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
Laminar Ulva Species: A Multi-Tool for Humankind?
Appl. Sci. 2024, 14(8), 3448; https://doi.org/10.3390/app14083448 (registering DOI) - 19 Apr 2024
Abstract
Green algae, phylum Chlorophyta, due to their green appearance as higher plants, are seen as one of the raw materials to be widely used by humanity for different purposes. How can these different purposes achieve ONU Sustainable Development Goals? The genus Ulva
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Green algae, phylum Chlorophyta, due to their green appearance as higher plants, are seen as one of the raw materials to be widely used by humanity for different purposes. How can these different purposes achieve ONU Sustainable Development Goals? The genus Ulva sp. is widely distributed through all continents, tolerating different ecosystems (freshwater and marine), different intensities of light, temperature, and salinity. The Ulva sp. life cycle is isomorphic and biphasic type, also affected by biotic factors such as thallus age, phytohormones, microbiome, sporulation inhibitors and metabolomic. Due to that, types of farming can be implemented depending on the cultivation method and it is final biomass exploitation. Thus, this critical review analyzes the laminar Ulva species from the ecology and demonstrates that the seaweed biomass application, may make significant contributions to marine ecosystems, humans, aquaculture, and biotechnological innovation, indicating its importance in both environmental and socioeconomic contexts based on experiments across the world, time and critical thinking. This means that explaining the actual road and future roads of laminar Ulva into a multi-tool development from humankind welfare. With right management of resources and human empowerment, Ulva sp. products can be produced facing climate change and support different industries. However, responsible management of Ulva populations and farming is essential to prevent overgrowth, green floods, and maintain environmental equilibrium.
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(This article belongs to the Section Ecology Science and Engineering)
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Assessing Gender Bias in Auditory-Perceptual Ratings of Tracheoesophageal Speakers
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Jenna L. Bucci, Nedeljko Jovanovic and Philip C. Doyle
Appl. Sci. 2024, 14(8), 3447; https://doi.org/10.3390/app14083447 (registering DOI) - 19 Apr 2024
Abstract
Objective: This study examined the relationship between gender and auditory-perceptual evaluation of tracheoesophageal (TE) speech. Method: We collected auditory-perceptual judgments of two features, speech acceptability and listener comfort, from normal-hearing young adult listeners (n = 16) who were naïve to TE
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Objective: This study examined the relationship between gender and auditory-perceptual evaluation of tracheoesophageal (TE) speech. Method: We collected auditory-perceptual judgments of two features, speech acceptability and listener comfort, from normal-hearing young adult listeners (n = 16) who were naïve to TE speech. Auditory-perceptual judgments were made for 12 TE speakers (6 men and 6 women) on two occasions separated by between 7 and 14 days. During the first session, listeners were deceived about the gender of the voice samples presented, and in the second session, listeners were informed of the true gender of the voice samples. Results: The findings suggest that a gender bias exists in perceptions of TE speech, and that female TE speakers tend to be disproportionately penalized when compared to their male counterparts when gender is known. Conclusions: These data provide insights into the potential influence of speaker gender on listener judgments of TE speech and the impact that such factors may have on communication. Our data indicate that listeners rate female TE speaker samples as less acceptable and less comfortable to listen to when the samples are known to be female speakers.
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(This article belongs to the Special Issue Computational Methods and Engineering Solutions to Voice III)
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Research on Factors Influencing Indoor PM2.5 Concentration in Curling Venues Based on CFD Simulation
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Xiaohui Du, Jiaxin Li, Ziying Tang and Shijing Hu
Appl. Sci. 2024, 14(8), 3446; https://doi.org/10.3390/app14083446 (registering DOI) - 19 Apr 2024
Abstract
This article explores the effects of outdoor PM2.5 concentration, venue airtightness and the distribution of indoor PM2.5 concentration on the curling venue of the National Aquatics Center. Research has found that when the filtration efficiency of the fresh air system is
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This article explores the effects of outdoor PM2.5 concentration, venue airtightness and the distribution of indoor PM2.5 concentration on the curling venue of the National Aquatics Center. Research has found that when the filtration efficiency of the fresh air system is 60%, the outdoor PM2.5 concentration increases by 20 μg/m3, an average increase of 6 μg/m3 in indoor PM2.5 concentration. When the venue air tightness is good, the outdoor air quality has no significant impact on the average indoor PM2.5 concentration. But as the number of infiltration air changes increases, the indoor PM2.5 concentration in each region shows an upward trend. The end of the air conditioning system in the competition area adopts bag air duct supply mode, which can reduce the concentration of PM2.5 in the competition area by 93%, and the moisture content is reduced to 2–2.5 g/kg, better meeting the requirements of curling competitions.
