Applied Sciences

21 pages, 7184 KiB

Open AccessArticle

Susceptibility and Remanent Magnetization Estimates from Orientation Tools in Borehole Imaging Logs

by Julio Cesar S. O. Lyrio, Ana Patrícia C. C. Laier, Jorge Campos Junior, Ana Natalia G. Rodrigues and Luciano dos Santos Martins

Appl. Sci. 2025, 15(5), 2873; https://doi.org/10.3390/app15052873 - 6 Mar 2025

Viewed by 591

Abstract

Orientation tools in borehole imaging logs acquire magnetic information that is currently used for spatial and geographical orientation of the images. We propose to use this magnetic field information to estimate both magnetic susceptibility and remanent magnetization of rocks inside wells. Measurements of [...] Read more.

Orientation tools in borehole imaging logs acquire magnetic information that is currently used for spatial and geographical orientation of the images. We propose to use this magnetic field information to estimate both magnetic susceptibility and remanent magnetization of rocks inside wells. Measurements of these magnetic parameters are not often available in hydrocarbon exploration to support forward modeling of magnetic data, an interpretation tool that has played important role in the exploration risk reduction in the Pre-Salt prospects of Campos Basin, Brazil. The acquired magnetic data requires corrections for tool rotation and diurnal variation of the Earth’s magnetic field before calculation. Then, using a set of simple equations and reasonable assumptions we were able to estimate the magnetic susceptibility of carbonates and basalts, as well as the remanent magnetization of the basalts, from a Pre-Salt well in Campos Basin. When compared to susceptibility values measured in laboratory for the same rock interval, our results show a significant match. This promising result shows the importance of our methodology in providing reliable information that can minimize uncertainties in forward modeling of magnetic data, which contributes to reduction of hydrocarbon exploration risks. Given that direct susceptibility and remanence measurements require oriented samples, a complex and expensive operation in wells, our results offer this rock information without any extra costs since imaging logs are commonly acquired in exploration wells. Besides its use in hydrocarbon exploration, our methodology can be applied to mineral exploration where magnetic susceptibility is an important property for rock identification. Full article

(This article belongs to the Special Issue Advances in Geophysical Exploration)

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23 pages, 2578 KiB

Open AccessArticle

The Significance of the Sorption Isotherm on the Simulated Performance of Grain Driers

by Graham R. Thorpe

Appl. Sci. 2025, 15(5), 2871; https://doi.org/10.3390/app15052871 - 6 Mar 2025

Viewed by 568

Abstract

Sorption isotherms enable postharvest technologists to estimate the degree and rate of drying of agricultural produce. They are also useful in the design and operation of desiccant systems that are used to condition air. However, the published data on sorption isotherms contain several [...] Read more.

Sorption isotherms enable postharvest technologists to estimate the degree and rate of drying of agricultural produce. They are also useful in the design and operation of desiccant systems that are used to condition air. However, the published data on sorption isotherms contain several inconsistencies. For example, under the conditions considered in this work, it is shown that the widely cited Chung–Pfost isotherm predicts moisture contents of canola that are less than zero as the relative humidity tends to zero. Furthermore, it is shown that a long-established form of empirical expression appears to grossly overestimate the differential heat of wetting, hence the integral heat of wetting of canola. In this work, algebraic expressions are derived that enable the relationship between the forms of isotherm equations on the speed of drying to be calculated. Prima facie, it is anticipated the heat of adsorption will augment the speed of temperature waves through beds of drying canola. However, it is found that this may not be the case. Anomalies in published isotherms for agricultural produce reinforce the need for accurate psychometric data to be measured over a wide range of temperatures and relative humidities. Full article

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

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24 pages, 4247 KiB

Open AccessArticle

Energy-Based Optimization of Seismic Isolation Parameters in RC Buildings Under Earthquake Action Using GWO

by Ali Erdem Çerçevik and Nihan Kazak Çerçevik

Appl. Sci. 2025, 15(5), 2870; https://doi.org/10.3390/app15052870 - 6 Mar 2025

Viewed by 617

Abstract

Modeling seismic isolators, one of the most effective installations in the design of earthquake-resistant buildings, is a very important challenge. In this study, we propose a new energy-based approach for the optimization of seismic isolation parameters. The hysteretic energy represents the dissipation of [...] Read more.

Modeling seismic isolators, one of the most effective installations in the design of earthquake-resistant buildings, is a very important challenge. In this study, we propose a new energy-based approach for the optimization of seismic isolation parameters. The hysteretic energy represents the dissipation of isolated structures in the isolation system. The minimization of input energy ensures that structural components are exposed to reduced seismic energy. For these reasons, this study aims to minimize the input energy and maximize the hysteretic energy. Additionally, an objective function is also generated with the energy ratio obtained from the input and hysteretic energy. The gray wolf optimizer (GWO) was applied to the optimization process. A four-story, 3D, and reinforced concrete superstructure was prepared and lead rubber bearings were placed under the base story. The isolation system is modeled nonlinearly, which requires two parameters: isolation period and characteristic strength. The inter-story drift ratio was selected as the structure constraint, while the isolator displacement and effective damping ratio were selected as the isolator constraints in the optimization process. The prepared base-isolated structure was optimized using 11 scaled ground motions. Nonlinear time history analyses were run in ETABS finite element software. Firstly, the optimum isolation parameters were obtained using peak roof story acceleration (PRA), in accordance with the methodology in previous studies. The outcomes generated by the PRA and energy components are compared considering the isolation parameters and structural responses. The energy ratio produced better results in terms of inter-story drift ratio than the other energy components. Secondly, the energy ratio was re-optimized with different constraints and its effectiveness was examined. Full article

(This article belongs to the Special Issue Novel Approaches for Optimal Design and Seismic Performance Assessment for Civil Structures)

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18 pages, 2439 KiB

Open AccessArticle

Reliability Assessment of a Series System with Weibull-Distributed Components Based on Zero-Failure Data

by Ziang Li, Huimin Fu and Jianchao Guo

Appl. Sci. 2025, 15(5), 2869; https://doi.org/10.3390/app15052869 - 6 Mar 2025

Viewed by 554

Abstract

This study focuses on the reliability assessment of a series system composed of Weibull-distributed components. Because high-reliability components rarely fail during life testing or actual operation, conventional system reliability analysis methods based on failure time data do not work well. This paper presents [...] Read more.

