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Appl. Sci., Volume 11, Issue 8 (April-2 2021) – 431 articles

Cover Story (view full-size image): We successfully demonstrated conversion of the indirect band gap nature of bilayer MoS2 to a direct band gap nature and enhanced the photoluminescence (PL) intensity of bilayer MoS2 dramatically. The procedure combines UV irradiation with superacid molecular treatment on bilayer MoS2. The process dramatically enhances the PL intensity of bilayer MoS2 by a factor of 700×. The monolayer-like top layer is generated and physically separated from the substrate by the intermediate bottom MoS2 layer. This situation is preferable for achieving a strong PL intensity. View this paper
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17 pages, 2606 KiB  
Article
A DMAIC Integrated Fuzzy FMEA Model: A Case Study in the Automotive Industry
by Radu Godina, Beatriz Gomes Rolis Silva and Pedro Espadinha-Cruz
Appl. Sci. 2021, 11(8), 3726; https://doi.org/10.3390/app11083726 - 20 Apr 2021
Cited by 30 | Viewed by 5808
Abstract
The growing competitiveness in the automotive industry and the strict standards to which it is subject, require high quality standards. For this, quality tools such as the failure mode and effects analysis (FMEA) are applied to quantify the risk of potential failure modes. [...] Read more.
The growing competitiveness in the automotive industry and the strict standards to which it is subject, require high quality standards. For this, quality tools such as the failure mode and effects analysis (FMEA) are applied to quantify the risk of potential failure modes. However, for qualitative defects with subjectivity and associated uncertainty, and the lack of specialized technicians, it revealed the inefficiency of the visual inspection process, as well as the limitations of the FMEA that is applied to it. The fuzzy set theory allows dealing with the uncertainty and subjectivity of linguistic terms and, together with the expert systems, allows modeling of the knowledge involved in tasks that require human expertise. In response to the limitations of FMEA, a fuzzy FMEA system was proposed. Integrated in the design, measure, analyze, improve and control (DMAIC) cycle, the proposed system allows the representation of expert knowledge and improves the analysis of subjective failures, hardly detected by visual inspection, compared to FMEA. The fuzzy FMEA system was tested in a real case study at an industrial manufacturing unit. The identified potential failure modes were analyzed and a fuzzy risk priority number (RPN) resulted, which was compared with the classic RPN. The main results revealed several differences between both. The main differences between fuzzy FMEA and classical FMEA come from the non-linear relationship between the variables and in the attribution of an RPN classification that assigns linguistic terms to the results, thus allowing a strengthening of the decision-making regarding the mitigation actions of the most “important” failure modes. Full article
(This article belongs to the Section Applied Industrial Technologies)
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16 pages, 65872 KiB  
Article
Implementation of Pavement Defect Detection System on Edge Computing Platform
by Yu-Chen Lin, Wen-Hui Chen and Cheng-Hsuan Kuo
Appl. Sci. 2021, 11(8), 3725; https://doi.org/10.3390/app11083725 - 20 Apr 2021
Cited by 16 | Viewed by 3893
Abstract
Road surfaces in Taiwan, as well as other developed countries, often experience structural failures, such as patches, bumps, longitudinal and lateral cracking, and potholes, which cause discomfort and pose direct safety risks to motorists. To minimize damage to vehicles from pavement defects or [...] Read more.
Road surfaces in Taiwan, as well as other developed countries, often experience structural failures, such as patches, bumps, longitudinal and lateral cracking, and potholes, which cause discomfort and pose direct safety risks to motorists. To minimize damage to vehicles from pavement defects or provide the corresponding comfortable ride promotion strategy later, in this study, we developed a pavement defect detection system using a deep learning perception scheme for implementation on Xilinx Edge AI platforms. To increase the detection distance and accuracy of pavement defects, two cameras with different fields of view, at 70∘ and 30∘, respectively, were used to capture the front views of a car, and then the YOLOv3 (you only look once, version 3) model was employed to recognize the pavement defects, such as potholes, cracks, manhole covers, patches, and bumps. In addition, to promote continuous pavement defect recognition rate, a tracking-via-detection strategy was employed, which first detects pavement defects in each frame and then associates them to different frames using the Kalman filter method. Thus, the average detection accuracy of the pothole category could reach 71%, and the miss rate was about 29%. To confirm the effectiveness of the proposed detection strategy, experiments were conducted on an established Taiwan pavement defect image dataset (TPDID), which is the first dataset for Taiwan pavement defects. Moreover, different AI methods were used to detect the pavement defects for quantitative comparative analysis. Finally, a field-programmable gate-array-based edge computing platform was used as an embedded system to implement the proposed YOLOv3-based pavement defect detection system; the execution speed reached 27.8 FPS while maintaining the accuracy of the original system model. Full article
(This article belongs to the Special Issue Deep Learning for Signal Processing Applications)
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11 pages, 2954 KiB  
Article
Conventional and Enzyme-Assisted Extraction of Rosemary Leaves (Rosmarinus officinalis L.): Toward a Greener Approach to High Added-Value Extracts
by Antonella Rozaria Nefeli Pontillo, Lydia Papakosta-Tsigkri, Theopisti Lymperopoulou, Diomi Mamma, Dimitris Kekos and Anastasia Detsi
Appl. Sci. 2021, 11(8), 3724; https://doi.org/10.3390/app11083724 - 20 Apr 2021
Cited by 25 | Viewed by 5154
Abstract
The effect of different extraction methods of rosemary leaves on the total phenolic content (TPC), and the antioxidant activity of the extracts was herein investigated. Firstly, the solid-liquid conventional extraction (CEM) and microwave-assisted extraction (MAE) were implemented in an effort to identify the [...] Read more.
The effect of different extraction methods of rosemary leaves on the total phenolic content (TPC), and the antioxidant activity of the extracts was herein investigated. Firstly, the solid-liquid conventional extraction (CEM) and microwave-assisted extraction (MAE) were implemented in an effort to identify the effect of the solvent and of microwave irradiation on the extract quality. The extract obtained from CEM at room temperature, using ethanol/water 95:5 v/v, showed the highest antioxidant activity (IC50 = 12.1 μg/mL). MAE using ethanol/water 50:50 v/v provided an extract with TPC and DPPH radical scavenging ability in a significantly shorter extraction time (1 h for MAE and 24 h for CEM). Enzyme-assisted extraction (EAE) using five commercial enzyme formulations was implemented, and the kinetic equation was calculated. Finally, the effect of EAE as a pretreatment method to CEM was examined. Pretreatment of the plant material with pectinolytic enzymes for 1 h prior to a 24 h CEM with 50% hydroethanolic solvent was found to be the optimum conditions for the extraction of rosemary leaves, providing an extract with higher DPPH radical scavenging ability (IC50 14.3 ± 0.8 μg/mL) and TPC (15.2 ± 0.3 mgGAE/grosemary) than the corresponding extract without the enzyme pretreatment. Full article
(This article belongs to the Special Issue Recent Advances in the Development and Application of Green Extraction Techniques)
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10 pages, 4148 KiB  
Article
Testing of Natural Sorbents for the Assessment of Heavy Metal Ions’ Adsorption
by Vera Yurak, Rafail Apakashev, Alexey Dushin, Albert Usmanov, Maxim Lebzin and Alexander Malyshev
Appl. Sci. 2021, 11(8), 3723; https://doi.org/10.3390/app11083723 - 20 Apr 2021
Cited by 19 | Viewed by 2858
Abstract
Nowadays, the sorption-oriented approach is on the agenda in the remediation practices of lands contaminated with heavy metals. The current growing quantity of research accounts for different sorbents. However, there is still a lack of studies utilizing the economic criteria. Therefore, to ensure [...] Read more.