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(This article belongs to the Section Environmental Sciences)
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Enhancing the Integration of Protein-Rich Oat Waste Material into Meat Formulations
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Joanna Tkaczewska and Marzena Zając
Appl. Sci. 2024, 14(8), 3445; https://doi.org/10.3390/app14083445 (registering DOI) - 19 Apr 2024
Abstract
The objective of this study was to modify a protein-rich by-product, generated during β-glucan production, to render it appropriate for incorporation into meat products. Additionally, the study sought to assess the quality of a prototype meat product containing oat additives, depending on its
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The objective of this study was to modify a protein-rich by-product, generated during β-glucan production, to render it appropriate for incorporation into meat products. Additionally, the study sought to assess the quality of a prototype meat product containing oat additives, depending on its concentration. Through hydrolyzation, its solubility was enhanced, making it suitable for broader applications in food products. With an average protein content of 52% and fat content of 6%, the pure hydrolysate exhibited a notable ferric ion reduction, as well as metal chelating properties. In meat formulations, the hydrolysate was integrated at concentrations of 1%, 2%, and 3%, relative to the meat mass. Following cooking and subsequent storage for 21 days, assessments were conducted every 7 days to evaluate colour retention, texture, and oxidation status. At concentrations of 2% to 3% (equivalent to 2–3 g/100 g), the hydrolysate significantly enhanced colour stability, while concurrently fostering oxidation. Notably, cohesiveness and resilience were augmented, with no discernible impact on hardness. The application of oat protein hydrolysate, particularly at 2–3 g/100 g, serves as a viable strategy for enhancing colour stability in meat formulations. However, its pro-oxidative effects necessitate supplementation with antioxidants to mitigate potential deterioration in the final product.
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(This article belongs to the Special Issue Recycling of Biological Materials)
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Fault-Coping Algorithm for Improving Leader–Follower Swarm-Control Algorithm of Unmanned Surface Vehicles
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Jihyeong Lee, Daehyeong Ji, Hyunjoon Cho, Saehun Baeg and Sangki Jeong
Appl. Sci. 2024, 14(8), 3444; https://doi.org/10.3390/app14083444 (registering DOI) - 19 Apr 2024
Abstract
This study presents a swarm-control algorithm to overcome the limitations inherent to single-object systems. The leader–follower swarm-control method was selected for its ease of mathematical interpretation and theoretical potential for the unlimited expansion of followers. However, a known drawback of this method is
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This study presents a swarm-control algorithm to overcome the limitations inherent to single-object systems. The leader–follower swarm-control method was selected for its ease of mathematical interpretation and theoretical potential for the unlimited expansion of followers. However, a known drawback of this method is the risk of swarm collapse when the leader breaks down. To address this, a fault-coping algorithm was developed and supplemented to the leader–follower swarm-control method, which enabled the detection and responsive handling of failures, thereby ensuring mission continuity. Comprehensive data, including voltage, current, thruster speed, position, and heading angle were acquired and analyzed using sensors on unmanned surface vehicles (USVs) to monitor potential failures. In the case of a failure, such as thruster malfunction, the nearest USV seamlessly takes charge of the mission under the guidance of the fault-coping algorithm. The leader–follower swarm-control and fault-coping algorithms were successfully validated through actual sea area tests, which confirmed their operational efficacy. This study affirms the well-formed nature of the USV swarm formation and demonstrates the effectiveness of the fault-coping algorithm in ensuring normal mission performance under the virtual failure scenarios applied to the leader USV.
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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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Fundamentals of Electron Cyclotron Resonance and Cyclotron Autoresonance in Gyro-Devices: A Comprehensive Review of Theory
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Svilen Sabchevski
Appl. Sci. 2024, 14(8), 3443; https://doi.org/10.3390/app14083443 (registering DOI) - 19 Apr 2024
Abstract
This paper aims to present some selected fundamentals of the theory of a broad class of gyro-devices in a systematic and consistent manner and with sufficient detail necessary for understanding the underlying physical principles of their operation. The focus of this work is
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This paper aims to present some selected fundamentals of the theory of a broad class of gyro-devices in a systematic and consistent manner and with sufficient detail necessary for understanding the underlying physical principles of their operation. The focus of this work is on the derivation and analysis of important invariants (constants of motion), as well as on comments concerning their analytical power and the physical insights they provide.