This study focuses on the reliability assessment of a series system composed of Weibull-distributed components. Because high-reliability components rarely fail during life testing or actual operation, conventional system reliability analysis methods based on failure time data do not work well. This paper presents a practical approach to address this issue, with a major interest in inferring the lower confidence limits of system reliability and reliable life. The proposed system reliability assessment method utilizes the minimum lifetime distribution theory to derive the closed-form confidence limits for system reliability indexes from Weibull zero-failure data. Furthermore, a system reliability update procedure is introduced, integrating life data at both the component and system levels. Monte Carlo simulations demonstrate that the proposed approach is more accurate than conventional methods. Finally, an engineering example of reliability assessment and life prediction for a satellite infrared Earth sensor is presented to illustrate the advantages and applications of the proposed method. Full article

(This article belongs to the Section Aerospace Science and Engineering)

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18 pages, 6174 KiB

Open AccessArticle

Sub-Pixel Displacement Measurement with Swin Transformer: A Three-Level Classification Approach

by Yongxing Lin, Xiaoyan Xu and Zhixin Tie

Appl. Sci. 2025, 15(5), 2868; https://doi.org/10.3390/app15052868 - 6 Mar 2025

Viewed by 511

Abstract

In order to avoid the dependence of traditional sub-pixel displacement methods on interpolation method calculation, image gradient calculation, initial value estimation and iterative calculation, a Swin Transformer-based sub-pixel displacement measurement method (ST-SDM) is proposed, and a square dataset expansion method is also proposed [...] Read more.

In order to avoid the dependence of traditional sub-pixel displacement methods on interpolation method calculation, image gradient calculation, initial value estimation and iterative calculation, a Swin Transformer-based sub-pixel displacement measurement method (ST-SDM) is proposed, and a square dataset expansion method is also proposed to rapidly expand the training dataset. The ST-SDM computes sub-pixel displacement values of different scales through three-level classification tasks, and solves the problem of positive and negative displacement with the rotation relative tag value method. The accuracy of the ST-SDM is verified by simulation experiments, and its robustness is verified by real rigid body experiments. The experimental results show that the ST-SDM model has higher accuracy and higher efficiency than the comparison algorithm. Full article

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23 pages, 4074 KiB

Open AccessArticle

A Method of Discriminating Between Power Swings and Faults Based on Principal Component Analysis

by Hao Wang, Qi Yang, Xiaopeng Li and Wenyue Zhou

Appl. Sci. 2025, 15(5), 2867; https://doi.org/10.3390/app15052867 - 6 Mar 2025

Viewed by 434

Abstract

Distance protection is widely applied in AC transmission systems. It may operate incorrectly under power swings, so a power swing blocking unit (PSBU) is needed to work with the distance protection relay. Such a unit should not only block the protection relay in [...] Read more.

Distance protection is widely applied in AC transmission systems. It may operate incorrectly under power swings, so a power swing blocking unit (PSBU) is needed to work with the distance protection relay. Such a unit should not only block the protection relay in time when a power swing occurs, but also deblock the protection relay after detecting a fault during the power swing. In this paper, a method that satisfies these requirements is proposed. To discriminate between power swings and faults, the characteristics of three-phase voltage under a power swing and fault situation are used. Principal Component Analysis (PCA) is applied to extract and quantify the characteristics. To detect faults during power swings, an index is proposed, and the change rate of the index is used to form the criterion. Simulations for different kinds of power swing and fault situations are conducted based on a two-end system and a nine-bus system in PSCAD/EMTDC. The simulation test results indicate that the proposed method can block the protection relay reliably under a power swing and deblock the relay quickly after detecting a fault during the power swing. Moreover, the proposed method is compared with other methods. The comparison results show that the proposed method has an advantage in terms of response speed and is less affected by measurement noise. Full article

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22 pages, 4450 KiB

Open AccessArticle

A Data-Driven Method for Determining DRASTIC Weights to Assess Groundwater Vulnerability to Nitrate: Application in the Lake Baiyangdian Watershed, North China Plain

by Xianglong Hou, Liqin Peng, Yuan Zhang, Yan Zhang, Yunxia Wang, Wenzhao Feng and Hui Yang

Appl. Sci. 2025, 15(5), 2866; https://doi.org/10.3390/app15052866 - 6 Mar 2025

Viewed by 467

Abstract

Nitrate pollution due to agricultural activities challenges the management of groundwater resources. The most popular technique used for groundwater vulnerability assessments is the DRASTIC. The subjectivity introduced by the DRASTIC has always been questioned. Therefore, the determination of rating scores and weights of [...] Read more.

Nitrate pollution due to agricultural activities challenges the management of groundwater resources. The most popular technique used for groundwater vulnerability assessments is the DRASTIC. The subjectivity introduced by the DRASTIC has always been questioned. Therefore, the determination of rating scores and weights of parameters has become the main difficulty in DRASTIC applications. In this paper, a new data-driven weighting method based on Monte Carlo or genetic algorithm was developed. The new method considers both single factors and the relationship among factors, overcomes the subjectivity of weight determination, and is theoretically applicable to various hydrogeological environments and as a general weight determination method. In addition, a new method for the verification of the evaluation results on a temporal scale was established, which is based on changes in the nitrate concentration over the past 20 years. To verify and test these methods, they were used for the evaluation of groundwater vulnerability to nitrate in the plain area of the Baiyangdian watershed in the North China Plain and compared with other commonly used methods. The Pearson correlation coefficient increased by 15%. From a time perspective, the changes in nitrate concentration confirmed that the correctness of the assessment is 88%. In this study, the effect of the revision of the rating ranges on the improvement of the evaluation results is very obvious. Therefore, the focus of future work should be on determining the rating ranges and their rating scores, and whether the corresponding weights based on the data-driven method will yield more reliable results. Full article

(This article belongs to the Special Issue The Mobilization, Speciation and Transformation of Organic and Inorganic Contaminants in Soil-Groundwater Ecosystems, 2nd Edition)

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15 pages, 5379 KiB

Open AccessArticle

Virtual Synchronous Generator Control of Grid Connected Modular Multilevel Converters with an Improved Capacitor Voltage Balancing Method

by Haroun Bensiali, Farid Khoucha, Abdeldjabar Benrabah, Lakhdar Benhamimid and Mohamed Benbouzid

Appl. Sci. 2025, 15(5), 2865; https://doi.org/10.3390/app15052865 - 6 Mar 2025

Viewed by 497

Abstract

Modular multilevel converters have emerged as a common solution in high-voltage and medium-voltage applications due to their scalability and modularity. However, these advantages come at the cost of increased control complexity, particularly when compared to other multilevel converter topologies. This paper proposes a [...] Read more.