Nowadays, the sorption-oriented approach is on the agenda in the remediation practices of lands contaminated with heavy metals. The current growing quantity of research accounts for different sorbents. However, there is still a lack of studies utilizing the economic criteria. Therefore, to ensure a wide application of opportunities, one of the necessary requirements is their economic efficiency in use. By utilizing these criteria, this manuscript researches the generally accepted natural sorbents for the assessment of heavy metal ions’ adsorption, such as peat, diatomite, vermiculite and their mixtures in different proportions and physical shapes. The methodological base of the study consists of the volumetric (titrimetric) method, X-ray fluorescence spectrometry and atomic absorption spectrometry. Experimental tests show a certain decline in the efficiency of heavy metal ions’ adsorption from aqueous salt solutes as follows: granular peat–diatomite > large-fraction vermiculite > medium-fraction vermiculite > non-granular peat–diatomite > diatomite. Full article
(This article belongs to the Special Issue Environmental Restoration of Metal-Contaminated Soils)
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14 pages, 635 KiB  
Article
CF-CNN: Coarse-to-Fine Convolutional Neural Network
by Jinho Park, Heegwang Kim and Joonki Paik
Appl. Sci. 2021, 11(8), 3722; https://doi.org/10.3390/app11083722 - 20 Apr 2021
Cited by 6 | Viewed by 4169
Abstract
In this paper, we present a coarse-to-fine convolutional neural network (CF-CNN) for learning multilabel classes. The basis of the proposed CF-CNN is a disjoint grouping method that first creates a class group with hierarchical association, and then assigns a new label to a [...] Read more.
In this paper, we present a coarse-to-fine convolutional neural network (CF-CNN) for learning multilabel classes. The basis of the proposed CF-CNN is a disjoint grouping method that first creates a class group with hierarchical association, and then assigns a new label to a class belonging to each group so that each class acquires multiple labels. CF-CNN consists of one main network and two subnetworks. Each subnetwork performs coarse prediction using the group labels created by the disjoint grouping method. The main network includes a refine convolution layer and performs fine prediction to fuse the feature maps acquired from the subnetwork. The generated class set in the upper level has the same classification boundary to that in the lower level. Since the classes belonging to the upper level label are classified with a higher priority, parameter optimization becomes easier. In experimental results, the proposed method is applied to various classification tasks to show a higher classification accuracy by up to 3% with a much smaller number of parameters without modification of the baseline model. Full article
(This article belongs to the Special Issue Advanced Intelligent Imaging Technology Ⅲ)
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11 pages, 831 KiB  
Article
Effectiveness of Shock Wave Therapy versus Intra-Articular Corticosteroid Injection in Diabetic Frozen Shoulder Patients’ Management: Randomized Controlled Trial
by Ahmed Ebrahim Elerian, David Rodriguez-Sanz, Abdelaziz Abdelaziz Elsherif, Hend Adel Dorgham, Dina Mohamed Ali Al-Hamaky, Mahmoud S. El Fakharany and Mahmoud Ewidea
Appl. Sci. 2021, 11(8), 3721; https://doi.org/10.3390/app11083721 - 20 Apr 2021
Cited by 7 | Viewed by 6384
Abstract
Frozen shoulder is a major musculoskeletal illness in diabetic patients. This study aimed to compare the effectiveness of shock wave and corticosteroid injection in the management of diabetic frozen shoulder patients. Fifty subjects with diabetic frozen shoulder were divided randomly into group A [...] Read more.
Frozen shoulder is a major musculoskeletal illness in diabetic patients. This study aimed to compare the effectiveness of shock wave and corticosteroid injection in the management of diabetic frozen shoulder patients. Fifty subjects with diabetic frozen shoulder were divided randomly into group A (the intra-articular corticosteroid injection group) and group B that received 12 sessions of shock wave therapy, while each patient in both groups received the traditional physiotherapy program. The level of pain and disability, the range of motion, as well as the glucose triad were evaluated before patient assignment to each group, during the study and at the end of the study. Compared to the pretreatment evaluations there were significant improvements of shoulder pain and disability and in shoulder flexion and abduction range of motion in both groups (p < 0.05). The shock wave group revealed a more significant improvement the intra-articular corticosteroid injection group, where p was 0.001 for shoulder pain and disability and shoulder flexion and abduction. Regarding the effect of both interventions on the glucose triad, there were significant improvements in glucose control with group B, where p was 0.001. Shock waves provide a more effective and safer treatment modality for diabetic frozen shoulder treatment than corticosteroid intra-articular injection. Full article
(This article belongs to the Special Issue Physical Therapy and Health)
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16 pages, 543 KiB  
Article
Adversarial Optimization-Based Knowledge Transfer of Layer-Wise Dense Flow for Image Classification
by Doyeob Yeo, Min-Suk Kim and Ji-Hoon Bae
Appl. Sci. 2021, 11(8), 3720; https://doi.org/10.3390/app11083720 - 20 Apr 2021
Cited by 2 | Viewed by 2355
Abstract
A deep-learning technology for knowledge transfer is necessary to advance and optimize efficient knowledge distillation. Here, we aim to develop a new adversarial optimization-based knowledge transfer method involved with a layer-wise dense flow that is distilled from a pre-trained deep neural network (DNN). [...] Read more.
A deep-learning technology for knowledge transfer is necessary to advance and optimize efficient knowledge distillation. Here, we aim to develop a new adversarial optimization-based knowledge transfer method involved with a layer-wise dense flow that is distilled from a pre-trained deep neural network (DNN). Knowledge distillation transferred to another target DNN based on adversarial loss functions has multiple flow-based knowledge items that are densely extracted by overlapping them from a pre-trained DNN to enhance the existing knowledge. We propose a semi-supervised learning-based knowledge transfer with multiple items of dense flow-based knowledge extracted from the pre-trained DNN. The proposed loss function would comprise a supervised cross-entropy loss for a typical classification, an adversarial training loss for the target DNN and discriminators, and Euclidean distance-based loss in terms of dense flow. For both pre-trained and target DNNs considered in this study, we adopt a residual network (ResNet) architecture. We propose methods of (1) the adversarial-based knowledge optimization, (2) the extended and flow-based knowledge transfer scheme, and (3) the combined layer-wise dense flow in an adversarial network. The results show that it provides higher accuracy performance in the improved target ResNet compared to the prior knowledge transfer methods. Full article
(This article belongs to the Special Issue Artificial Intelligence and Control Technology for Unmanned Transport Systems)
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15 pages, 3684 KiB  
Article
A Technique of Recursive Reliability-Based Missing Data Imputation for Collaborative Filtering
by Sun-Young Ihm, Shin-Eun Lee, Young-Ho Park, Aziz Nasridinov, Miyeon Kim and So-Hyun Park
Appl. Sci. 2021, 11(8), 3719; https://doi.org/10.3390/app11083719 - 20 Apr 2021
Cited by 5 | Viewed by 2600
Abstract
Collaborative filtering (CF) is a recommendation technique that analyzes the behavior of various users and recommends the items preferred by users with similar preferences. However, CF methods suffer from poor recommendation accuracy when the user preference data used in the recommendation process is [...] Read more.
