Applied Sciences

26 pages, 3627 KiB

Open AccessEditor’s ChoiceArticle

Improving Soil Stability with Alum Sludge: An AI-Enabled Approach for Accurate Prediction of California Bearing Ratio

by Abolfazl Baghbani, Minh Duc Nguyen, Ali Alnedawi, Nick Milne, Thomas Baumgartl and Hossam Abuel-Naga

Appl. Sci. 2023, 13(8), 4934; https://doi.org/10.3390/app13084934 - 14 Apr 2023

Cited by 16 | Viewed by 3048

Abstract

Alum sludge is a byproduct of water treatment plants, and its use as a soil stabilizer has gained increasing attention due to its economic and environmental benefits. Its application has been shown to improve the strength and stability of soil, making it suitable [...] Read more.

Alum sludge is a byproduct of water treatment plants, and its use as a soil stabilizer has gained increasing attention due to its economic and environmental benefits. Its application has been shown to improve the strength and stability of soil, making it suitable for various engineering applications. However, to go beyond just measuring the effects of alum sludge as a soil stabilizer, this study investigates the potential of artificial intelligence (AI) methods for predicting the California bearing ratio (CBR) of soils stabilized with alum sludge. Three AI methods, including two black box methods (artificial neural network and support vector machines) and one grey box method (genetic programming), were used to predict CBR, based on a database with nine input parameters. The results demonstrate the effectiveness of AI methods in predicting CBR with good accuracy (R² values ranging from 0.94 to 0.99 and MAE values ranging from 0.30 to 0.51). Moreover, a novel approach, using genetic programming, produced an equation that accurately estimated CBR, incorporating seven inputs. The analysis of parameter sensitivity and importance, revealed that the number of hammer blows for compaction was the most important parameter, while the parameters for maximum dry density of soil and mixture were the least important. This study highlights the potential of AI methods as a useful tool for predicting the performance of alum sludge as a soil stabilizer. Full article

(This article belongs to the Section Environmental Sciences)

► Show Figures

Figure 1

13 pages, 490 KiB

Open AccessEditor’s ChoiceArticle

Incorporating Foreshocks in an Epidemic-like Description of Seismic Occurrence in Italy

by Giuseppe Petrillo and Eugenio Lippiello

Appl. Sci. 2023, 13(8), 4891; https://doi.org/10.3390/app13084891 - 13 Apr 2023

Cited by 11 | Viewed by 1664

Abstract

The Epidemic Type Aftershock Sequence (ETAS) model is a widely used tool for cluster analysis and forecasting, owing to its ability to accurately predict aftershock occurrences. However, its capacity to explain the increase in seismic activity prior to large earthquakes—known as foreshocks—has been [...] Read more.

The Epidemic Type Aftershock Sequence (ETAS) model is a widely used tool for cluster analysis and forecasting, owing to its ability to accurately predict aftershock occurrences. However, its capacity to explain the increase in seismic activity prior to large earthquakes—known as foreshocks—has been called into question due to inconsistencies between simulated and experimental catalogs. To address this issue, we introduce a generalization of the ETAS model, called the Epidemic Type Aftershock Foreshock Sequence (ETAFS) model. This model has been shown to accurately describe seismicity in Southern California. In this study, we demonstrate that the ETAFS model is also effective in the Italian catalog, providing good agreement with the instrumental Italian catalogue (ISIDE) in terms of not only the number of aftershocks, but also the number of foreshocks—where the ETAS model fails. These findings suggest that foreshocks cannot be solely explained by cascades of triggered events, but can be reasonably considered as precursory phenomena reflecting the nucleation process of the main event. Full article

(This article belongs to the Special Issue Comprehensive Research in Earthquake Forecasting and Seismic Hazard Assessment)

► Show Figures

Figure 1

21 pages, 4860 KiB

Open AccessEditor’s ChoiceArticle

A Ship Trajectory Prediction Model Based on Attention-BILSTM Optimized by the Whale Optimization Algorithm

by Hongyu Jia, Yaoyu Yang, Jintang An and Rui Fu

Appl. Sci. 2023, 13(8), 4907; https://doi.org/10.3390/app13084907 - 13 Apr 2023

Cited by 22 | Viewed by 2721

Abstract

Nowadays, maritime transportation has become one of the most important ways of international trade. However, with the increase in ship transportation, the complex maritime environment has led to frequent traffic accidents, causing huge economic losses and safety hazards. For ships in maritime transportation, [...] Read more.

Nowadays, maritime transportation has become one of the most important ways of international trade. However, with the increase in ship transportation, the complex maritime environment has led to frequent traffic accidents, causing huge economic losses and safety hazards. For ships in maritime transportation, collision avoidance and route planning can be achieved by predicting the ship’s trajectory, which can give crews warning to avoid dangers. How to predict the ship’s trajectory more accurately is of great significance for risk avoidance. However, existing ship trajectory prediction models suffer from problems such as poor prediction accuracy, poor applicability, and difficult hyperparameter design. To address these issues, this paper adopts the Bidirectional Long Short-Term Memory (BILSTM) model as the base model, as it considers contextual information of time-series data more comprehensively. Meanwhile, to improve the accuracy and fitness of complex ship trajectories, this paper adds an attention mechanism to the BILSTM model to improve the weight of key information. In addition, to solve the problem of difficult hyperparameter design, this paper optimizes the hyperparameters of the Attention-BILSTM network by fusing the Whale Optimization Algorithm (WOA). In this paper, the AIS data are filtered, and the trajectory is complemented by the cubic spline interpolation method. Using the pre-processed AIS data, this WOA-Attention-BILSTM model is compared and assessed with traditional models. The results show that compared with other models, the WOA-Attention-BILSTM prediction model has high prediction accuracy, high applicability, and high stability, which provides an effective and feasible method for ship collision avoidance, maritime surveillance, and intelligent shipping. Full article

(This article belongs to the Special Issue Applied Maritime Engineering and Transportation Problems 2022)

► Show Figures

Figure 1

18 pages, 13225 KiB

Open AccessEditor’s ChoiceArticle

Assessing Vulnerability in Flood Prone Areas Using Analytic Hierarchy Process—Group Decision Making and Geographic Information System: A Case Study in Portugal

by Sandra Mourato, Paulo Fernandez, Luísa Gomes Pereira and Madalena Moreira

Appl. Sci. 2023, 13(8), 4915; https://doi.org/10.3390/app13084915 - 13 Apr 2023

Cited by 19 | Viewed by 5277

Abstract

A flood vulnerability index was constructed by coupling Geographic Information System (GIS) mapping capabilities with an Analytic Hierarchy Process (AHP) Group Decision-Making (GDM) resulting from a paired comparison matrix of expert groups to assign weights to each of the standardised criteria. A survey [...] Read more.

A flood vulnerability index was constructed by coupling Geographic Information System (GIS) mapping capabilities with an Analytic Hierarchy Process (AHP) Group Decision-Making (GDM) resulting from a paired comparison matrix of expert groups to assign weights to each of the standardised criteria. A survey was sent to 25 flood experts from government organisations, universities, research institutes, NGOs, and the private sector (56% academics and 44% non-academics). Respondents made pairwise comparisons for several criteria (population, socio-economic, buildings, and exposed elements) and sub-criteria. The group priorities were obtained by combining the Consistency Ratio (CR) and Euclidean Distance (ED) measures to assess the weight of each expert and obtain a final weight for each criterion and sub-criteria. In Portugal, 23 flood-prone areas were considered, and this work contributes with a tool to assess the flood vulnerability and consequently the flood risk. The flood vulnerability index was calculated, and the relevance of the proposed framework is demonstrated for flood-prone areas, in mainland Portugal. The results showed that in all five hydrographic regions, flood-prone areas with very high vulnerability were found, corresponding to areas with a high probability of flooding. The most vulnerable areas are Ponte de Lima in the North, Coimbra, and Pombal in the Centre; Loures in the Tagus and West Region; Setúbal and Alcácer do Sal in the Alentejo Region and Monchique in the Algarve Region. This methodology has the potential to be successfully applied to other flood-prone areas, combining the opinions of stakeholders validated by a mathematical model, which allows the vulnerability of the site to be assessed. Full article

(This article belongs to the Special Issue Topical Advisory Panel Members’ Collection Series: Environmental Hydrology)

► Show Figures

Figure 1

29 pages, 32060 KiB

Open AccessEditor’s ChoiceArticle

Study on Kinematic Structure Performance and Machining Characteristics of 3-Axis Machining Center

by Tzu-Chi Chan, Chia-Chuan Chang, Aman Ullah and Han-Huei Lin

Appl. Sci. 2023, 13(8), 4742; https://doi.org/10.3390/app13084742 - 10 Apr 2023

Cited by 7 | Viewed by 2922

Abstract

The rigidity and natural frequency of machine tools considerably influence cutting and generate great forces when the tool is in contact with the workpiece. The poor static rigidity of these Vertical Machining Centre machines can cause deformations and destroy the workpiece. If the [...] Read more.

The rigidity and natural frequency of machine tools considerably influence cutting and generate great forces when the tool is in contact with the workpiece. The poor static rigidity of these Vertical Machining Centre machines can cause deformations and destroy the workpiece. If the natural frequency of the machines is low or close to the commonly used cutting frequency, they vibrate considerably, resulting in poor workpiece surfaces and thus shortening the lifespan of the tool. The main objective of this study was to develop an experimental technique for measuring the effect of machine tool stiffness. The static rigidity of the X-axis was found to be 2.20 kg/μm, while the first-, second-, and third-order natural frequencies were 27.3, 34.4, and 48.3 Hz, respectively. When an external force of 1000 N was applied, the Y-axis motor load was found to be approximately 2740 N-mm. In this study, the finite element method was mainly used to analyze the structure, static force, modal, frequency spectrum, and transient state of machine tools. The results of the static analysis were verified and compared to the experimental results. The analysis model and conditions were modified to ensure that the analysis results were consistent with the experimental results. Multi-body dynamics analyses were conducted by examining the force of each component and casting of the machine tools and the load of the motor during the cutting stroke. Moreover, an external force was applied to simulate the load condition of the motor when the machine tool is cutting to confirm the feed. In this study, we used topology optimization for effective structural optimization designs. The optimal conditions for topology optimization included lightweight structures, which resulted in reduced structural deformation and increased natural frequency. Full article

(This article belongs to the Special Issue Dynamic, Magnetic and Thermal Properties of Nanofluids)

► Show Figures

Figure 1

23 pages, 16844 KiB

Open AccessEditor’s ChoiceArticle

A Vision Detection Scheme Based on Deep Learning in a Waste Plastics Sorting System

by Shengping Wen, Yue Yuan and Jingfu Chen

Appl. Sci. 2023, 13(7), 4634; https://doi.org/10.3390/app13074634 - 6 Apr 2023

Cited by 20 | Viewed by 5280

Abstract

The preliminary sorting of plastic products is a necessary step to improve the utilization of waste resources. To improve the quality and efficiency of sorting, a plastic detection scheme based on deep learning is proposed in this paper for a waste plastics sorting [...] Read more.

The preliminary sorting of plastic products is a necessary step to improve the utilization of waste resources. To improve the quality and efficiency of sorting, a plastic detection scheme based on deep learning is proposed in this paper for a waste plastics sorting system based on vision detection. In this scheme, the YOLOX (You Only Look Once) object detection model and the DeepSORT (Deep Simple Online and Realtime Tracking) multiple object tracking algorithm are improved and combined to make them more suitable for plastic sorting. For plastic detection, multiple data augmentations are combined to improve the detection effect, while BN (Batch Normalization) layer fusion and mixed precision inference are adopted to accelerate the model. For plastic tracking, the improved YOLOX is used as a detector, and the tracking effect is further improved by optimizing the deep cosine metric learning and the metric in the matching stage. Based on this, virtual detection lines are set up to filter and extract information to determine the sorted objects. The experimental results show that the scheme proposed in this paper makes full use of vision information to achieve dynamic and real-time detection of plastics. The system is effective and versatile for sorting complex objects. Full article

(This article belongs to the Section Applied Industrial Technologies)

► Show Figures

Figure 1

20 pages, 2058 KiB

Open AccessEditor’s ChoiceReview

Human-Focused Digital Twin Applications for Occupational Safety and Health in Workplaces: A Brief Survey and Research Directions

by Jin-Sung Park, Dong-Gu Lee, Jesus A. Jimenez, Sung-Jin Lee and Jun-Woo Kim

Appl. Sci. 2023, 13(7), 4598; https://doi.org/10.3390/app13074598 - 5 Apr 2023

Cited by 19 | Viewed by 4452

Abstract

Occupational safety and health is among the most challenging issues in many industrial workplaces, in that various factors can cause occupational illness and injury. Robotics, automation, and other state-of-the-art technologies represent risks that can cause further injuries and accidents. However, the tools currently [...] Read more.