Modular multilevel converters have emerged as a common solution in high-voltage and medium-voltage applications due to their scalability and modularity. However, these advantages come at the cost of increased control complexity, particularly when compared to other multilevel converter topologies. This paper proposes a new combined control strategy based on virtual synchronous generator (VSG) control and capacitor voltage balancing (CVB) method. The VSG control is applied for power sharing and inertia emulation to increase the dynamic response and improve system stability while the CVB method is used to redistribute the energy stored in the capacitors of the submodules (SMs) in order to ensure uniform voltage levels and equalize the voltage across the capacitors. The simulation results as well as experimental ones confirm the feasibility and effectiveness of the proposed method, enhancing the performance of the energy conversion system. Full article

(This article belongs to the Section Electrical, Electronics and Communications Engineering)

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27 pages, 729 KiB

Open AccessArticle

Privacy Illusion: Subliminal Channels in Schnorr-like Blind-Signature Schemes

by Mirosław Kutyłowski and Oliwer Sobolewski

Appl. Sci. 2025, 15(5), 2864; https://doi.org/10.3390/app15052864 - 6 Mar 2025

Viewed by 496

Abstract

Blind signatures are one of the key techniques of Privacy-Enhancing Technologies (PETs). They appear as a component of many schemes, including, in particular, the Privacy Pass technology. Blind-signature schemes provide provable privacy: the signer cannot derive any information about a message signed at [...] Read more.

Blind signatures are one of the key techniques of Privacy-Enhancing Technologies (PETs). They appear as a component of many schemes, including, in particular, the Privacy Pass technology. Blind-signature schemes provide provable privacy: the signer cannot derive any information about a message signed at user’s request. Unfortunately, in practice, this might be just an illusion. We consider a novel but realistic threat model where the user does not participate in the protocol directly but instead uses a provided black-box device. We then show that the black-box device may be implemented in such a way that, despite a provably secure unblinding procedure, a malicious signer can link the signing protocol transcript with a resulting unblinded signature. Additionally, we show how to transmit any short covert message between the black-box device and the signer. We prove the stealthiness of these attacks in anamorphic cryptography model, where the attack cannot be detected even if all private keys are given to an auditor. At the same time, an auditor will not detect any irregular behavior even if the secret keys of the signer and the device are revealed for audit purposes (anamorphic cryptography model). We analyze the following schemes: (1) Schnorr blind signatures, (2) Tessaro–Zhu blind signatures, and their extensions. We provide a watchdog countermeasure and conclude that similar solutions are necessary in practical implementations to defer most of the threats. Full article

(This article belongs to the Special Issue Advances in Cloud/Edge Computing: AI, Application, Blockchain and Security)

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31 pages, 6391 KiB

Open AccessReview

Sphingolipidoses and Retinal Involvement: A Comprehensive Review

by Chiara Carrozzi, Daniele Fumi, Davide Fasciolo, Federico Di Tizio, Serena Fragiotta, Mariachiara Di Pippo and Solmaz Abdolrahimzadeh

Appl. Sci. 2025, 15(5), 2863; https://doi.org/10.3390/app15052863 - 6 Mar 2025

Viewed by 592

Abstract

Sphingolipidoses are a class of inherited lysosomal storage diseases, characterized by enzymatic deficiencies that impair sphingolipid degradation. This enzymatic malfunction results in the pathological accumulation of sphingolipids within lysosomes, leading to tissue damage across multiple organ systems. Among the various organs involved, the [...] Read more.

Sphingolipidoses are a class of inherited lysosomal storage diseases, characterized by enzymatic deficiencies that impair sphingolipid degradation. This enzymatic malfunction results in the pathological accumulation of sphingolipids within lysosomes, leading to tissue damage across multiple organ systems. Among the various organs involved, the eye and particularly the retina, can be affected and this will be the primary focus of this study. This article will explore the various subtypes of sphingolipidoses, detailing their associated retinal abnormalities, with an emphasis on multimodal imaging findings and clinical recognition of these rare disorders. Full article

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

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17 pages, 1257 KiB

Open AccessArticle

Enhanced Emotion Recognition Through Dynamic Restrained Adaptive Loss and Extended Multimodal Bottleneck Transformer

by Dang-Khanh Nguyen, Eunchae Lim, Soo-Hyung Kim, Hyung-Jeong Yang and Seungwon Kim

Appl. Sci. 2025, 15(5), 2862; https://doi.org/10.3390/app15052862 - 6 Mar 2025

Viewed by 592

Abstract

Emotion recognition in video aims to estimate human emotions using acoustic, visual, and linguistic information. This problem is considered multimodal and requires learning different modalities, such as visual, verbal, and vocal cues. Although previous studies have focused on developing sophisticated deep learning models, [...] Read more.

Emotion recognition in video aims to estimate human emotions using acoustic, visual, and linguistic information. This problem is considered multimodal and requires learning different modalities, such as visual, verbal, and vocal cues. Although previous studies have focused on developing sophisticated deep learning models, this work proposes a different approach using dynamic restrained adaptive loss inspired by multitask learning to understand multimodal inputs jointly. This training strategy allows predictions from one modality to enhance the accuracy of predictions from other modalities, mirroring the concept of multitask learning, where the results of one task can improve the performance of related tasks. Furthermore, this work introduces the extended multimodal bottleneck transformer, an efficient and effective mid-fusion method designed for problems involving more than two modalities to enhance the performance of emotion recognition systems. The proposed method significantly improves results compared to other end-to-end multimodal fusion techniques on three multimodal benchmarks—Interactive Emotional Dyadic Motion Capture (IEMOCAP), Carnegie Mellon University Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI), and the Chinese Multimodal Sentiment Analysis dataset with independent unimodal annotations (CH-SIMS). Full article

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18 pages, 3789 KiB

Open AccessArticle

Multivariate Time Series Anomaly Detection Using Working Memory Connections in Bi-Directional Long Short-Term Memory Autoencoder Network

by Xianghua Ding, Jingnan Wang, Yiqi Liu and Uk Jung

Appl. Sci. 2025, 15(5), 2861; https://doi.org/10.3390/app15052861 - 6 Mar 2025

Viewed by 593

Abstract

“Normal” events are characterized as data patterns or behaviors that align with expected operational conditions, while “anomalies” are defined as deviations from these patterns, potentially signaling faults, errors, or unexpected system behaviors. The timely and accurate detection of anomalies plays a critical role [...] Read more.

“Normal” events are characterized as data patterns or behaviors that align with expected operational conditions, while “anomalies” are defined as deviations from these patterns, potentially signaling faults, errors, or unexpected system behaviors. The timely and accurate detection of anomalies plays a critical role in domains such as industrial manufacturing, financial transactions, and other related domains. In the context of Industry 4.0, the proliferation of sensors has resulted in a massive influx of time series data, making the anomaly detection of such multivariate time series data a popular research area. Long Short-Term Memory (LSTM) has been extensively recognized as an effective framework for modeling and processing time series data. Previous studies have combined Bi-directional Long Short-Term Memory (Bi-LSTM) architecture with Autoencoder (AE) for multivariate time series anomaly detection. However, due to the inherent limitations of LSTM, Bi-LSTM-AE still cannot overcome these drawbacks. Our study replaces the LSTM units within the Bi-LSTM-AE architecture of existing research with Working Memory Connections for LSTM units and demonstrates that this architecture performs better in the field of multivariate time series anomaly detection compared to using standard LSTM units. The model we proposed not only outperforms the baseline models but also demonstrates greater robustness across various scenarios. Full article

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71 pages, 32082 KiB

Open AccessArticle

Developing New Design Procedure for Bridge Construction Equipment Based on Advanced Structural Analysis

by Shaoxiong Jiang and Faham Tahmasebinia

Appl. Sci. 2025, 15(5), 2860; https://doi.org/10.3390/app15052860 - 6 Mar 2025

Viewed by 678

Abstract

Bridge construction equipment (BCE) is crucial for efficiently executing large-scale infrastructure projects, particularly those involving continuous long-span bridges. Current BCE technologies, like the Overhead Movable Scaffolding System (OMSS), are often chosen for their high efficiency and cost-effective reusability. However, the lack of a [...] Read more.

