Journal Description
Applied Sciences
Applied Sciences
is an international, peer-reviewed, open access journal on all aspects of applied natural sciences published semimonthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), Inspec, CAPlus / SciFinder, and other databases.
- Journal Rank: JCR - Q2 (Engineering, Multidisciplinary) / CiteScore - Q1 (General Engineering)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 16.9 days after submission; acceptance to publication is undertaken in 2.6 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Testimonials: See what our authors say about Applied Sciences.
- Companion journals for Applied Sciences include: Applied Nano, AppliedChem, Applied Biosciences, Virtual Worlds, Spectroscopy Journal and JETA.
Impact Factor:
2.7 (2022);
5-Year Impact Factor:
2.9 (2022)
Latest Articles
Level of Agreement in Subjective Selection of Gingival Colour
Appl. Sci. 2024, 14(10), 4025; https://doi.org/10.3390/app14104025 (registering DOI) - 9 May 2024
Abstract
Background and Objectives: Primary outcome: To assess the level of agreement between the objective and subjective methods for recording gingival colour in each area of the gingiva. Secondary outcome: To compare performance of the subjective visual method of gingival colour selection by
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Background and Objectives: Primary outcome: To assess the level of agreement between the objective and subjective methods for recording gingival colour in each area of the gingiva. Secondary outcome: To compare performance of the subjective visual method of gingival colour selection by a male observer and a female observer. Materials and Methods: A chromatic study was conducted on a total of 101 participants, in five gingival zones, from the free gingival margin to the mucogingival line, using a SpectroShade Micro spectrophotometer for the objective method and 21 ad hoc ceramic gingival shade tabs for the subjective method. A man and a woman of the same age, with the same amount of clinical experience in dentistry, selected the tab that most resembled the colour of participants’ gingiva. The “chromatic error” was then assessed by calculating the colour difference (using the Euclidean and CIEDE2000 formulae) between the CIELAB coordinates of the shade tab selected and the objective coordinates of the gingiva. The unweighted Kappa coefficient was used to calculate the level of agreement between observers. Results: For the male observer, the mean chromatic errors varied between ΔEab* 10.3 and 13.1 units, while for the female observer, the mean errors varied between ΔEab* 11.1 and 12.8: these differences were not statistically significant. Similarly, no statistically significant differences were found between the mean chromatic errors for the five gingival zones in either the male operator (p = 0.100) or the female operator (p = 0.093). The minimum level of agreement (unweighted Kappa) between the observers ranged from 0.1 to 0.4. Conclusions: Subjective selection of gingival colour was very inaccurate, by both the male observer and the female observer, for any area of the gingiva, with no differences identified between them. The level of agreement between the observers was low. These findings suggest that gingival colour should not be determined using solely subjective methods, given that the chromatic errors significantly exceeded the clinical acceptability threshold for gingiva (4.1 units for ΔEab* and 2.9 units for ΔE00). Both observers showed a tendency to select gingival shade tabs that were redder and bluer than the objective colours.
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(This article belongs to the Section Applied Dentistry and Oral Sciences)
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Fault-Tolerant Multiport Converter for Hybrid Distribution Systems: Configuration, Control Principles and Fault Analysis
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Simone Negri, Giovanni Ubezio and Roberto Sebastiano Faranda
Appl. Sci. 2024, 14(10), 4024; https://doi.org/10.3390/app14104024 (registering DOI) - 9 May 2024
Abstract
Multiport converters (MCs) are widely adopted in many applications, from renewable energy sources and storage integration to automotive applications and distribution systems. They are used in order to interface different energy sources, storage devices and loads with one single, simple converter topology in
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Multiport converters (MCs) are widely adopted in many applications, from renewable energy sources and storage integration to automotive applications and distribution systems. They are used in order to interface different energy sources, storage devices and loads with one single, simple converter topology in contrast to the traditional approach, which can require different solutions made by two-port converters. MCs allow for a reduction in the number of components and cascaded conversion stages with respect to an equivalent system of two-port converters, resulting in reduced complexity, dimensions and costs, as well as in improved reliability and enhanced efficiency. Nevertheless, some aspects related to the design of MCs are still worth further discussion when MCs are applied to hybrid AC/DC distribution systems. First, most converters are developed for one specific application and are not modular in structure. Furthermore, many of the proposed solutions are not equally suitable for AC and DC applications and they can introduce significant issues in hybrid distribution systems, with earthing management being particularly critical. Even though most available solutions offer satisfying steady-state and dynamic performances, fault behavior is often not considered and the possibility of maintaining controllability during faults is overlooked. Building on these three aspects, in this paper, a new MC for hybrid distribution systems is presented. An innovative circuit topology integrating three-phase AC ports and three-wire DC ports and characterized by a unique connection between the AC neutral wire and the DC midpoint neutral wire is presented. Its control principles and properties during external faults are highlighted, and extensive numerical simulations support the presented discussion.
