Loading [MathJax]/jax/output/HTML-CSS/jax.js
 
 
Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Article Types

Countries / Regions

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Search Results (13,918)

Search Parameters:
Keywords = architecture modeling

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
20 pages, 7885 KiB  
Article
Fault Diagnosis Method for Transformer Winding Based on Differentiated M-Training Classification Optimized by White Shark Optimization Algorithm
by Guochao Qian, Kun Yang, Jin Hu, Hongwen Liu, Shun He, Dexu Zou, Weiju Dai, Haozhou Wang and Dongyang Wang
Energies 2025, 18(9), 2290; https://doi.org/10.3390/en18092290 - 30 Apr 2025
Viewed by 245
Abstract
Transformers, serving as critical components in power systems, are predominantly affected by winding faults that compromise their operational safety and reliability. Frequency Response Analysis (FRA) has emerged as the prevailing methodology for the status assessment of transformer windings in contemporary power engineering practice. [...] Read more.
Transformers, serving as critical components in power systems, are predominantly affected by winding faults that compromise their operational safety and reliability. Frequency Response Analysis (FRA) has emerged as the prevailing methodology for the status assessment of transformer windings in contemporary power engineering practice. To mitigate the accuracy limitations of single-classifier approaches in winding status assessment, this paper proposes a differentiated M-training classification algorithm based on White Shark Optimization (WSO). The principal contributions are threefold: First, building upon the fundamental principles of the M-training algorithm, we establish a classification model incorporating diversified classifiers. For each base classifier, a parameter optimization method leveraging WSO is developed to enhance diagnostic precision. Second, an experimental platform for transformer fault simulation is constructed, capable of replicating various fault types with programmable severity levels. Through controlled experiments, frequency response curves and associated characteristic parameters are systematically acquired under diverse winding statuses. Finally, the model undergoes comprehensive training and validation using experimental datasets, and the model is verified and analyzed by the actual transformer test results. The experimental findings demonstrate that implementing WSO for base classifier optimization enhances the M-training algorithm’s diagnostic precision by 8.92% in fault-type identification and 8.17% in severity-level recognition. The proposed differentiated M-training architecture achieves classification accuracies of 98.33% for fault-type discrimination and 97.17% for severity quantification, representing statistically significant improvements over standalone classifiers. Full article
Show Figures

Figure 1

18 pages, 260 KiB  
Article
Evaluating the Performance of DenseNet in ECG Report Automation
by Gazi Husain, Ayesha Siddiqua and Milan Toma
Electronics 2025, 14(9), 1837; https://doi.org/10.3390/electronics14091837 - 30 Apr 2025
Viewed by 192
Abstract
Ongoing advancements in machine learning show great promise for automating medical data interpretation, potentially saving valuable time in life-threatening situations. One such area is the analysis of electrocardiograms (ECGs). In this study, we investigate the effectiveness of using a DenseNet121 encoder with three [...] Read more.
Ongoing advancements in machine learning show great promise for automating medical data interpretation, potentially saving valuable time in life-threatening situations. One such area is the analysis of electrocardiograms (ECGs). In this study, we investigate the effectiveness of using a DenseNet121 encoder with three decoder architectures: Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and a Transformer-based approach. We utilize these models to generate automated ECG reports from the publicly available PTB-XL dataset. Our results show that the DenseNet121 encoder paired with a GRU decoder yields higher performance than previously achieved. It achieves a METEOR (Metric for Evaluation of Translation with Explicit Ordering) score of 72.19%, outperforming the previous best result of 55.53% from a ResNet34-based model that used LSTM and Transformer components. We also discuss several important design choices, such as how to initialize decoders, how to use attention mechanisms, and how to apply data augmentation. These findings offer valuable insights into creating more robust and reliable deep learning tools for ECG interpretation. Full article
(This article belongs to the Special Issue Digital Intelligence Technology and Applications)
Show Figures