Bridge construction equipment (BCE) is crucial for efficiently executing large-scale infrastructure projects, particularly those involving continuous long-span bridges. Current BCE technologies, like the Overhead Movable Scaffolding System (OMSS), are often chosen for their high efficiency and cost-effective reusability. However, the lack of a standardised design framework tailored to Australian conditions complicates the design process, potentially leading to increased inefficiencies and safety concerns. This research project seeks to establish a novel design procedure for BCE, using the OMSS in Australia as a case study. The project adopts parametric design techniques using Rhinoceros (Rhino) 3D and Grasshopper to create a three-dimensional linear model. This model undergoes initial structural optimisation with Karamba3D. Subsequent advanced analyses include linear static design assessments performed in Strand7, a sophisticated finite element analysis software. The evaluation primarily utilises Australian standards to assess performance against various load types and combinations, such as permanent (dead), imposed (live), and wind loads. The structural integrity, including maximum displacement, axial forces, and bending moments, is manually verified against the analysis outcomes. The results confirm that the OMSS model adheres to ultimate and serviceability limit state requirements, affirming the effectiveness of the proposed design procedure for BCE. The research culminates in a design procedure flowchart and further suggests future research directions to refine BCE design methodologies for complex bridge construction scenarios. Full article

(This article belongs to the Special Issue Current Technological, Methodological, and Organizational Research Trends in the Construction Industry, Second Edition)

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34 pages, 12341 KiB

Open AccessArticle

Development and Validation of Digital Twin Behavioural Model for Virtual Commissioning of Cyber-Physical System

by Roman Ruzarovsky, Tibor Horak, Roman Zelník, Richard Skypala, Martin Csekei, Ján Šido, Eduard Nemlaha and Michal Kopcek

Appl. Sci. 2025, 15(5), 2859; https://doi.org/10.3390/app15052859 - 6 Mar 2025

Viewed by 790

Abstract

Modern manufacturing systems are influenced by the growing complexity of mechatronics, control systems, IIoT, and communication technologies integrated into cyber-physical systems. These systems demand flexibility, modularity, and rapid project execution, making digital tools critical for their design. Virtual commissioning, based on digital twins, [...] Read more.

Modern manufacturing systems are influenced by the growing complexity of mechatronics, control systems, IIoT, and communication technologies integrated into cyber-physical systems. These systems demand flexibility, modularity, and rapid project execution, making digital tools critical for their design. Virtual commissioning, based on digital twins, enables the testing and validation of control systems and designs in virtual environments, reducing risks and accelerating time-to-market. This research explores the development of digital twin models to bridge the gap between simulation and real-world validation. The models identify design flaws, validate the PLC control code, and ensure interoperability across software platforms. A case study involving a modular Festo manufacturing system modelled in Tecnomatix Process Simulate demonstrates the ability of digital twins to detect inefficiencies, such as collision risks, and to validate automation systems virtually. This study highlights the advantages of virtual commissioning for optimizing manufacturing systems. Communication testing showed compatibility across platforms but revealed limitations with certain data types due to software constraints. This research provides practical insights into creating robust digital twin models, improving the flexibility, efficiency, and quality of manufacturing system design. It also offers recommendations to address current challenges in interoperability and system performance. Full article

(This article belongs to the Special Issue Digital Twins and Cyber-Physical Systems Towards Cybersecurity and Smart Manufacturing)

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20 pages, 8921 KiB

Open AccessArticle

A Survey of IEEE 802.11ax WLAN Temporal Duty Cycle for the Assessment of RF Electromagnetic Exposure

by Yizhen Yang, Günter Vermeeren, Leen Verloock, Mònica Guxens and Wout Joseph

Appl. Sci. 2025, 15(5), 2858; https://doi.org/10.3390/app15052858 - 6 Mar 2025

Viewed by 538

Abstract

The increasing deployment of IEEE 802.11ax (Wi-Fi 6) networks necessitates an accurate assessment of radiofrequency electromagnetic field (RF-EMF) exposure under realistic usage scenarios. This study investigates the duty cycle (DC) and corresponding exposure levels of Wi-Fi 6 in controlled laboratory conditions, focusing on [...] Read more.

The increasing deployment of IEEE 802.11ax (Wi-Fi 6) networks necessitates an accurate assessment of radiofrequency electromagnetic field (RF-EMF) exposure under realistic usage scenarios. This study investigates the duty cycle (DC) and corresponding exposure levels of Wi-Fi 6 in controlled laboratory conditions, focusing on bandwidth variations, multi-user scenarios, and application types. DC measurements reveal significant variability across internet services, with FTP upload exhibiting the highest mean DC (94.3%) under 20 MHz bandwidth, while YouTube 4K video streaming showed bursts with a maximum DC of 89.2%. Under poor radio conditions, DC increased by up to 5× for certain applications, emphasizing the influence of degraded signal-to-noise ratio (SNR) on retransmissions and modulation. Weighted exposure results indicate a reduction in average electric-field strength by up to 10× when incorporating DC, with maximum weighted exposure at 4.2 V/m (6.9% of ICNIRP limits) during multi-user scenarios. These findings highlight the critical role of realistic DC assessments in refining exposure evaluations, ensuring regulatory compliance, and advancing the understanding of Wi-Fi 6’s EMF exposure implications. Full article

(This article belongs to the Special Issue Electromagnetic Radiation and Human Environment)

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20 pages, 3601 KiB

Open AccessArticle

Full-Scale Piano Score Recognition

by Xiang-Yi Zhang and Jia-Lien Hsu

Appl. Sci. 2025, 15(5), 2857; https://doi.org/10.3390/app15052857 - 6 Mar 2025

Viewed by 456

Abstract

Sheet music is one of the most efficient methods for storing music. Meanwhile, a large amount of sheet music-image data is stored in paper form, but not in a computer-readable format. Therefore, digitizing sheet music is an essential task, such that the encoded [...] Read more.