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(This article belongs to the Special Issue Multiport Converters for Renewable Energy Sources and EV (Electric Vehicle) Applications)
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A New Approach to Detect Hand-Drawn Dashed Lines in Engineering Sketches
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Raquel Plumed, Manuel Contero, Ferran Naya and Pedro Company
Appl. Sci. 2024, 14(10), 4023; https://doi.org/10.3390/app14104023 (registering DOI) - 9 May 2024
Abstract
Sketched drawings sometimes include non-solid lines drawn as sets of consecutive strokes. They represent dashed lines, which are useful for various purposes. Recognizing such dashed lines while parsing drawings is reasonably straightforward if they are outlined with a ruler and compass but becomes
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Sketched drawings sometimes include non-solid lines drawn as sets of consecutive strokes. They represent dashed lines, which are useful for various purposes. Recognizing such dashed lines while parsing drawings is reasonably straightforward if they are outlined with a ruler and compass but becomes challenging when they are hand-drawn. The problem is manageable if the strokes are drawn consecutively so we can leverage the entire sequence. However, it becomes more challenging if they are drawn unordered, and/or we do not have access to the sequence (like in batch vectorization). In this paper, we describe a new approach to identify groups of strokes as depicting single hand-drawn dashed lines. The approach does not use sequence information and is tolerant with irregularities and imprecisions of the strokes. Our goal is to identify hidden lines of sketched engineering line-drawings, which would enable the interpretation of line-drawings with hidden edges, which currently cannot be efficiently vectorized. We speculate that other fields like hand-drawn graph interpretation may also benefit from our approach.
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(This article belongs to the Section Computing and Artificial Intelligence)
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Predicting the Spread of a Pandemic Using Machine Learning: A Case Study of COVID-19 in the UAE
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Donthi Sankalpa, Salam Dhou, Michel Pasquier and Assim Sagahyroon
Appl. Sci. 2024, 14(10), 4022; https://doi.org/10.3390/app14104022 (registering DOI) - 9 May 2024
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Pandemics can result in large morbidity and mortality rates that can cause significant adverse effects on the social and economic situations of communities. Monitoring and predicting the spread of pandemics helps the concerned authorities manage the required resources, formulate preventive measures, and control
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Pandemics can result in large morbidity and mortality rates that can cause significant adverse effects on the social and economic situations of communities. Monitoring and predicting the spread of pandemics helps the concerned authorities manage the required resources, formulate preventive measures, and control the spread effectively. In the specific case of COVID-19, the UAE (United Arab Emirates) has undertaken many initiatives, such as surveillance and contact tracing by introducing mobile apps such as Al Hosn, containment of spread by limiting the gathering of people, online schooling and remote work, sanitation drives, and closure of public places. The aim of this paper is to predict the trends occurring in pandemic outbreak, with COVID-19 in the UAE being a specific case study to investigate. In this paper, a predictive modeling approach is proposed to predict the future number of cases based on the recorded history, taking into consideration the enforced policies and provided vaccinations. Machine learning models such as LASSO Regression and Exponential Smoothing, and deep learning models such as LSTM, LSTM-AE, and bi-directional LSTM-AE, are utilized. The dataset used is publicly available from the UAE government, Federal Competitiveness and Statistics Centre (FCSC) and consists of several attributes, such as the numbers of confirmed cases, recovered cases, deaths, tests, and vaccinations. An additional categorical attribute is manually added to the dataset describing whether an event has taken place, such as a national holiday or a sanitization drive, to study the effect of such events on the pandemic trends. Experimental results showed that the Univariate LSTM model with an input of a five-day history of Confirmed Cases achieved the best performance with an RMSE of 275.85, surpassing the current state of the art related to the UAE by over 30%. It was also found that the bi-directional LSTMs performed relatively well. The approach proposed in the paper can be applied to monitor similar infectious disease outbreaks and thus contribute to strengthening the authorities’ preparedness for future pandemics.