Occupational safety and health is among the most challenging issues in many industrial workplaces, in that various factors can cause occupational illness and injury. Robotics, automation, and other state-of-the-art technologies represent risks that can cause further injuries and accidents. However, the tools currently used to assess risks in workplaces require manual work and are highly subjective. These tools include checklists and work assessments conducted by experts. Modern Industry 4.0 technologies such as a digital twin, a computerized representation in the digital world of a physical asset in the real world, can be used to provide a safe and healthy work environment to human workers and can reduce occupational injuries and accidents. These digital twins should be designed to collect, process, and analyze data about human workers. The problem is that building a human-focused digital twin is quite challenging and requires the integration of various modern hardware and software components. This paper aims to provide a brief survey of recent research papers on digital twins, focusing on occupational safety and health applications, which is considered an emerging research area. The authors focus on enabling technologies for human data acquisition and human representation in a virtual environment, on data processing procedures, and on the objectives of such applications. Additionally, this paper discusses the limitations of existing studies and proposes future research directions. Full article

(This article belongs to the Special Issue Intelligent Digital Twins: Trends and Applications in the Human-Centered Manufacturing Context)

► Show Figures

Figure 1

16 pages, 5008 KiB

Open AccessEditor’s ChoiceArticle

Spatial Assessment of Soil Erosion Risk Using RUSLE Embedded in GIS Environment: A Case Study of Jhelum River Watershed

by Muhammad Waseem, Fahad Iqbal, Muhammad Humayun, Muhammad Umais Latif, Tayyaba Javed and Megersa Kebede Leta

Appl. Sci. 2023, 13(6), 3775; https://doi.org/10.3390/app13063775 - 15 Mar 2023

Cited by 13 | Viewed by 4220

Abstract

The watershed area of the Mangla Reservoir spans across the Himalayan region of India and Pakistan, primarily consisting of the Jhelum River basin. The area is rugged with highly elevated, hilly terrain and relatively thin vegetation cover, which significantly increases the river’s sediment [...] Read more.

The watershed area of the Mangla Reservoir spans across the Himalayan region of India and Pakistan, primarily consisting of the Jhelum River basin. The area is rugged with highly elevated, hilly terrain and relatively thin vegetation cover, which significantly increases the river’s sediment output, especially during the monsoon season, leading to a decline in the reservoir’s storage capacity. This work assesses the soil erosion risk in the Jhelum River watershed (Azad Jammu and Kashmir (AJ&K), Pakistan) using the Revised Universal Soil Loss Equation of (RUSLE). The RUSLE components, including the conservation support or erosion control practice factor (P), soil erodibility factor (K), slope length and slope steepness factor (LS), rainfall erosivity factor (R), and crop cover factor (C), were integrated to compute soil erosion. Soil erosion risk and intensity maps were generated by computing the RUSLE parameters, which were then integrated with physical factors such as terrain units, elevation, slope, and land uses/cover to examine how these factors affect the spatial patterns of soil erosion loss. The 2021 rainfall data were utilized to compute the rainfall erosivity factor (R), and the soil erodibility (K) map was created using the world surface soil map prepared by the Food and Agriculture Organization (FAO). The slope length and slope steepness factor (LS) were generated in the highly rough terrain using Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM). The analysis revealed that the primary land use in the watershed was cultivated land, accounting for 27% of the area, and slopes of 30% or higher were present across two-thirds of the watershed. By multiplying the five variables, the study determined that the annual average soil loss was 23.47 t ha⁻¹ yr⁻¹. In areas with dense mixed forest cover, soil erosion rates ranged from 0.23 t ha⁻¹ yr⁻¹ to 25 t ha⁻¹ yr⁻¹. The findings indicated that 55.18% of the research area has a low erosion risk, 18.62% has a medium erosion risk, 13.66% has a high risk, and 11.6% has a very high erosion risk. The study’s findings will provide guidelines to policy/decision makers for better management of the Mangla watershed. Full article

(This article belongs to the Special Issue GIS and Spatial Planning for Natural Hazards Mitigation)

► Show Figures

Figure 1

14 pages, 1999 KiB

Open AccessEditor’s ChoiceArticle

Calculating the Limits of Detection in Laser-Induced Breakdown Spectroscopy: Not as Easy as It Might Seem

by Francesco Poggialini, Stefano Legnaioli, Beatrice Campanella, Bruno Cocciaro, Giulia Lorenzetti, Simona Raneri and Vincenzo Palleschi

Appl. Sci. 2023, 13(6), 3642; https://doi.org/10.3390/app13063642 - 13 Mar 2023

Cited by 21 | Viewed by 4700

Abstract

The objectives of this paper will be to discuss the issues related to the determination of the limits of detection (LOD) in laser-induced breakdown spectroscopy (LIBS) analytical applications. The derivation of the commonly used ‘3-sigma over slope’ rule and [...] Read more.

The objectives of this paper will be to discuss the issues related to the determination of the limits of detection (LOD) in laser-induced breakdown spectroscopy (LIBS) analytical applications. The derivation of the commonly used ‘3-sigma over slope’ rule and its evolution towards the new official definition recently adopted by the International Union of Pure and Applied Chemistry (IUPAC) will be illustrated. Methods for extending the calculation of the LOD to LIBS multivariate analysis will also be discussed, using as an example the detection of Cu traces in cast iron samples by LIBS. Full article

(This article belongs to the Section Optics and Lasers)

► Show Figures

Figure 1

21 pages, 5162 KiB

Open AccessEditor’s ChoiceArticle

Quality Assessment of Banana Ripening Stages by Combining Analytical Methods and Image Analysis

by Vassilia J. Sinanoglou, Thalia Tsiaka, Konstantinos Aouant, Elizabeth Mouka, Georgia Ladika, Eftichia Kritsi, Spyros J. Konteles, Alexandros-George Ioannou, Panagiotis Zoumpoulakis, Irini F. Strati and Dionisis Cavouras

Appl. Sci. 2023, 13(6), 3533; https://doi.org/10.3390/app13063533 - 10 Mar 2023

Cited by 20 | Viewed by 13335

Abstract

Currently, the evaluation of fruit ripening progress in relation to physicochemical and texture-quality parameters has become an increasingly important issue, particularly when considering consumer acceptance. Therefore, the purpose of the present study was the application of rapid, nondestructive, and conventional methods to assess [...] Read more.

Currently, the evaluation of fruit ripening progress in relation to physicochemical and texture-quality parameters has become an increasingly important issue, particularly when considering consumer acceptance. Therefore, the purpose of the present study was the application of rapid, nondestructive, and conventional methods to assess the quality of banana peels and flesh in terms of ripening and during storage in controlled temperatures and humidity. For this purpose, we implemented various analytical techniques, such as attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy for texture, colorimetrics, and physicochemical features, along with image-analysis methods and discriminant as well as statistical analysis. Image-analysis outcomes showed that storage provoked significant degradation of banana peels based on the increased image-texture dissimilarity and the loss of the structural order of the texture. In addition, the computed features were sufficient to discriminate four ripening stages with high accuracy. Moreover, the results revealed that storage led to significant changes in the color parameters and dramatic decreases in the texture attributes of banana flesh. The combination of image and chemical analyses pinpointed that storage caused water migration to the flesh and significant starch decomposition, which was then converted into soluble sugars. The redness and yellowness of the peel; the flesh moisture content; the texture attributes; Brix; and the storage time were all strongly interrelated. The combination of these techniques, coupled with statistical tools, to monitor the physicochemical and organoleptic quality of bananas during storage could be further applied for assessing the quality of other fruits and vegetables under similar conditions. Full article

(This article belongs to the Special Issue Innovative Technologies in Food Detection)

► Show Figures

Figure 1

18 pages, 4690 KiB

Open AccessEditor’s ChoiceArticle

Effectiveness of Machine-Learning and Deep-Learning Strategies for the Classification of Heat Treatments Applied to Low-Carbon Steels Based on Microstructural Analysis

by Jorge Muñoz-Rodenas, Francisco García-Sevilla, Juana Coello-Sobrino, Alberto Martínez-Martínez and Valentín Miguel-Eguía

Appl. Sci. 2023, 13(6), 3479; https://doi.org/10.3390/app13063479 - 9 Mar 2023

Cited by 14 | Viewed by 2751

Abstract

This work aims to compare the effectiveness of different machine-learning techniques for the image classification of steel microstructures. For this, we use a set of samples of hypoeutectoid steels subjected to three heat treatments: annealing, quenching and quenching with tempering. Logically, the samples [...] Read more.

This work aims to compare the effectiveness of different machine-learning techniques for the image classification of steel microstructures. For this, we use a set of samples of hypoeutectoid steels subjected to three heat treatments: annealing, quenching and quenching with tempering. Logically, the samples contain the typical constituents expected, and these are different for each treatment. Images are obtained by optical microscopy at 400× magnification and from different low-carbon steels to generate the data with some heterogeneity. Learning models are created with an image dataset for classification into three classes based on the respective heat treatments. Likewise, we develop two kinds of models by using, on the one hand, classical machine-learning methods based on the “bag of features” technique and, on the other hand, convolutional neural networks (CNN) with a transfer-learning approach by using GoogLeNet and ResNet50. We demonstrate the superiority of deep-learning techniques (CNN) over classical machine-learning methods. Full article

(This article belongs to the Special Issue Data Mining and Machine Learning in Industrial World)

► Show Figures

Figure 1

15 pages, 5960 KiB

Open AccessEditor’s ChoiceReview

Review of Flexible Supercapacitors Using Carbon Nanotube-Based Electrodes

by Yurim Han, Heebo Ha, Chunghyeon Choi, Hyungsub Yoon, Paolo Matteini, Jun Young Cheong and Byungil Hwang

Appl. Sci. 2023, 13(5), 3290; https://doi.org/10.3390/app13053290 - 4 Mar 2023

Cited by 31 | Viewed by 5512

Abstract

Carbon nanotube (CNT)-based electrodes in flexible supercapacitors have received significant attention in recent years. Carbon nanotube fiber fabrics (CNT-FF) have emerged as promising materials due to their high surface area, excellent conductivity, and mechanical strength. Researchers have attempted to improve the energy density [...] Read more.

Carbon nanotube (CNT)-based electrodes in flexible supercapacitors have received significant attention in recent years. Carbon nanotube fiber fabrics (CNT-FF) have emerged as promising materials due to their high surface area, excellent conductivity, and mechanical strength. Researchers have attempted to improve the energy density and rate performance of CNT-FF supercapacitor electrodes through various strategies, such as functionalization with conductive materials like MnO₂ nanoparticles and/or incorporation of graphene into them. In addition, the utilization of CNTs in combination with thin metal film electrodes has also gained widespread attention. Research has focused on enhancing electrochemical performance through functionalizing CNTs with conductive materials such as graphene and metal nanoparticles, or by controlling their morphology. This review paper will discuss the recent developments in supercapacitor technology utilizing carbon nanotube-based electrodes, including CNT fiber fabrics and CNTs on thin metal film electrodes. Various strategies employed for improving energy storage performance and the strengths and weaknesses of these strategies will be discussed. Finally, the paper will conclude with a discussion on the challenges that need to be addressed in order to realize the full potential of carbon nanotube-based electrodes in supercapacitor technology. Full article

(This article belongs to the Special Issue Printed Function Sensors and Materials)

► Show Figures

Figure 1

20 pages, 1537 KiB

Open AccessEditor’s ChoiceReview

Use of Machine Learning and Remote Sensing Techniques for Shoreline Monitoring: A Review of Recent Literature

by Chrysovalantis-Antonios D. Tsiakos and Christos Chalkias

Appl. Sci. 2023, 13(5), 3268; https://doi.org/10.3390/app13053268 - 3 Mar 2023

Cited by 39 | Viewed by 7709

Abstract

Climate change and its effects (i.e., sea level rise, extreme weather events) as well as anthropogenic activities, determine pressures to the coastal environments and contribute to shoreline retreat and coastal erosion phenomena. Coastal zones are dynamic and complex environments consisting of heterogeneous and [...] Read more.