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(This article belongs to the Section Applied Physics General)
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A Mongolian–Chinese Neural Machine Translation Method Based on Semantic-Context Data Augmentation
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Huinuan Zhang, Yatu Ji, Nier Wu and Min Lu
Appl. Sci. 2024, 14(8), 3442; https://doi.org/10.3390/app14083442 (registering DOI) - 19 Apr 2024
Abstract
Neural machine translation (NMT) typically relies on a substantial number of bilingual parallel corpora for effective training. Mongolian, as a low-resource language, has relatively few parallel corpora, resulting in poor translation performance. Data augmentation (DA) is a practical and promising method to solve
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Neural machine translation (NMT) typically relies on a substantial number of bilingual parallel corpora for effective training. Mongolian, as a low-resource language, has relatively few parallel corpora, resulting in poor translation performance. Data augmentation (DA) is a practical and promising method to solve problems related to data sparsity and single semantic structure by expanding the size and structure of available data. In order to address the issues of data sparsity and semantic inconsistency in Mongolian–Chinese NMT processes, this paper proposes a new semantic-context DA method. This method adds an additional semantic encoder based on the original translation model, which utilizes both source and target sentences to generate different semantic vectors to enhance each training instance. The results show that this method significantly improves the quality of Mongolian–Chinese NMT tasks, with an increase of approximately 2.5 BLEU values compared to the basic Transformer model. Compared to the basic model, this method can achieve the same translation results with about half of the data, greatly improving translation efficiency.
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(This article belongs to the Special Issue Natural Language Processing: Theory, Methods and Applications)
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Dynamic Grouping within Minimax Optimal Strategy for Stochastic Multi-ArmedBandits in Reinforcement Learning Recommendation
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Jiamei Feng, Junlong Zhu, Xuhui Zhao and Zhihang Ji
Appl. Sci. 2024, 14(8), 3441; https://doi.org/10.3390/app14083441 (registering DOI) - 18 Apr 2024
Abstract
The multi-armed bandit (MAB) problem is a typical problem of exploration and exploitation. As a classical MAB problem, the stochastic multi-armed bandit (SMAB) is the basis of reinforcement learning recommendation. However, most existing SMAB and MAB algorithms have two limitations: (1) they do
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The multi-armed bandit (MAB) problem is a typical problem of exploration and exploitation. As a classical MAB problem, the stochastic multi-armed bandit (SMAB) is the basis of reinforcement learning recommendation. However, most existing SMAB and MAB algorithms have two limitations: (1) they do not make full use of feedback from the environment or agent, such as the number of arms and rewards contained in user feedback; (2) they overlook the utilization of different action selections, which can affect the exploration and exploitation of the algorithm. These limitations motivate us to propose a novel dynamic grouping within the minimax optimal strategy in the stochastic case (DG-MOSS) algorithm for reinforcement learning recommendation for small and medium-sized data scenarios. DG-MOSS does not require additional contextual data and can be used for recommendation of various types of data. Specifically, we designed a new exploration calculation method based on dynamic grouping which uses the feedback information automatically in the selection process and adopts different action selections. During the thorough training of the algorithm, we designed an adaptive episode length to effectively improve the training efficiency. We also analyzed and proved the upper bound of DG-MOSS’s regret. Our experimental results for different scales, densities, and field datasets show that DG-MOSS can yield greater rewards than nine baselines with sufficiently trained recommendation and demonstrate that it has better robustness.