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Data Acquisition, Processing, and Aggregation in a Low-Cost IoT System for Indoor Environmental Quality Monitoring
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Alberto Barbaro, Pietro Chiavassa, Virginia Isabella Fissore, Antonio Servetti, Erica Raviola, Gustavo Ramírez-Espinosa, Edoardo Giusto, Bartolomeo Montrucchio, Arianna Astolfi and Franco Fiori
Appl. Sci. 2024, 14(10), 4021; https://doi.org/10.3390/app14104021 (registering DOI) - 9 May 2024
Abstract
The rapid spread of Internet of Things technologies has enabled a continuous monitoring of indoor environmental quality in office environments by integrating monitoring devices equipped with low-cost sensors and cloud platforms for data storage and visualization. Critical aspects in the development of such
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The rapid spread of Internet of Things technologies has enabled a continuous monitoring of indoor environmental quality in office environments by integrating monitoring devices equipped with low-cost sensors and cloud platforms for data storage and visualization. Critical aspects in the development of such monitoring systems are effective data acquisition, processing, and visualization strategies, which significantly influence the performance of the system both at monitoring device and at cloud platform level. This paper proposes novel strategies to address the challenges in the design of a complete monitoring system for indoor environmental quality. By adopting the proposed solution, one can reduce the data rate transfer between the monitoring devices and the server without loss of information, as well as achieve efficient data storage and aggregation on the server side to minimize retrieval times. Finally, enhanced flexibility in the dashboard for data visualization is obtained, thus enabling graph modifications without extensive coding efforts. The functionality of the developed system was assessed, with the collected data in good agreement with those from other instruments used as references.
Full article
(This article belongs to the Special Issue Air Quality in Indoor Environments, 2nd Edition)
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Exploring the Role of Self-Adaptive Feature Words in Relation Quintuple Extraction for Scientific Literature
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Yujiang Liu, Lijun Fu, Xiaojun Xia and Yonghong Zhang
Appl. Sci. 2024, 14(10), 4020; https://doi.org/10.3390/app14104020 - 9 May 2024
Abstract
Extracting relation quintuple and feature words from unstructured text is a prelude to the construction of the scientific knowledge base. At present, the prior works use explicit clues between entities to study this task but ignore the use and the association of the
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Extracting relation quintuple and feature words from unstructured text is a prelude to the construction of the scientific knowledge base. At present, the prior works use explicit clues between entities to study this task but ignore the use and the association of the feature words. In this work, we propose a new method to generate self-adaptive feature words from the original text for every single sample. These words can add additional correlation information to the knowledge graph. We allow the model to generate a new word representation and apply it to the original sentence to judge the relation type and locate the head and tail of the relation quintuple. Compared with the previous works, the feature words increase the flexibility of relying on information and improve the explanatory ability. Extensive experiments on scientific field datasets illustrate that the self-adaptive feature words method (SAFW) is good at ferreting out the unique feature words and obtaining the core part for the quintuple. It achieves good performance on four public datasets and obtains a markable performance improvement compared with other baselines.
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(This article belongs to the Special Issue Machine-Learning-Based Feature Extraction and Selection)
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Enhancing Botnet Detection in Network Security Using Profile Hidden Markov Models
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Rucha Mannikar and Fabio Di Troia
Appl. Sci. 2024, 14(10), 4019; https://doi.org/10.3390/app14104019 (registering DOI) - 9 May 2024
Abstract
A botnet is a network of compromised computer systems, or bots, remotely controlled by an attacker through bot controllers. This covert network poses a threat through large-scale cyber attacks, including phishing, distributed denial of service (DDoS), data theft, and server crashes. Botnets often
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A botnet is a network of compromised computer systems, or bots, remotely controlled by an attacker through bot controllers. This covert network poses a threat through large-scale cyber attacks, including phishing, distributed denial of service (DDoS), data theft, and server crashes. Botnets often camouflage their activity by utilizing common internet protocols, such as HTTP and IRC, making their detection challenging. This paper addresses this threat by proposing a method to identify botnets based on distinctive communication patterns between command and control servers and bots. Recognizable traits in botnet behavior, such as coordinated attacks, heartbeat signals, and periodic command distribution, are analyzed. Probabilistic models, specifically Hidden Markov Models (HMMs) and Profile Hidden Markov Models (PHMMs), are employed to learn and identify these activity patterns in network traffic data. This work utilizes publicly available datasets containing a combination of botnet, normal, and background traffic to train and test these models. The comparative analysis reveals that both HMMs and PHMMs are effective in detecting botnets, with PHMMs exhibiting superior accuracy in botnet detection compared to HMMs.
