs, with the AOSMA-trained MLP achieving the highest performance, indicated by an R² value of 0.8514. The proposed AOSMA-MLP approach shows significant potential for yielding effective outcomes in various regression problems. Full article

(This article belongs to the Special Issue Current Trends and Perspectives on Advances in Geosciences)

► Show Figures

Figure 1

17 pages, 3025 KiB

Open AccessArticle

A TEE-Based Federated Privacy Protection Method: Proposal and Implementation

by Libo Zhang, Bing Duan, Jinlong Li, Zhan’gang Ma and Xixin Cao

Appl. Sci. 2024, 14(8), 3533; https://doi.org/10.3390/app14083533 - 22 Apr 2024

Viewed by 1721

Abstract

With the continuous enhancement of privacy protection globally, there is a problem for the traditional machine learning paradigm, which is that training data cannot be obtained from a single place. Federated learning is considered a viable technique for preserving privacy that can train [...] Read more.

With the continuous enhancement of privacy protection globally, there is a problem for the traditional machine learning paradigm, which is that training data cannot be obtained from a single place. Federated learning is considered a viable technique for preserving privacy that can train deep models with decentralized data. Aiming at two-party vertical federated learning, and at common attack problems such as model inversion, gradient leakage, and data theft, we provide a formal definition of Intel SGX’s trusted computing base, remote attestation, integrity verification, and encrypted storage, and propose a general federated learning privacy enhancement algorithm in the scenario of a malicious adversary model, and we extend this method to support horizontal federated learning, secure outsourced computation, etc. Furthermore, the method is developed in a Fedlearner framework of open-sourced machine learning to achieve privacy protection of the training data and model without any modification to the existing neural network and algorithm running on the framework. The experimental results show that this scheme substantially improves on the existing schemes in terms of training efficiency, without losing model accuracy. Full article

► Show Figures

Figure 1

16 pages, 339 KiB

Open AccessArticle

RumorLLM: A Rumor Large Language Model-Based Fake-News-Detection Data-Augmentation Approach

by Jianqiao Lai, Xinran Yang, Wenyue Luo, Linjiang Zhou, Langchen Li, Yongqi Wang and Xiaochuan Shi

Appl. Sci. 2024, 14(8), 3532; https://doi.org/10.3390/app14083532 - 22 Apr 2024

Cited by 5 | Viewed by 3016

Abstract

With the rapid development of the Internet and social media, false information, rumors, and misleading content have become pervasive, posing significant threats to public opinion and social stability, and even causing serious societal harm. This paper introduces a novel solution to address the [...] Read more.

With the rapid development of the Internet and social media, false information, rumors, and misleading content have become pervasive, posing significant threats to public opinion and social stability, and even causing serious societal harm. This paper introduces a novel solution to address the challenges of fake news detection, presenting the “Rumor Large Language Models” (RumorLLM), a large language model finetuned with rumor writing styles and content. The key contributions include the development of RumorLLM and a data-augmentation method for small categories, effectively mitigating the issue of category imbalance in real-world fake-news datasets. Experimental results on the BuzzFeed and PolitiFact datasets demonstrate the superiority of the proposed model over baseline methods, particularly in F1 score and AUC-ROC. The model’s robust performance highlights its effectiveness in handling imbalanced datasets and provides a promising solution to the pressing issue of false-information proliferation. Full article

(This article belongs to the Special Issue Data Mining and Machine Learning in Social Network Analysis)

► Show Figures

Figure 1

19 pages, 2010 KiB

Open AccessReview

Emerging Technologies for Automation in Environmental Sensing: Review

by Shekhar Suman Borah, Aaditya Khanal and Prabha Sundaravadivel

Appl. Sci. 2024, 14(8), 3531; https://doi.org/10.3390/app14083531 - 22 Apr 2024

Cited by 1 | Viewed by 3773

Abstract

This article explores the impact of automation on environmental sensing, focusing on advanced technologies that revolutionize data collection analysis and monitoring. The International Union of Pure and Applied Chemistry (IUPAC) defines automation as integrating hardware and software components into modern analytical systems. Advancements [...] Read more.

This article explores the impact of automation on environmental sensing, focusing on advanced technologies that revolutionize data collection analysis and monitoring. The International Union of Pure and Applied Chemistry (IUPAC) defines automation as integrating hardware and software components into modern analytical systems. Advancements in electronics, computer science, and robotics drive the evolution of automated sensing systems, overcoming traditional limitations in manual data collection. Environmental sensor networks (ESNs) address challenges in weather constraints and cost considerations, providing high-quality time-series data, although issues in interoperability, calibration, communication, and longevity persist. Unmanned Aerial Systems (UASs), particularly unmanned aerial vehicles (UAVs), play an important role in environmental monitoring due to their versatility and cost-effectiveness. Despite challenges in regulatory compliance and technical limitations, UAVs offer detailed spatial and temporal information. Pollution monitoring faces challenges related to high costs and maintenance requirements, prompting the exploration of cost-efficient alternatives. Smart agriculture encounters hurdle in data integration, interoperability, device durability in adverse weather conditions, and cybersecurity threats, necessitating privacy-preserving techniques and federated learning approaches. Financial barriers, including hardware costs and ongoing maintenance, impede the widespread adoption of smart technology in agriculture. Integrating robotics, notably underwater vehicles, proves indispensable in various environmental monitoring applications, providing accurate data in challenging conditions. This review details the significant role of transfer learning and edge computing, which are integral components of robotics and wireless monitoring frameworks. These advancements aid in overcoming challenges in environmental sensing, underscoring the ongoing necessity for research and innovation to enhance monitoring solutions. Some state-of-the-art frameworks and datasets are analyzed to provide a comprehensive review on the basic steps involved in the automation of environmental sensing applications. Full article

(This article belongs to the Special Issue Autonomous Systems in Cyber-Physical Systems and Smart Industry: Innovations and Challenges)

► Show Figures

Figure 1

21 pages, 6017 KiB

Open AccessArticle

The Optimization of the Geometry of the Centrifugal Fan at Different Design Points

by Paulius Ragauskas, Ina Tetsmann and Raimondas Jasevičius

Appl. Sci. 2024, 14(8), 3530; https://doi.org/10.3390/app14083530 - 22 Apr 2024

Viewed by 2869

Abstract

The optimization of the geometry of a centrifugal fan is performed at maximum power and high-efficiency design points (DPs) to improve impeller efficiency. Two design variables defining the shape of fan blade are selected for the optimization. The optimal values of the geometry [...] Read more.