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(This article belongs to the Section Computing and Artificial Intelligence)
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A Year in the Life of Sea Fennel: Annual Phytochemical Variations of Major Bioactive Secondary Metabolites
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Marijana Popović, Sanja Radman, Ivana Generalić Mekinić, Tonka Ninčević Runjić, Branimir Urlić and Maja Veršić Bratinčević
Appl. Sci. 2024, 14(8), 3440; https://doi.org/10.3390/app14083440 (registering DOI) - 18 Apr 2024
Abstract
Sea fennel (Crithmum maritimum L.) is one of the most abundant and widespread Mediterranean halophytes, traditionally harvested and used in the summer months. As the plant bioactive metabolites are strongly influenced by the plant vegetation period and environmental conditions, we investigated some
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Sea fennel (Crithmum maritimum L.) is one of the most abundant and widespread Mediterranean halophytes, traditionally harvested and used in the summer months. As the plant bioactive metabolites are strongly influenced by the plant vegetation period and environmental conditions, we investigated some of the main bioactive compounds from sea fennel leaves over a one-year period to gain a deeper insight into their annual changes. A comprehensive phytochemical analysis of the essential oils using GC-MS, as well as the major phenolic and carotenoid compounds using HPLC, was performed. The results showed a high positive correlation between temperature and all major bioactive compounds, especially phenolic acids, cryptochlorogenic acid, and chlorogenic acid (r = 0.887, p = 0.0001 and r = 0.794, p = 0.002, respectively), as well as the limonene content in the essential oil (r = 0.694, p = 0.012). PCA analysis clearly distinguishes the period from February to April from the rest of the year, which contained the least bioactive metabolites overall. The overall data analyzed show great variations in sea fennel phytochemicals during the period of a year, with β-carotene content being the least effected. Therefore, it can be concluded that the plant can be used as a functional food or in other industries, such as the cosmetic and/or pharmaceutic industries, beyond its typical harvest period (early to midsummer).
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(This article belongs to the Special Issue Biological Activity, Chemical Characterization and Contaminants of Plants and Waste)
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The Synthesis and Characterization of Geopolymers Based on Metakaolin and on Automotive Glass Waste
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Ivana Perná, Martina Havelcová, Monika Šupová, Margit Žaloudková and Olga Bičáková
Appl. Sci. 2024, 14(8), 3439; https://doi.org/10.3390/app14083439 - 18 Apr 2024
Abstract
The presented article studies a metakaolin-based geopolymer matrix for which two types of non-recyclable automotive glass waste (AGW) have been used as an alternative aggregate. Their composition and character, as well as their influence on the properties and structure of geopolymer composites (AGW-Gs),
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The presented article studies a metakaolin-based geopolymer matrix for which two types of non-recyclable automotive glass waste (AGW) have been used as an alternative aggregate. Their composition and character, as well as their influence on the properties and structure of geopolymer composites (AGW-Gs), have been investigated by means of X-ray fluorescence and X-ray diffraction analyses, scanning electron microscopy, Fourier transform infrared spectrometry and gas chromatography/mass spectrometry. Infrared analysis has proven that the use of AGW does not affect the formation of geopolymer bonds. GC/MS analysis has revealed the presence of triethylene glycol bis(2-ethylhexanoate) in AGW and geopolymers, whose concentration varied according to the size of the fractions used. Preliminary compressive-strength tests have shown the promising potential of AGW-Gs. From the presented results, based on the study of two types of automotive glass waste, it is possible to assume that automotive glass will generally behave in the same or a similar manner in metakaolin-based geopolymer matrices and can be considered as potential alternative aggregates. The result is promising for the current search for new sources of raw materials, for ensuring resource security, for the promotion of sustainability and innovation and for meeting the needs of the growing world population while reducing dependence on limited resources.
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(This article belongs to the Special Issue Development, Characterization, Application and Recycling of Novel Construction Materials)
Open AccessArticle
Research on a Highway Passenger Volume Prediction Model Based on a Multilayer Perceptron Neural Network
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He Lu, Baohua Guo, Zhezhe Zhang and Weifan Gu
Appl. Sci. 2024, 14(8), 3438; https://doi.org/10.3390/app14083438 - 18 Apr 2024
Abstract
The accurate prediction of highway passenger volume is very important for China’s transportation planning and economic development. Based on a neural network, this paper establishes a prediction model by using historical road passenger traffic and related influencing factor data, aiming to provide an
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The accurate prediction of highway passenger volume is very important for China’s transportation planning and economic development. Based on a neural network, this paper establishes a prediction model by using historical road passenger traffic and related influencing factor data, aiming to provide an accurate road passenger traffic prediction. Firstly, the historical highway passenger volume data and the factor data affecting passenger volume are collected. Then, a multilayer perceptron neural network is established by using SPSS software (PASW Statistics 18) to analyze the significant relationship between highway passenger volume and influencing factors. Then, through the training and verification of the model by MATLAB software (R2021a), the reliability of the prediction model is proved. Finally, the model is used to predict the passenger traffic volume in 2020–2022, and the actual passenger traffic volume is compared and analyzed. It is concluded that the highway passenger traffic volume decreased significantly in 2020–2022 due to various factors such as the epidemic situation and policies, which have had an impact on China’s economic development.