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(This article belongs to the Special Issue Network Information Theory and Its Applications in Security and Privacy)
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Water-Filling Characteristics and Water Source of Weakly Rich Water and Weakly Conducting Water Aquifers in the Changxing Formation after Mining Damage
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Xianzhi Shi, Guosheng Xu and Shuyun Zhu
Appl. Sci. 2024, 14(10), 4018; https://doi.org/10.3390/app14104018 - 9 May 2024
Abstract
The escalation of mining activities in the karst regions of Guizhou Province has heightened the occurrence of water-inrush incidents in deep coal mines. This study focused on water-inrush phenomena within the Xinhua mining area of Jinsha County, Guizhou Province, aiming to investigate the
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The escalation of mining activities in the karst regions of Guizhou Province has heightened the occurrence of water-inrush incidents in deep coal mines. This study focused on water-inrush phenomena within the Xinhua mining area of Jinsha County, Guizhou Province, aiming to investigate the sources of these incidents. The findings indicated that the overlying limestone of the Changxing Formation in the coal seam served as a vulnerable aquifer under certain conditions, leading to water inrushes. The analysis of the spatiotemporal distribution patterns of water-inrush incidents at the working face indicated that previous mining operations damaged the shallow Changxing Formation limestone, resulting in the accumulation of goaf water and the formation of numerous mining-induced fractures. These fractures served as rapid conduits for water inrushes from both atmospheric precipitation and underground sources at the deep working face. The examination of surface water and mine water quality demonstrated that both exhibited similar characteristics, predominantly featuring bicarbonate, sulfate, and sodium compositions. Investigation into the relationship between mine water inflow and atmospheric precipitation established that atmospheric precipitation influenced the mine water supply cycle, with a replenishment period of ~10 months during the operational phase of the Jinyuan Coal Mine and about one month post-closure. The fractures induced by mining activities within the Changxing Formation limestone facilitated water flow, with atmospheric precipitation serving as the primary water source for the mine. This study offered a valuable scientific foundation for addressing water-related damage resulting from atmospheric precipitation in mines susceptible to water inrushes under analogous hydrogeological conditions.
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(This article belongs to the Topic Dynamic Disaster Control, Mine Multi-Source Disaster Monitoring and Intelligent Analysis)
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Prediction of Glass Chemical Composition and Type Identification Based on Machine Learning Algorithms
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Ziwei Chen, Yang Xu, Chao Zhang and Min Tang
Appl. Sci. 2024, 14(10), 4017; https://doi.org/10.3390/app14104017 - 9 May 2024
Abstract
Ancient glass artifacts were susceptible to weathering from the environment, causing changes in their chemical composition, which pose significant obstacles to the identification of glass products. Analyzing the chemical composition of ancient glass has been beneficial for evaluating their weathering status and proposing
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Ancient glass artifacts were susceptible to weathering from the environment, causing changes in their chemical composition, which pose significant obstacles to the identification of glass products. Analyzing the chemical composition of ancient glass has been beneficial for evaluating their weathering status and proposing measures to reduce glass weathering. The objective of this study was to explore the optimal machine learning algorithm for glass type classification based on chemical composition. A set of glass artifact data including color, emblazonry, weathering, and chemical composition was employed and various methods including logistic regression and machine learning techniques were used. The results indicated that a significant correlation (p < 0.05) could only observed between surface weathering and the glass types (high-potassium and lead–barium). Based on the random forest and logistic regression models, the primary chemical components that signify glass types and weathering status were determined using PbO, K2O, BaO, SiO2, Al2O3, and P2O5. The random forest model presented a superior ability to identify glass types and weathering status, with a global accuracy of 96.3%. This study demonstrates the great potential of machine learning for glass chemical component estimation and glass type and weathering status identification, providing technical guidance for the appraisal of ancient glass artifacts.
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(This article belongs to the Section Computing and Artificial Intelligence)
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Lactic Acid Bacteria and Bacillus subtilis as Potential Protective Cultures for Biopreservation in the Food Industry
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Nerea Garin-Murguialday, Laura Espina, Raquel Virto and Rafael Pagán
Appl. Sci. 2024, 14(10), 4016; https://doi.org/10.3390/app14104016 - 9 May 2024
Abstract
The use of bacteria and/or their compounds is an alternative to the use of positive-list additives that the food industry is using as a tool to meet consumer demands for more natural, long-shelf-life, and healthy products, in short, to offer clean label foods.
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The use of bacteria and/or their compounds is an alternative to the use of positive-list additives that the food industry is using as a tool to meet consumer demands for more natural, long-shelf-life, and healthy products, in short, to offer clean label foods. The aim of this study is to investigate the suitability of cell-free supernatants (CFSs) from Qualified Presumption of Safety strains as bioprotective cultures. Out of an initial screening panel of about 200 isolates, strains Pediococcus acidilactici CNTA 1059, Lactiplantibacillus plantarum CNTA 600, Levilactobacillus brevis CNTA 1374, and Bacillus subtilis CNTA 517 demonstrated strong antimicrobial activity against, especially, Gram-positive bacteria. The CFSs of these four strains showed minimum inhibitory concentration values between 0.15% and 5% against Listeria monocytogenes and Lentilactobacillus parabuchneri. None of the four selected strains exhibited acquired resistance to target antibiotics, and the non-toxigenicity of all the CFSs was demonstrated. In the case of the three lactic acid bacteria, the presence of bacteriocin-like inhibitory substances was confirmed following the decline in antimicrobial activity due to treatment with proteases. Regarding B. subtilis, biosynthetic gene clusters for different bacteriocin-like substances, including protease-resistant lipoproteins, were found via whole-genome sequencing. In addition, all of the CFSs exhibited stable antimicrobial activity at a wide range of temperatures (70–121 °C) used for the pasteurization and sterilization of food products, with a loss of antimicrobial activity ranging from 3% to 28%. These results point to the possibility that CFSs from these strains could be used in the food industry as a biocontrol tool to develop new products.