Climate change and its effects (i.e., sea level rise, extreme weather events) as well as anthropogenic activities, determine pressures to the coastal environments and contribute to shoreline retreat and coastal erosion phenomena. Coastal zones are dynamic and complex environments consisting of heterogeneous and different geomorphological features, while exhibiting different scales and spectral responses. Thus, the monitoring of changes in the coastal land classes and the extraction of coastlines/shorelines can be a challenging task. Earth Observation data and the application of spatiotemporal analysis methods can facilitate shoreline change analysis and detection. Apart from remote sensing methods, the advent of machine learning-based techniques presents an emerging trend, being capable of supporting the monitoring and modeling of coastal ecosystems at large scales. In this context, this study aims to provide a review of the relevant literature falling within the period of 2015–2022, where different machine learning approaches were applied for cases of coast-line/shoreline extraction and change analysis, and/or coastal dynamic monitoring. Particular emphasis is given on the analysis of the selected studies, including details about their performances, as well as their advantages and weaknesses, and information about the different environmental data employed. Full article

(This article belongs to the Special Issue GIS and Spatial Planning for Natural Hazards Mitigation)

► Show Figures

Figure 1

22 pages, 6166 KiB

Open AccessEditor’s ChoiceArticle

Comparative Analysis of Lithium-Ion and Lead–Acid as Electrical Energy Storage Systems in a Grid-Tied Microgrid Application

by Cry S. Makola, Peet F. Le Roux and Jaco A. Jordaan

Appl. Sci. 2023, 13(5), 3137; https://doi.org/10.3390/app13053137 - 28 Feb 2023

Cited by 18 | Viewed by 3834

Abstract

Microgrids (MGs) are a valuable substitute for traditional generators. They can supply inexhaustible, sustainable, constant, and efficient energy with minimized losses and curtail network congestion. Nevertheless, the optimum contribution of renewable energy resource (RER)-based generators in an MG is prohibited by its variable [...] Read more.

Microgrids (MGs) are a valuable substitute for traditional generators. They can supply inexhaustible, sustainable, constant, and efficient energy with minimized losses and curtail network congestion. Nevertheless, the optimum contribution of renewable energy resource (RER)-based generators in an MG is prohibited by its variable attribute. It cannot be effectively deployed due to its application’s power quality and stability issues. Therefore, an energy storage system is employed to alleviate the variability of RERs by stabilizing the power demand against irregular generation. Electrical energy storage systems (EESSs) are regarded as one of the most beneficial methods for storing dependable energy supply while integrating RERs into the utility grid. Conventionally, lead–acid (LA) batteries are the most frequently utilized electrochemical storage system for grid-stationed implementations thus far. However, due to their low life cycle and low efficiency, another contending technology known as lithium-ion (Li-ion) is utilized. This research presents a feasibility study approach using ETAP software 20.6 to analyze the performance of LA and Li-ion batteries under permissible charging constraints. The design of an optimal model is a grid-connected microgrid system consisting of a PV energy source and dynamic load encompassed by Li-ion and LA batteries. Finally, the comparative study led to significant conclusions regarding the specific attributes of both battery technologies analyzed through the operation, revealing that Li-ion is a more conducive energy storage system than LA. Full article

(This article belongs to the Special Issue Advancing Grid-Connected Renewable Generation Systems 2021-2022)

► Show Figures

Figure 1

34 pages, 6208 KiB

Open AccessEditor’s ChoiceArticle

Hydrological Drought Frequency Analysis in Water Management Using Univariate Distributions

by Cristian Gabriel Anghel and Cornel Ilinca

Appl. Sci. 2023, 13(5), 3055; https://doi.org/10.3390/app13053055 - 27 Feb 2023

Cited by 14 | Viewed by 3215

Abstract

The study of extreme phenomena in hydrology generally involves frequency analysis and a time series analysis. In this article we provide enough mathematics to enable hydrology researchers to apply a wide range of probability distributions in frequency analyses of hydrological drought. The article [...] Read more.

The study of extreme phenomena in hydrology generally involves frequency analysis and a time series analysis. In this article we provide enough mathematics to enable hydrology researchers to apply a wide range of probability distributions in frequency analyses of hydrological drought. The article presents a hydrological drought frequency analysis methodology for the determination of minimum annual flows, annual drought durations and annual deficit volumes for exceedance probabilities common in water management. Eight statistical distributions from different families and with different numbers of parameters are used for the frequency analysis. The method of ordinary moments and the method of linear moments are used to estimate the parameters of the distributions. All the mathematical characteristics necessary for the application of the eight analyzed distributions, for the method of ordinary moments and the method of linear moments, are presented. The performance of the analyzed distributions is evaluated using relative mean error and relative absolute error. For the frequency analysis of the annual minimum flows, only distributions that have a lower bound close to the annual minimum value should be used, a defining characteristic having the asymptotic distributions at this value. A case study of hydrological drought frequency analysis is presented for the Prigor River. We believe that the use of software without the knowledge of the mathematics behind it is not beneficial for researchers in the field of technical hydrology; thus, the dissemination of mathematical methods and models is necessary. All the research was conducted within the Faculty of Hydrotechnics. Full article

(This article belongs to the Special Issue Hydrology and Water Resources)

► Show Figures

Figure 1

19 pages, 7110 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Comparative Analysis of Primary Photosynthetic Reactions Assessed by OJIP Kinetics in Three Brassica Crops after Drought and Recovery

by Jasenka Antunović Dunić, Selma Mlinarić, Iva Pavlović, Hrvoje Lepeduš and Branka Salopek-Sondi

Appl. Sci. 2023, 13(5), 3078; https://doi.org/10.3390/app13053078 - 27 Feb 2023

Cited by 19 | Viewed by 2722

Abstract

Plant drought tolerance depends on adaptations of the photosynthetic apparatus to changing environments triggered by water deficit. The seedlings of three Brassica crops differing in drought sensitivity, Brassica oleracea L. var. capitata—white cabbage, Brassica oleracea L. var. acephala—kale, and Brassica rapa [...] Read more.

Plant drought tolerance depends on adaptations of the photosynthetic apparatus to changing environments triggered by water deficit. The seedlings of three Brassica crops differing in drought sensitivity, Brassica oleracea L. var. capitata—white cabbage, Brassica oleracea L. var. acephala—kale, and Brassica rapa L. var. pekinensis—Chinese cabbage, were exposed to drought by withholding water. Detailed insight into the photosynthetic machinery was carried out when the seedling reached a relative water content of about 45% and after re-watering by analyzing the OJIP kinetics. The key objective of this study was to find reliable parameters for distinguishing drought−tolerant and drought-sensitive varieties before permanent structural and functional changes in the photosynthetic apparatus occur. According to our findings, an increase in the total performance index (PI_total) and structure–function index (SFI), positive L and K bands, total driving forces (ΔDF), and drought resistance index (DRI) suggest drought tolerance. At the same time, susceptible varieties can be distinguished based on negative L and K bands, PI_total, SFI, and the density of reaction centers (RC/CS₀). Kale proved to be the most tolerant, Chinese cabbage was moderately susceptible, and white cabbage showed high sensitivity to the investigated drought stress. The genetic variation revealed among the selected Brassica crops could be used in breeding programs and high-precision crop management. Full article

(This article belongs to the Special Issue Biophysical Properties of Agricultural Crops)

► Show Figures

Figure 1

42 pages, 7626 KiB

Open AccessEditor’s ChoiceReview

Ceramic Matrix Composites for Aero Engine Applications—A Review

by George Karadimas and Konstantinos Salonitis

Appl. Sci. 2023, 13(5), 3017; https://doi.org/10.3390/app13053017 - 26 Feb 2023

Cited by 83 | Viewed by 25621

Abstract

Ceramic matrix materials have attracted great attention from researchers and industry due to their material properties. When used in engineering systems, and especially in aero-engine applications, they can result in reduced weight, higher temperature capability, and/or reduced cooling needs, each of which increases [...] Read more.

Ceramic matrix materials have attracted great attention from researchers and industry due to their material properties. When used in engineering systems, and especially in aero-engine applications, they can result in reduced weight, higher temperature capability, and/or reduced cooling needs, each of which increases efficiency. This is where high-temperature ceramics have made considerable progress, and ceramic matrix composites (CMCs) are in the foreground. CMCs are classified into non-oxide and oxide-based ones. Both families have material types that have a high potential for use in high-temperature propulsion applications. The oxide materials discussed will focus on alumina and aluminosilicate/mullite base material families, whereas for non-oxides, carbon, silicon carbide, titanium carbide, and tungsten carbide CMC material families will be discussed and analyzed. Typical oxide-based ones are composed of an oxide fiber and oxide matrix (Ox-Ox). Some of the most common oxide subcategories are alumina, beryllia, ceria, and zirconia ceramics. On the other hand, the largest number of non-oxides are technical ceramics that are classified as inorganic, non-metallic materials. The most well-known non-oxide subcategories are carbides, borides, nitrides, and silicides. These matrix composites are used, for example, in combustion liners of gas turbine engines and exhaust nozzles. Until now, a thorough study on the available oxide and non-oxide-based CMCs for such applications has not been presented. This paper will focus on assessing a literature survey of the available oxide and non-oxide ceramic matrix composite materials in terms of mechanical and thermal properties, as well as the classification and fabrication methods of those CMCs. The available manufacturing and fabrication processes are reviewed and compared. Finally, the paper presents a research and development roadmap for increasing the maturity of these materials allowing for the wider adoption of aero-engine applications. Full article

(This article belongs to the Special Issue Processing, Properties and Applications of Composite Materials)

► Show Figures

Figure 1

24 pages, 2558 KiB

Open AccessEditor’s ChoiceArticle

Comparison of the Spreadability of Butter and Butter Substitutes

by Małgorzata Ziarno, Dorota Derewiaka, Anna Florowska and Iwona Szymańska

Appl. Sci. 2023, 13(4), 2600; https://doi.org/10.3390/app13042600 - 17 Feb 2023

Cited by 17 | Viewed by 7859

Abstract

There are many types of butter, soft margarine, and blends, e.g., a mixture of butter and vegetable fats, on the market as bread spreads. Among these, butter and blends of butter with vegetable fats are very popular. The consumer’s choice of product is [...] Read more.

There are many types of butter, soft margarine, and blends, e.g., a mixture of butter and vegetable fats, on the market as bread spreads. Among these, butter and blends of butter with vegetable fats are very popular. The consumer’s choice of product is often determined by functional properties, such as texture, and the physicochemical composition of butter and butter substitutes. The aim of this study was to compare sixteen market samples of butter and butter substitutes in terms of spreadability and other selected structural (spreadability, hardness, adhesive force, and adhesiveness) and physicochemical parameters (water content, water distribution, plasma pH, color, acid value, peroxide number, saponification number, and instrumentally measured fatty acid profile) to investigate their correlation with spreadability. The parameters determined here were correlated with factors such as the type of sample, measuring temperature, and physicochemical composition. The statistical analysis revealed a very strong positive correlation between hardness and spreadability for all samples tested at 4 °C, as well as between hardness and spreadability for all samples tested 30 min after removal from the refrigerator; however, the interpretation of the results was different if the butter and butter substitute samples were subjected to a multivariate analysis separately. Full article

(This article belongs to the Special Issue Unconventional Raw Materials for Food Products)

► Show Figures

Figure 1

25 pages, 8892 KiB

Open AccessEditor’s ChoiceArticle

Effects of Heat Treatment and Diamond Burnishing on Fatigue Behaviour and Corrosion Resistance of AISI 304 Austenitic Stainless Steel

by Jordan Maximov, Galya Duncheva, Angel Anchev, Vladimir Dunchev, Yaroslav Argirov and Maria Nikolova

Appl. Sci. 2023, 13(4), 2570; https://doi.org/10.3390/app13042570 - 16 Feb 2023

Cited by 15 | Viewed by 2466

Abstract

The surface cold working (SCW) of austenitic stainless steel (SS) causes martensitic transformation in the surface layers, and the percentage fraction of the strain-induced martensite depends on the degree of SCW. Higher content of α′−martensite increases the surface micro-hardness and fatigue strength, but [...] Read more.