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(This article belongs to the Section Transportation and Future Mobility)
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Evaluation of the Dark Fermentation Process as an Alternative for the Energy Valorization of the Organic Fraction of Municipal Solid Waste (OFMSW) for Bogotá, Colombia
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Ana-Paola Becerra-Quiroz, Santiago-Andrés Rodríguez-Morón, Paola-Andrea Acevedo-Pabón, Javier Rodrigo-Ilarri and María-Elena Rodrigo-Clavero
Appl. Sci. 2024, 14(8), 3437; https://doi.org/10.3390/app14083437 - 18 Apr 2024
Abstract
In the context of valorizing the organic fraction of urban solid waste (OFMSW) in megacities, dark fermentation emerges as a central strategy alongside composting and anaerobic digestion. This article focuses on assessing the environmental, technical, and energy viability of dark fermentation using life
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In the context of valorizing the organic fraction of urban solid waste (OFMSW) in megacities, dark fermentation emerges as a central strategy alongside composting and anaerobic digestion. This article focuses on assessing the environmental, technical, and energy viability of dark fermentation using life cycle assessment (LCA) and circular economy principles. Dark fermentation for biohydrogen production is an active and promising research field in the quest for sustainable biofuels. In this context, defining operational parameters such as organic loading and the substrate-inoculum ratio is relevant for achieving better production yields. Laboratory tests were conducted using organic loading values of 5, 10, and 15 g of volatile solids per liter (gVS/L) and with substrate-inoculum ratios (s/x) of 1, 0.75, and 0.5 g of volatile solids of substrate per gram of volatile solids of inoculum (gVSs/gVSi). The combination with the best performance turned out to be an initial organic loading of 10 gVS/L and an s/x of 1 gVSs/gVSi. From this result, it was determined that the s/x had a greater impact on production. Finally, a valorization plant was dimensioned with the scaled-up process, starting from the municipal solid waste generated by Bogotá projected for 2042. The scaling was demonstrated to be energetically sustainable, producing a power of 2,368,358.72 kWh per day.
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(This article belongs to the Special Issue Application of Municipal/Industrial Solid and Liquid Waste in Energy Area, 2nd Edition)
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Analyzing Data Reduction Techniques: An Experimental Perspective
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Vítor Fernandes, Gonçalo Carvalho, Vasco Pereira and Jorge Bernardino
Appl. Sci. 2024, 14(8), 3436; https://doi.org/10.3390/app14083436 - 18 Apr 2024
Abstract
The exponential growth in data generation has become a ubiquitous phenomenon in today’s rapidly growing digital technology. Technological advances and the number of connected devices are the main drivers of this expansion. However, the exponential growth of data presents challenges across different architectures,
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The exponential growth in data generation has become a ubiquitous phenomenon in today’s rapidly growing digital technology. Technological advances and the number of connected devices are the main drivers of this expansion. However, the exponential growth of data presents challenges across different architectures, particularly in terms of inefficient energy consumption, suboptimal bandwidth utilization, and the rapid increase in data stored in cloud environments. Therefore, data reduction techniques are crucial to reduce the amount of data transferred and stored. This paper provides a comprehensive review of various data reduction techniques and introduces a taxonomy to classify these methods based on the type of data loss. The experiments conducted in this study include distinct data types, assessing the performance and applicability of these techniques across different datasets.
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(This article belongs to the Special Issue Knowledge and Data Engineering)
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Earthquake-Induced Landslides in Italy: Evaluation of the Triggering Potential Based on Seismic Hazard
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Sina Azhideh, Simone Barani, Gabriele Ferretti and Davide Scafidi
Appl. Sci. 2024, 14(8), 3435; https://doi.org/10.3390/app14083435 (registering DOI) - 18 Apr 2024
Abstract
In this study, we defined screening maps for Italy that classify sites based on their potential for triggering landslides. To this end, we analyzed seismic hazard maps and hazard disaggregation results on a national scale considering four spectral periods (0.01 s, 0.2 s,
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In this study, we defined screening maps for Italy that classify sites based on their potential for triggering landslides. To this end, we analyzed seismic hazard maps and hazard disaggregation results on a national scale considering four spectral periods (0.01 s, 0.2 s, 0.5 s, and 1.0 s) and three return periods (475, 975, and 2475 years). First, joint distributions of magnitude ( ) and distance ( ) from hazard disaggregation were analyzed by means of an innovative approach based on image processing techniques to find all modal scenarios contributing to the hazard. In order to obtain the - scenarios controlling the triggering of earthquake-induced landslides at any computation node, mean and modal - pairs were compared to empirical curves defining the - bounds associated with landslide triggering. Three types of landslides were considered (i.e., disrupted slides and falls, coherent slides, and lateral spreads and flows). As a result, screening maps for all of Italy showing the potential for triggering landslides based on the level of seismic hazard were obtained. The maps and the related data are freely accessible.