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(This article belongs to the Special Issue Natural Products and Bioactive Compounds)
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Five-Axis Finish Milling Machining for an Inconel 718 Alloy Monolithic Blisk
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Ming-Hsu Tsai, Teng-Hui Chen, Jeng-Nan Lee, Tai-Lin Hsu and Dong-Ke Huang
Appl. Sci. 2024, 14(10), 4015; https://doi.org/10.3390/app14104015 - 9 May 2024
Abstract
Blisks subjected to rough machining for channel creation must undergo finishing processes, and such processes must achieve the required tolerance limits. A high-quality surface finish and predictable long tool life are critical for the finish milling of blisks. Accordingly, the aim of this
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Blisks subjected to rough machining for channel creation must undergo finishing processes, and such processes must achieve the required tolerance limits. A high-quality surface finish and predictable long tool life are critical for the finish milling of blisks. Accordingly, the aim of this study was to optimize parameters for the finish machining of an Inconel 718 alloy monolithic blisk. Ball-cone mills were used to machine the blade surface at a constant depth. A sensory tool holder was used to collect cutting force signals during machining, and a digital microscope was used to examine tool wear. The surface texture measuring instrument was used to measure blisk blade surface roughness to evaluate processing quality. This study manipulated two cutting parameters, namely cutting speed and feed per tooth, and investigated their effects. The relationship between cutting conditions and machining efficiency was analyzed. According to the experimental results, we identified a set of optimal parameters at effective cutting speeds of 46.53 m/min, feed per tooth of 0.1 mm/tooth, and depth of cut of 0.05 mm for marginal tool wear and fast cutting speeds. Then the corresponding tool life was estimated by using the derived parameters.
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(This article belongs to the Special Issue Selected Papers from the 12th International Multi-Conference on Engineering and Technology Innovation (IMETI 2023))
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The Synthesis of Ag/TiO2 via the DC Magnetron Sputtering Method and Its Application in the Photocatalytic Degradation of Methyl Orange in Na2SO4 Solution
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Li Sun, Zhuoqun Que, Ting Ruan, Zhigang Yuan, Wenbang Gong, Shunqi Mei, Zhen Chen and Ying Liu
Appl. Sci. 2024, 14(10), 4014; https://doi.org/10.3390/app14104014 - 9 May 2024
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TiO2 and TiO2 films modified with Ag (Ag/TiO2) were prepared via the DC magnetron sputtering method and the degree of modification was controlled via the sputtering power and time of Ag. The microstructures and properties of these films were
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TiO2 and TiO2 films modified with Ag (Ag/TiO2) were prepared via the DC magnetron sputtering method and the degree of modification was controlled via the sputtering power and time of Ag. The microstructures and properties of these films were characterized using field emission scanning electron microscopy, X-ray diffractometry, ultraviolet–visible diffuse reflectance spectrometry, atomic force microscopy, and X-ray photoelectron spectroscopy (XPS). The results show that the prepared films have an anatase structure. Compared with pure TiO2, Ag deposition can improve the utilization of light. The three-dimensional images of Ag/TiO2 clearly show that with the increase in Ag sputtering power and sputtering time, Ag particles on the surface of the film gradually increase, and the structure of the film is relatively dense. The photocatalytic effect of Ag/TiO2 films is the best when the Ag sputtering power is 5 W and the sputtering time is 50 s. Under high-pressure mercury lamp irradiation, the photocatalytic degradation rate of methyl orange (MO) in pure MO solution with Ag/TiO2-5 W-50 s can reach 100% within 55 min, whereas that in MO-Na2SO4 mixed solution can reach 99.55% within 65 min. The results suggest that the presence of Na2SO4 in MO solution can inhibit the degradation of MO using Ag/TiO2, the result of XPS suggests that Na2SO4 accelerates the oxidation of Ag, which may lead to an increase in the recombination rate of photogenerated electron–hole pairs and a decrease in the degradation rate. During the process of recycling photocatalysts, the degradation rate of MO was apparently reduced. A possible reason is that the Ag particles have been oxidized and products of photocatalytic degradation are on the surface of the photocatalyst. The photocatalytic degradation mechanism was studied.