The optimization of the geometry of a centrifugal fan is performed at maximum power and high-efficiency design points (DPs) to improve impeller efficiency. Two design variables defining the shape of fan blade are selected for the optimization. The optimal values of the geometry parameters of the impeller blades are identified by employing virtual flow simulations. The results of virtual experiments indicate the influence of the parameters of the blade geometry on its efficiency. With the optimization of impeller blade geometry, the efficiency of the fan is improved with respect to the reference model, as confirmed by comparing the performance curves. Herein, we discuss the results obtained in virtual tests by identifying the influence of DPs on the performance characteristics of centrifugal fans. Full article

(This article belongs to the Special Issue Advances in Structural Optimization)

► Show Figures

Figure 1

17 pages, 4552 KiB

Open AccessArticle

Enhancing Surgical Outcomes via Three-Dimensional-Assisted Techniques Combined with Orthognathic Treatment: A Case Series Study of Skeletal Class III Malocclusions

by Monica Macrì, Abdulaziz Alhotan, Gabriella Galluccio, Ersilia Barbato and Felice Festa

Appl. Sci. 2024, 14(8), 3529; https://doi.org/10.3390/app14083529 - 22 Apr 2024

Viewed by 1644

Abstract

(•) Orthognathic surgery is a necessary procedure for the correction of severe skeletal discrepancies, among which are skeletal Class III malocclusions. Currently, both conventional fixed braces and clear aligners can be used in orthognathic surgery. However, the use of clear aligners remains a [...] Read more.

(•) Orthognathic surgery is a necessary procedure for the correction of severe skeletal discrepancies, among which are skeletal Class III malocclusions. Currently, both conventional fixed braces and clear aligners can be used in orthognathic surgery. However, the use of clear aligners remains a little-chosen option. The present study aimed to evaluate the skeletal and aesthetic improvements in adults with Class III malocclusion after surgical treatment and compare the results achieved by fixed appliances versus clear aligners. The study sample included four patients (three males and one female, aged 18 to 34 years) with skeletal Class III malocclusion, three of whom underwent a bimaxillary surgery and one of whom underwent only a bilateral sagittal split osteotomy. Two patients were treated with fixed appliances and two with clear aligners. The pre- and post-surgical hard and soft tissue cephalometric measurements were performed and compared for each patient and between fixed appliances and clear aligners. One year after surgery, all patients showed an essential modification of the face’s middle and lower third with an increase in the convexity of the profile and the Wits index and a reduction in the FH^NB angle. No differences were noted between fixed appliances and aligners. Therefore, thanks to the 3D-assisted surgery associated with orthodontics, every participant achieved proper occlusal function and an improved facial aesthetics. In addition, the clear aligners can be considered a valid alternative for pre- and post-surgical orthodontic treatment. Full article

(This article belongs to the Special Issue Advanced Technologies in Oral Surgery)

► Show Figures

Figure 1

28 pages, 2596 KiB

Open AccessArticle

Optimization of Well Placement in Carbon Capture and Storage (CCS): Bayesian Optimization Framework under Permutation Invariance

by Sofianos Panagiotis Fotias, Ismail Ismail and Vassilis Gaganis

Appl. Sci. 2024, 14(8), 3528; https://doi.org/10.3390/app14083528 - 22 Apr 2024

Viewed by 2202

Abstract

Carbon Capture and Storage (CCS) stands as a pivotal technological stride toward a sustainable future, with the practice of injecting supercritical CO₂ into subsurface formations being already an established practice for enhanced oil recovery operations. The overarching objective of CCS is to [...] Read more.

Carbon Capture and Storage (CCS) stands as a pivotal technological stride toward a sustainable future, with the practice of injecting supercritical CO₂ into subsurface formations being already an established practice for enhanced oil recovery operations. The overarching objective of CCS is to protract the operational viability and sustainability of platforms and oilfields, thereby facilitating a seamless transition towards sustainable practices. This study introduces a comprehensive framework for optimizing well placement in CCS operations, employing a derivative-free method known as Bayesian Optimization. The development plan is tailored for scenarios featuring aquifers devoid of flow boundaries, incorporating production wells tasked with controlling pressure buildup and injection wells dedicated to CO₂ sequestration. Notably, the wells operate under group control, signifying predefined injection and production targets and constraints that must be adhered to throughout the project’s lifespan. As a result, the objective function remains invariant under specific permutations of the well locations. Our investigation delves into the efficacy of Bayesian Optimization under the introduced permutation invariance. The results reveal that it demonstrates critical efficiency in handling the optimization task extremely fast. In essence, this study advocates for the efficacy of Bayesian Optimization in the context of optimizing well placement for CCS operations, emphasizing its potential as a preferred methodology for enhancing sustainability in the energy sector. Full article

(This article belongs to the Special Issue Novel Applications of Machine Learning and Bayesian Optimization)

► Show Figures

Figure 1

29 pages, 7345 KiB

Open AccessArticle

Practical Steps towards Establishing an Underwater Acoustic Network in the Context of the Marine Internet of Things

by Konstantin Kebkal, Aleksey Kabanov, Oleg Kramar, Maksim Dimin, Timur Abkerimov, Vadim Kramar and Veronika Kebkal-Akbari

Appl. Sci. 2024, 14(8), 3527; https://doi.org/10.3390/app14083527 - 22 Apr 2024

Viewed by 920

Abstract

When several hydroacoustic modems operate simultaneously in an area of mutual coverage, collisions of data packets received from several sources may occur, which lead to information loss. With an increase in the number of simultaneously operating hydroacoustic modems, physical layer algorithms do not [...] Read more.