Sheet music is one of the most efficient methods for storing music. Meanwhile, a large amount of sheet music-image data is stored in paper form, but not in a computer-readable format. Therefore, digitizing sheet music is an essential task, such that the encoded music object could be effectively utilized for tasks such as editing or playback. Although there have been a few studies focused on recognizing sheet music images with simpler structures—such as monophonic scores or more modern scores with relatively simple structures, only containing clefs, time signatures, key signatures, and notes—in this paper we focus on the issue of classical sheet music containing dynamics symbols and articulation signs, more than only clefs, time signatures, key signatures, and notes. Therefore, this study augments the data from the GrandStaff dataset by concatenating single-line scores into multi-line scores and adding various classical music dynamics symbols not included in the original GrandStaff dataset. Given a full-scale piano score in pages, our approach first applies three YOLOv8 models to perform the three tasks: 1. Converting a full page of sheet music into multiple single-line scores; 2. Recognizing the classes and absolute positions of dynamics symbols in the score; and 3. Finding the relative positions of dynamics symbols in the score. Then, the identified dynamics symbols are removed from the original score, and the remaining score serves as the input into a Convolutional Recurrent Neural Network (CRNN) for the following steps. The CRNN outputs KERN notation (KERN, a core pitch/duration representation for common practice music notation) without dynamics symbols. By combining the CRNN output with the relative and absolute position information of the dynamics symbols, the final output is obtained. The results show that with the assistance of YOLOv8, there is a significant improvement in accuracy. Full article

(This article belongs to the Special Issue Integration of AI in Signal and Image Processing)

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21 pages, 1860 KiB

Open AccessArticle

Nonparametric Comparative Analysis of Driver Behaviors in Signalized and Non-Signalized Roundabouts: A Study on Road Safety in Qatar

by Mohammed Abul Fahed, Pilsung Choe and Al-Harith Umlai

Appl. Sci. 2025, 15(5), 2856; https://doi.org/10.3390/app15052856 - 6 Mar 2025

Viewed by 486

Abstract

This study investigated and compared driver behaviors at signalized and non-signalized roundabouts in Qatar, focusing on turn signal usage, lane change behavior, and correct lane usage. The primary objectives were to determine the frequency of turn signal usage, assess correct lane usage, analyze [...] Read more.

This study investigated and compared driver behaviors at signalized and non-signalized roundabouts in Qatar, focusing on turn signal usage, lane change behavior, and correct lane usage. The primary objectives were to determine the frequency of turn signal usage, assess correct lane usage, analyze lane change behavior, and compare these behaviors between the two types of roundabouts. Data were collected through a field study at selected roundabouts, where driver behaviors were observed and analyzed. The results revealed significant differences between signalized and non-signalized roundabouts. Turn signal compliance was higher in signalized roundabouts (up to 45%) compared to non-signalized roundabouts (20%). The rate of lane change in signalized roundabouts was observed to be 31%, whereas it was 14% in non-signalized roundabouts, and correct lane usage compliance was higher in signalized roundabouts (60%) compared to non-signalized roundabouts (35%). These findings suggest that traffic signals contribute to safer and more predictable driver behavior, although congestion and long waiting times in signalized roundabouts present challenges. The study recommends improving signage visibility, optimizing signal timings, enhancing road markings, and enforcing traffic regulations to address these issues. The findings can inform traffic engineers and policymakers in enhancing the safety and efficiency of roundabouts. Full article

(This article belongs to the Special Issue Road Safety in Sustainable Urban Transport)

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27 pages, 11370 KiB

Open AccessArticle

Research on Real-Time Control Strategy for HVAC Systems in University Libraries

by Yiquan Zou, Wentao Zou, Han Chen, Xingyao Dong, Luxi Zhu and Hong Shu

Appl. Sci. 2025, 15(5), 2855; https://doi.org/10.3390/app15052855 - 6 Mar 2025

Viewed by 528

Abstract

The energy consumption of library facilities in college buildings is significant, with the HVAC system accounting for 40–60% of the total energy use. Many university libraries, particularly those constructed in earlier years, rely on manual control methods, making the real-time control of HVAC [...] Read more.

The energy consumption of library facilities in college buildings is significant, with the HVAC system accounting for 40–60% of the total energy use. Many university libraries, particularly those constructed in earlier years, rely on manual control methods, making the real-time control of HVAC systems crucial. This study explored the optimization of a building’s HVAC system control using the Levenberg–Marquardt algorithm combined with the universal global optimization algorithm to reduce energy consumption. A university library building was used as a case study to model the overall energy consumption of the HVAC equipment. The proposed strategy was then applied to optimize the energy-saving control of the building’s HVAC system. The results, based on real operational data, demonstrate that this method achieves an energy-saving rate of over 30% while also significantly improving the comfort of library users. The findings of this study provide valuable insights into the energy-saving control of HVAC systems in libraries, which can help advance building energy efficiency and sustainability in the future. Full article

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12 pages, 10631 KiB

Open AccessArticle

Reimagining Historical Exploration: Multi-User Mixed Reality Systems for Cultural Heritage Sites

by Agapi Chrysanthakopoulou, Theofilos Chrysikopoulos, Gerasimos Arvanitis and Konstantinos Moustakas

Appl. Sci. 2025, 15(5), 2854; https://doi.org/10.3390/app15052854 - 6 Mar 2025

Viewed by 655

Abstract

This work presents a mixed reality (MR) system designed to explore inaccessible cultural heritage sites through immersive and interactive experiences. The application features two versions: an asynchronous personalized guided system offering interactions tailored to individual users’ requests and a synchronous guided system providing [...] Read more.

This work presents a mixed reality (MR) system designed to explore inaccessible cultural heritage sites through immersive and interactive experiences. The application features two versions: an asynchronous personalized guided system offering interactions tailored to individual users’ requests and a synchronous guided system providing a shared, collective navigation experience for all users. Both versions integrate innovative mechanics that allow users to explore virtual recreations of cultural sites. Multi-user functionality ensures the visibility of other users as avatars in the virtual environment, enabling collaborative exploration. The proposed application offers a GPS localization system for on-site experiences and a non-location-dependent option for remote settings. A user evaluation was conducted to assess the effectiveness and engagement of the system, providing insights into user preferences and the potential for MR technologies in preserving and promoting cultural heritage. The results highlight the application’s impact on accessibility, immersion, and multi-user interaction, paving the way for further innovation in MR cultural heritage exploration. Full article

(This article belongs to the Special Issue Advanced Virtual, Augmented, and Mixed Reality: Immersive Applications and Innovative Techniques: 2nd Edition)

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20 pages, 6787 KiB

Open AccessArticle

Analysis of Passenger Flow Characteristics and Origin–Destination Passenger Flow Prediction in Urban Rail Transit Based on Deep Learning

by Zhongwei Hou, Jin Han and Guang Yang

Appl. Sci. 2025, 15(5), 2853; https://doi.org/10.3390/app15052853 - 6 Mar 2025

Viewed by 552

Abstract

Traditional station passenger flow prediction can no longer meet the application needs of urban rail transit vehicle scheduling. Station passenger flow can only predict station distribution, and the passenger flow distribution in general sections is unknown. Accurate short-term travel origin and destination (OD) [...] Read more.