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Large-Scale Fire Tests of Battery Electric Vehicle (BEV): Slovak Case Study
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Jozef Svetlík, Zoltan Tancos, Petr Tancos, Iveta Markova and Kristian Slastan
Appl. Sci. 2024, 14(10), 4013; https://doi.org/10.3390/app14104013 - 9 May 2024
Abstract
Due to the increasing number of battery electric vehicles (BEV) on the roads and the number of BEV accidents with the occurrence of a fire, full-scale fire tests of BEVs were carried out. For initiation, the BEVs were mechanically damaged, forming a gap
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Due to the increasing number of battery electric vehicles (BEV) on the roads and the number of BEV accidents with the occurrence of a fire, full-scale fire tests of BEVs were carried out. For initiation, the BEVs were mechanically damaged, forming a gap with a size of 15 cm × 15 cm. The external heat source was a 300 kW propane burner with a maximum power of 54.0 kW and a length of 54 cm. The flame of the propane–butane fuel mixed in air at a temperature of 1970 °C was inserted directly into the battery pack. The increase in the temperature was monitored as a function of time through thermocouples at selected locations of the BEV until the point of initiation. Thermocouples were placed 10, 30, and 50 cm from the place of BEV surface. Accordingly, to obtain the temperature–time curves from the experiment measurement, critical temperatures were subsequently evaluated. The fire tests on BEVs can be described according to the individual phases of the fire. The external heat source started the initiation process at the 25 min time mark. Consequently, the phase of a developed fire with a dynamic course started. A sharp rise in temperature occurred. Within two minutes, the temperature rose to 1056.9 °C. After the initiation source was removed, there was decline in temperature and re-ignition to the stage of a fully developed fire. Thermocouples recorded temperatures in the range of 900 °C. The resulting dynamic process of a BEV fire with a sharp increase in temperature is a problem for the implementation of firefighting works and the liquidation of traffic accidents. Furthermore, foam extinguishing was part of the experiments. In both cases after the foam application, the temperature on the thermocouple T1 (distance was 10 cm from the surface of the BEV) dropped from 486.1 °C to 76 °C after 10 s of application.
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(This article belongs to the Special Issue Advanced Technologies in Environment Protection and Environmental Risk Assessment)
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Towards Personally Relevant Navigation: The Differential Effects of Cognitive Style and Map Orientation on Spatial Knowledge Development
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Hannah Park, Manish K. Dixit and Fatemeh Pariafsai
Appl. Sci. 2024, 14(10), 4012; https://doi.org/10.3390/app14104012 - 9 May 2024
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Under emergencies such as floods and fires or during indoor navigation where cues from local landmarks and a Global Positioning System (GPS) are no longer available, the acquisition of comprehensive environmental representation becomes particularly important. Several studies demonstrated that individual differences in cognitive
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Under emergencies such as floods and fires or during indoor navigation where cues from local landmarks and a Global Positioning System (GPS) are no longer available, the acquisition of comprehensive environmental representation becomes particularly important. Several studies demonstrated that individual differences in cognitive style might play an important role in creating a complete environmental representation and spatial navigation. However, this relationship between cognitive style and spatial navigation is not well researched. This study hypothesized that a specific type of map orientation (north-up vs. forward-up) might be more efficient for individuals with different cognitive styles. Forty participants were recruited to perform spatial tasks in a virtual maze environment to understand how cognitive style may relate to spatial navigation abilities, particularly the acquisition of survey and route knowledge. To measure survey knowledge, pointing direction tests and sketch map tests were employed, whereas, for route knowledge, the landmark sequencing test and route retracing test were employed. The results showed that both field-dependent and field-independent participants showed more accurate canonical organization in their sketch map task with a north-up map than with a forward-up map, with field-independent participants outperforming field-dependent participants in canonical organization scores. The map orientation did not influence the performance of Field-Independent participants on the pointing direct test, with field-dependent participants showing higher angular error with north-up maps. Regarding route knowledge, field-independent participants had more accurate responses in the landmark sequencing tests with a north-up map than with a forward-up map. On the other hand, field-dependent participants had higher accuracy in landmark sequencing tests in the forward-up map condition than in the north-up map condition. In the route retracing test, however, the map orientation had no statistically significant effect on different cognitive style groups. The results indicate that cognitive style may affect the relationship between map orientation and spatial knowledge acquisition.
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Deep Feature Retention Module Network for Texture Classification
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Sung-Hwan Park, Sung-Yoon Ahn and Sang-Woong Lee
Appl. Sci. 2024, 14(10), 4011; https://doi.org/10.3390/app14104011 - 9 May 2024
Abstract
Texture describes the unique features of an image. Therefore, texture classification is a crucial task in computer vision. Various CNN-based deep learning methods have been developed to classify textures. During training, the deep-learning model undergoes an end-to-end procedure of learning features from low
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Texture describes the unique features of an image. Therefore, texture classification is a crucial task in computer vision. Various CNN-based deep learning methods have been developed to classify textures. During training, the deep-learning model undergoes an end-to-end procedure of learning features from low to high levels. Most CNN architectures depend on high-level features for the final classification. Hence, other low- and mid-level information was not prioritized for the final classification. However, in the case of texture classification, it is essential to determine detailed feature information within the pattern to classify textures as they have diversity and irregularity in images within the same class. Therefore, the feature information at the low- and mid-levels can also provide meaningful information to distinguish the classes. In this study, we introduce a CNN model with a feature retention module (FRM) to utilize features from numerous levels. FRM maintains the texture information extracted at each level and extracts feature information through filters of various sizes. We used three texture datasets to evaluate the proposed model combined with the FRM. The experimental results showed that learning using different levels of features together assists in improving learning performance more than learning using high-level features.