When several hydroacoustic modems operate simultaneously in an area of mutual coverage, collisions of data packets received from several sources may occur, which lead to information loss. With an increase in the number of simultaneously operating hydroacoustic modems, physical layer algorithms do not provide stable data transmission and the likelihood of collisions increases, which makes the operation of modems ineffective. To ensure effective operation in a hydroacoustic signal propagation environment and to reduce collisions when exchanging data between two modems that do not have the ability to operate synchronously and to reduce the access time to the signal propagation environment, methods of the medium access control layer using link layer protocols are required. Typically, this problem is solved using code separation of hydroacoustic channels. If you need to transfer over a network, this option will not work, since network transfer involves working on the basis of “broadcast” messages, particularly between data source and data sink that remain too far from each other, outside of their mutual audibility. In practical use, it is convenient to place these protocols into a software environment for developing specific user applications for solving network communication problems. This software framework allows for custom modification of existing network algorithms, as well as the inclusion of new network hydroacoustic communication algorithms. To build a predictive model, the DACAP, T-Lohi, Flooding, and ICRP protocols were used in this work. The implementation is performed in Erlang. The paper presents algorithms for implementing these protocols. A comparative analysis of network operation with and without protocols is provided. Efficiency of operation, i.e., data rates and probabilities of data delivery, was assessed. Full article

(This article belongs to the Special Issue Autonomous Underwater Vehicles (AUVs): Applications and Technologies)

► Show Figures

Figure 1

18 pages, 1493 KiB

Open AccessArticle

Hypergraph Position Attention Convolution Networks for 3D Point Cloud Segmentation

by Yanpeng Rong, Liping Nong, Zichen Liang, Zhuocheng Huang, Jie Peng and Yiping Huang

Appl. Sci. 2024, 14(8), 3526; https://doi.org/10.3390/app14083526 - 22 Apr 2024

Viewed by 1507

Abstract

Point cloud segmentation, as the basis for 3D scene understanding and analysis, has made significant progress in recent years. Graph-based modeling and learning methods have played an important role in point cloud segmentation. However, due to the inherent complexity of point cloud data, [...] Read more.

Point cloud segmentation, as the basis for 3D scene understanding and analysis, has made significant progress in recent years. Graph-based modeling and learning methods have played an important role in point cloud segmentation. However, due to the inherent complexity of point cloud data, it is difficult to capture higher-order and complex features of 3D data using graph learning methods. In addition, how to quickly and efficiently extract important features from point clouds also poses a great challenge to the current research. To address these challenges, we propose a new framework, called hypergraph position attention convolution networks (HGPAT), for point cloud segmentation. Firstly, we use hypergraph to model the higher-order relationships among point clouds. Secondly, in order to effectively learn the feature information of point cloud data, a hyperedge position attention convolution module is proposed, which utilizes the hyperedge–hyperedge propagation pattern to extract and aggregate more important features. Finally, we design a ResNet-like module to reduce the computational complexity of the network and improve its efficiency. We have conducted point cloud segmentation experiments on the ShapeNet Part and S3IDS datasets, and the experimental results demonstrate the effectiveness of the proposed method compared with the state-of-the-art ones. Full article

(This article belongs to the Special Issue New Insights into Computer Vision and Graphics)

► Show Figures

Figure 1

16 pages, 8529 KiB

Open AccessArticle

Comparative Analysis of the Stability of Overlying Rock Mass for Two Types of Lined Rock Caverns Based on Rock Mass Classification

by Qi Yi, Zhen Shen, Guanhua Sun, Shan Lin and Hongming Luo

Appl. Sci. 2024, 14(8), 3525; https://doi.org/10.3390/app14083525 - 22 Apr 2024

Viewed by 1122

Abstract

Lined rock caverns (LRCs) are becoming the preferred option for air storage at sites where there are no natural cavities, such as salt caverns, and this storage technology is being developed and utilized in markets around the world. The stability of the overlying [...] Read more.

Lined rock caverns (LRCs) are becoming the preferred option for air storage at sites where there are no natural cavities, such as salt caverns, and this storage technology is being developed and utilized in markets around the world. The stability of the overlying rock mass is one of the key factors to ensure the successful operation of LRCs. In this paper, a stability assessment method is presented that first calculates the potential fracture surfaces of the surrounding rock based on the limiting stress field and the Mohr–Coulomb damage criterion, and then, based on these fracture surfaces, solves for the factor of safety defined on the basis of the concept of strength reserve. Using this method, this study evaluates the stability of two types of LRCs, tunnel- and silo-type, under three different geological conditions. The results of the analysis show that the silo-type LRCs are more economical for engineering purposes. Also, this paper provides some guidance for engineers in site selection and preliminary design. Full article

(This article belongs to the Special Issue Advances and Challenges in Rock Mechanics and Rock Engineering)

► Show Figures

Figure 1

18 pages, 4592 KiB

Open AccessArticle

Text Triplet Extraction Algorithm with Fused Graph Neural Networks and Improved Biaffine Attention Mechanism

by Yinghao Piao and Jin-Xi Zhang

Appl. Sci. 2024, 14(8), 3524; https://doi.org/10.3390/app14083524 - 22 Apr 2024

Cited by 1 | Viewed by 1033

Abstract

In the realm of aspect-based sentiment analysis (ABSA), a paramount task is the extraction of triplets, which define aspect terms, opinion terms, and their respective sentiment orientations within text. This study introduces a novel extraction model, BiLSTM-BGAT-GCN, which seamlessly integrates graph neural networks [...] Read more.

In the realm of aspect-based sentiment analysis (ABSA), a paramount task is the extraction of triplets, which define aspect terms, opinion terms, and their respective sentiment orientations within text. This study introduces a novel extraction model, BiLSTM-BGAT-GCN, which seamlessly integrates graph neural networks with an enhanced biaffine attention mechanism. This model amalgamates the sophisticated capabilities of both graph attention and convolutional networks to process graph-structured data, substantially enhancing the interpretation and extraction of textual features. By optimizing the biaffine attention mechanism, the model adeptly uncovers the subtle interplay between aspect terms and emotional expressions, offering enhanced flexibility and superior contextual analysis through dynamic weight distribution. A series of comparative experiments confirm the model’s significant performance improvements across various metrics, underscoring its efficacy and refined effectiveness in ABSA tasks. Full article

(This article belongs to the Special Issue Applications of Advanced Deep Learning Technology in Control and Intelligent Systems)

► Show Figures

Figure 1

12 pages, 3062 KiB

Open AccessArticle

Structural and Tribological Analysis of Brake Disc–Pad Pair Material for Cars

by Filip Ilie and Andreea Catalina Ctristescu

Appl. Sci. 2024, 14(8), 3523; https://doi.org/10.3390/app14083523 - 22 Apr 2024

Cited by 2 | Viewed by 1708

Abstract

The study of the tribological behavior of the braking system in auto vehicles requires knowing the characteristics of the material in contact and, in the work process, the friction pair brake disc pads. Material structural analysis is necessary because the wear process depends [...] Read more.