Full article
(This article belongs to the Special Issue Recent Advances in Modeling, Assessment, and Mitigation of Landslide Hazards)
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A Variable-Scale Attention Mechanism Guided Time-Frequency Feature Fusion Transfer Learning Method for Bearing Fault Diagnosis in an Annealing Kiln Roller System
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Yu Xin, Kangqu Zhou, Songlin Liu and Tianchuang Liu
Appl. Sci. 2024, 14(8), 3434; https://doi.org/10.3390/app14083434 (registering DOI) - 18 Apr 2024
Abstract
Effective real-time health condition monitoring of the roller table and through shaft bearings in the annealing kiln roller system of glass production lines is crucial for maintaining their operational safety and stability for the quality and production efficiency of glass products. However, the
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Effective real-time health condition monitoring of the roller table and through shaft bearings in the annealing kiln roller system of glass production lines is crucial for maintaining their operational safety and stability for the quality and production efficiency of glass products. However, the collected vibration signal of the roller bearing system is affected by the low rotating frequency and strong mechanical background noise, which shows the width impact interval and non-stationary multi-component characteristics. Moreover, the distribution characteristics of monitoring data and probability of fault occurrence of the roller bearing and through shaft bearing improve the difficulty of the fault diagnosis and condition monitoring of the annealing kiln roller system, as well as the reliance on professional experience and prior knowledge. Therefore, this paper proposes a variable-scale attention mechanism guided time-frequency feature fusion transfer learning method for a bearing fault diagnosis at different installation positions in an annealing kiln roller system. Firstly, the instinct time decomposition method and the Gini–Kurtosis composed index are used to decompose and reconstruct the signal for noise reduction, wavelet transform with the Morlet basic function is used to extract the time-frequency features, and histogram equalization is introduced to reform the time-frequency map for the blur and implicit time-frequency features. Secondly, a variable-scale attention mechanism guided time-frequency feature fusion framework is established to extract multiscale time-dependency features from the time-frequency representation for the distinguished fault diagnosis of roller table bearings. Then, for through shaft bearings, the vibration signal of the roller table bearing is used as the source domain and the signal of the through shaft bearing is used as the target domain, based on the feature fusion framework and the multi-kernel maximum mean differences metric function, and the transfer diagnosis method is proposed to reduce the distribution differences and extract the across-domain invariant feature to diagnose the through shaft bearing fault speed under different working conditions, using a small sample. Finally, the effectiveness of the proposed method is verified based on the vibration signal from the experimental platform and the roller bearing system of the glass production line. Results show that the proposed method can effectively diagnose roller table and through shaft bearings’ fault information in the annealing kiln roller system.
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(This article belongs to the Section Applied Industrial Technologies)
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Salient Object Detection via Fusion of Multi-Visual Perception
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Wenjun Zhou, Tianfei Wang, Xiaoqin Wu, Chenglin Zuo, Yifan Wang, Quan Zhang and Bo Peng
Appl. Sci. 2024, 14(8), 3433; https://doi.org/10.3390/app14083433 - 18 Apr 2024
Abstract
Salient object detection aims to distinguish the most visually conspicuous regions, playing an important role in computer vision tasks. However, complex natural scenarios can challenge salient object detection, hindering accurate extraction of objects with rich morphological diversity. This paper proposes a novel method
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Salient object detection aims to distinguish the most visually conspicuous regions, playing an important role in computer vision tasks. However, complex natural scenarios can challenge salient object detection, hindering accurate extraction of objects with rich morphological diversity. This paper proposes a novel method for salient object detection leveraging multi-visual perception, mirroring the human visual system’s rapid identification, and focusing on impressive objects/regions within complex scenes. First, a feature map is derived from the original image. Then, salient object detection results are obtained for each perception feature and combined via a feature fusion strategy to produce a saliency map. Finally, superpixel segmentation is employed for precise salient object extraction, removing interference areas. This multi-feature approach for salient object detection harnesses complementary features to adapt to complex scenarios. Competitive experiments on the MSRA10K and ECSSD datasets place our method in the first tier, achieving 0.1302 MAE and 0.9382 F-measure for the MSRA10K dataset and 0.0783 MAE and and 0.9635 F-measure for the ECSSD dataset, demonstrating superior salient object detection performance in complex natural scenarios.