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(This article belongs to the Special Issue State-of-the-Art of Computer Vision and Pattern Recognition)
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Recommendations for the Model-Based Systems Engineering Modeling Process Based on the SysML Model and Domain Knowledge
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Jia Zhang and Shuqun Yang
Appl. Sci. 2024, 14(10), 4010; https://doi.org/10.3390/app14104010 - 8 May 2024
Abstract
Model-based systems engineering (MBSE) is a modeling approach used in industry to support the formalization, analysis, design, checking and verification of systems. In MBSE modeling, domain knowledge is the basis of the modeling. However, modeling does not happen overnight; it requires systematic training
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Model-based systems engineering (MBSE) is a modeling approach used in industry to support the formalization, analysis, design, checking and verification of systems. In MBSE modeling, domain knowledge is the basis of the modeling. However, modeling does not happen overnight; it requires systematic training and a significant investment of resources. Unfortunately, many domain experts lack the expertise required for modeling, even though they know the domain well. The question arises about how to provide system modelers with domain knowledge at the right time to support the efficient completion of modeling. Since MBSE research that integrates AI is just beginning to take off, no public dataset is available. In this paper, aerospace SysML models are constructed based on spacecraft-related domain knowledge to form SysML model data. The validation rules are studied to validate the SysML model data, and combined with the concept of the recommended system, a recommendation method for the MBSE modeling process based on the knowledge and SysML model is proposed. A GLOVE language model is pre-trained by using domain knowledge and general knowledge; the model data are also used to fine-tune the GLOVE language model combined with the pre-training to recommend some domain development processes. The recommendation list is manually quality-verified and fed into the pre-training phase, while new requirement texts are continuously added in the fine-tuning phase, resulting in a more relevant and accurate recommendation list. Experiments show that the incremental recommender system can not only effectively recommend SysML models, but also improve the quality and efficiency of MBSE development.
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Open AccessArticle
Non-Contact Tilapia Mass Estimation Method Based on Underwater Binocular Vision
by
Guofu Feng, Bo Pan and Ming Chen
Appl. Sci. 2024, 14(10), 4009; https://doi.org/10.3390/app14104009 - 8 May 2024
Abstract
The non-destructive measurement of fish is an important link in intelligent aquaculture, and realizing the accurate estimation of fish mass is the key to the stable operation of this link. Taking tilapia as the object, this study proposes an underwater tilapia mass estimation
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The non-destructive measurement of fish is an important link in intelligent aquaculture, and realizing the accurate estimation of fish mass is the key to the stable operation of this link. Taking tilapia as the object, this study proposes an underwater tilapia mass estimation method, which can accurately estimate the mass of free-swimming tilapia under non-contact conditions. First, image enhancement is performed on the original image, and the depth image is obtained by correcting and stereo matching the enhanced image using binocular stereo vision technology. And the fish body is segmented by an SAM model. Then, the segmented fish body is labeled with key points, thus realizing the 3D reconstruction of tilapia. Five mass estimation models are established based on the relationship between the body length and the mass of tilapia, so as to realize the mass estimation of tilapia. The results showed that the average relative errors of the method models were 5.34%~7.25%. The coefficient of determination of the final tilapia mass estimation with manual measurement was 0.99, and the average relative error was 5.90%. The improvement over existing deep learning methods is about 1.54%. This study will provide key technical support for the non-destructive measurement of tilapia, which is of great significance to the information management of aquaculture, the assessment of fish growth condition, and baiting control.