The study of the tribological behavior of the braking system in auto vehicles requires knowing the characteristics of the material in contact and, in the work process, the friction pair brake disc pads. Material structural analysis is necessary because the wear process depends both on the friction-pair chemical composition (brake disc pads) and on the work process parameters (pressing force, rotational speed, traffic conditions, etc.). The material of the brake discs is generally the same, gray cast iron, and the brake pads can be semimetallic (particles of steel, copper, brass, and graphite, all united with a special resin), organic materials (particles of rubber, glass, and Kevlar, all joined with the help of a resin), composite materials that contain different constituents, and ceramic materials (rarely have small copper particles). Therefore, the purpose of this paper is to analyze the crystalline structure, tribological behavior (at friction and wear), and the mechanical properties of the materials of the brake disc–pad friction pair specific to the field through study and analysis. Full article

(This article belongs to the Special Issue Feature Papers in 'Surface Sciences and Technology' Section, 2nd Edition)

► Show Figures

Figure 1

18 pages, 1399 KiB

Open AccessArticle

Encapsulation of Fennel Essential Oil in Calcium Alginate Microbeads via Electrostatic Extrusion

by Erika Dobroslavić, Ena Cegledi, Katarina Robić, Ivona Elez Garofulić, Verica Dragović-Uzelac and Maja Repajić

Appl. Sci. 2024, 14(8), 3522; https://doi.org/10.3390/app14083522 - 22 Apr 2024

Cited by 2 | Viewed by 1132

Abstract

Fennel essential oil (EO) is well known for its biological activities and wide potential for use in the food, cosmetic, and pharmaceutical industries, where the main challenge is to achieve higher stability of EO. This study aimed to evaluate the potential of electrostatic [...] Read more.

Fennel essential oil (EO) is well known for its biological activities and wide potential for use in the food, cosmetic, and pharmaceutical industries, where the main challenge is to achieve higher stability of EO. This study aimed to evaluate the potential of electrostatic extrusion for encapsulation of fennel EO by examining the effects of alginate (1%, 1.5%, and 2%) and whey protein (0%, 0.75%, and 1.5%) concentrations and drying methods on the encapsulation efficiency, loading capacity, bead characteristics, and swelling behavior of the produced fennel EO microbeads. Results revealed that electrostatic extrusion proved to be effective for encapsulating fennel EO, with whey protein addition enhancing the examined characteristics of the obtained microbeads. Freeze-drying exhibited superior performance compared to air-drying. Optimal encapsulation efficiency (51.95%) and loading capacity (78.28%) were achieved by using 1.5% alginate and 0.75% whey protein, followed by freeze-drying. GC-MS analysis revealed no differences in the qualitative aspect of the encapsulated and initial EO, with the encapsulated EO retaining 58.95% of volatile compounds. This study highlighted the potential of electrostatic extrusion using alginate and whey protein as a promising technique for fennel EO encapsulation while also emphasizing the need for further exploration into varied carrier materials and process parameters to optimize the encapsulation process and enhance product quality. Full article

(This article belongs to the Special Issue Natural Products and Bioactive Compounds)

► Show Figures

Figure 1

19 pages, 6065 KiB

Open AccessArticle

Automatic Object Detection in Radargrams of Multi-Antenna GPR Systems Based on Simulation Data for Railway Infrastructure Analysis

by Lukas Lahnsteiner, David Größbacher, Martin Bürger and Gerald Zauner

Appl. Sci. 2024, 14(8), 3521; https://doi.org/10.3390/app14083521 - 22 Apr 2024

Viewed by 1187

Abstract

Ground-penetrating radar (GPR) is a non-invasive technology that uses electromagnetic pulses for subsurface exploration. In the railroad sector, it is crucial to assessing soil layers and infrastructure, offering insights into soil stratification and geological features and aiding in identifying subsurface hazards. However, the [...] Read more.

Ground-penetrating radar (GPR) is a non-invasive technology that uses electromagnetic pulses for subsurface exploration. In the railroad sector, it is crucial to assessing soil layers and infrastructure, offering insights into soil stratification and geological features and aiding in identifying subsurface hazards. However, the automation of radargram analysis is impeded by the lack of ground truth—accurate real-world data used to validate machine learning models—thus affecting the deployment of advanced algorithms. This study focuses on generating high-quality simulated data to address the shortage of real-world data in the context of object detection along railroad tracks and presents a fully automated pipeline that includes data generation, algorithm training, and validation using real-world data. By doing so, it paves the way for significantly easing the future task of object detection algorithms in the railway sector. A simulation environment, including the digital twin of a GPR antenna, was developed for artificial data generation. The process involves pre- and post-processing techniques to transform the three-dimensional data from the multichannel GPR system into two-dimensional datasets. This ensures minimal information loss and suitability for established two-dimensional object detection algorithms like the well-known YOLO (You Only Look Once) framework. Validation involved real-world measurements on a track with predefined buried objects. The entire pipeline, encompassing data generation, processing, training, and application, was automated for efficient algorithm testing and implementation. Artificial data show promise for better performance with increased training. Future AI and sensor advancements will enhance subsurface exploration, contributing to safer and more reliable railroad operations. Full article

(This article belongs to the Special Issue Ground Penetrating Radar (GPR): Theory, Methods and Applications)

► Show Figures

Figure 1

17 pages, 675 KiB

Open AccessArticle

Can Windows 11 Stop Well-Known Ransomware Variants? An Examination of Its Built-in Security Features

by Yousef Mahmoud Al-Awadi, Ali Baydoun and Hafeez Ur Rehman

Appl. Sci. 2024, 14(8), 3520; https://doi.org/10.3390/app14083520 - 22 Apr 2024

Viewed by 1907

Abstract

The ever-evolving landscape of cyber threats, with ransomware at its forefront, poses significant challenges to the digital world. Windows 11 Pro, Microsoft’s latest operating system, claims to offer enhanced security features designed to tackle such threats. This paper aims to comprehensively evaluate the [...] Read more.