Traditional station passenger flow prediction can no longer meet the application needs of urban rail transit vehicle scheduling. Station passenger flow can only predict station distribution, and the passenger flow distribution in general sections is unknown. Accurate short-term travel origin and destination (OD) passenger flow prediction is the main basis for formulating urban rail transit operation organization plans. To simultaneously consider the spatiotemporal characteristics of passenger flow distribution and achieve high precision estimation of origin and destination (OD) passenger flow quickly, a predictive model based on a temporal convolutional network and a long short-term memory network (TCN–LSTM) combined with an attention mechanism was established to process passenger flow data in urban rail transit. Firstly, according to the passenger flow data of the urban rail transit section, the existing data characteristics were summarized, and the impact of external factors on section passenger flow was studied. Then, a temporal convolutional network and long short-term memory (TCN–LSTM) deep learning model based on an attention mechanism was constructed to predict interval passenger flow. The model combines some external factors such as time, date attributes, weather conditions, and air quality that affect passenger flow in the interval to improve the shortcomings of the original model in predicting origin and destination (OD) passenger flow. Taking Chongqing Rail Transit as an example, the model was validated, and the results showed that the deep learning model had significantly better prediction results than other baseline models. The applicability analysis in scenarios such as high/medium/low passenger flow could achieve stable prediction results. Full article

(This article belongs to the Special Issue Damage Monitoring and Defect Identification Based on Deep/Machine Learning (2nd Edition))

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23 pages, 673 KiB

Open AccessArticle

Generative Adversarial Network Based on Self-Attention Mechanism for Automatic Page Layout Generation

by Peng Sun, Xiaomei Liu, Liguo Weng and Ziheng Liu

Appl. Sci. 2025, 15(5), 2852; https://doi.org/10.3390/app15052852 - 6 Mar 2025

Viewed by 625

Abstract

Automatic page layout generation is a challenging and promising research task, which improves the design efficiency and quality of various documents, web pages, etc. However, the current generation of layouts that are both reasonable and aesthetically pleasing still faces many difficulties, such as [...] Read more.

Automatic page layout generation is a challenging and promising research task, which improves the design efficiency and quality of various documents, web pages, etc. However, the current generation of layouts that are both reasonable and aesthetically pleasing still faces many difficulties, such as the shortcomings of existing methods in terms of structural rationality, element alignment, text and image relationship processing, and insufficient consideration of element details and mutual influence within the page. To address these issues, this article proposes a Transformer-based Generative Adversarial Network (TGAN). Generative Adversarial Networks (GANs) innovatively introduce the self-attention mechanism into the network, enabling the model to focus more on key local information that affects page layout. By introducing conditional variables in the generator and discriminator, more accurate sample generation and discrimination can be achieved. The experimental results show that the TGAN outperforms other methods in both subjective and objective ratings when generating page layouts. The generated layouts perform better in element alignment, avoiding overlap, and exhibit higher layout quality and stability, providing a more effective solution for automatic page layout generation. Full article

(This article belongs to the Special Issue Big Data Analysis and Management Based on Deep Learning: 2nd Edition)

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14 pages, 1657 KiB

Open AccessArticle

An Efficient Method for Lung Lesions Classification Using Automatic Vascularization Evaluation on Color Doppler Ultrasound

by Roxana Rusu-Both, Adrian Satmari, Romeo-Ioan Chira, Alexandra Chira and Camelia Avram

Appl. Sci. 2025, 15(5), 2851; https://doi.org/10.3390/app15052851 - 6 Mar 2025

Viewed by 459

Abstract

Lung cancer still represents one of the main causes of cancer-related mortality, highlighting the necessity for precise, effective, and minimally intrusive diagnostic methods. This research presents an innovative approach to classifying lung lesions using Doppler ultrasound imagery combined with a feed-forward neural network [...] Read more.

Lung cancer still represents one of the main causes of cancer-related mortality, highlighting the necessity for precise, effective, and minimally intrusive diagnostic methods. This research presents an innovative approach to classifying lung lesions using Doppler ultrasound imagery combined with a feed-forward neural network (FNN). This study integrates Doppler mode ultrasound vascularization features—blood vessel area, tortuosity index, and orientation—into an FNN to classify lung lesions as benign or malignant. A dataset of 565 Doppler ultrasound pictures was extended using augmentation techniques to enhance robustness, yielding a training dataset of 3390 images. The FNN architecture was trained utilizing the Levenberg–Marquardt algorithm, achieving a classification accuracy of 98%, demonstrating its potential as a diagnostic aid. The results indicate that integrating all three vascularization factors significantly improves diagnosis accuracy compared with individual modules. This method offers a non-invasive and cost-effective complementary tool to conventional techniques such as CT scans, with the potential to improve early detection and treatment planning for lung cancer patients. Full article

(This article belongs to the Special Issue Advances in Diagnostic Radiology)

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16 pages, 908 KiB

Open AccessArticle

Development and Implementation of a Machine Learning Model to Identify Emotions in Children with Severe Motor and Communication Impairments

by Caryn Vowles, Kate Patterson and T. Claire Davies

Appl. Sci. 2025, 15(5), 2850; https://doi.org/10.3390/app15052850 - 6 Mar 2025

Viewed by 432

Abstract

Children with severe motor and communication impairments (SMCIs) face significant challenges in expressing emotions, often leading to unmet needs and social isolation. This study investigated the potential of machine learning to identify emotions in children with SMCIs through the analysis of physiological signals. [...] Read more.

Children with severe motor and communication impairments (SMCIs) face significant challenges in expressing emotions, often leading to unmet needs and social isolation. This study investigated the potential of machine learning to identify emotions in children with SMCIs through the analysis of physiological signals. A model was created based on the data from the DEAP online dataset to identify the emotions of typically developing (TD) participants. The DEAP model was then adapted for use by participants with SMCIs using data collected within the Building and Designing Assistive Technology Lab (BDAT). Key adaptations to the DEAP model resulted in the exclusion of respiratory signals, a reduction in wavelet levels, and the analysis of shorter-duration data segments to enhance the model’s applicability. The adapted SMCI model demonstrated an accuracy comparable to the DEAP model, performing better than chance in TD populations and showing promise for adaptation to SMCI contexts. The models were not reliable for the effective identification of emotions; however, these findings highlight the feasibility of using machine learning to bridge communication gaps for children with SMCIs, enabling better emotional understanding. Future efforts should focus on expanding the data collection of physiological signals for diverse populations and developing personalized models to account for individual differences. This study underscores the importance of collecting data from populations with SMCIs for the development of inclusive technologies to promote empathetic care and enhance the quality of life of children with communication difficulties. Full article

(This article belongs to the Special Issue Assistive Technologies for Rehabilitation: Challenges and Applications)

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22 pages, 3751 KiB

Open AccessArticle

Bio-Inspired Traffic Pattern Generation for Multi-AMR Systems

by Rok Vrabič, Andreja Malus, Jure Dvoršak, Gregor Klančar and Tena Žužek

Appl. Sci. 2025, 15(5), 2849; https://doi.org/10.3390/app15052849 - 6 Mar 2025

Viewed by 499

Abstract

In intralogistics, autonomous mobile robots (AMRs) operate without predefined paths, leading to complex traffic patterns and potential conflicts that impact system efficiency. This paper proposes a bio-inspired optimization method for autonomously generating spatial movement constraints for autonomous mobile robots (AMRs). Unlike traditional multi-agent [...] Read more.