The surface cold working (SCW) of austenitic stainless steel (SS) causes martensitic transformation in the surface layers, and the percentage fraction of the strain-induced martensite depends on the degree of SCW. Higher content of α′−martensite increases the surface micro-hardness and fatigue strength, but deterioration of the corrosion resistance is possible. Therefore, the desired operational behaviour of austenitic SS can be ensured by the corresponding degree of SCW and heat treatment. This article evaluates the effects of SCW performed by diamond burnishing (DB) and heat treatment on the surface integrity (SI), rotating fatigue strength, and corrosion resistance of AISI 304 austenitic SS for two initial states: as-received hot-rolled bar and initially heat-treated at 1100 °C for one hour followed by quenching in water. Then, DB was implemented as a smoothing and hardening process, both alone and in combination with heat treatment at 350 °C for three hours after DB. The electrochemical performance was examined by open circuit potential measurements, followed by potentiodynamic tests. For both initial states, smoothing DB provided the lowest roughness, whereas an improvement in the maximum surface micro-hardness was obtained after hardening DB and subsequent heat treatment. The maximum fatigue strength was obtained by hardening multi-pass DB without subsequent heat treatment for the as-received initial state. Smoothing DB and subsequent heat treatment maximised the surface corrosion resistance for the two initial states, whereas a minimum corrosion rate was obtained for the initially heat-treated state. For the as-received state, smoothing DB and subsequent heat treatment simultaneously lead to a high fatigue limit (equal to that obtained by hardening single-pass DB) and a low corrosion rate. Full article

(This article belongs to the Special Issue Application of Diamond Burnishing to Improve the Performance of Materials)

► Show Figures

Figure 1

19 pages, 1225 KiB

Open AccessEditor’s ChoiceReview

Review of Studies on Emotion Recognition and Judgment Based on Physiological Signals

by Wenqian Lin and Chao Li

Appl. Sci. 2023, 13(4), 2573; https://doi.org/10.3390/app13042573 - 16 Feb 2023

Cited by 67 | Viewed by 8940

Abstract

People’s emotions play an important part in our daily life and can not only reflect psychological and physical states, but also play a vital role in people’s communication, cognition and decision-making. Variations in people’s emotions induced by external conditions are accompanied by variations [...] Read more.

People’s emotions play an important part in our daily life and can not only reflect psychological and physical states, but also play a vital role in people’s communication, cognition and decision-making. Variations in people’s emotions induced by external conditions are accompanied by variations in physiological signals that can be measured and identified. People’s psychological signals are mainly measured with electroencephalograms (EEGs), electrodermal activity (EDA), electrocardiograms (ECGs), electromyography (EMG), pulse waves, etc. EEG signals are a comprehensive embodiment of the operation of numerous neurons in the cerebral cortex and can immediately express brain activity. EDA measures the electrical features of skin through skin conductance response, skin potential, skin conductance level or skin potential response. ECG technology uses an electrocardiograph to record changes in electrical activity in each cardiac cycle of the heart from the body surface. EMG is a technique that uses electronic instruments to evaluate and record the electrical activity of muscles, which is usually referred to as myoelectric activity. EEG, EDA, ECG and EMG have been widely used to recognize and judge people’s emotions in various situations. Different physiological signals have their own characteristics and are suitable for different occasions. Therefore, a review of the research work and application of emotion recognition and judgment based on the four physiological signals mentioned above is offered. The content covers the technologies adopted, the objects of application and the effects achieved. Finally, the application scenarios for different physiological signals are compared, and issues for attention are explored to provide reference and a basis for further investigation. Full article

(This article belongs to the Special Issue Recent Advances in Biological Science and Technology)

► Show Figures

Figure 1

26 pages, 6738 KiB

Open AccessEditor’s ChoiceArticle

Tannin Extraction from Chestnut Wood Waste: From Lab Scale to Semi-Industrial Plant

by Clelia Aimone, Giorgio Grillo, Luisa Boffa, Samuele Giovando and Giancarlo Cravotto

Appl. Sci. 2023, 13(4), 2494; https://doi.org/10.3390/app13042494 - 15 Feb 2023

Cited by 20 | Viewed by 7168

Abstract

The chestnut tree (Castanea sativa, Mill.) is a widespread plant in Europe whose fruits and wood has a relevant economic impact. Chestnut wood (CW) is rich in high-value compounds that exhibit various biological activities, such as antioxidant as well as anticarcinogenic [...] Read more.

The chestnut tree (Castanea sativa, Mill.) is a widespread plant in Europe whose fruits and wood has a relevant economic impact. Chestnut wood (CW) is rich in high-value compounds that exhibit various biological activities, such as antioxidant as well as anticarcinogenic and antimicrobial properties. These metabolites can be mainly divided into monomeric polyphenols and tannins. In this piece of work, we investigated a sustainable protocol to isolate enriched fractions of the above-mentioned compounds from CW residues. Specifically, a sequential extraction protocol, using subcritical water, was used as a pre-fractionation step, recovering approximately 88% of tannins and 40% of monomeric polyphenols in the first and second steps, respectively. The optimized protocol was also tested at pre-industrial levels, treating up to 13.5 kg CW and 160 L of solution with encouraging results. Ultra- and nanofiltrations were used to further enrich the recovered fractions, achieving more than 98% of the tannin content in the heavy fraction, whilst the removed permeate achieved up to 752.71 mg GAE/g_ext after the concentration (75.3%). Samples were characterized by means of total phenolic content (TPC), antioxidant activity (DPPH· and ABTS·), and tannin composition (hydrolysable and condensed). In addition, LC-MS-DAD was used for semiqualitative purposes to detect vescalagin/castalagin and vescalin/castalin, as well as gallic acid and ellagic acid. The developed valorization protocol allows the efficient fractionation and recovery of the major polyphenolic components of CW with a sustainable approach that also evaluates pre-industrial scaling-up. Full article

(This article belongs to the Section Green Sustainable Science and Technology)

► Show Figures

Figure 1

23 pages, 5293 KiB

Open AccessEditor’s ChoiceReview

Reinforcement Learning in Game Industry—Review, Prospects and Challenges

by Konstantinos Souchleris, George K. Sidiropoulos and George A. Papakostas

Appl. Sci. 2023, 13(4), 2443; https://doi.org/10.3390/app13042443 - 14 Feb 2023

Cited by 25 | Viewed by 18127

Abstract

This article focuses on the recent advances in the field of reinforcement learning (RL) as well as the present state–of–the–art applications in games. First, we give a general panorama of RL while at the same time we underline the way that it has [...] Read more.

This article focuses on the recent advances in the field of reinforcement learning (RL) as well as the present state–of–the–art applications in games. First, we give a general panorama of RL while at the same time we underline the way that it has progressed to the current degree of application. Moreover, we conduct a keyword analysis of the literature on deep learning (DL) and reinforcement learning in order to analyze to what extent the scientific study is based on games such as ATARI, Chess, and Go. Finally, we explored a range of public data to create a unified framework and trends for the present and future of this sector (RL in games). Our work led us to conclude that deep RL accounted for roughly 25.1% of the DL literature, and a sizable amount of this literature focuses on RL applications in the game domain, indicating the road for newer and more sophisticated algorithms capable of outperforming human performance. Full article

(This article belongs to the Special Issue Deep Reinforcement Learning for Robots and Agents)

► Show Figures

Figure 1

37 pages, 2260 KiB

Open AccessEditor’s ChoiceReview

Robots in Inspection and Monitoring of Buildings and Infrastructure: A Systematic Review

by Srijeet Halder and Kereshmeh Afsari

Appl. Sci. 2023, 13(4), 2304; https://doi.org/10.3390/app13042304 - 10 Feb 2023

Cited by 94 | Viewed by 22600

Abstract

Regular inspection and monitoring of buildings and infrastructure, that is collectively called the built environment in this paper, is critical. The built environment includes commercial and residential buildings, roads, bridges, tunnels, and pipelines. Automation and robotics can aid in reducing errors and increasing [...] Read more.

Regular inspection and monitoring of buildings and infrastructure, that is collectively called the built environment in this paper, is critical. The built environment includes commercial and residential buildings, roads, bridges, tunnels, and pipelines. Automation and robotics can aid in reducing errors and increasing the efficiency of inspection tasks. As a result, robotic inspection and monitoring of the built environment has become a significant research topic in recent years. This review paper presents an in-depth qualitative content analysis of 269 papers on the use of robots for the inspection and monitoring of buildings and infrastructure. The review found nine different types of robotic systems, with unmanned aerial vehicles (UAVs) being the most common, followed by unmanned ground vehicles (UGVs). The study also found five different applications of robots in inspection and monitoring, namely, maintenance inspection, construction quality inspection, construction progress monitoring, as-built modeling, and safety inspection. Common research areas investigated by researchers include autonomous navigation, knowledge extraction, motion control systems, sensing, multi-robot collaboration, safety implications, and data transmission. The findings of this study provide insight into the recent research and developments in the field of robotic inspection and monitoring of the built environment and will benefit researchers, and construction and facility managers, in developing and implementing new robotic solutions. Full article

(This article belongs to the Special Issue Recent Advances in Mechatronic and Robotic Systems)

► Show Figures

Figure 1

17 pages, 630 KiB

Open AccessEditor’s ChoiceArticle

A Secure and Decentralized Authentication Mechanism Based on Web 3.0 and Ethereum Blockchain Technology

by Adrian Petcu, Bogdan Pahontu, Madalin Frunzete and Dan Alexandru Stoichescu

Appl. Sci. 2023, 13(4), 2231; https://doi.org/10.3390/app13042231 - 9 Feb 2023

Cited by 20 | Viewed by 7605

Abstract

Over the past decade, there has been significant evolution in the security field, specifically in the authentication and authorization part. The standard authentication protocol nowadays is OAuth 2.0-based authentication. This method relies on a third-party authentication service provider with complete control over the [...] Read more.

Over the past decade, there has been significant evolution in the security field, specifically in the authentication and authorization part. The standard authentication protocol nowadays is OAuth 2.0-based authentication. This method relies on a third-party authentication service provider with complete control over the users’ data, which it can filter or modify at will. Blockchain and decentralization have generated much interest in recent years, and the decentralized web is considered the next significant improvement in the world wide web (also known as Web 3.0). Web3 authentication, also known as decentralized authentication, allows for the secure and decentralized authentication of users on the web. The use cases for this technology include online marketplaces, social media platforms, and other online communities that require user authentication. The advantages of Web3 authentication include increased security and privacy for users and the ability for users to have more control over their data. The proposed system implementation uses Ethereum as the blockchain and a modern web stack to enhance user interaction and usability. The solution brings benefits both to the private and the public sector, proving that it has the capability of becoming the preferred authentication mechanism for any decentralized web application. Full article

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

► Show Figures

Figure 1

19 pages, 12841 KiB

Open AccessEditor’s ChoiceArticle

Design of a Smart Factory Based on Cyber-Physical Systems and Internet of Things towards Industry 4.0

by Mutaz Ryalat, Hisham ElMoaqet and Marwa AlFaouri

Appl. Sci. 2023, 13(4), 2156; https://doi.org/10.3390/app13042156 - 8 Feb 2023

Cited by 143 | Viewed by 17007

Abstract

The rise of Industry 4.0, which employs emerging powerful and intelligent technologies and represents the digital transformation of manufacturing, has a significant impact on society, industry, and other production sectors. The industrial scene is witnessing ever-increasing pressure to improve its agility and versatility [...] Read more.