The ever-evolving landscape of cyber threats, with ransomware at its forefront, poses significant challenges to the digital world. Windows 11 Pro, Microsoft’s latest operating system, claims to offer enhanced security features designed to tackle such threats. This paper aims to comprehensively evaluate the effectiveness of these Windows 11 Pro, built-in security measures against prevalent ransomware strains, with a particular emphasis on crypto-ransomware. Utilizing a meticulously crafted experimental environment, the research adopted a two-phased testing approach, examining both the default and a hardened configuration of Windows 11 Pro. This dual examination offered insights into the system’s inherent and potential defenses against ransomware threats. The study’s findings revealed that Windows 11 Pro does present formidable defenses. This paper not only contributes valuable insights into cybersecurity, but also furnishes practical recommendations for both technology developers and end-users in the ongoing battle against ransomware. The significance of these findings extends beyond the immediate evaluation of Windows 11 Pro, serving as a reference point for the broader discourse on enhancing digital security measures. Full article

(This article belongs to the Special Issue Advances in Cybersecurity: Challenges and Solutions)

► Show Figures

Figure 1

16 pages, 1543 KiB

Open AccessArticle

Stability Analysis in Multi-VSC (Voltage Source Converter) Systems of Wind Turbines

by Dimitrios Dimitropoulos, Xiongfei Wang and Frede Blaabjerg

Appl. Sci. 2024, 14(8), 3519; https://doi.org/10.3390/app14083519 - 22 Apr 2024

Cited by 1 | Viewed by 1117

Abstract

In this paper, a holistic nonlinear state-space model of a system with multiple converters is developed, where the converters correspond to the wind turbines in a wind farm and are equipped with grid-following control. A novel generalized methodology is developed, based on the [...] Read more.

In this paper, a holistic nonlinear state-space model of a system with multiple converters is developed, where the converters correspond to the wind turbines in a wind farm and are equipped with grid-following control. A novel generalized methodology is developed, based on the number of the system’s converters, to compute the equilibrium points around which the model is linearized. This is a more solid approach compared with selecting operating points for linearizing the model or utilizing EMT simulation tools to estimate the system’s steady state. The dynamics of both the inner and outer control loops of the power converters are included, as well as the dynamics of the electrical elements of the system and the digital time delay, in order to study the dynamic issues in both high- and low-frequency ranges. The system’s stability is assessed through an eigenvalue-based stability analysis. A participation factor analysis is also used to give an insight into the interactions caused by the control topology of the converters. Time domain simulations and the corresponding frequency analysis are performed in order to validate the model for all the control interactions under study. Full article

(This article belongs to the Section Energy Science and Technology)

► Show Figures

Figure 1

16 pages, 2185 KiB

Open AccessReview

Users’ Expectations of Smart Devices during Physical Activity—A Literature Review

by Kitti Tóth, Péter Takács and Ildikó Balatoni

Appl. Sci. 2024, 14(8), 3518; https://doi.org/10.3390/app14083518 - 22 Apr 2024

Viewed by 1627

Abstract

Background: The field of smart devices and physical activity is evolving rapidly, with a wide range of devices measuring a wide range of parameters. Scientific articles look at very different populations in terms of the impact of smart devices but do not take [...] Read more.

Background: The field of smart devices and physical activity is evolving rapidly, with a wide range of devices measuring a wide range of parameters. Scientific articles look at very different populations in terms of the impact of smart devices but do not take into account which characteristics of the devices are important for the group and which may influence the effectiveness of the device. In our study, we aimed to analyse articles about the impact of smart devices on physical activity and identify the characteristics of different target groups. Methods: Queries were run on two major databases (PubMed and Web of Science) between 2017 and 2024. Duplicates were filtered out, and according to a few main criteria, inappropriate studies were excluded so that 37 relevant articles were included in a more detailed analysis. Results: Four main target groups were identified: healthy individuals, people with chronic diseases, elderly people, and competitive athletes. We identified the essential attributes of smart devices by target groups. For the elderly, an easy-to-use application is needed. In the case of women, children, and elderly people, gamification can be used well, but for athletes, specific measurement tools and accuracy may have paramount importance. For most groups, regular text messages or notifications are important. Conclusions: The use of smart devices can have a positive impact on physical activity, but the context and target group must be taken into account to achieve effectiveness. Full article

► Show Figures

Figure 1

16 pages, 2500 KiB

Open AccessArticle

Soil and Sediments in Natural Underground Ecosystems as a Source of Culturable Micromycetes: A Case Study of the Brestovská Cave (Western Tatras, Slovakia)

by Rafał Ogórek, Justyna Borzęcka, Klaudyna Spychała, Agata Piecuch and Jakub Suchodolski

Appl. Sci. 2024, 14(8), 3517; https://doi.org/10.3390/app14083517 - 22 Apr 2024

Cited by 1 | Viewed by 937

Abstract

Soil and sediment host microorganisms are able to survive in extremely resource-limited environments. Therefore, more and more attention is being paid to cave sediments as a reservoir of microbiota. The aim of this study is the speleomycological evaluation of the culturable soil and [...] Read more.

Soil and sediment host microorganisms are able to survive in extremely resource-limited environments. Therefore, more and more attention is being paid to cave sediments as a reservoir of microbiota. The aim of this study is the speleomycological evaluation of the culturable soil and sediment fungal communities in the Brestovská Cave. To explore the origins of fungi, speleomycological studies were conducted both inside and outside the cave under investigation. Additionally, two incubation temperatures (5 and 24 °C) were used to increase the species spectrum of isolated fungi. To achieve the most accurate species identification, we combined an assessment of morphological characteristics of the isolates with molecular sequencing (ITS, internal transcribed spacer). Twenty different species were found and the most frequent was Penicillium commune, followed by Trichosporiella cerebriformis and Pseudogymnoascus pannorum. To our knowledge, our study has enabled the first identification of fungal species such as Penicillium swiecicki, Cephalotrichum hinnuleum, Cosmpospora berkeleyana, Lecythophora hoffmannii, Ambomucor seriatoinflatus, and Mortierella minutissima in underground sites. Our data showed that the abundance and composition of the fungal community varied between the indoor and outdoor samples and thus from the entrance and less visited sites deeper in the cave. Full article

(This article belongs to the Section Applied Microbiology)

► Show Figures

Figure 1

23 pages, 4610 KiB

Open AccessArticle

Exploring the Performance of Continuous-Time Dynamic Link Prediction Algorithms

by Raphaël Romero, Maarten Buyl, Tijl De Bie and Jefrey Lijffijt

Appl. Sci. 2024, 14(8), 3516; https://doi.org/10.3390/app14083516 - 22 Apr 2024

Viewed by 958

Abstract

Dynamic Link Prediction (DLP) addresses the prediction of future links in evolving networks. However, accurately portraying the performance of DLP algorithms poses challenges that might impede progress in the field. Importantly, common evaluation pipelines usually calculate ranking or binary classification metrics, where the [...] Read more.