In intralogistics, autonomous mobile robots (AMRs) operate without predefined paths, leading to complex traffic patterns and potential conflicts that impact system efficiency. This paper proposes a bio-inspired optimization method for autonomously generating spatial movement constraints for autonomous mobile robots (AMRs). Unlike traditional multi-agent pathfinding (MAPF) approaches, which focus on temporal coordination, our approach proactively reduces conflicts by adapting a weighted directed grid graph to improve traffic flow. This is achieved through four mechanisms inspired by ant colony systems: (1) a movement reward that decreases the weight of traversed edges, similar to pheromone deposition, (2) a delay penalty that increases edge weights along delayed paths, (3) a collision penalty that increases weights at conflict locations, and (4) an evaporation mechanism that prevents premature convergence to suboptimal solutions. Compared to the existing approaches, the proposed approach addresses the entire intralogistic problem, including plant layout, task distribution, release and dispatching algorithms, and fleet size. Its autonomous movement rule generation and low computational complexity make it well suited for dynamic intralogistic environments. Validated through physics-based simulations in Gazebo across three scenarios, a standard MAPF benchmark, and two industrial environments, the movement constraints generated using the proposed method improved the system throughput by up to 10% compared to unconstrained navigation and up to 4% compared to expert-designed solutions while reducing the need for conflict-resolution interventions. Full article

(This article belongs to the Special Issue Advancements in Multi-Agent Systems and Artificial Intelligence: Methodologies, Applications, and Future Trends)

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28 pages, 10564 KiB

Open AccessArticle

Aging-Friendly Design Research: Knowledge Graph Construction for Elderly Advantage Applications

by Xiaoying Li, Xingda Wang and Guangran Li

Appl. Sci. 2025, 15(5), 2848; https://doi.org/10.3390/app15052848 - 6 Mar 2025

Viewed by 541

Abstract

In the field of aging design, obtaining elderly advantage data is a challenge. In this study, we developed a visualization tool using knowledge graph technology to assist designers in studying elderly advantages, promoting their application in design practice. First, brainstorming sessions and workshops [...] Read more.

In the field of aging design, obtaining elderly advantage data is a challenge. In this study, we developed a visualization tool using knowledge graph technology to assist designers in studying elderly advantages, promoting their application in design practice. First, brainstorming sessions and workshops were held to analyze the challenges of applying elderly advantages in design. Based on these challenges, the concept and functional design of an elderly advantages knowledge graph were proposed. Next, the elderly advantages knowledge graph was constructed by following these steps: (1) The KJ-AHP method was used to process raw data, making them structured and quantitative. (2) The ontology of the knowledge graph was reverse-engineered based on the functional requirements of the graph, allowing the construction of the knowledge graph model layer. (3) The processed data were applied to the knowledge graph ontology through AHP-ontology mapping rules, allowing the knowledge content construction. (4) The programming language Cypher was used for the functional verification of the elderly advantages knowledge graph, and a satisfaction survey was conducted through questionnaires to assess the verification process. The elderly advantages knowledge graph constructed in this study initially fulfilled the expected functions and was met with high satisfaction. The application of knowledge graph technology provides a new reference for advantage mining in the design field. Based on the innovative combination of KJ-AHP and knowledge graph technology, this study enhances the structuring and quantification of graph data, significantly facilitating designers’ understanding of data structures, clarifying data relationships, and expanding design thinking. Full article

(This article belongs to the Special Issue Knowledge Graphs: State-of-the-Art and Applications)

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23 pages, 5525 KiB

Open AccessArticle

Automatic Identification and Segmentation of Overlapping Fog Droplets Using XGBoost and Image Segmentation

by Dongde Liao, Xiongfei Chen, Muhua Liu, Yihan Zhou, Peng Fang, Jinlong Lin, Zhaopeng Liu and Xiao Wang

Appl. Sci. 2025, 15(5), 2847; https://doi.org/10.3390/app15052847 - 6 Mar 2025

Viewed by 474

Abstract

Water-sensitive paper (WSP) has been widely used to assess the quality of pesticide sprays. However, fog droplets tend to overlap on WSP. In order to accurately measure the droplet size and grasp the droplet distribution pattern, this study proposes a method based on [...] Read more.

Water-sensitive paper (WSP) has been widely used to assess the quality of pesticide sprays. However, fog droplets tend to overlap on WSP. In order to accurately measure the droplet size and grasp the droplet distribution pattern, this study proposes a method based on the optimized XGBoost classification model combined with improved concave-point matching to achieve multi-level overlapping-droplet segmentation. For different types of overlapping droplets, the corresponding improved segmentation algorithm is used to improve the segmentation accuracy. For parallel overlapping droplets, the centre-of-mass segmentation method is used; for non-parallel overlapping droplets, the minimum-distance segmentation method is used; and for strong overlapping of a single concave point, the vertical-linkage segmentation method is used. Complex overlapping droplets were gradually segmented by loop iteration until a single droplet was obtained or no further segmentation was possible, and then ellipse fitting was used to obtain the final single-droplet profile. Up to 105 WSPs were obtained in an orchard field through drone spraying experiments, and were used to validate the effectiveness of the method. The experimental results show that the classification model proposed in this paper achieves an average accuracy of 98% in identifying overlapping-droplet types, which effectively meets the needs of subsequent segmentation. The overall segmentation accuracy of the method is 91.35%, which is significantly better than the contour-solidity and watershed-based algorithm (76.19%) and the improved-concave-point-segmentation algorithm (68.82%). In conclusion, the method proposed in this paper provides an efficient and accurate new approach for pesticide spraying quality assessment. Full article

(This article belongs to the Special Issue Advances in Image Recognition and Processing Technologies)

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15 pages, 2994 KiB

Open AccessArticle

Underwater Ionic Current Signal Sensing and Information Transmission by Contact-Induced Charge Transfer

by Boru Su, Junyan Zhang, Yunfei Deng and Lin Chi

Appl. Sci. 2025, 15(5), 2846; https://doi.org/10.3390/app15052846 - 6 Mar 2025

Cited by 1 | Viewed by 514 | Correction

Abstract

Underwater ionic current signal sensing shows great potential for electric-field-sensing-based target detection, information transmission and communication. Nevertheless, it is still a challenging task. Herein, a self-powered underwater ionic current signal sensing system using contact-induced charge transfer is presented. The system mainly consists of [...] Read more.