Collaborative filtering (CF) is a recommendation technique that analyzes the behavior of various users and recommends the items preferred by users with similar preferences. However, CF methods suffer from poor recommendation accuracy when the user preference data used in the recommendation process is sparse. Data imputation can alleviate the data sparsity problem by substituting a virtual part of the missing user preferences. In this paper, we propose a k-recursive reliability-based imputation (k-RRI) that first selects data with high reliability and then recursively imputes data with additional selection while gradually lowering the reliability criterion. We also propose a new similarity measure that weights common interests and indifferences between users and items. The proposed method can overcome disregarding the importance of missing data and resolve the problem of poor data imputation of existing methods. The experimental results demonstrate that the proposed approach significantly improves recommendation accuracy compared to those resulting from the state-of-the-art methods while demanding less computational complexity. Full article
(This article belongs to the Collection The Development and Application of Fuzzy Logic)
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16 pages, 3492 KiB  
Article
Sustained Antibacterial Effect and Wear Behavior of Quaternary Ammonium Contact-Killing Dental Polymers after One-Year of Hydrolytic Degradation
by Abdulrahman A. Balhaddad, Lamia S. Mokeem, Michael D. Weir, Huakun Xu and Mary Anne S. Melo
Appl. Sci. 2021, 11(8), 3718; https://doi.org/10.3390/app11083718 - 20 Apr 2021
Cited by 7 | Viewed by 3302
Abstract
This study intended to investigate the long-term antibacterial effect, mechanical performance, and surface topography of new anticaries dental composites. While most artificial aging studies of dental resins lasted for 30–90 days, this study prolonged the water-aging to one year to be more clinically [...] Read more.
This study intended to investigate the long-term antibacterial effect, mechanical performance, and surface topography of new anticaries dental composites. While most artificial aging studies of dental resins lasted for 30–90 days, this study prolonged the water-aging to one year to be more clinically relevant. The base resin was loaded with dimethylaminohexadecyl methacrylate (DMAHDM) at 3 or 5 wt.% and nano-sized amorphous calcium phosphate (NACP) at 20 wt.%. Composites were subjected to one-year water storage and wear. Following water aging, samples were evaluated for flexural strength, elastic modulus, and microbiological assays. Biofilm plate counting method, metabolic assay, colorimetric quantification of lactic acid, and Baclight bacterial viability assay were measured after one year. Topography changes (ΔRa, ΔRq, ΔRv, ΔRt) were examined after wear and observed by scanning electron microscopy. Biofilm assays and topography changes data were analyzed via one-way ANOVA and Tukey’s tests. Mechanical properties and normalized data were verified using a t-test. The flexural strength values for the formulations that contained 5% DMAHDM-20% NACP, 3% DMAHDM, and 5% DMAHDM were reduced significantly (p < 0.05) in relation to the baseline but the values were still above the ISO standards. No significant differences were observed between the groups concerning the topography changes, except for the ΔRt, where there was a significant increase in the 5% DMAHDM-20% NACP group. All the groups demonstrated robust biofilm-inhibition, with slightly reduced antibacterial properties following water aging. The aged samples reduced the total microorganisms, total streptococci, and mutans streptococci by 1.5 to 3-log, compared to the experimental control. The new formulations containing DMAHDM and NACP were able to sustain the antibacterial performance after one-year of aging. Mechanical properties and surface topography were slightly affected over time. Full article
(This article belongs to the Special Issue 10th Anniversary of Applied Sciences: Invited Papers in Applied Dentistry Section)
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40 pages, 2621 KiB  
Review
Selenium-Containing Polysaccharides—Structural Diversity, Biosynthesis, Chemical Modifications and Biological Activity
by Sandra Górska, Anna Maksymiuk and Jadwiga Turło
Appl. Sci. 2021, 11(8), 3717; https://doi.org/10.3390/app11083717 - 20 Apr 2021
Cited by 33 | Viewed by 6448
Abstract
Selenosugars are a group of sugar derivatives of great structural diversity (e.g., molar masses, selenium oxidation state, and selenium binding), obtained as a result of biosynthesis, chemical modification of natural compounds, or chemical synthesis. Seleno-monosaccharides and disaccharides are known to be non-toxic products [...] Read more.
Selenosugars are a group of sugar derivatives of great structural diversity (e.g., molar masses, selenium oxidation state, and selenium binding), obtained as a result of biosynthesis, chemical modification of natural compounds, or chemical synthesis. Seleno-monosaccharides and disaccharides are known to be non-toxic products of the natural metabolism of selenium compounds in mammals. In the case of the selenium-containing polysaccharides of natural origin, their formation is also postulated as a form of detoxification of excess selenium in microorganisms, mushroom, and plants. The valency of selenium in selenium-containing polysaccharides can be: 0 (encapsulated nano-selenium), IV (selenites of polysaccharides), or II (selenoglycosides or selenium built into the sugar ring to replace oxygen). The great interest in Se-polysaccharides results from the expected synergy between selenium and polysaccharides. Several plant- and mushroom-derived polysaccharides are potent macromolecules with antitumor, immunomodulatory, antioxidant, and other biological properties. Selenium, a trace element of fundamental importance to human health, has been shown to possess several analogous functions. The mechanism by which selenium exerts anticancer and immunomodulatory activity differs from that of polysaccharide fractions, but a similar pharmacological effect suggests a possible synergy of these two agents. Various functions of Se-polysaccharides have been explored, including antitumor, immune-enhancement, antioxidant, antidiabetic, anti-inflammatory, hepatoprotective, and neuroprotective activities. Due to being non-toxic or much less toxic than inorganic selenium compounds, Se-polysaccharides are potential dietary supplements that could be used, e.g., in chemoprevention. Full article
(This article belongs to the Special Issue Polysaccharides: From Extraction to Applications)
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16 pages, 4181 KiB  
Article
Research on O-ring Dimension Measurement Algorithm Based on Cubic Spline Interpolation
by Hu Haibing, Xipeng Zheng, Jiajie Yin and Yueyan Wang
Appl. Sci. 2021, 11(8), 3716; https://doi.org/10.3390/app11083716 - 20 Apr 2021
Cited by 10 | Viewed by 2694
Abstract
Current O-ring dimension measurement algorithms based on machine vision are mainly whole-pixel level algorithms, which have the disadvantage of a low measurement accuracy. In order to improve the stability and accuracy of O-ring dimension measurement, a sub-pixel edge detection algorithm based on cubic [...] Read more.