Dynamic Link Prediction (DLP) addresses the prediction of future links in evolving networks. However, accurately portraying the performance of DLP algorithms poses challenges that might impede progress in the field. Importantly, common evaluation pipelines usually calculate ranking or binary classification metrics, where the scores of observed interactions (positives) are compared with those of randomly generated ones (negatives). However, a single metric is not sufficient to fully capture the differences between DLP algorithms, and is prone to overly optimistic performance evaluation. Instead, an in-depth evaluation should reflect performance variations across different nodes, edges, and time segments. In this work, we contribute tools to perform such a comprehensive evaluation. (1) We propose Birth–Death diagrams, a simple but powerful visualization technique that illustrates the effect of time-based train–test splitting on the difficulty of DLP on a given dataset. (2) We describe an exhaustive taxonomy of negative sampling methods that can be used at evaluation time. (3) We carry out an empirical study of the effect of the different negative sampling strategies. Our comparison between heuristics and state-of-the-art memory-based methods on various real-world datasets confirms a strong effect of using different negative sampling strategies on the test area under the curve (AUC). Moreover, we conduct a visual exploration of the prediction, with additional insights on which different types of errors are prominent over time. Full article

(This article belongs to the Special Issue Data Mining and Machine Learning in Social Network Analysis)

► Show Figures

Figure 1

9 pages, 354 KiB

Open AccessArticle

Comparison of the Operator and Surrounding Dose When Using Portable Intraoral X-ray Devices

by Mehrdad Abdinian, Maedeh Aminian, Forouzan Keymasi, Parisa Soltani, Mariangela Cernera, Niccolo Giuseppe Armogida and Gianrico Spagnuolo

Appl. Sci. 2024, 14(8), 3515; https://doi.org/10.3390/app14083515 - 22 Apr 2024

Viewed by 1493

Abstract

This study aimed to investigate the scattered radiation dose using three portable dental radiographic units: iRay D3, EZRay Air, and Epix. The absorbed dose was measured at 0.5 and 1 m distances, every 15° in the horizontal plane, using an ionization chamber. The [...] Read more.

This study aimed to investigate the scattered radiation dose using three portable dental radiographic units: iRay D3, EZRay Air, and Epix. The absorbed dose was measured at 0.5 and 1 m distances, every 15° in the horizontal plane, using an ionization chamber. The maximum number of radiographs per day using the portable units was calculated considering a dose limit of 50 mSv/year and 20 mSv/year. The doses were higher in the Epix unit compared to the other two devices. Anterior exposure was generally higher than the sides or posterior exposure. With a dose limit of 50 mSv/year, considering a distance of 0.5 m between the operator and the X-ray unit, a maximum of 961, 565, and 38 radiographs are permitted daily using iRay D3, EZRay Air, and Epix, respectively. Considering a dose limit of 20 mSv/year, with a distance of 0.5 m between the operator and the radiographic device, a maximum of 384, 226, and 15 radiographs are permitted daily using iRay D3, EZRay Air, and Epix portable units, respectively. It is highly unlikely that an operator would reach occupational dose limits when using iRay D3 and EZRay Air. The Epix radiographic device allows for fewer daily radiographs and should be avoided for daily use. Full article

(This article belongs to the Section Applied Dentistry and Oral Sciences)

► Show Figures

Figure 1

16 pages, 7103 KiB

Open AccessArticle

Research on Optimization of an Open-Bench Deep-Hole Blasting Parameter Using an Improved Gray Wolf Algorithm

by Li Zhao, Dengfeng Su, Zhengguo Li, Banghong Chen, Rui Wang and Rongkai Chen

Appl. Sci. 2024, 14(8), 3514; https://doi.org/10.3390/app14083514 - 22 Apr 2024

Viewed by 1217

Abstract

The blasting quality of open-pit mining can be enhanced and the production cost of stope reduced by establishing a mathematical model for step drilling and blasting costs based on stope consumption. By enhancing the Gray Wolf algorithm, the parameters for step drilling and [...] Read more.

The blasting quality of open-pit mining can be enhanced and the production cost of stope reduced by establishing a mathematical model for step drilling and blasting costs based on stope consumption. By enhancing the Gray Wolf algorithm, the parameters for step drilling and blasting are optimized, resulting in improved effectiveness for step blasting mining, as demonstrated through modeling and calculation. The enhanced Gray Wolf algorithm effectively enhances the blasting performance, reduces production costs, and increases production efficiency. Taking a limestone mine as an example, the optimized drilling and blasting parameters are as follows: hole spacing of 4.62 m, row spacing of 4 m, and explosive consumption rate of 0.23 kg/t; based on these parameters, the stope’s production cost is reduced to CNY 7.7. Full article

(This article belongs to the Special Issue Recent Advances in the Effect of Blast Loads on Structures)

► Show Figures

Figure 1

17 pages, 1894 KiB

Open AccessArticle

Enhanced Visual Performance for In–Vehicle Reading Task Evaluated by Preferences, Emotions and Sustained Attention

by Yichen Ni, Christopher Weirich and Yandan Lin

Appl. Sci. 2024, 14(8), 3513; https://doi.org/10.3390/app14083513 - 22 Apr 2024

Viewed by 1102

Abstract

The proliferation of electric and hybrid vehicles has made it possible for people to read and work in a stationary vehicle for extended periods. However, the current commonly used in–vehicle lighting design is still centered around driving and driving safety. Following recommendations from [...] Read more.

The proliferation of electric and hybrid vehicles has made it possible for people to read and work in a stationary vehicle for extended periods. However, the current commonly used in–vehicle lighting design is still centered around driving and driving safety. Following recommendations from the literature, a neutral white color band (4000 K–5000 K) with 50–100 lx at the vehicle table area is favored. Whether this lighting environment can meet the needs to enhance the reading performance in a modern vehicle was investigated in this presented study. Therefore, in total, 12 lighting settings were designed based on combinations of four illuminance levels (50 lx, 100 lx, 150 lx and 200 lx) and three correlated color temperatures (3000 K, 4000 K and 5000 K); we recruited 19 subjects (12 females, 7 males) and let study participants evaluate each condition based on electronic and paper reading. Next, subjective preferences, positive and negative emotions, feeling of fatigue and sustained attention were tested. We found that higher illuminance and higher CCT (Correlated Color Temperature) can significantly improve the performance of in–vehicle readers in most aspects following Kruithof’s law (p < 0.05). Among them, we recommend the combination of 150 lx and 4000 K as the light parameters for in–vehicle reading as a new development guideline. In addition, we also discovered the inconsistency of people’s lighting preferences between in–vehicle spaces and conventional spaces. For indoor lighting, illuminance values up to 1000 lx are still favored. For an in–vehicle function, starting with 200 lx, the preference level and reading performance already declined. In comparison between electronic and paper reading, both were similarly evaluated. These results show that a neutral white light color should be chosen with a horizontal illuminance of maximal 150 lx for a reading light function independent of the reading device. Interdisciplinarily speaking, our findings can be applied in similar small spaces or transportation modes with gentle acceleration and deceleration such as small space hotel rooms, trains, airplanes or ships. Full article