Underwater ionic current signal sensing shows great potential for electric-field-sensing-based target detection, information transmission and communication. Nevertheless, it is still a challenging task. Herein, a self-powered underwater ionic current signal sensing system using contact-induced charge transfer is presented. The system mainly consists of a working electrode, a metal sheet and a sensing electrode that is immersed in electrolyte solution. Upon touching the working electrode with a metal sheet with a different work function, charge transfer occurs on the interface, and a corresponding ionic current is induced. The generated ionic current can be detected with the sensing electrode far away from the working electrode. It was found that the magnitude and direction of the generated ionic current are determined by the contact potential difference (CPD) between the working electrode and the contacting metal sheet. Additionally, the effects of water temperature, the ionic concentration of the electrolyte solution, electrode surface area and hydrostatic pressure are systematically investigated. The detected signal magnitude decreased with an increase in the distance between the working electrode and the sensing electrode. A proof-of-concept demonstration of underwater information transmission via Morse code with this method was successfully achieved. Full article

(This article belongs to the Section Surface Sciences and Technology)

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24 pages, 6438 KiB

Open AccessArticle

Establishing Two-Dimensional Dependencies for Multi-Label Image Classification

by Jiuhang Wang, Yuewen Zhang, Tengjing Wang, Hongying Tang and Baoqing Li

Appl. Sci. 2025, 15(5), 2845; https://doi.org/10.3390/app15052845 - 6 Mar 2025

Viewed by 441

Abstract

As a fundamental upstream task, multi-label image classification (MLIC) work has made a great deal of progress in recent years. Establishing dependencies between targets is crucial for MLIC as targets in the real world always co-occur simultaneously. However, due to the complex spatial [...] Read more.

As a fundamental upstream task, multi-label image classification (MLIC) work has made a great deal of progress in recent years. Establishing dependencies between targets is crucial for MLIC as targets in the real world always co-occur simultaneously. However, due to the complex spatial relationships and semantic relationships among targets, existing methods fail to effectively establish the dependencies between targets. In this paper, we propose a Two-Dimensional Dependency Model (TDDM) for MLIC. The network consists of an Spatial Feature Dependency Module (SFDM) and a Label Semantic Dependency Module (LSDM), which establish effective dependencies in the dimensions of image spatial features and label semantics, respectively. Our method was tested on three publicly available multi-label image datasets, PASCAL VOC 2007, PASCAL VOC 2012, and MS-COCO, and it produced superior results compared to existing state-of-the-art methods, as demonstrated in our experiments. Full article

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

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24 pages, 1304 KiB

Open AccessArticle

Impact of Augmented Reality and Game-Based Learning for Science Teaching: Lessons from Pre-Service Teachers

by Valerie Czok and Holger Weitzel

Appl. Sci. 2025, 15(5), 2844; https://doi.org/10.3390/app15052844 - 6 Mar 2025

Viewed by 611

Abstract

Technological advancement and growing interest in digitalizing education increased Augmented Reality (AR) use in education. However, previous research findings on AR’s potential for knowledge acquisition are inconclusive. Furthermore, computer self-efficacy has seldom been evaluated. AR is frequently combined with game-based approaches (GAME), yet [...] Read more.

Technological advancement and growing interest in digitalizing education increased Augmented Reality (AR) use in education. However, previous research findings on AR’s potential for knowledge acquisition are inconclusive. Furthermore, computer self-efficacy has seldom been evaluated. AR is frequently combined with game-based approaches (GAME), yet the specific impact of each feature, “AR” and “GAME”, is often not differentiated in the research design. This work analyzed an AR game-based learning environment for science teaching. It was conducted with German pre-service teachers, assessing “Knowledge” and “Computer Self-Efficacy”. These measures were used to analyze the effect of AR and GAME in four intervention groups. The results showed a significant time effect for all groups in both variables, indicating all intervention designs led to knowledge and self-efficacy gains. However, no interaction effect was found, indicating the groups did not significantly differ in their knowledge and self-efficacy gains over time. The results further indicate no clear advantage of either AR or GAME for the design of science teaching. However, AR and GAME also did not hinder learning and both led to successful knowledge and self-efficacy gains. This indicates that AR and game-based learning support the learning process and strengthen learners’ computer self-efficacy. Combining both features aids in easing the transition toward technology-enhanced learning by providing a playful learning experience, using digital as well as analog components. Full article

(This article belongs to the Special Issue Virtual and Augmented Reality Technology in Education and Entertainment)

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24 pages, 7005 KiB

Open AccessArticle

Electricity Demand Forecasting Using Deep Polynomial Neural Networks and Gene Expression Programming During COVID-19 Pandemic

by Cagatay Cebeci and Kasım Zor

Appl. Sci. 2025, 15(5), 2843; https://doi.org/10.3390/app15052843 - 6 Mar 2025

Viewed by 707

Abstract

The power-generation mix of future grids will be quite diversified with the ever-increasing share of renewable energy technologies. Therefore, the prediction of electricity demand will become crucial for resource optimization and grid stability. Machine learning- and artificial intelligence-based methods are widely studied by [...] Read more.

The power-generation mix of future grids will be quite diversified with the ever-increasing share of renewable energy technologies. Therefore, the prediction of electricity demand will become crucial for resource optimization and grid stability. Machine learning- and artificial intelligence-based methods are widely studied by researchers to tackle the demand forecasting problem. However, since the COVID-19 pandemic broke out, new challenges have surfaced for forecasting research. In such a short amount of time, significant shifts have emerged in electricity demand trends, making it apparent that the pandemic and the possibility of similar crises in the future have escalated the complexity of energy management problems. Motivated by the circumstances, this research presents an hour-ahead and day-ahead electricity demand forecasting benchmark using Deep Polynomial Neural Networks (DNN) and Gene Expression Programming (GEP) methods. The DNN and GEP algorithms utilize on-site electricity consumption data collected from a university hospital for over two years with a temporal granularity of 15-minute intervals. Quarter-hourly meteorological, calendar, and daily COVID-19 data, including new cases and cumulative cases divided by four restriction levels, were also considered. These datasets are used not only to predict the electricity demand but also to investigate the impact of the COVID-19 pandemic on the electricity consumption of the hospital. The hour-ahead and day-ahead nRMSE results show that the DNN outperforms the GEP by

8.27 %

and

14.32 %

, respectively. For the computational times, the DNN appears to be much faster than the GEP by

82.83 %

and

78.56 %

in the hour-ahead and day-ahead forecasting, respectively. Full article

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

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