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(This article belongs to the Special Issue Engineering of Smart Agriculture—2nd Edition)
Open AccessArticle
Inversion of the Permeability Coefficient of a High Core Wall Dam Based on a BP Neural Network and the Marine Predator Algorithm
by
Junrong Duan and Zhenzhong Shen
Appl. Sci. 2024, 14(10), 4008; https://doi.org/10.3390/app14104008 - 8 May 2024
Abstract
The parameters’ inversion of saturated–unsaturated is important in ensuring the safety of earth dams; many scholars have conducted some research regarding the inversion of hydraulic conductivity based on seepage pressure monitoring data. The van Genuchten model is widely used in saturated–unsaturated seepage analysis,
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The parameters’ inversion of saturated–unsaturated is important in ensuring the safety of earth dams; many scholars have conducted some research regarding the inversion of hydraulic conductivity based on seepage pressure monitoring data. The van Genuchten model is widely used in saturated–unsaturated seepage analysis, which considers the permeability connected to the water content of the soil and the soil’s shape parameters. A BP neural artificial network is a mature prediction technique based on enough data, and the marine predator algorithm is a new nature-inspired metaheuristic inspired by the movement of animals in the ocean. The BP neural artificial network and marine predator algorithm are applied in the permeability coefficient inversion of a core-rock dam in China; the results show that in the normal operation status, the BP network shows better accuracy, and the average of the absolute error and variance of the absolute error are both minimum values, which are 2.21 m and 1.43 m, respectively. While the water storage speed changes, the marine predator algorithm shows better accuracy; the objective function is calculated to be 0.253. So, the marine predator algorithm is able to accurately reverse the desired results in some situations. According to the actual condition, employing suitable methods for the inverse permeability coefficient of a dam can effectively ensure the safe operation of dams.
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(This article belongs to the Special Issue Novel Advances in Computational Fluid Mechanics (CFM))
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Healthcare in the Time of COVID-19: An Environmental Perspective on the Pandemic’s Impact on Hospitals
by
Vanesa Jiménez-Lacarra, Eduardo Martínez-Cámara, Jacinto Santamaría-Peña, Emilio Jiménez-Macías and Julio Blanco-Fernández
Appl. Sci. 2024, 14(10), 4007; https://doi.org/10.3390/app14104007 - 8 May 2024
Abstract
Hospitals have demonstrated their enormous capacity to adapt to the rapidly changing situation imposed by the pandemic: increasing the number of intensive care units and intermediate and inpatient beds, with the corresponding human resources, services and facilities required. Internationally, the enormous demand to
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Hospitals have demonstrated their enormous capacity to adapt to the rapidly changing situation imposed by the pandemic: increasing the number of intensive care units and intermediate and inpatient beds, with the corresponding human resources, services and facilities required. Internationally, the enormous demand to manage the COVID-19 pandemic has challenged hospitals in terms of staffing, supplies and equipment. This article analyses the effect of the COVID-19 pandemic on hospital activities, from the perspective of its environmental impact. It compares a year of normal hospital activities, 2019, with data on hospital activities from 2020. The aim of this research is to analyse the changes produced by the pandemic in the regular activities of the hospital and to determine the environmental impact, which allows reflecting on the exceptional situation generated. The results show that the hospital’s environmental impact increased significantly in 2020 compared to 2019, with a 17.2% increase in overall environmental efficiency indices. The main contributors to this increase were waste generation and medical gas consumption, which are critical aspects of hospital activities during the pandemic.
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(This article belongs to the Section Biomedical Engineering)
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Influence of the Road Model on the Optimal Maneuver of a Racing Motorcycle
by
Jan Biniewicz and Mariusz Pyrz
Appl. Sci. 2024, 14(10), 4006; https://doi.org/10.3390/app14104006 - 8 May 2024
Abstract
Motorcycle motion is largely influenced by the road geometry, which alters the allowable accelerations in longitudinal and lateral directions and influences the vertical wheel loads. Recently, a method for three-dimensional road reconstruction and its incorporation into transient and quasi-steady-state (QSS) minimum lap time
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Motorcycle motion is largely influenced by the road geometry, which alters the allowable accelerations in longitudinal and lateral directions and influences the vertical wheel loads. Recently, a method for three-dimensional road reconstruction and its incorporation into transient and quasi-steady-state (QSS) minimum lap time simulations (MLTSs) has been proposed. The main purpose of this work is to demonstrate how significantly different results from a minimum lap time optimal control problem can be obtained when using inappropriate elevation data sources in the track reconstruction problem, and how the road model reconstructed using poor input data can lead to misleading conclusions when analyzing real vehicle and driver performances. Two road models derived from high- and low-resolution digital elevation models (DEMs) are compared and their impact on the optimal maneuver of a racing motorcycle is examined. The essentials of track identification are presented, as well as vehicle positioning on the 3D road and the generalized QSS motorcycle model. Obtained 3D and 2D road models are analyzed in detail, on a case example of the Road Atlanta racetrack, and used in minimum lap time simulations, which are validated by the experimental data recorded on the Supersport motorcycle. The comparative analysis showed that great care should be taken when selecting the elevation dataset in the track reconstruction process, and that the 1 m resolution local DEMs seem to be sufficient to obtain MLTS results close to the measured ones. The example of using the 3D free-trajectory QSS minimum lap time problem to localize the track segments where real driver actions can be improved is also presented. The differences between simulation results on different road models of the same racetrack can be large and influence the interpretation of optimal maneuver.
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(This article belongs to the Section Transportation and Future Mobility)
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