(This article belongs to the Section Transportation and Future Mobility)

► Show Figures

Figure 1

22 pages, 8535 KiB

Open AccessArticle

A Meta-Analysis of the Effect of Moisture Content of Recycled Concrete Aggregate on the Compressive Strength of Concrete

by Sung-Won Cho, Sung Eun Cho and Alexander S. Brand

Appl. Sci. 2024, 14(8), 3512; https://doi.org/10.3390/app14083512 - 22 Apr 2024

Viewed by 1123

Abstract

To reduce the environmental impact of concrete, recycled aggregates are of significant interest. Recycled concrete aggregate (RCA) presents a significant resource opportunity, although its performance as an aggregate in concrete is variable. This study presents a meta-analysis of the published literature to refine [...] Read more.

To reduce the environmental impact of concrete, recycled aggregates are of significant interest. Recycled concrete aggregate (RCA) presents a significant resource opportunity, although its performance as an aggregate in concrete is variable. This study presents a meta-analysis of the published literature to refine the understanding of how the moisture content of RCA, as well as other parameters, affects the compressive strength of concrete. Seven machine learning models were used to predict the compressive strength of concrete with RCA, including linear regression, support vector regression (SVR), and k-nearest neighbors (KNN) as single models, and decision tree, random forest, XGBoost, and LightGBM as ensemble models. The results of this study demonstrate that ensemble models, particularly the LightGBM model, exhibited superior prediction accuracy compared to single models. The LightGBM model yielded the highest prediction accuracy with R² = 0.94, RMSE = 4.16 MPa, MAE = 3.03 MPa, and Delta RMSE = 1.4 MPa, making it the selected final model. The study, employing feature importance with LightGBM as the final model, identified age, water/cement ratio, and fine RCA aggregate content as key factors influencing compressive strength in concrete with RCA. In an interaction plot analysis using the final model, lowering the water–cement ratio consistently improved compressive strength, especially between 0.3 and 0.4, while increasing the fine RCA ratio decreased compressive strength, particularly in the range of 0.4 to 0.6. Additionally, it was found that maintaining moisture conditions of RCA typically between 0.0 and 0.8 was crucial for maximizing strength, whereas extreme moisture conditions, like fully saturated surface dry (SSD) state, negatively impacted strength. Full article

(This article belongs to the Special Issue Development, Characterization, Application and Recycling of Novel Construction Materials)

► Show Figures

Figure 1

32 pages, 4173 KiB

Open AccessArticle

Insight into Adsorption Kinetics of Cs⁺, Rb⁺, Co²⁺, and Sr²⁺ on a Zeolites-Based Composite: Comprehensive Diffusional Explanation and Modelling

by Abdel Boughriet, Gildas Doyemet, Nicole Poumaye, Oscar Allahdin and Michel Wartel

Appl. Sci. 2024, 14(8), 3511; https://doi.org/10.3390/app14083511 - 22 Apr 2024

Viewed by 1072

Abstract

Kaolinite-rich soils were used to prepare zeolite-based composites via alkaline activation. The porous material was characterized by conducting XRD and microporosity measurements, as well as ESEM microscopy. The Weber and Morris (W-M) model was used for studying adsorption kinetics of radioactive cations on [...] Read more.

Kaolinite-rich soils were used to prepare zeolite-based composites via alkaline activation. The porous material was characterized by conducting XRD and microporosity measurements, as well as ESEM microscopy. The Weber and Morris (W-M) model was used for studying adsorption kinetics of radioactive cations on synthesized alkali-activated material. These investigations evidenced the effects of pore structure and the importance of the intrinsic characteristics of hydrated cations (ionic potential; hydrated radius; B-viscosity parameter; molar Gibbs energy of hydration of cation) on W-M kinetic rate constants. The application of diffusion-based models permitted us to assess the key diffusion parameters controlling successive diffusion regimes, and to reveal strong contributions of surface diffusion to adsorption kinetics during the course of the second and third kinetics stages of the W-M model. The magnitude of the surface diffusion coefficient was related to the capacity of hydrated cationic species to lose water molecules when penetrating brick pores. The HSDM model were tested for predicting radionuclide adsorption in a fixed-bed column. A breakthrough curve simulation indicated the predominance of the surface diffusion regime, which was in agreement with mathematical analysis of (batch) adsorption kinetics data. Ionic diffusion was linked to the characteristics of capillary porosity and connectivity of capillary pores in the composite, suggesting the generation of hydrated nuclides and their immobilization in the form of outer-sphere complexes. Full article

(This article belongs to the Special Issue Novel Ceramic Materials: Processes, Properties and Applications)

► Show Figures

Figure 1

16 pages, 2259 KiB

Open AccessArticle

PnV: An Efficient Parallel Consensus Protocol Integrating Proof and Voting

by Han Wang, Hui Li, Ping Fan, Jian Kang, Selwyn Deng and Xiang Zhu

Appl. Sci. 2024, 14(8), 3510; https://doi.org/10.3390/app14083510 - 22 Apr 2024

Viewed by 1126

Abstract

Consensus protocols, as crucial components of blockchain technology, play a vital role in ensuring data consistency among distributed nodes. However, the existing voting-based and proof-based consensus protocols encounter scalability issues within the blockchain system. Moreover, most consensus protocols are serialized, which further limits [...] Read more.