Current O-ring dimension measurement algorithms based on machine vision are mainly whole-pixel level algorithms, which have the disadvantage of a low measurement accuracy. In order to improve the stability and accuracy of O-ring dimension measurement, a sub-pixel edge detection algorithm based on cubic spline interpolation is proposed for O-ring dimension measurement. After image pre-processing of the O-ring graphics, the whole-pixel-level O-ring edges are obtained by using a noise-resistant mathematical morphology method, and then the sub-pixel edge contours are obtained using a sub-pixel edge detection algorithm based on cubic spline interpolation. Finally, the edge curve is fitted with the least squares method to obtain its inner and outer diameter as well as the size of the wire diameter. The experimental data show that the algorithm has a mean square error of 4.8 μm for the outer diameter and 0.18 μm for the wire diameter. The outer diameter error is kept within ±100 μm and the wire diameter error can be kept within ±15 μm. Compared with the whole pixel algorithm, the measurement accuracy has been greatly improved. Full article
(This article belongs to the Section Electrical, Electronics and Communications Engineering)
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16 pages, 16032 KiB  
Article
Detoxified Spent Pot Lining from Aluminum Production as (Alumino-)Silicate Source for Composite Cement and AutoClaved Aerated Concrete
by Arne Peys, Mateja Košir, Ruben Snellings, Ana Mladenovič and Liesbeth Horckmans
Appl. Sci. 2021, 11(8), 3715; https://doi.org/10.3390/app11083715 - 20 Apr 2021
Cited by 7 | Viewed by 3886
Abstract
New sources of supplementary cementitious materials (SCMs) are needed to meet the future demand. A potential new source of SCM is spent pot lining, a residue from aluminum production. The present work showed that the refined aluminosilicate part of spent pot lining (SPL) [...] Read more.
New sources of supplementary cementitious materials (SCMs) are needed to meet the future demand. A potential new source of SCM is spent pot lining, a residue from aluminum production. The present work showed that the refined aluminosilicate part of spent pot lining (SPL) has a moderate chemical reactivity in a cementitious system measured in the R3 calorimetry test, comparable to commercially used coal fly ash. The reaction of SPL led to the consumption of Ca(OH)2 in a cement paste beyond 7 days after mixing. At 28 and 90 days a significant contribution to strength development was therefore observed, reaching a relative strength, which is similar to composite cements with coal fly ash. At early age a retardation of the cement hydration is caused by the SPL, which should most likely be associated with the presence of trace amounts of NH3. The spent pot lining is also investigated as silica source for autoclaved aerated concrete blocks. The replacement of quartz by spent pot lining did not show an adverse effect on the strength-density relation of the lightweight blocks up to 50 wt% quartz substitution. Overall, spent pot lining can be used in small replacement volumes (30 wt%) as SCM or as replacement of quartz (50 wt%) in autoclaved aerated concrete blocks. Full article
(This article belongs to the Special Issue Eco-Performance of Alternative Binder Systems)
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20 pages, 8790 KiB  
Article
Straight Gait Research of a Small Electric Hexapod Robot
by Feng Zhang, Shidong Zhang, Qian Wang, Yujie Yang and Bo Jin
Appl. Sci. 2021, 11(8), 3714; https://doi.org/10.3390/app11083714 - 20 Apr 2021
Cited by 8 | Viewed by 2828
Abstract
Gait is an important research content of hexapod robots. To better improve the motion performance of hexapod robots, many researchers have adopted some high-cost sensors or complex gait control algorithms. This paper studies the straight gait of a small electric hexapod robot with [...] Read more.
Gait is an important research content of hexapod robots. To better improve the motion performance of hexapod robots, many researchers have adopted some high-cost sensors or complex gait control algorithms. This paper studies the straight gait of a small electric hexapod robot with a low cost, which can be used in daily life. The strategy of “increasing duty factor” is put forward in the gait planning, which aims to reduce foot sliding and attitude fluctuation in robot motion. The straight gaits of the robot include tripod gait, quadrangular gait, and pentagonal gait, which can be described conveniently by discretization and a time sequence diagram. In order to facilitate the user to control the robot to achieve all kinds of motion, an online gait transformation algorithm based on the adjustment of foot positions is proposed. In addition, according to the feedback of the actual attitude information, a yaw angle correction algorithm based on kinematics analysis and PD controller is designed to reduce the motion error of the robot. The experiments show that the designed gait planning scheme and control algorithm are effective, and the robot can achieve the expected motion. The RMSE of the row, pitch, and yaw angle was reduced by 35%, 25%, and 12%, respectively, using the “increasing duty factor” strategy, and the yaw angle was limited in the range −3°~3° using the yaw angle correction algorithm. Finally, the comparison with related works and the limitations are discussed. Full article
(This article belongs to the Section Robotics and Automation)
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17 pages, 4009 KiB  
Article
Verification and Optimization of an Ultra-Low Volume (ULV) Sprayer Used for the Inactivation of Indoor Total Bacteria
by Yun-Hee Choi, Da-An Huh, Ju-Yeon Lee, Ji Yoon Choi and Kyong Whan Moon
Appl. Sci. 2021, 11(8), 3713; https://doi.org/10.3390/app11083713 - 20 Apr 2021
Cited by 5 | Viewed by 2875
Abstract
Physical and chemical cleaning for the removal of indoor microorganisms, which can cause allergic reactions and respiratory diseases, is labor-intensive and time-consuming. An ultra-low volume (ULV) sprayer, a newly introduced device to inactivate pathogenic microorganisms, allows the disinfectant particles to reach hard-to-reach spaces [...] Read more.
Physical and chemical cleaning for the removal of indoor microorganisms, which can cause allergic reactions and respiratory diseases, is labor-intensive and time-consuming. An ultra-low volume (ULV) sprayer, a newly introduced device to inactivate pathogenic microorganisms, allows the disinfectant particles to reach hard-to-reach spaces indoors and is more cost-effective than the existing methods. However, few studies have been conducted to verify the efficiency of the ULV sprayer. Here, we verified the disinfection efficiency of the ULV sprayer for inactivating total bacteria present on indoor surfaces, considering the factors affecting bacteria inactivation, and presented the optimal ULV sprayer usage conditions to achieve the highest disinfection efficiency depending on room size. The total bacteria removal efficiency was high (range: 0.56–2.46 log10 reductions), including hard-to-reach spaces. A response surface model was developed to identify the individual and interactive effects of the disinfectant concentration, spray amount, and room size on total bacteria disinfection efficiency. These three variables had interactive effects on the total bacteria disinfection efficiency. The experimental data were fitted to a second-order polynomial model, with high coefficients of determination (R2) for all models (R2 > 0.82). The optimum conditions were a spray amount of 3.08–6.40 L in 160 m3, 3.78–7.22 L in 230 m3, and 5.68–8 L in 300 m3 surface area when using dilution rates of 100 times. These conditions predicted a bacterial disinfection efficiency of >1.10 log10 reductions (92%) on all surfaces. Our results clearly indicate that the ULV sprayer effectively inactivates total bacteria present on indoor surfaces. Full article
(This article belongs to the Section Environmental Sciences)
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18 pages, 1610 KiB  
Review
Haptic-Enabled Hand Rehabilitation in Stroke Patients: A Scoping Review
by Mohamed-Amine Choukou, Sophia Mbabaali, Jasem Bani Hani and Carol Cooke
Appl. Sci. 2021, 11(8), 3712; https://doi.org/10.3390/app11083712 - 20 Apr 2021
Cited by 13 | Viewed by 6834
Abstract
There is a plethora of technology-assisted interventions for hand therapy, however, less is known about the effectiveness of these interventions. This scoping review aims to explore studies about technology-assisted interventions targeting hand rehabilitation to identify the most effective interventions. It is expected that [...] Read more.