The rise of Industry 4.0, which employs emerging powerful and intelligent technologies and represents the digital transformation of manufacturing, has a significant impact on society, industry, and other production sectors. The industrial scene is witnessing ever-increasing pressure to improve its agility and versatility to accommodate the highly modularized, customized, and dynamic demands of production. One of the key concepts within Industry 4.0 is the smart factory, which represents a manufacturing/production system with interconnected processes and operations via cyber-physical systems, the Internet of Things, and state-of-the-art digital technologies. This paper outlines the design of a smart cyber-physical system that complies with the innovative smart factory framework for Industry 4.0 and implements the core industrial, computing, information, and communication technologies of the smart factory. It discusses how to combine the key components (pillars) of a smart factory to create an intelligent manufacturing system. As a demonstration of a simplified smart factory model, a smart manufacturing case study with a drilling process is implemented, and the feasibility of the proposed method is demonstrated and verified with experiments. Full article

(This article belongs to the Section Mechanical Engineering)

► Show Figures

Figure 1

14 pages, 5185 KiB

Open AccessEditor’s ChoiceArticle

Speech Emotion Recognition Based on Two-Stream Deep Learning Model Using Korean Audio Information

by A-Hyeon Jo and Keun-Chang Kwak

Appl. Sci. 2023, 13(4), 2167; https://doi.org/10.3390/app13042167 - 8 Feb 2023

Cited by 21 | Viewed by 5245

Abstract

Identifying a person’s emotions is an important element in communication. In particular, voice is a means of communication for easily and naturally expressing emotions. Speech emotion recognition technology is a crucial component of human–computer interaction (HCI), in which accurately identifying emotions is key. [...] Read more.

Identifying a person’s emotions is an important element in communication. In particular, voice is a means of communication for easily and naturally expressing emotions. Speech emotion recognition technology is a crucial component of human–computer interaction (HCI), in which accurately identifying emotions is key. Therefore, this study presents a two-stream-based emotion recognition model based on bidirectional long short-term memory (Bi-LSTM) and convolutional neural networks (CNNs) using a Korean speech emotion database, and the performance is comparatively analyzed. The data used in the experiment were obtained from the Korean speech emotion recognition database built by Chosun University. Two deep learning models, Bi-LSTM and YAMNet, which is a CNN-based transfer learning model, were connected in a two-stream architecture to design an emotion recognition model. Various speech feature extraction methods and deep learning models were compared in terms of performance. Consequently, the speech emotion recognition performance of Bi-LSTM and YAMNet was 90.38% and 94.91%, respectively. However, the performance of the two-stream model was 96%, which was a minimum of 1.09% and up to 5.62% improved compared with a single model. Full article

(This article belongs to the Special Issue Deep Learning and Machine Learning in Biomedical Signal and Image Processing)

► Show Figures

Figure 1

26 pages, 5134 KiB

Open AccessEditor’s ChoiceReview

Noble Metal-Based Heterogeneous Catalysts for Electrochemical Hydrogen Evolution Reaction

by Huajie Niu, Qingyan Wang, Chuanxue Huang, Mengyang Zhang, Yu Yan, Tong Liu and Wei Zhou

Appl. Sci. 2023, 13(4), 2177; https://doi.org/10.3390/app13042177 - 8 Feb 2023

Cited by 20 | Viewed by 5857

Abstract

Hydrogen energy, a green renewable energy, has shown great potential in developing new energy and alleviating environmental problems. Water electrolysis is an effective method to achieve large-scale clean hydrogen production, but this process needs to consume a huge amount of electric energy. It [...] Read more.

Hydrogen energy, a green renewable energy, has shown great potential in developing new energy and alleviating environmental problems. Water electrolysis is an effective method to achieve large-scale clean hydrogen production, but this process needs to consume a huge amount of electric energy. It is urgent to develop high-activity, high-stability and low-cost catalysts to reduce the consumption of electric energy. At present, the noble metal catalyst is the star material in the hydrogen evolution reaction (HER), but its stability and high cost restrict its large-scale application. In this review, we comprehensively discussed the research progress on noble metal-based heterogeneous electrocatalysts used in water electrolysis for hydrogen production. Firstly, we analyzed the influence factors for hydrogen production performance, including the mass transfer process, the adsorption–desorption process, the catalytic process, and the influence of the working electrode and electrolyte. Then, we discussed the relationship between catalytic activity and electronic structure and chemical composition in view of theoretical calculations and summarized the strategies for developing efficient catalysts (alloying and interface engineering). Finally, we highlighted the challenges for the practical application of noble metal-based hydrogen evolution electrocatalysts. Full article

(This article belongs to the Section Chemical and Molecular Sciences)

► Show Figures

Figure 1

22 pages, 3501 KiB

Open AccessEditor’s ChoiceReview

Evaluation and Current State of Primary and Secondary Zinc Production—A Review

by Henryk Kania and Mariola Saternus

Appl. Sci. 2023, 13(3), 2003; https://doi.org/10.3390/app13032003 - 3 Feb 2023

Cited by 50 | Viewed by 10911

Abstract

This article presents the history of zinc, its production and demand. The quantity of zinc production, both primary zinc from ores and concentrates, and secondary zinc from scrap and zinc-rich waste, was discussed. A comprehensive economic analysis covers zinc prices in the years [...] Read more.

This article presents the history of zinc, its production and demand. The quantity of zinc production, both primary zinc from ores and concentrates, and secondary zinc from scrap and zinc-rich waste, was discussed. A comprehensive economic analysis covers zinc prices in the years 1960–2021. The basic methods of obtaining zinc from ores, including pyrometallurgical (Imperial Smelting Process ISP, Kivcet, Ausmelt) and hydrometallurgical (roasting–leaching–electrowinning RLE, atmospheric direct leaching ADL, Engitec Zinc Extraction EZINEX, zinc pressure leach) and their short characteristics, are presented. The global zinc market and the main areas of its application were analyzed. Technologies used for the recovery of zinc from scrap are discussed along with their characteristics. Galvanized steel is the main source of secondary zinc, both in the galvanizing process and in the remelting of galvanized steel. It can be easily recycled with other scrap steel in the electric arc furnace (EAF) for steel production. Currently, with high volatility in the price of zinc, as well as its natural resources in the earth’s crust, recycling is an important activity, despite the fact that zinc concentrates have a relatively constant chemical composition, while the resulting zinc waste contains zinc in varying amounts. Full article

(This article belongs to the Special Issue Selected Papers in the Section Materials 2022)

► Show Figures

Figure 1

27 pages, 12170 KiB

Open AccessEditor’s ChoiceArticle

Design and Validation of a U-Net-Based Algorithm for Star Sensor Image Segmentation

by Marco Mastrofini, Ivan Agostinelli and Fabio Curti

Appl. Sci. 2023, 13(3), 1947; https://doi.org/10.3390/app13031947 - 2 Feb 2023

Cited by 9 | Viewed by 2949

Abstract

The present work focuses on the investigation of an artificial intelligence (AI) algorithm for brightest objects segmentation in night sky images’ field of view (FOV). This task is mandatory for many applications that want to focus on the brightest objects in an optical [...] Read more.

The present work focuses on the investigation of an artificial intelligence (AI) algorithm for brightest objects segmentation in night sky images’ field of view (FOV). This task is mandatory for many applications that want to focus on the brightest objects in an optical sensor image with a particular shape: point-like or streak. The algorithm is developed as a dedicated application for star sensors both for attitude determination (AD) and onboard space surveillance and tracking (SST) tasks. Indeed, in the former, the brightest objects of most concern are stars, while in the latter they are resident space objects (RSOs). Focusing attention on these shapes, an AI-based segmentation approach can be investigated. This will be carried out by designing, developing and testing a convolutional neural network (CNN)-based algorithm. In particular, a U-Net will be used to tackle this problem. A dataset for the design process of the algorithm, network training and tests is created using both real and simulated images. In the end, comparison with traditional segmentation algorithms will be performed, and results will be presented and discussed together with the proposal of an electro-optical payload for a small satellite for an in-orbit validation (IOV) mission. Full article

(This article belongs to the Special Issue Small Satellites Missions and Applications)

► Show Figures

Figure 1

19 pages, 37316 KiB

Open AccessEditor’s ChoiceArticle

Anomaly Detection Method for Multivariate Time Series Data of Oil and Gas Stations Based on Digital Twin and MTAD-GAN

by Yuanfeng Lian, Yueyao Geng and Tian Tian

Appl. Sci. 2023, 13(3), 1891; https://doi.org/10.3390/app13031891 - 1 Feb 2023

Cited by 31 | Viewed by 5898

Abstract

Due to the complexity of the oil and gas station system, the operational data, with various temporal dependencies and inter-metric dependencies, has the characteristics of diverse patterns, variable working conditions and imbalance, which brings great challenges to multivariate time series anomaly detection. Moreover, [...] Read more.

Due to the complexity of the oil and gas station system, the operational data, with various temporal dependencies and inter-metric dependencies, has the characteristics of diverse patterns, variable working conditions and imbalance, which brings great challenges to multivariate time series anomaly detection. Moreover, the time-series reconstruction information of data from digital twin space can be used to identify and interpret anomalies. Therefore, this paper proposes a digital twin-driven MTAD-GAN (Multivariate Time Series Data Anomaly Detection with GAN) oil and gas station anomaly detection method. Firstly, the operational framework consisting of digital twin model, virtual-real synchronization algorithm, anomaly detection strategy and realistic station is constructed, and an efficient virtual-real mapping is achieved by embedding a stochastic Petri net (SPN) to describe the station-operating logic of behavior. Secondly, based on the potential correlation and complementarity among time series variables, we present a MTAD-GAN anomaly detection method to reconstruct the error of multivariate time series by combining mechanism of knowledge graph attention and temporal Hawkes attention to judge the abnormal samples by a given threshold. The experimental results show that the digital twin-driven anomaly detection method can achieve accurate identification of anomalous data with complex patterns, and the performance of MTAD-GAN anomaly detection is improved by about 2.6% compared with other methods based on machine learning and deep learning, which proves the effectiveness of the method. Full article

(This article belongs to the Special Issue Unsupervised Anomaly Detection)

► Show Figures

Figure 1

17 pages, 11377 KiB

Open AccessEditor’s ChoiceArticle

Short-Term Power Prediction of Wind Turbine Applying Machine Learning and Digital Filter

by Shujun Liu, Yaocong Zhang, Xiaoze Du, Tong Xu and Jiangbo Wu

Appl. Sci. 2023, 13(3), 1751; https://doi.org/10.3390/app13031751 - 30 Jan 2023

Cited by 9 | Viewed by 2850

Abstract

As wind energy development increases, accurate wind energy forecasting helps to develop sensible power generation plans and ensure a balance between supply and demand. Machine-learning-based forecasting models possess exceptional predictive capabilities, and data manipulation prior to model training is also a key focus [...] Read more.

As wind energy development increases, accurate wind energy forecasting helps to develop sensible power generation plans and ensure a balance between supply and demand. Machine-learning-based forecasting models possess exceptional predictive capabilities, and data manipulation prior to model training is also a key focus of this research. This study trained a deep Long Short-Term Memory (LSTM) neural network to learn the processing results of the Savitzky-Golay filter, which can avoid overfitting due to fluctuations and noise in measurements, improving the generalization performance. The optimum data frame length to match the second-order filter was determined by comparison. In a single-step prediction, the method reduced the root-mean-square error by 3.8% compared to the model trained directly with the measurements. The method also produced the smallest errors in all steps of the multi-step advance prediction. The proposed method ensures the accuracy of the forecasting and, on that basis, also improves the timeliness of the effective forecasts. Full article

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

► Show Figures

Figure 1

25 pages, 3039 KiB

Open AccessEditor’s ChoiceArticle

Is It Worth It? Comparing Six Deep and Classical Methods for Unsupervised Anomaly Detection in Time Series

by Ferdinand Rewicki, Joachim Denzler and Julia Niebling

Appl. Sci. 2023, 13(3), 1778; https://doi.org/10.3390/app13031778 - 30 Jan 2023

Cited by 28 | Viewed by 8148

Abstract

Detecting anomalies in time series data is important in a variety of fields, including system monitoring, healthcare and cybersecurity. While the abundance of available methods makes it difficult to choose the most appropriate method for a given application, each method has its strengths [...] Read more.