Consensus protocols, as crucial components of blockchain technology, play a vital role in ensuring data consistency among distributed nodes. However, the existing voting-based and proof-based consensus protocols encounter scalability issues within the blockchain system. Moreover, most consensus protocols are serialized, which further limits their scalability potential. To address this limitation, parallelization methods have been employed in both types of consensus protocols. Surprisingly, however, novel fusion consensus protocols demonstrate superior scalability compared with these two types but lack the utilization of parallelization techniques. In this paper, we present PnV, an efficient parallel fusion protocol integrating proof-based and voting-based consensus features. It enhances the data structure, consensus process, transaction allocation, and timeout handling mechanisms to enable concurrent block generation by multiple nodes within a consensus round. Experimental results demonstrate that PnV exhibits superior efficiency, excellent scalability, and acceptable delay compared with Proof of Vote (PoV) and BFT-SMART. Moreover, at the system level, the performance of the PnV-based blockchain system optimally surpasses that of the FISCO BCOS platform. Our proposed protocol contributes to advancing blockchain technology by providing a more efficient and practical solution for achieving decentralized consensus in distributed systems. Full article

(This article belongs to the Special Issue Advanced Technologies in Data and Information Security III)

► Show Figures

Figure 1

17 pages, 14564 KiB

Open AccessArticle

P D N: A Priori Dictionary Network for Fashion Parsing

by Jue Hou, Yinwen Lu, Yang Yang and Zheng Liu

Appl. Sci. 2024, 14(8), 3509; https://doi.org/10.3390/app14083509 - 22 Apr 2024

Viewed by 945

Abstract

The task of fashion parsing aims to assign pixel-level labels to clothing targets; thereby, parsing models are required to have good contextual recognition ability. However, the shapes of clothing components are complex, and the types are difficult to distinguish. Recent solutions focus on [...] Read more.

The task of fashion parsing aims to assign pixel-level labels to clothing targets; thereby, parsing models are required to have good contextual recognition ability. However, the shapes of clothing components are complex, and the types are difficult to distinguish. Recent solutions focus on improving datasets and supplying abundant priori information, but the utilization of features by more efficient methods is rarely explored. In this paper, we propose a multi-scale fashion parsing model called the Priori Dictionary Network (PDN), which includes a priori attention module and a multi-scale backbone. The priori attention module extracts high dimensional features from our designed clothing average template as a priori information dictionary (priori dictionary, PD), and the PD is utilized to activate the feature maps of a CNN from a multi-scale attention mechanism. The backbone is derived from classical models, and five side paths are designed to leverage the richer features of local and global contextual representations. To measure the performance of our method, we evaluated the model on four public datasets, the CFPD, UTFR-SBD3, ModaNet and LIP, and the experimental results show that our model stands out from other State of the Art in all four datasets. This method can assist with the labeling problem of clothing datasets. Full article

(This article belongs to the Special Issue AI-Based Image Processing: 2nd Edition)

► Show Figures

Figure 1

13 pages, 281 KiB

Open AccessArticle

The Quality of Goose Breast Muscle Products Depending on the Cooking Method Used

by Adam Więk, Wacław Mozolewski, Sylwester Rybaczek and Monika Modzelewska-Kapituła

Appl. Sci. 2024, 14(8), 3508; https://doi.org/10.3390/app14083508 - 22 Apr 2024

Viewed by 987

Abstract

This study was conducted to compare the quality characteristics of White Kołuda goose breast muscle products, heated using the sous vide (SV) and the convection–steam oven (OV) methods. The qualitative analysis included instrumental evaluation of texture and colour parameters and the content of [...] Read more.

This study was conducted to compare the quality characteristics of White Kołuda goose breast muscle products, heated using the sous vide (SV) and the convection–steam oven (OV) methods. The qualitative analysis included instrumental evaluation of texture and colour parameters and the content of histidine dipeptide anserine. The research material consisted of breast muscles without skin, heated using the sous vide (SV) method at 65 °C for 4 h and 10 h and in a convection–steam oven (OV) in a steam environment at 80 °C and 90 °C (to obtain the final temperature of 65 °C in the geometric centre of meat pieces). Extending the heating time using the SV method and increasing the temperature in OV resulted in increased hardness, cohesiveness and chewiness. The use of heat treatment resulted in a significant reduction in the initial anserine content. A greater anserine reduction was found in SV samples compared to OV. The SV processing time did not significantly differentiate the dipeptide content, nor did the temperature used in OV processing. Pectoral muscles heated using the sous vide method were characterised by higher values of the parameters L* and b* and the hue angle (h) compared to OV processing, in which the value of the a* parameter was higher. The low-temperature processing methods (SV 65 °C/4 h and OV 80 °C) of goose breast meat allowed for obtaining products with similar textural characteristics: hardness, adhesiveness, elasticity and chewiness. Full article

(This article belongs to the Section Food Science and Technology)

20 pages, 2467 KiB

Open AccessArticle

Chosen Biochemical and Physical Properties of Beetroot Treated with Ultrasound and Dried with Infrared–Hot Air Method

by Malgorzata Nowacka, Katarzyna Rybak, Magdalena Trusinska, Magdalena Karwacka, Aleksandra Matys, Katarzyna Pobiega and Dorota Witrowa-Rajchert

Appl. Sci. 2024, 14(8), 3507; https://doi.org/10.3390/app14083507 - 22 Apr 2024

Cited by 1 | Viewed by 1424

Abstract

Beetroots are sources of bioactive compounds and valued pigments such as betalains. The purpose of this study was to determine the influence of ultrasound pretreatment on the beetroot infrared–hot air drying process and the functional properties of the obtained product. In this study, [...] Read more.

Beetroots are sources of bioactive compounds and valued pigments such as betalains. The purpose of this study was to determine the influence of ultrasound pretreatment on the beetroot infrared–hot air drying process and the functional properties of the obtained product. In this study, there were two used frequencies—21 and 35 kHz—and three different periods of time—10, 20, and 30 min. Since beetroots are usually subjected to thermal treatment, another aim was to examine the influence of blanching and soaking on the beetroot tissue properties in order to compare traditional and ultrasound-treated methods. As a result of this study, it was found that ultrasound pretreatment changed the dry matter content, water activity, thickness of the tissue, total color difference, and contents of betanin pigments in the beetroot. It was revealed that the drying process is shorter after ultrasound pretreatment using a 21 kHz frequency. Drying tissue exposed to ultrasounds showed a significant increase in the L* parameter; however, the decrease in the a* parameter was caused by a reduced content of betalain pigments. Taking into consideration parameters important from a technological point of view, it was found that the best condition for beetroot pretreatment is 20 min treatment, regardless of the frequency used. Full article

(This article belongs to the Special Issue Functional Foods and Natural Products: Bioactive Compounds and Beneficial Effects on Health – Volume II)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Appl. Sci., Volume 14, Issue 8 (April-2 2024) – 402 articles

Further Information

Guidelines

MDPI Initiatives

Follow MDPI