There is a plethora of technology-assisted interventions for hand therapy, however, less is known about the effectiveness of these interventions. This scoping review aims to explore studies about technology-assisted interventions targeting hand rehabilitation to identify the most effective interventions. It is expected that multifaceted interventions targeting hand rehabilitation are more efficient therapeutic approaches than mono-interventions. The scoping review will aim to map the existing haptic-enabled interventions for upper limb rehabilitation and investigates their effects on motor and functional recovery in patients with stroke. The methodology used in this review is based on the Arksey and O’Malley framework, which includes the following stages: identifying the research question, identifying relevant studies, study selection, charting the data, and collating, summarizing, and reporting the results. Results show that using three or four different technologies was more positive than using two technologies (one technology + haptics). In particular, when standardized as a percentage of outcomes, the combination of three technologies showed better results than the combination of haptics with one technology or with three other technologies. To conclude, this study portrayed haptic-enabled rehabilitation approaches that could help therapists decide which technology-enabled hand therapy approach is best suited to their needs. Those seeking to undertake research and development anticipate further opportunities to develop haptic-enabled hand telerehabilitation platforms. Full article
(This article belongs to the Special Issue Advances in Technological Rehabilitation)
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20 pages, 21295 KiB  
Article
Time Travel to the Past of Bosnia and Herzegovina through Virtual and Augmented Reality
by Selma Rizvić, Dušanka Bošković, Vensada Okanović, Ivona Ivković Kihić, Irfan Prazina and Bojan Mijatović
Appl. Sci. 2021, 11(8), 3711; https://doi.org/10.3390/app11083711 - 20 Apr 2021
Cited by 10 | Viewed by 5210
Abstract
Bosnia and Herzegovina (BH) has a very picturesque past. Founded in 11th century, it has always been a crossroads of faiths and civilizations. Extended Reality (XR) technologies can finally take us to time travel into this history, enable us to experience past events [...] Read more.
Bosnia and Herzegovina (BH) has a very picturesque past. Founded in 11th century, it has always been a crossroads of faiths and civilizations. Extended Reality (XR) technologies can finally take us to time travel into this history, enable us to experience past events and meet historical characters. In this paper, we overview the latest applications we developed that use Virtual Reality (VR) video, Virtual and Augmented Reality (AR) for interactive digital storytelling about BH history. “Nine dissidents” is the first BH VR documentary, tackling a still tricky subject of dissidents in the Socialist Yugoslavia, artists and writers falsely accused, persecuted and still forbidden. “Virtual Museum of Old Crafts” aims to present and preserve crafts intangible heritage through Virtual Reality. “Battle on Neretva VR” is recreating a famous WWII battle offering the users to experience it and meet comrade Tito, the commander of the Yugoslav Liberation Army. “Sarajevo 5D” shows the cultural monuments from Sarajevo that do not exist anymore in physical form using Augmented Reality. Through user experience studies, we measure the user immersion and edutainment of these applications and show the potential of XR for the presentation and preservation of cultural heritage. Full article
(This article belongs to the Special Issue Extended Reality: From Theory to Applications)
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22 pages, 3289 KiB  
Article
Intelligent Scheduling with Reinforcement Learning
by Bruno Cunha, Ana Madureira, Benjamim Fonseca and João Matos
Appl. Sci. 2021, 11(8), 3710; https://doi.org/10.3390/app11083710 - 20 Apr 2021
Cited by 17 | Viewed by 6401
Abstract
In this paper, we present and discuss an innovative approach to solve Job Shop scheduling problems based on machine learning techniques. Traditionally, when choosing how to solve Job Shop scheduling problems, there are two main options: either use an efficient heuristic that provides [...] Read more.
In this paper, we present and discuss an innovative approach to solve Job Shop scheduling problems based on machine learning techniques. Traditionally, when choosing how to solve Job Shop scheduling problems, there are two main options: either use an efficient heuristic that provides a solution quickly, or use classic optimization approaches (e.g., metaheuristics) that take more time but will output better solutions, closer to their optimal value. In this work, we aim to create a novel architecture that incorporates reinforcement learning into scheduling systems in order to improve their overall performance and overcome the limitations that current approaches present. It is also intended to investigate the development of a learning environment for reinforcement learning agents to be able to solve the Job Shop scheduling problem. The reported experimental results and the conducted statistical analysis conclude about the benefits of using an intelligent agent created with reinforcement learning techniques. The main contribution of this work is proving that reinforcement learning has the potential to become the standard method whenever a solution is necessary quickly, since it solves any problem in very few seconds with high quality, approximate to the optimal methods. Full article
(This article belongs to the Collection Machine Learning in Computer Engineering Applications)
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27 pages, 7363 KiB  
Article
Towards Flexible Distribution Systems: Future Adaptive Management Schemes
by Hannu Laaksonen, Hosna Khajeh, Chethan Parthasarathy, Miadreza Shafie-khah and Nikos Hatziargyriou
Appl. Sci. 2021, 11(8), 3709; https://doi.org/10.3390/app11083709 - 20 Apr 2021
Cited by 24 | Viewed by 5560
Abstract
During the ongoing evolution of energy systems toward increasingly flexible, resilient, and digitalized distribution systems, many issues need to be developed. In general, a holistic multi-level systemic view is required on the future enabling technologies, control and management methods, operation and planning principles, [...] Read more.
During the ongoing evolution of energy systems toward increasingly flexible, resilient, and digitalized distribution systems, many issues need to be developed. In general, a holistic multi-level systemic view is required on the future enabling technologies, control and management methods, operation and planning principles, regulation as well as market and business models. Increasing integration of intermittent renewable generation and electric vehicles, as well as industry electrification during the evolution, requires a huge amount of flexibility services at multiple time scales and from different voltage levels, resources, and sectors. Active use of distribution network-connected flexible energy resources for flexibility services provision through new marketplaces will also be needed. Therefore, increased collaboration between system operators in operation and planning of the future power system will also become essential during the evolution. In addition, use of integrated cyber-secure, resilient, cost-efficient, and advanced communication technologies and solutions will be of key importance. This paper describes a potential three-stage evolution path toward fully flexible, resilient, and digitalized electricity distribution networks. A special focus of this paper is the evolution and development of adaptive control and management methods as well as compatible collaborative market schemes that can enable the improved provision of flexibility services by distribution network-connected flexible energy resources for local (distribution system operator) and system-wide (transmission system operator) needs. Full article
(This article belongs to the Special Issue Future Distribution Network Solutions)
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17 pages, 5096 KiB  
Article
Characteristics of Warm Mix Asphalt Incorporating Coarse Steel Slag Aggregates
by Adham Mohammed Alnadish, Mohamad Yusri Aman, Herda Yati Binti Katman and Mohd Rasdan Ibrahim
Appl. Sci. 2021, 11(8), 3708; https://doi.org/10.3390/app11083708 - 20 Apr 2021
Cited by 9 | Viewed by 2947
Abstract
The major goal of sustainable practices is to preserve raw resources through the utilization of waste materials as an alternative to natural resources. Decreasing the temperature required to produce asphalt mixes contributes to environmental sustainability by reducing energy consumption and toxic emissions. In [...] Read more.