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(This article belongs to the Topic Computer Vision and Image Processing, 2nd Edition)
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Research on the Vehicle-Behavior Boundary of Intersection Traffic Based on Naturalistic Driving Data Study
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Biao Wu, Zhixiong Ma, Xichan Zhu and Yu Lin
Appl. Sci. 2024, 14(8), 3432; https://doi.org/10.3390/app14083432 - 18 Apr 2024
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With the development and application of vehicle-infrastructure cooperative technology, the traffic regional safety related to intelligent connected vehicles (ICVs) has become the hotspot of the intelligent transportation system (ITS), and the integration of mixed autonomous and non-autonomous vehicles that are not cooperative in
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With the development and application of vehicle-infrastructure cooperative technology, the traffic regional safety related to intelligent connected vehicles (ICVs) has become the hotspot of the intelligent transportation system (ITS), and the integration of mixed autonomous and non-autonomous vehicles that are not cooperative in intersection areas has become a significant challenge due to the rapid advancement of autonomous vehicle technology. Autonomous vehicles in intersections with strong-structure and weak-rule characteristics pose a potential hazard in complex traffic situations. Studying the driving behavior of vehicles in intersections is of great significance due to the complex traffic environment, frequent traffic signals, and traffic violations, which can optimize the vehicle driving behavior and improve the safety and efficiency of intersection traffic. By using naturalistic driving data from the DAIR V2X-Seq dataset and general vehicle dynamic parameters, it is possible to obtain the joint-probability-density distribution of the bivariate dynamic parameters of a vehicle. This distribution represents the driving characteristics of vehicles in intersection traffic. The three vehicle dynamic parameters that have an impact on vehicles driving through the intersection area are velocity, angular velocity, and acceleration. The driving behavior characteristics of human-driven vehicles (HVs) and autonomous vehicles (AVs) were analyzed using the multivariate kernel density estimation (MKDE) method to establish the vehicle-behavior boundary. The assessment of the boundary model showed that it accurately characterizes the driving characteristics of HVs and AVs. This boundary can be used to improve the safety detection of intersection areas, enhancing the performance of autonomous vehicles and optimizing intersection traffic.
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Research on Settlement and Section Optimization of Cemented Sand and Gravel (CSG) Dam Based on BP Neural Network
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Shuyan Wang, Haixia Yang and Zhanghuan Lin
Appl. Sci. 2024, 14(8), 3431; https://doi.org/10.3390/app14083431 - 18 Apr 2024
Abstract
In order to predict the settlement and compressive stress of the cemented sand and gravel (CSG) dam, and optimize its section design, relying on a CSG dam in the design phase, using finite element software ANSYS, the influence of the dam’s own geometric
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In order to predict the settlement and compressive stress of the cemented sand and gravel (CSG) dam, and optimize its section design, relying on a CSG dam in the design phase, using finite element software ANSYS, the influence of the dam’s own geometric dimensions and the material parameters of the overburden, including upstream and downstream slope coefficients of the first and the second stage of the dam body, the elastic modulus and the Poisson’s ratio of the overburden on the dam’s settlement and compressive stress are studied. An orthogonal experiment with six factors and three levels is conducted for a grey relational analysis of the dam’s maximum settlement and maximum compressive stress separately on these six parameters. Based on the BP neural network, the six selected factors are used as input layers for the neural network prediction model, and the maximum settlement and compressive stress of the dam are taken as the result to be output. The mapping relationship between the geometric dimensions of the dam body and the maximum settlement and the maximum compressive stress in the trained prediction model is combined with the global optimization tool Pattern Search in the MATLAB toolbox to optimize the section design of the dam. The results reveal that the six selected factors have a high correlation degree with the dam’s maximum settlement and maximum compressive stress. In dimension parameters, the downstream slope coefficient of the second stage of the dam has the greatest impact on the maximum settlement, with a grey correlation degree of 0.7367, and the upstream slope coefficient of the second stage of the dam has the greatest impact on the maximum compressive stress, with a grey correlation degree of 0.7012. The influence of the elastic modulus of the overburden on the maximum settlement and maximum compressive stress of the dam body is greater than its Poisson’s ratio. The BP neural network is applicable for predicting the dam’s settlement based on geometric dimension parameters of the dam and material parameters of the surrounding environment, with R2 reaching 0.9996 and RMSE only 0.0109 cm. Based on the optimization method combined with BP neural network, the material consumption is saved by 11.83%, the maximum settlement is reduced by 2.6%, the maximum compressive stress is reduced by 37.35%, and the optimization time is shortened by 40.92%, compared to the traditional method. The findings have certain reference value for site selection, dimension design, overburden treatment, and design optimization of CSG dams.