Detecting anomalies in time series data is important in a variety of fields, including system monitoring, healthcare and cybersecurity. While the abundance of available methods makes it difficult to choose the most appropriate method for a given application, each method has its strengths in detecting certain types of anomalies. In this study, we compare six unsupervised anomaly detection methods of varying complexity to determine whether more complex methods generally perform better and if certain methods are better suited to certain types of anomalies. We evaluated the methods using the UCR anomaly archive, a recent benchmark dataset for anomaly detection. We analyzed the results on a dataset and anomaly-type level after adjusting the necessary hyperparameters for each method. Additionally, we assessed the ability of each method to incorporate prior knowledge about anomalies and examined the differences between point-wise and sequence-wise features. Our experiments show that classical machine learning methods generally outperform deep learning methods across a range of anomaly types. Full article

(This article belongs to the Special Issue Unsupervised Anomaly Detection)

► Show Figures

Figure 1

23 pages, 16418 KiB

Open AccessEditor’s ChoiceArticle

Robotics in Search and Rescue (SAR) Operations: An Ethical and Design Perspective Framework for Response Phase

by Hareesh Chitikena, Filippo Sanfilippo and Shugen Ma

Appl. Sci. 2023, 13(3), 1800; https://doi.org/10.3390/app13031800 - 30 Jan 2023

Cited by 24 | Viewed by 22918

Abstract

Every year, especially in urban areas, the population density rises quickly. The effects of catastrophes (i.e., war, earthquake, fire, tsunami) on people are therefore significant and grave. Assisting the impacted people will soon involve human-robot Search and Rescue (SAR) operations. Therefore, it is [...] Read more.

Every year, especially in urban areas, the population density rises quickly. The effects of catastrophes (i.e., war, earthquake, fire, tsunami) on people are therefore significant and grave. Assisting the impacted people will soon involve human-robot Search and Rescue (SAR) operations. Therefore, it is crucial to connect contemporary technology (i.e., robots and cognitive approaches) to SAR to save human lives. However, these operations also call for careful consideration of several factors, including safety, severity, and resources. Hence, ethical issues with technologies in SAR must be taken into consideration at the development stage. In this study, the most relevant ethical and design issues that arise when using robotic and cognitive technology in SAR are discussed with a focus on the response phase. Among the vast variety of SAR robots that are available nowadays, snake robots have shown huge potential; as they could be fitted with sensors and used for transporting tools to hazardous or confined areas that other robots and humans are unable to access. With this perspective, particular emphasis has been put on snake robotics in this study by considering ethical and design issues. This endeavour will contribute to providing a broader knowledge of ethical and technological factors that must be taken into account throughout the design and development of snake robots. Full article

(This article belongs to the Special Issue Advances in Intelligent Robotics in the Era 4.0)

► Show Figures

Figure 1

14 pages, 1926 KiB

Open AccessEditor’s ChoiceArticle

Assessing the Relationship between Cognitive Workload, Workstation Design, User Acceptance and Trust in Collaborative Robots

by Tommaso Panchetti, Luca Pietrantoni, Gabriele Puzzo, Luca Gualtieri and Federico Fraboni

Appl. Sci. 2023, 13(3), 1720; https://doi.org/10.3390/app13031720 - 29 Jan 2023

Cited by 28 | Viewed by 5137

Abstract

Collaborative robots are revolutionising the manufacturing industry and the way workers perform their tasks. When designing shared workspaces between robots and humans, human factors and ergonomics are often overlooked. This study assessed the relationship between cognitive workload, workstation design, user acceptance and trust [...] Read more.

Collaborative robots are revolutionising the manufacturing industry and the way workers perform their tasks. When designing shared workspaces between robots and humans, human factors and ergonomics are often overlooked. This study assessed the relationship between cognitive workload, workstation design, user acceptance and trust in collaborative robots. We combined subjective and objective data to evaluate the cognitive workload during an assembly task in three different scenarios in which we manipulated various features of the workstation and interaction modalities. Our results showed that participants experienced a reduction in cognitive workload in each of the three trials, indicating an improvement in cognitive performance. Additionally, we found that user acceptance predicted perceived stress across the trials but did not significantly impact the cognitive workload. Trust was not found to moderate the relationship between cognitive workload and perceived stress. This study has the potential to make a significant contribution to the field of collaborative assembly systems by providing valuable insights and helping to bridge the gap between researchers and practitioners. This study can potentially impact companies looking to improve safety, productivity and efficiency. Full article

(This article belongs to the Special Issue Design and Application of Collaborative Robotics)

► Show Figures

Figure 1

51 pages, 3550 KiB

Open AccessEditor’s ChoiceReview

Solid Lipid Nanoparticles (SLNs) and Nanostructured Lipid Carriers (NLCs) as Food-Grade Nanovehicles for Hydrophobic Nutraceuticals or Bioactives

by Chuan-He Tang, Huan-Le Chen and Jin-Ru Dong

Appl. Sci. 2023, 13(3), 1726; https://doi.org/10.3390/app13031726 - 29 Jan 2023

Cited by 56 | Viewed by 8334

Abstract

Although solid lipid nanoparticles (SLNs) and nanostructured lipid carriers (NLCs) have been successfully used as drug delivery systems for about 30 years, the usage of these nanoparticles as food-grade nanovehicles for nutraceuticals or bioactive compounds has been, relatively speaking, scarcely investigated. With fast-increasing [...] Read more.

Although solid lipid nanoparticles (SLNs) and nanostructured lipid carriers (NLCs) have been successfully used as drug delivery systems for about 30 years, the usage of these nanoparticles as food-grade nanovehicles for nutraceuticals or bioactive compounds has been, relatively speaking, scarcely investigated. With fast-increasing interest in the incorporation of a wide range of bioactives in food formulations, as well as health awareness of consumers, there has been a renewed urge for the development of food-compatible SLNs and/or NLCs as nanovehicles for improving water dispersibility, stability, bioavailability, and bioactivities of many lipophilic nutraceuticals or poorly soluble bioactives. In this review, the development of food-grade SLNs and NLCs, as well as their utilization as nanosized delivery systems for lipophilic or hydrophobic nutraceuticals, was comprehensively reviewed. First, the structural composition and preparation methods of food-grade SLNs and NLCs were simply summarized. Next, some key issues about the usage of such nanoparticles as oral nanovehicles, e.g., incorporation and release of bioactives, oxidative stability, lipid digestion and absorption, and intestinal transport, were critically discussed. Then, recent advances in the utilization of SLNs and NLCs as nanovehicles for encapsulation and delivery of different liposoluble or poorly soluble nutraceuticals or bioactives were comprehensively reviewed. The performance of such nanoparticles as nanovehicles for improving stability, bioavailability, and bioactivities of curcuminoids (and curcumin in particular) was also highlighted. Lastly, some strategies to improve the oral bioavailability and delivery of loaded nutraceuticals in such nanoparticles were presented. The review will be relevant, providing state-of-the-art knowledge about the development of food-grade lipid-based nanovehicles for improving the stability and bioavailability of many nutraceuticals. Full article

(This article belongs to the Special Issue Editorial Board Members' Collection Series: Functional Foods)

► Show Figures

Graphical abstract

18 pages, 1715 KiB

Open AccessEditor’s ChoiceArticle

TeleFE: A New Tool for the Tele-Assessment of Executive Functions in Children

by Carlotta Rivella, Costanza Ruffini, Clara Bombonato, Agnese Capodieci, Andrea Frascari, Gian Marco Marzocchi, Alessandra Mingozzi, Chiara Pecini, Laura Traverso, Maria Carmen Usai and Paola Viterbori

Appl. Sci. 2023, 13(3), 1728; https://doi.org/10.3390/app13031728 - 29 Jan 2023

Cited by 14 | Viewed by 3492

Abstract

In recent decades, the utility of cognitive tele-assessment has increasingly been highlighted, both in adults and in children. The present study aimed to present TeleFE, a new tool for the tele-assessment of EF in children aged 6–13. TeleFE consists of a web platform [...] Read more.

In recent decades, the utility of cognitive tele-assessment has increasingly been highlighted, both in adults and in children. The present study aimed to present TeleFE, a new tool for the tele-assessment of EF in children aged 6–13. TeleFE consists of a web platform including four tasks based on robust neuropsychological paradigms to evaluate inhibition, interference suppression, working memory, cognitive flexibility, and planning. It also includes questionnaires on EF for teachers and parents, to obtain information on the everyday functioning of the children. As TeleFE allows the assessment of EF both remotely and in-person, a comparison of the two modalities was conducted by administering TeleFE to 1288 Italian primary school children. A series of ANOVA was conducted, showing no significant effect of assessment modality (p > 0.05 for all the measures). In addition, significant differences by class emerged for all the measures (p < 0.001 for all the measures except p = 0.008 for planning). Finally, a significant sex effect emerged for inhibition (p < 0.001) and for the reaction times in both interference control (p = 0.013) and cognitive flexibility (p < 0.001), with boys showing a lower inhibition and faster reaction times. The implications of these results along with the indications for the choice of remote assessment are discussed. Full article

(This article belongs to the Special Issue New Digital Technologies for Diagnosis and Rehabilitation of Neurodevelopmental Disorders)

► Show Figures

Figure 1

17 pages, 7391 KiB

Open AccessEditor’s ChoiceArticle

Data Augmentation Method for Plant Leaf Disease Recognition

by Byeongjun Min, Taehyun Kim, Dongil Shin and Dongkyoo Shin

Appl. Sci. 2023, 13(3), 1465; https://doi.org/10.3390/app13031465 - 22 Jan 2023

Cited by 28 | Viewed by 5643

Abstract

Recently, several plant pathogens have become more active due to temperature increases arising from climate change, which has caused damage to various crops. If climate change continues, it will likely be very difficult to maintain current crop production, and the problem of a [...] Read more.

Recently, several plant pathogens have become more active due to temperature increases arising from climate change, which has caused damage to various crops. If climate change continues, it will likely be very difficult to maintain current crop production, and the problem of a shortage of expert manpower is also deepening. Fortunately, research on various early diagnosis systems based on deep learning is actively underway to solve these problems, but the problem of lack of diversity in some hard-to-collect disease samples remains. This imbalanced data increases the bias of machine learning models, causing overfitting problems. In this paper, we propose a data augmentation method based on an image-to-image translation model to solve the bias problem by supplementing these insufficient diseased leaf images. The proposed augmentation method performs translation between healthy and diseased leaf images and utilizes attention mechanisms to create images that reflect more evident disease textures. Through these improvements, we generated a more plausible diseased leaf image compared to existing methods and conducted an experiment to verify whether this data augmentation method could further improve the performance of a classification model for early diagnosis of plants. In the experiment, the PlantVillage dataset was used, and the extended dataset was built using the generated images and original images, and the performance of the classification models was evaluated through the test set. Full article

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

► Show Figures

Figure 1

20 pages, 1413 KiB

Open AccessEditor’s ChoiceReview

A Perspective on Ethernet in Automotive Communications—Current Status and Future Trends

by Lucia Lo Bello, Gaetano Patti and Luca Leonardi

Appl. Sci. 2023, 13(3), 1278; https://doi.org/10.3390/app13031278 - 18 Jan 2023

Cited by 31 | Viewed by 7673

Abstract

Automated driving requires correct perception of the surrounding environment in any driving condition. To achieve this result, not only are many more sensors than in current Advanced Driver Assistant Systems (ADAS) needed, but such sensors are also of different types, such as radars, [...] Read more.