The major goal of sustainable practices is to preserve raw resources through the utilization of waste materials as an alternative to natural resources. Decreasing the temperature required to produce asphalt mixes contributes to environmental sustainability by reducing energy consumption and toxic emissions. In this study, warm mix asphalt incorporating coarse steel slag aggregates was investigated. Warm mix asphalt was produced at different temperatures lower than the control asphalt mixes (hot mix asphalt) by 10, 20, and 30 °C. The performances of the control and warm mix asphalt were assessed through laboratory tests examining stiffness modulus, dynamic creep, and moisture sensitivity. Furthermore, a response surface methodology (RSM) was conducted by means of DESIGN EXPERT 11 to develop prediction models for the performance of warm mix asphalt. The findings of this study illustrate that producing warm mix asphalt at a temperature 10 °C lower than that of hot mix asphalt exhibited the best results, compared to the other mixes. Additionally, the warm mix asphalt produced at 30 °C lower than the hot mix asphalt exhibited comparable performance to the hot mix asphalt. However, as the production temperature increases, the performance of the warm mix asphalt improves. Full article
(This article belongs to the Special Issue Advances in Asphalt Pavement Technologies and Practices)
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15 pages, 1872 KiB  
Article
The Impact of Additive Manufacturing on the Flexibility of a Manufacturing Supply Chain
by Ageel Abdulaziz Alogla, Martin Baumers, Christopher Tuck and Waiel Elmadih
Appl. Sci. 2021, 11(8), 3707; https://doi.org/10.3390/app11083707 - 20 Apr 2021
Cited by 30 | Viewed by 5082
Abstract
There is an increasing need for supply chains that can rapidly respond to fluctuating demands and can provide customised products. This supply chain design requires the development of flexibility as a critical capability. To this end, firms are considering Additive Manufacturing (AM) as [...] Read more.
There is an increasing need for supply chains that can rapidly respond to fluctuating demands and can provide customised products. This supply chain design requires the development of flexibility as a critical capability. To this end, firms are considering Additive Manufacturing (AM) as one strategic option that could enable such a capability. This paper develops a conceptual model that maps AM characteristics relevant to flexibility against key market disruption scenarios. Following the development of this model, a case study is undertaken to indicate the impact of adopting AM on supply chain flexibility from four major flexibility-related aspects: volume, mix, delivery, and new product introduction. An inter-process comparison is implemented in this case study using data collected from a manufacturing company that produces pipe fittings using Injection Moulding (IM). The supply chain employing IM in this case study shows greater volume and delivery flexibility levels (i.e., 65.68% and 92.8% for IM compared to 58.70% and 75.35% for AM, respectively) while the AM supply chain shows greater mix and new product introduction flexibility, indicated by the lower changeover time and cost of new product introduction to the system (i.e., 0.33 h and €0 for AM compared to 4.91 h and €30,000 for IM, respectively). This work will allow decision-makers to take timely decisions by providing useful information on the effect of AM adoption on supply chain flexibility in different sudden disruption scenarios such as demand uncertainty, demand variability, lead-time compression and product variety. Full article
(This article belongs to the Special Issue Smart Manufacturing Systems for Industry 5.0: Challenges and Opportunities)
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25 pages, 12595 KiB  
Article
Prediction of Non-Uniform Distorted Flows, Effects on Transonic Compressor Using CFD, Regression Analysis and Artificial Neural Networks
by Muhammad Umer Sohail, Hossein Raza Hamdani, Asad Islam, Khalid Parvez, Abdul Munem Khan, Usman Allauddin, Muhammad Khurram and Hassan Elahi
Appl. Sci. 2021, 11(8), 3706; https://doi.org/10.3390/app11083706 - 20 Apr 2021
Cited by 13 | Viewed by 3105
Abstract
Non-uniform inlet flows frequently occur in aircrafts and result in chronological distortions of total temperature and total pressure at the engine inlet. Distorted inlet flow operation of the axial compressor deteriorates aerodynamic performance, which reduces the stall margin and increases blade stress levels, [...] Read more.
Non-uniform inlet flows frequently occur in aircrafts and result in chronological distortions of total temperature and total pressure at the engine inlet. Distorted inlet flow operation of the axial compressor deteriorates aerodynamic performance, which reduces the stall margin and increases blade stress levels, which in turn causes compressor failure. Deep learning is an efficient approach to predict catastrophic compressor failure, and its stability for better performance at minimum computational cost and time. The current research focuses on the development of a transonic compressor instability prediction tool for the comprehensive modeling of axial compressor dynamics. A novel predictive approach founded by an extensive CFD-based dataset for supervised learning has been implemented to predict compressor performance and behavior at different ambient temperatures and flow conditions. Artificial Neural Network-based results accurately predict compressor performance parameters by minimizing the Root Mean Square Error (RMSE) loss function. Computational results show that, as compared to the tip radial pressure distortion, hub radial pressure distortion has improved the stability range of the compressor. Furthermore, the combined effect of pressure distortion with the bulk flow has a qualitative and deteriorator effect on the compressor. Full article
(This article belongs to the Section Aerospace Science and Engineering)
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17 pages, 3592 KiB  
Article
Prediction of Peak Particle Velocity Caused by Blasting through the Combinations of Boosted-CHAID and SVM Models with Various Kernels
by Jie Zeng, Panayiotis C. Roussis, Ahmed Salih Mohammed, Chrysanthos Maraveas, Seyed Alireza Fatemi, Danial Jahed Armaghani and Panagiotis G. Asteris
Appl. Sci. 2021, 11(8), 3705; https://doi.org/10.3390/app11083705 - 20 Apr 2021
Cited by 36 | Viewed by 3523
Abstract
This research examines the feasibility of hybridizing boosted Chi-Squared Automatic Interaction Detection (CHAID) with different kernels of support vector machine (SVM) techniques for the prediction of the peak particle velocity (PPV) induced by quarry blasting. To achieve this objective, a boosting-CHAID technique was [...] Read more.
This research examines the feasibility of hybridizing boosted Chi-Squared Automatic Interaction Detection (CHAID) with different kernels of support vector machine (SVM) techniques for the prediction of the peak particle velocity (PPV) induced by quarry blasting. To achieve this objective, a boosting-CHAID technique was applied to a big experimental database comprising six input variables. The technique identified four input parameters (distance from blast-face, stemming length, powder factor, and maximum charge per delay) as the most significant parameters affecting the prediction accuracy and utilized them to propose the SVM models with various kernels. The kernel types used in this study include radial basis function, polynomial, sigmoid, and linear. Several criteria, including mean absolute error (MAE), correlation coefficient (R), and gains, were calculated to evaluate the developed models’ accuracy and applicability. In addition, a simple ranking system was used to evaluate the models’ performance systematically. The performance of the R and MAE index of the radial basis function kernel of SVM in training and testing phases, respectively, confirm the high capability of this SVM kernel in predicting PPV values. This study successfully demonstrates that a combination of boosting-CHAID and SVM models can identify and predict with a high level of accuracy the most effective parameters affecting PPV values. Full article
(This article belongs to the Collection Heuristic Algorithms in Engineering and Applied Sciences)
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19 pages, 7638 KiB  
Article
FEM-CFD Simulation and Experimental Study of Compound Parabolic Concentrator (CPC) Solar Collectors with and without Fins for Residential Applications
by Javier E. Barrón-Díaz, Emmanuel A. Flores-Johnson, Danny G. Chan-Colli, J. Francisco Koh-Dzul, Ali Bassam, Luis D. Patiño-Lopez and Jose G. Carrillo
Appl. Sci. 2021, 11(8), 3704; https://doi.org/10.3390/app11083704 - 20 Apr 2021
Cited by 5 | Viewed by 3371
Abstract
Compound parabolic concentrator (CPC) solar collectors are widely used for solar energy systems in industry; however, CPC collectors for residential applications have not been fully investigated. In this work, the thermal performance of non-tracking, small-size and low-cost CPC collectors with an absorber with [...] Read more.