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(This article belongs to the Section Civil Engineering)
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Open AccessArticle
Investigation of Steep Waste Dump Slope Stability of Iron Ore Mine—A Case Study
by
Zhongao Yang, Xin Liu, Weimin Qian, Xiaohua Ding, Zhongchen Ao, Zhiyuan Zhang, Izhar Mithal Jiskani, Ya Tian, Bokang Xing and Abdoul Wahab
Appl. Sci. 2024, 14(8), 3430; https://doi.org/10.3390/app14083430 - 18 Apr 2024
Abstract
Using a combination of experimental and numerical methods, this study examines the stability of the slope of Waste Dump#1 in Ziluoyi Iron Mine. We conducted direct shear tests on soil samples taken from the waste dump, which provided important insights into slope stability.
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Using a combination of experimental and numerical methods, this study examines the stability of the slope of Waste Dump#1 in Ziluoyi Iron Mine. We conducted direct shear tests on soil samples taken from the waste dump, which provided important insights into slope stability. The tests identified key mechanical parameters, including an average cohesion of 4.80 kPa and an internal friction angle of 25.63°. By implementing GEO-SLOPE software, we could determine that the slope stability factor is 1.047, which is far from the required safety standards. To address this issue, we proposed an appropriate rectification strategy including the construction of safety platforms and reconfiguration of the slope structure. This approach effectively improved the slope stability factor to 1.219 and met the safety criteria. In addition, particle flow code (PFC) simulations were methodically performed to model the slope morphology and particle displacement before and after rectification. The obtained results revealed a remarkable reduction in sliding areas and particle displacement post-rectification, enhancing mine safety and efficiency. Our findings provide valuable insights into the application of combined experimental and numerical methods to assess and improve slope stability in open-pit mines, which will substantially contribute to the field of geotechnical engineering and mining safety.
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(This article belongs to the Special Issue Advances in Rock Fracture Mechanics: From Microscale Interactions to Macroscopic Failure)
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Open AccessArticle
Exploring Deep Neural Networks in Simulating Human Vision through Five Optical Illusions
by
Hongtao Zhang and Shinichi Yoshida
Appl. Sci. 2024, 14(8), 3429; https://doi.org/10.3390/app14083429 - 18 Apr 2024
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Recent research has delved into the biological parallels between deep neural networks (DNNs) in vision and human perception through the study of visual illusions. However, the bulk of this research is currently constrained to the investigation of visual illusions within a single model
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Recent research has delved into the biological parallels between deep neural networks (DNNs) in vision and human perception through the study of visual illusions. However, the bulk of this research is currently constrained to the investigation of visual illusions within a single model focusing on a singular type of illusion. There exists a need for a more comprehensive explanation of visual illusions in DNNs, as well as an expansion in the variety of illusions studied. This study is pioneering in its application of representational dissimilarity matrices and feature activation visualization techniques for a detailed examination of how five classic visual illusions are processed by DNNs. Our findings uncover the potential of DNNs to mimic human visual illusions, particularly highlighting notable differences in how these networks process illusions pertaining to color, contrast, length, angle, and spatial positioning. Although there are instances of consistency between DNNs and human perception in certain illusions, the performance distribution and focal points of interest within the models diverge from those of human observers. This study significantly advances our comprehension of DNNs’ capabilities in handling complex visual tasks and their potential to emulate the human biological visual system. It also underscores the existing gaps in our understanding and processing of intricate visual information. While DNNs have shown progress in simulating human vision, their grasp of the nuance and intricacy of complex visual data still requires substantial improvement.
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