Automated driving requires correct perception of the surrounding environment in any driving condition. To achieve this result, not only are many more sensors than in current Advanced Driver Assistant Systems (ADAS) needed, but such sensors are also of different types, such as radars, ultrasonic sensors, LiDARs, and video cameras. Given the high number of sensors and the bandwidth requirements of some of them, high-bandwidth automotive-grade networks are required. Ethernet technology is a suitable candidate, as it offers a broad selection of automotive-grade Ethernet physical layers, with transmission speeds ranging from 10 Mbps to 10 Gbps. In addition, the Time-Sensitive Networking (TSN) family of standards offers several features for Ethernet-based networks that are suitable for automotive communications, such as high reliability, bounded delays, support for scheduled traffic, etc. In this context, this paper provides an overview of Ethernet-based in-car networking and discusses novel trends and future developments in automotive communications. Full article

(This article belongs to the Special Issue Technologies, Materials and Digital Management Solutions for Automotive Industry)

► Show Figures

Figure 1

17 pages, 2632 KiB

Open AccessFeature PaperEditor’s ChoiceReview

Conductive Polymer and Nanoparticle-Promoted Polymer Hybrid Coatings for Metallic Bipolar Plates in Proton Membrane Exchange Water Electrolysis

by Gaoyang Liu, Faguo Hou, Xindong Wang and Baizeng Fang

Appl. Sci. 2023, 13(3), 1244; https://doi.org/10.3390/app13031244 - 17 Jan 2023

Cited by 23 | Viewed by 4646

Abstract

Proton exchange membrane water electrolysis (PEMWE) is a green hydrogen production technology with great development prospects. As an important part of PEMWE, bipolar plates (BPs) play an important role and put forward special requirements due to the harsh environments on both the anode [...] Read more.

Proton exchange membrane water electrolysis (PEMWE) is a green hydrogen production technology with great development prospects. As an important part of PEMWE, bipolar plates (BPs) play an important role and put forward special requirements due to the harsh environments on both the anode and cathode. Recently, metal-based BPs, particularly stainless steel and titanium BPs have attracted much attention from researchers all over the world because of their advantages of high corrosion resistance, low resistivity, high thermal conductivity, and low permeability. However, these metallic BPs are still prone to being oxidized and are facing with hydrogen embrittlement problems in the PEMWE working environment, which would result in reduced output power and premature failure of the PEMWE stack. In order to reduce the corrosion rate and maintain low interfacial contact resistance, the surface modification of the metallic BPs with protective coatings, such as precious metals (e.g., Au, Pt, etc.) and metal nitrides/carbides, etc., have been extensively investigated. However, the above-mentioned coating materials are restricted by the high-cost materials, complex equipment, and the complicated operation process. In this review, the surface modification of metallic BPs based on silane treatment, conductive polymers, e.g., polyaniline (PANI) and polypyrrole (PPy) as well as some nanoparticles-promoted polymer hybrid coatings which have been investigated for PEMWE, are summarized and reviewed. As for the silane treatment, the dense silane can not only effectively enhance the corrosion resistance but also improve the adhesion between the substrate and the conductive polymers. As for PANI and PPy, the typical value of corrosion current density of a PANI coating is 5.9 μA cm⁻², which is significantly lower than 25.68 μA cm⁻² of the bare metal plate. The introduction of nanosized conductive particles in PANI can further reduce the corrosion current density to 0.15 μA cm⁻². However, further improvement in the electrical conductivity is still desired to decrease the interface contact resistance (ICR) to be lower than 10 mΩ cm². In addition, serious peeling off of the coating during long-term operation also needs to be solved. Typically, the conductive polymer reinforced by graphene, noble metals, and their compounds in the form of nanoparticle-promoted polymer hybrid coatings could be a good choice to obtain higher corrosion resistance, durability, and conductivity and to extend the service life of PEMWE. Especially, nanoparticle-promoted polymer hybrid coatings consisting of polymers and conductive noble metals or nitrides/carbides can be controlled to balance the conductivity and mechanical properties. Due to the advantages of a simple preparation process, low cost, and large-scale production, nanoparticle-promoted polymer hybrid coatings have gradually become a research hotspot. This review is believed to enrich the knowledge of the large-scale preparation process and applications of BPs for PEMWE. Full article

(This article belongs to the Special Issue Feature Review Papers in "Nanotechnology and Applied Nanosciences" Section)

► Show Figures

Figure 1

16 pages, 951 KiB

Open AccessEditor’s ChoiceReview

Pasteurization of Food and Beverages by High Pressure Processing (HPP) at Room Temperature: Inactivation of Staphylococcus aureus, Escherichia coli, Listeria monocytogenes, Salmonella, and Other Microbial Pathogens

by Filipa Vinagre M. Silva and Evelyn

Appl. Sci. 2023, 13(2), 1193; https://doi.org/10.3390/app13021193 - 16 Jan 2023

Cited by 41 | Viewed by 13977

Abstract

Vegetative pathogens actively grow in foods, metabolizing and dividing their cells. They have consequently become a focus of concern for the food industry, food regulators and food control agencies. Although much has been done by the food industry and food regulatory agencies, foodborne [...] Read more.

Vegetative pathogens actively grow in foods, metabolizing and dividing their cells. They have consequently become a focus of concern for the food industry, food regulators and food control agencies. Although much has been done by the food industry and food regulatory agencies, foodborne outbreaks are still reported globally, causing illnesses, hospitalizations, and in certain cases, deaths, together with product recalls and subsequent economic losses. Major bacterial infections from raw and processed foods are caused by Escherichia coli serotype O157:H7, Salmonella enteritidis, and Listeria monocytogenes. High pressure processing (HPP) (also referred to as high hydrostatic pressure, HHP) is a non-thermal pasteurization technology that relies on very high pressures (400–600 MPa) to inactivate pathogens, instead of heat, thus causing less negative impact in the food nutrients and quality. HPP can be used to preserve foods, instead of chemical food additives. In this study, a review of the effect of HPP treatments on major vegetative bacteria in specific foods was carried out. HPP at 600 MPa, commonly used by the food industry, can achieve the recommended 5–8-log reductions in E. coli, S. enteritidis, L. monocytogenes, and Vibrio. Staphylococcus aureus presented the highest resistance to HPP among the foodborne vegetative pathogens investigated, followed by E. coli. More susceptible L. monocytogenes and Salmonella spp. bacteria were reduced by 6 logs at pressures within 500–600 MPa. Vibrio spp. (e.g., raw oysters), Campylobacter jejuni, Yersinia enterocolitica, Citrobacter freundii and Aeromonas hydrophila generally required lower pressures (300–400 MPa) for inactivation. Bacterial species and strain, as well as the food itself, with a characteristic composition, affect the microbial inactivation. This review demonstrates that HPP is a safe pasteurization technology, which is able to achieve at least 5-log reduction in major food bacterial pathogens, without the application of heat. Full article

(This article belongs to the Special Issue Non-thermal Technologies for Food Processing)

► Show Figures

Figure 1

24 pages, 13784 KiB

Open AccessEditor’s ChoiceArticle

Implicit to Explicit Algorithm for ABAQUS Standard User-Subroutine UMAT for a 3D Hashin-Based Orthotropic Damage Model

by M. R. T. Arruda, M. Trombini and A. Pagani

Appl. Sci. 2023, 13(2), 1155; https://doi.org/10.3390/app13021155 - 15 Jan 2023

Cited by 21 | Viewed by 6141

Abstract

This study examines a new approach to facilitate the convergence of upcoming user-subroutines UMAT when the secant material matrix is applied rather than the conventional tangent (also known as Jacobian) material matrix. This algorithm makes use of the viscous regularization technique to stabilize [...] Read more.

This study examines a new approach to facilitate the convergence of upcoming user-subroutines UMAT when the secant material matrix is applied rather than the conventional tangent (also known as Jacobian) material matrix. This algorithm makes use of the viscous regularization technique to stabilize the numerical solution of softening material models. The Newton–Raphson algorithm predictor-corrector of ABAQUS then applies this type of viscous regularization to a UMAT using only the secant matrix. When the time step is smaller than the viscosity parameter, this type of regularization may be unsuitable for a predictor-corrector with the secant matrix because its implicit convergence is incorrect, transforming the algorithm into an undesirable explicit version that may cause convergence problems. A novel 3D orthotropic damage model with residual stresses is proposed for this study, and it is analyzed using a new algorithm. The method’s convergence is tested using the proposed implicit-to-explicit secant matrix as well as the traditional implicit and explicit secant matrices. Furthermore, all numerical models are compared to experimental data. It was concluded that both the new 3D orthotropic damage model and the new proposed time step algorithm were stable and robust. Full article

(This article belongs to the Special Issue Machine Learning, Materials Informatics and Other Emerging Technologies in Materials Science)

► Show Figures

Figure 1

27 pages, 1989 KiB

Open AccessEditor’s ChoiceReview

A Review of Recent Progress of Carbon Capture, Utilization, and Storage (CCUS) in China

by Jia Yao, Hongdou Han, Yang Yang, Yiming Song and Guihe Li

Appl. Sci. 2023, 13(2), 1169; https://doi.org/10.3390/app13021169 - 15 Jan 2023

Cited by 70 | Viewed by 10429

Abstract

The continuous temperature rise has raised global concerns about CO₂ emissions. As the country with the largest CO₂ emissions, China is facing the challenge of achieving large CO₂ emission reductions (or even net-zero CO₂ emissions) in a short period. [...] Read more.

The continuous temperature rise has raised global concerns about CO₂ emissions. As the country with the largest CO₂ emissions, China is facing the challenge of achieving large CO₂ emission reductions (or even net-zero CO₂ emissions) in a short period. With the strong support and encouragement of the Chinese government, technological breakthroughs and practical applications of carbon capture, utilization, and storage (CCUS) are being aggressively pursued, and some outstanding accomplishments have been realized. Based on the numerous information from a wide variety of sources including publications and news reports only available in Chinese, this paper highlights the latest CCUS progress in China after 2019 by providing an overview of known technologies and typical projects, aiming to provide theoretical and practical guidance for achieving net-zero CO₂ emissions in the future. Full article

(This article belongs to the Special Issue Applications and Challenges in Carbon Capture, Utilization and Storage)

► Show Figures

Figure 1

19 pages, 3202 KiB

Open AccessEditor’s ChoiceReview

The Role of Renewable Energy Sources and Industry 4.0 Focus for Africa: A Review

by Kingsley Ukoba, Thokozani Justin Kunene, Pieter Harmse, Valantine Takwa Lukong and Tien Chien Jen

Appl. Sci. 2023, 13(2), 1074; https://doi.org/10.3390/app13021074 - 13 Jan 2023

Cited by 27 | Viewed by 5647

Abstract

The fourth industrial revolution presents an upspring opportunity for the African continent to adopt technologies such as artificial intelligence, big data, internet-enabled industrial platforms, 3D printing, robotics, nanotechnology, and blockchains. This is more so because the past three industrial revolutions saw the African [...] Read more.

The fourth industrial revolution presents an upspring opportunity for the African continent to adopt technologies such as artificial intelligence, big data, internet-enabled industrial platforms, 3D printing, robotics, nanotechnology, and blockchains. This is more so because the past three industrial revolutions saw the African continent being left out of its opportunities despite its affluent population and natural resources. Africa stands to benefit from industrial development, digitalization, and greater integration, which would result in more excellent opportunities for the growing youthful populations. However, for the digital transformation strategy and other key industry 4.0 opportunities to be successful, reliable infrastructure, affordable and stable electricity, and greater awareness are critical and imperative. This review examines the possible energy options that the continent of Africa can explore and implement for the successful deployment of Industry 4.0. The impact, difficulties, and opportunities of the fourth industrial revolution technologies on African development are discussed. Also discussed are various forms of renewable energy options based on Africa’s geographic location. This review will assist researchers and policymakers in implementing Industry 4.0 in Africa. Full article

(This article belongs to the Special Issue Industry 4.0 Technologies Supporting the Energy Transition)

► Show Figures

Figure 1

15 pages, 4646 KiB

Open AccessEditor’s ChoiceArticle

Improvement of Electrical and Mechanical Properties of PLA/PBAT Composites Using Coconut Shell Biochar for Antistatic Applications

by Justin George, Daeseung Jung and Debes Bhattacharyya

Appl. Sci. 2023, 13(2), 902; https://doi.org/10.3390/app13020902 - 9 Jan 2023

Cited by 31 | Viewed by 5380

Abstract

Biochar-based environment-friendly polymer composites are suitable substitutes for conventional non-biodegradable polymer composites. In this work, we developed polylactic acid (PLA)/polybutylene adipate-co-terephthalate (PBAT)/biochar (BC) composites with improved mechanical and electrical properties for antistatic applications. Coconut shell biochar was obtained through the pyrolysis of coconut [...] Read more.