Compound parabolic concentrator (CPC) solar collectors are widely used for solar energy systems in industry; however, CPC collectors for residential applications have not been fully investigated. In this work, the thermal performance of non-tracking, small-size and low-cost CPC collectors with an absorber with and without segmented fins was studied experimentally and by means of a proposed numerical methodology that included ray tracing simulation and a coupled heat transfer finite element method (FEM)-computational fluid dynamics (CFD) simulation, which was validated with experimental data. The experimental results showed that the CPC with a finned absorber has better thermal performance than that of the CPC with absorber without fins, which was attributed to the increase in thermal energy on the absorber surface. The numerical results showed that ray tracing simulation can be used to estimate the heat flux on the absorber surface and the FEM-CFD simulation can be used to estimate the heat transfer from the absorber to the water running through the pipe along with its temperature. The numerical results showed that mass flow rate is an important parameter for the design of the CPC collectors. The numerical methodology developed in this work was capable of describing the thermal performance of the CPC collectors and can be used for the modeling of the thermal behavior of other CPCs solar systems. Full article
(This article belongs to the Special Issue Recent Progress in Solar Thermal Technologies and Applications)
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31 pages, 3505 KiB  
Article
Path Planning Based on Obstacle-Dependent Gaussian Model Predictive Control for Autonomous Driving
by Dong-Sung Pae, Geon-Hee Kim, Tae-Koo Kang and Myo-Taeg Lim
Appl. Sci. 2021, 11(8), 3703; https://doi.org/10.3390/app11083703 - 20 Apr 2021
Cited by 19 | Viewed by 4510
Abstract
Path planning research plays a vital role in terms of safety and comfort in autonomous driving systems. This paper focuses on safe driving and comfort riding through path planning in autonomous driving applications and proposes autonomous driving path planning through an optimal controller [...] Read more.
Path planning research plays a vital role in terms of safety and comfort in autonomous driving systems. This paper focuses on safe driving and comfort riding through path planning in autonomous driving applications and proposes autonomous driving path planning through an optimal controller integrating obstacle-dependent Gaussian (ODG) and model prediction control (MPC). The ODG algorithm integrates the information from the sensors and calculates the risk factors in the driving environment. The MPC function finds vehicle control signals close to the objective function under limited conditions, such as the structural shape of the vehicle and road driving conditions. The proposed method provides safe control and minimizes vehicle shaking due to the tendency to respond to avoid obstacles quickly. We conducted an experiment using mobile robots, similar to an actual vehicle, to verify the proposed algorithm performance. The experimental results show that the average safety metric is 72.34%, a higher ISO-2631 comport score than others, while the average processing time is approximately 14.2 ms/frame. Full article
(This article belongs to the Special Issue Artificial Intelligence and Control Technology for Unmanned Transport Systems)
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10 pages, 3635 KiB  
Article
Pulpal Response to the Combined Use of Mineral Trioxide Aggregate and Iloprost for Direct Pulp Capping
by AlAnoud Almeshari, Rita Khounganian, Wael Mahdi, Fahd Aljarbou, Shilpa Bhandi and Sara Alsubait
Appl. Sci. 2021, 11(8), 3702; https://doi.org/10.3390/app11083702 - 20 Apr 2021
Cited by 2 | Viewed by 2833
Abstract
Purpose: The present study aims to assess the combined effects of mineral trioxide aggregate (MTA) and iloprost when used as a pulp capping material on pulpal inflammation and tertiary dentin formation compared with MTA and iloprost alone in rat molar teeth. Methods: Eighty [...] Read more.
Purpose: The present study aims to assess the combined effects of mineral trioxide aggregate (MTA) and iloprost when used as a pulp capping material on pulpal inflammation and tertiary dentin formation compared with MTA and iloprost alone in rat molar teeth. Methods: Eighty maxillary first molar rat teeth were exposed and capped with iloprost solution, MTA, or MTA mixed with iloprost (MTA-iloprost). The cavities were then filled with resin-modified glass ionomer. The cavity was restored with glass ionomer without the use of pulp capping agent in the control group. The rats were sacrificed after one and four weeks. Block sections of the molar specimens were prepared and subjected to hematoxylin and eosin staining for evaluation. Statistical analysis was done using the Kruskal–Wallis test, followed by Dunnett’s test. Results: At week one, the control group showed significantly more severe pulpal inflammatory reactions than the iloprost (p = 0.00), MTA (p = 0.04), and MTA-iloprost (p = 0.00) groups. Hard tissue formation was commonly found in the iloprost, MTA, and MTA-iloprost groups. After four weeks, pulpal tissue degeneration was observed in the control group. Complete hard tissue barriers were found in 50%, 72.7%, and 77.8% of the specimens in iloprost, MTA, and MTA-iloprost groups, respectively, with no significant differences among the experimental groups. The dentinal tubule patterns were mostly regular in the MTA-iloprost group and irregular in the iloprost and MTA groups. Conclusions: The application of iloprost, MTA, and MTA-iloprost as a pulp capping material resulted in similar pulpal responses in the mechanically exposed pulp of rat molars. Therefore, mixing MTA with iloprost might not be clinically significant. Full article
(This article belongs to the Special Issue Innovative Techniques in Endodontics)
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16 pages, 4328 KiB  
Article
Measurement of Temperature and H2O Concentration in Premixed CH4/Air Flame Using Two Partially Overlapped H2O Absorption Signals in the Near Infrared Region
by Sunghyun So, Nakwon Jeong, Aran Song, Jungho Hwang, Daehae Kim and Changyeop Lee
Appl. Sci. 2021, 11(8), 3701; https://doi.org/10.3390/app11083701 - 20 Apr 2021
Cited by 8 | Viewed by 2833
Abstract
It is important to monitor the temperature and H2O concentration in a large combustion environment in order to improve combustion (and thermal) efficiency and reduce harmful combustion emissions. However, it is difficult to simultaneously measure both internal temperature and gas concentration [...] Read more.
It is important to monitor the temperature and H2O concentration in a large combustion environment in order to improve combustion (and thermal) efficiency and reduce harmful combustion emissions. However, it is difficult to simultaneously measure both internal temperature and gas concentration in a large combustion system because of the harsh environment with rapid flow. In regard, tunable diode laser absorption spectroscopy, which has the advantages of non-intrusive, high-speed response, and in situ measurement, is highly attractive for measuring the concentration of a specific gas species in the combustion environment. In this study, two partially overlapped H2O absorption signals were used in the tunable diode laser absorption spectroscopy (TDLAS) to measure the temperature and H2O concentration in a premixed CH4/air flame due to the wide selection of wavelengths with high temperature sensitivity and advantages where high frequency modulation can be applied. The wavelength regions of the two partially overlapped H2O absorptions were 1.3492 and 1.34927 μm. The measured signals separated the multi-peak Voigt fitting. As a result, the temperature measured by TDLAS based on multi-peak Voigt fitting in the premixed CH4/air flame was the highest at 1385.80 K for an equivalence ratio of 1.00. It also showed a similarity to those tendencies to the temperature measured by the corrected R-type T/C. In addition, the H2O concentrations measured by TDLAS based on the total integrated absorbance area for various equivalent ratios were consistent with those calculated by the chemical equilibrium simulation. Additionally, the H2O concentration measured at an equivalence ratio of 1.15 was the highest at 18.92%. Full article
(This article belongs to the Special Issue Diode Laser Spectroscopy – Robust Sensing for Environmental and Industrial Applications)
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8 pages, 626 KiB  
Article
Odor Emissions Factors for Bitumen-Related Production Sites
by Enrico Davoli, Giancarlo Bianchi, Anna Bonura, Marzio Invernizzi and Selena Sironi
Appl. Sci. 2021, 11(8), 3700; https://doi.org/10.3390/app11083700 - 20 Apr 2021
Cited by 1 | Viewed by 2489
Abstract
Bitumen-related production sites are facing increasing difficulties with nearby residents due to odor emissions. This parameter is still not regulated for these plants and little is known about the emissions that these plants have put into the atmosphere with the technologies available today. [...] Read more.
Bitumen-related production sites are facing increasing difficulties with nearby residents due to odor emissions. This parameter is still not regulated for these plants and little is known about the emissions that these plants have put into the atmosphere with the technologies available today. In this study, emission data from 47 Italian production plants were collected and analyzed to assess which values could describe the current situation in Italy. The results of the analysis showed that emissions are very variable, with odor concentration values between 200 to 37,000 ouE/m3, but data have a normal distribution. The mean value of the stack odor concentration was found to be 2424 ouE/m3. It was also possible to calculate emission factors of the plants, such as odor emission rate (OER), which represents the quantity of odor emitted per unit of time, and is expressed in odor units per second (ouE∙s−1) and odor emission factor (OEF) per ton of product, expressed in ouE/t. The values obtained were 7.1 × 104 ouE/s and 1.4 × 106 ouE/t. respectively. These data could provide a starting point for the definition of shared values among various stakeholders for the definition of regional guidelines for the emissions of these plants, in order to adjust available technologies towards emission parameters that are protective of the surrounding environment. Full article
(This article belongs to the Special Issue New Frontiers in the Assessment, Monitoring and Control of Environmental Odors)
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16 pages, 4366 KiB  
Article
Non-Deterministic Methods and Surrogates in the Design of Rockfill Dams
by Rajeev Das and Azzedine Soulaimani
Appl. Sci. 2021, 11(8), 3699; https://doi.org/10.3390/app11083699 - 20 Apr 2021
Cited by 8 | Viewed by 2570
Abstract
The parameters of the constitutive models used in the design of rockfill dams are associated with a high degree of uncertainty. This occurs because rockfill dams are comprised of numerous zones, each with different soil materials, and it is not feasible to extract [...] Read more.
The parameters of the constitutive models used in the design of rockfill dams are associated with a high degree of uncertainty. This occurs because rockfill dams are comprised of numerous zones, each with different soil materials, and it is not feasible to extract materials from such structures to accurately ascertain their behavior or their respective parameters. The general approach involves laboratory tests using small material samples or empirical data from the literature. However, such measures lack an accurate representation of the actual scenario, resulting in uncertainties. This limits the suitability of the model in the design process. Inverse analysis provides an option to better understand dam behavior. This procedure involves the use of real monitored data, such as deformations and stresses, from the dam structure via installed instruments. Fundamentally, it is a non-destructive approach that considers optimization methods and actual performance data to determine the values of the parameters by minimizing the differences between simulated and observed results. This paper considers data from an actual rockfill dam and proposes a surrogate assisted non-deterministic framework for its inverse analysis. A suitable error/objective function that measures the differences between the actual and simulated displacement values is defined first. Non-deterministic algorithms are used as the optimization technique, as they can avoid local optima and are more robust when compared to the conventional deterministic methods. Three such approaches, the genetic algorithm, differential evolution, and particle swarm optimization are evaluated to identify the best strategy in solving problems of this nature. A surrogate model in the form of a polynomial regression is studied and recommended in place of the actual numerical model of the dam to reduce computation cost. Finally, this paper presents the relevant dam parameters estimated by the analysis and provides insights into the performance of the three procedures to solve the inverse problem. Full article
(This article belongs to the Section Civil Engineering)
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14 pages, 18230 KiB  
Article
Chaotic Communication System with Symmetry-Based Modulation
by Timur Karimov, Vyacheslav Rybin, Georgii Kolev, Ekaterina Rodionova and Denis Butusov
Appl. Sci. 2021, 11(8), 3698; https://doi.org/10.3390/app11083698 - 20 Apr 2021
Cited by 36 | Viewed by 3578
Abstract
Communication systems based on chaotic synchronization are gaining interest in the area of secure and covert data transmission. In this paper, a novel digital communication technique based on a coherent chaotic data transmission approach is proposed. In general, this technique resembles the well-known [...] Read more.
Communication systems based on chaotic synchronization are gaining interest in the area of secure and covert data transmission. In this paper, a novel digital communication technique based on a coherent chaotic data transmission approach is proposed. In general, this technique resembles the well-known approach based on the modulation of nonlinearity parameters. The key idea of this study is to modulate a signal by varying not the system parameter but the symmetry coefficient in discrete chaotic models obtained by the special numerical integration method. For this purpose, the self-adjoint semi-implicit integration method of order 2 is used to obtain discrete master and slave models of the considered chaotic oscillator. The experimental results explicitly show that, like during parameter modulation, transmitting and receiving oscillators may completely synchronize only if the symmetry coefficients are equal in both systems. The architecture of the communication system based on the proposed modulation is presented. The practical applicability of the approach is confirmed by transmitting a test binary sequence between the transmitter and receiver models and preliminary benchmarking of the obtained communication system. Since the symmetry coefficient modulation does not significantly impact the chaotic behavior of the transmitting digital system, its better suitability for covert messaging was experimentally confirmed by comparing it with the parameter modulation technique. Full article
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18 pages, 3195 KiB  
Article
Framework for Social Media Analysis Based on Hashtag Research
by Ladislav Pilař, Lucie Kvasničková Stanislavská, Roman Kvasnička, Petr Bouda and Jana Pitrová
Appl. Sci. 2021, 11(8), 3697; https://doi.org/10.3390/app11083697 - 20 Apr 2021
Cited by 21 | Viewed by 7315
Abstract
Social networks have become a common part of many people’s daily lives. Users spend more and more time on these platforms and create an active and passive digital footprint through their interaction with other subjects. These data have high research potential in many [...] Read more.
Social networks have become a common part of many people’s daily lives. Users spend more and more time on these platforms and create an active and passive digital footprint through their interaction with other subjects. These data have high research potential in many fields, because understanding people’s communication on social media is essential to understanding their attitudes, experiences and behaviours. Social media analysis is a relatively new subject. There is still a need to develop methods and tools for researchers to help solve typical problems associated with this area. A researcher will be able to focus on the subject of research entirely. This article describes the Social Media Analysis based on Hashtag Research (SMAHR) framework, which uses social network analysis methods to explore social media communication through a network of hashtags. The results show that social media analysis based on hashtags provides information applicable to theoretical research and practical strategic marketing and management applications. Full article
(This article belongs to the Special Issue Social Network Analysis)
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