Biochar-based environment-friendly polymer composites are suitable substitutes for conventional non-biodegradable polymer composites. In this work, we developed polylactic acid (PLA)/polybutylene adipate-co-terephthalate (PBAT)/biochar (BC) composites with improved mechanical and electrical properties for antistatic applications. Coconut shell biochar was obtained through the pyrolysis of coconut shell in an inert atmosphere, and characterised using scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS) and X-ray diffraction (XRD), to investigate the morphology and structural properties. The biochar was converted to powder form, sieved to reduce the particle size (

\leq

30

μ m

diameters), and melt-mixed with PLA and PBAT to form composites. The composites were extruded to produce 3D printing filaments and, eventually, 3D-printed tensile specimens. The tensile strength and tensile modulus of the 3D-printed PLA/PBAT/BC (79/20/1) composite with 1 wt% of biochar improved by 45% and 18%, respectively, compared to those of PLA/PBAT (80/20). The interfacial interaction between the biochar and polymer matrix was strong, and the biochar particles improved the compatibility of the PLA and PBAT in the composites, improving the tensile strength. Additionally, the electrical resistivity of the composite did reduce with the addition of biochar, and PLA/PBAT/BC (70/20/10) showed the surface resistivity of

~ 10^{11} Ω / sq

, making it a suitable material for antistatic applications. Full article

(This article belongs to the Section Chemical and Molecular Sciences)

► Show Figures

Figure 1

12 pages, 543 KiB

Open AccessEditor’s ChoiceArticle

Ensemble-NQG-T5: Ensemble Neural Question Generation Model Based on Text-to-Text Transfer Transformer

by Myeong-Ha Hwang, Jikang Shin, Hojin Seo, Jeong-Seon Im, Hee Cho and Chun-Kwon Lee

Appl. Sci. 2023, 13(2), 903; https://doi.org/10.3390/app13020903 - 9 Jan 2023

Cited by 21 | Viewed by 4925

Abstract

Deep learning chatbot research and development is exploding recently to offer customers in numerous industries personalized services. However, human resources are used to create a learning dataset for a deep learning chatbot. In order to augment this, the idea of neural question generation [...] Read more.

Deep learning chatbot research and development is exploding recently to offer customers in numerous industries personalized services. However, human resources are used to create a learning dataset for a deep learning chatbot. In order to augment this, the idea of neural question generation (NQG) has evolved, although it has restrictions on how questions can be expressed in different ways and has a finite capacity for question generation. In this paper, we propose an ensemble-type NQG model based on the text-to-text transfer transformer (T5). Through the proposed model, the number of generated questions for each single NQG model can be greatly increased by considering the mutual similarity and the quality of the questions using the soft-voting method. For the training of the soft-voting algorithm, the evaluation score and mutual similarity score weights based on the context and the question–answer (QA) dataset are used as the threshold weight. Performance comparison results with existing T5-based NQG models using the SQuAD 2.0 dataset demonstrate the effectiveness of the proposed method for QG. The implementation of the proposed ensemble model is anticipated to span diverse industrial fields, including interactive chatbots, robotic process automation (RPA), and Internet of Things (IoT) services in the future. Full article

(This article belongs to the Special Issue Natural Language Processing (NLP) and Applications)

► Show Figures

Figure 1

21 pages, 1011 KiB

Open AccessEditor’s ChoiceArticle

A Deep Learning Method for Lightweight and Cross-Device IoT Botnet Detection

by Marta Catillo, Antonio Pecchia and Umberto Villano

Appl. Sci. 2023, 13(2), 837; https://doi.org/10.3390/app13020837 - 7 Jan 2023

Cited by 30 | Viewed by 4869

Abstract

Ensuring security of Internet of Things (IoT) devices in the face of threats and attacks is a primary concern. IoT plays an increasingly key role in cyber–physical systems. Many existing intrusion detection systems (IDS) proposals for the IoT leverage complex machine learning architectures, [...] Read more.

Ensuring security of Internet of Things (IoT) devices in the face of threats and attacks is a primary concern. IoT plays an increasingly key role in cyber–physical systems. Many existing intrusion detection systems (IDS) proposals for the IoT leverage complex machine learning architectures, which often provide one separate model per device or per attack. These solutions are not suited to the scale and dynamism of modern IoT networks. This paper proposes a novel IoT-driven cross-device method, which allows learning a single IDS model instead of many separate models atop the traffic of different IoT devices. A semi-supervised approach is adopted due to its wider applicability for unanticipated attacks. The solution is based on an all-in-one deep autoencoder, which consists of training a single deep neural network with the normal traffic from different IoT devices. Extensive experimentation performed with a widely used benchmarking dataset indicates that the all-in-one approach achieves within 0.9994–0.9997 recall, 0.9999–1.0 precision, 0.0–0.0071 false positive rate and 0.9996–0.9998 F1 score, depending on the device. The results obtained demonstrate the validity of the proposal, which represents a lightweight and device-independent solution with considerable advantages in terms of transferability and adaptability. Full article

(This article belongs to the Collection Innovation in Information Security)

► Show Figures

Figure 1

22 pages, 33375 KiB

Open AccessEditor’s ChoiceArticle

Using UAS-Aided Photogrammetry to Monitor and Quantify the Geomorphic Effects of Extreme Weather Events in Tectonically Active Mass Waste-Prone Areas: The Case of Medicane Ianos

by Evelina Kotsi, Emmanuel Vassilakis, Michalis Diakakis, Spyridon Mavroulis, Aliki Konsolaki, Christos Filis, Stylianos Lozios and Efthymis Lekkas

Appl. Sci. 2023, 13(2), 812; https://doi.org/10.3390/app13020812 - 6 Jan 2023

Cited by 12 | Viewed by 2573

Abstract

Extreme weather events can trigger various hydrogeomorphic phenomena and processes including slope failures. These shallow instabilities are difficult to monitor and measure due to the spatial and temporal scales in which they occur. New technologies such as unmanned aerial systems (UAS), photogrammetry and [...] Read more.

Extreme weather events can trigger various hydrogeomorphic phenomena and processes including slope failures. These shallow instabilities are difficult to monitor and measure due to the spatial and temporal scales in which they occur. New technologies such as unmanned aerial systems (UAS), photogrammetry and the structure-from-motion (SfM) technique have recently demonstrated capabilities useful in performing accurate terrain observations that have the potential to provide insights into these geomorphic processes. This study explores the use of UAS-aided photogrammetry and change detection, using specialized techniques such as the digital elevation model (DEM) of differences (DoD) and cloud-to-cloud distance (C2C) to monitor and quantify geomorphic changes before and after an extreme medicane event in Myrtos, a highly visited touristic site on Cephalonia Island, Greece. The application demonstrates that the combination of UAS with photogrammetry allows accurate delineation of instabilities, volumetric estimates of morphometric changes, insights into erosion and deposition processes and the delineation of higher-risk areas in a rapid, safe and practical way. Overall, the study illustrates that the combination of tools facilitates continuous monitoring and provides key insights into geomorphic processes that are otherwise difficult to observe. Through this deeper understanding, this approach can be a stepping stone to risk management of this type of highly-visited sites, which in turn is a key ingredient to sustainable development in high-risk areas. Full article

(This article belongs to the Section Earth Sciences)

► Show Figures

Figure 1

16 pages, 2814 KiB

Open AccessEditor’s ChoiceArticle

State of the Art of High-Flux Compton/Thomson X-rays Sources

by Vittoria Petrillo, Illya Drebot, Marcel Ruijter, Sanae Samsam, Alberto Bacci, Camilla Curatolo, Michele Opromolla, Marcello Rossetti Conti, Andrea Renato Rossi and Luca Serafini

Appl. Sci. 2023, 13(2), 752; https://doi.org/10.3390/app13020752 - 5 Jan 2023

Cited by 17 | Viewed by 3662

Abstract

In this paper, we present the generalities of the Compton interaction process; we analyse the different paradigms of Inverse Compton Sources, implemented or in commissioning phase at various facilities, or proposed as future projects. We present an overview of the state of the [...] Read more.

In this paper, we present the generalities of the Compton interaction process; we analyse the different paradigms of Inverse Compton Sources, implemented or in commissioning phase at various facilities, or proposed as future projects. We present an overview of the state of the art, with a discussion of the most demanding challenges. Full article

(This article belongs to the Special Issue RF-Based Undulators and High-Gradient Accelerator Fed by High-Frequency Electromagnetic Devices)

► Show Figures

Figure 1

33 pages, 4439 KiB

Open AccessEditor’s ChoiceReview

Mitigation of Non-Steroidal Anti-Inflammatory and Antiretroviral Drugs as Environmental Pollutants by Adsorption Using Nanomaterials as Viable Solution—A Critical Review

by Sisonke Sigonya, Thabang Hendrica Mokhothu, Teboho Clement Mokhena and Talent Raymond Makhanya

Appl. Sci. 2023, 13(2), 772; https://doi.org/10.3390/app13020772 - 5 Jan 2023

Cited by 13 | Viewed by 4004

Abstract

Traces of pharmaceuticals of various classes have been reported as emerging pollutants, and they continue to be detected in aquatic environments. The steady growth of pharmaceuticals in water, as well as the related negative consequences, has made it a major priority to discover [...] Read more.

Traces of pharmaceuticals of various classes have been reported as emerging pollutants, and they continue to be detected in aquatic environments. The steady growth of pharmaceuticals in water, as well as the related negative consequences, has made it a major priority to discover effective ways for their removal from water. Various strategies have been used in the past in order to address this issue. Recently, nanotechnology has emerged as a topic of intense interest for this purpose, and different technologies for removing pharmaceuticals from water have been devised and implemented, such as photolysis, nanofiltration, reverse osmosis, and oxidation. Nanotechnological approaches including adsorption and degradation have been comprehensively examined in this paper, along with the applications and limits, in which various types of nanoparticles, nanocomposites, and nanomembranes have played important roles in removing these pharmaceutical pollutants. However, this review focuses on the most often used method, adsorption, as it is regarded as the superior approach due to its low cost, efficiency, and ease of application. Adsorption kinetic models are explained to evaluate the effectiveness of nano-adsorbents in evaluating mass transfer processes in terms of how much can be adsorbed by each method. Several robust metals, metal oxides, and functionalized magnetic nanoparticles have been highlighted, classified, and compared for the removal of pharmaceuticals, such as non-steroidal, anti-inflammatory and antiretroviral drugs, from water. Additionally, current research difficulties and prospects have been highlighted. Full article

(This article belongs to the Special Issue Wastewater Treatment Technologies II)

► Show Figures

Figure 1

10 pages, 626 KiB

Open AccessEditor’s ChoiceArticle

Knowing Knowledge: Epistemological Study of Knowledge in Transformers

by Leonardo Ranaldi and Giulia Pucci

Appl. Sci. 2023, 13(2), 677; https://doi.org/10.3390/app13020677 - 4 Jan 2023

Cited by 48 | Viewed by 3932

Abstract

Statistical learners are leading towards auto-epistemic logic, but is it the right way to progress in artificial intelligence (AI)? Ways to discover AI fit the senses and the intellect. The structure of symbols–the operations by which the intellectual solution is realized–and the search [...] Read more.

Statistical learners are leading towards auto-epistemic logic, but is it the right way to progress in artificial intelligence (AI)? Ways to discover AI fit the senses and the intellect. The structure of symbols–the operations by which the intellectual solution is realized–and the search for strategic reference points evoke essential issues in the analysis of AI. Studying how knowledge can be represented through methods of theoretical generalization and empirical observation is only the latest step in a long process of evolution. In this paper, we try to outline the origin of knowledge and how modern artificial minds have inherited it. Full article

(This article belongs to the Special Issue Deep Learning Based on Neural Network Design)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Editor’s Choice Articles

Further Information

Guidelines

MDPI Initiatives

Follow MDPI