Figure 1

18 pages, 1459 KiB  
Article
Inferring Mechanical Properties of Wire Rods via Transfer Learning Using Pre-Trained Neural Networks
by Adriany A. F. Eduardo, Gustavo A. S. Martinez, Ted W. Grant, Lucas B. S. Da Silva and Wei-Liang Qian
J 2025, 8(2), 15; https://doi.org/10.3390/j8020015 - 30 Apr 2025
Viewed by 221
Abstract
The primary objective of this study is to explore how machine learning techniques can be incorporated into the analysis of material deformation. Neural network algorithms are applied to the study of mechanical properties of wire rods subjected to cold plastic deformations. Specifically, this [...] Read more.
The primary objective of this study is to explore how machine learning techniques can be incorporated into the analysis of material deformation. Neural network algorithms are applied to the study of mechanical properties of wire rods subjected to cold plastic deformations. Specifically, this study explores how pre-trained neural networks with appropriate architecture can be exploited to predict apparently distinct but internally related features. Tentative predictions are made by observing only an insignificant cropped fraction of the material’s profile. The neural network models are trained and calibrated using 6400 image fractions with a resolution of 120×90 pixels. Different architectures are developed with a focus on two particular aspects. Firstly, different possible architectures are compared, particularly between multi-output and multi-label convolutional neural networks (CNNs). Moreover, a hybrid model is employed, essentially a conjunction of a CNN with a multi-layer perceptron (MLP). The neural network’s input constitutes combined numerical and visual data, and its architecture primarily consists of seven dense layers and eight convolutional layers. By proper calibration and fine-tuning, observed improvements over the standard CNN models are reflected by good training and test accuracies in order to predict the material’s mechanical properties, with efficiency demonstrated by the loss function’s rapid convergence. Secondly, the role of the pre-training process is investigated. The obtained CNN-MLP model can inherit the learning from a pre-trained multi-label CNN, initially developed for distinct features such as localization and number of passes. It is demonstrated that the pre-training effectively accelerates the learning process for the target feature. Therefore, it is concluded that appropriate architecture design and pre-training are essential for applying machine learning techniques to realistic problems. Full article
Show Figures

Figure 1

25 pages, 1891 KiB  
Article
Classification Improvement with Integration of Radial Basis Function and Multilayer Perceptron Network Architectures
by László Kovács
Mathematics 2025, 13(9), 1471; https://doi.org/10.3390/math13091471 - 30 Apr 2025
Viewed by 191
Abstract
The radial basis function architecture and the multilayer perceptron architecture are very different approaches to neural networks in theory and practice. Considering their classification efficiency, both have different strengths; thus, the integration of these tools is an interesting but understudied problem domain. This [...] Read more.
The radial basis function architecture and the multilayer perceptron architecture are very different approaches to neural networks in theory and practice. Considering their classification efficiency, both have different strengths; thus, the integration of these tools is an interesting but understudied problem domain. This paper presents a novel initialization method based on a distance-weighted homogeneity measure to construct a radial basis function network with fast convergence. The proposed radial basis function network is utilized in the development of an integrated RBF-MLP architecture. The proposed neural network model was tested in various classification tasks and the test results show superiority of the proposed architecture. The RBF-MLP model achieved nearly 40 percent better accuracy in the tests than the baseline MLP or RBF neural network architectures. Full article
Show Figures

Figure 1

14 pages, 8583 KiB  
Article
A Spatial Five-Bar Linkage as a Tilting Joint of the Breeding Blanket Transporter for the Remote Maintenance of EU DEMO
by Hjalte Durocher, Christian Bachmann, Rocco Mozzillo, Günter Janeschitz and Xuping Zhang
Machines 2025, 13(5), 371; https://doi.org/10.3390/machines13050371 - 29 Apr 2025
Viewed by 95
Abstract
The future fusion power plant EU DEMO will generate its own tritium fuel through the use of segmented breeding blankets (BBs), which must be replaced from time to time due to material damage caused by high-energy neutrons from the plasma. A vertical maintenance [...] Read more.
The future fusion power plant EU DEMO will generate its own tritium fuel through the use of segmented breeding blankets (BBs), which must be replaced from time to time due to material damage caused by high-energy neutrons from the plasma. A vertical maintenance architecture has been proposed, using a robotic remote handling tool (transporter) to disengage the 180 t and 125 t outboard and inboard segments and manipulate them through an upper port. Safe disengagement without damaging the support structures requires the use of high-capacity tilting joints in the transporter. The trolley tilting mechanism (TTM) is proposed as a novel, compact, high-capacity robotic joint consisting of a five-bar spatial mechanism integrated in the BB transporter trolley link. A kinematic model of the TTM is established, and the analytical input–output relationships, including the position-dependent transmission ratio, are derived and used to guide the design and optimization of the mechanism. The model predictions are compared to an ADAMS multibody simulation and to the results of an experiment conducted on a down-scaled prototype, both of which validate the model accuracy. Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
Show Figures

Figure 1

20 pages, 610 KiB  
Article
TC-Verifier: Trans-Compiler-Based Code Translator Verifier with Model-Checking
by Amira T. Mahmoud, Walaa Medhat, Sahar Selim, Hala Zayed, Ahmed H. Yousef and Nahla Elaraby
Appl. Syst. Innov. 2025, 8(3), 60; https://doi.org/10.3390/asi8030060 - 29 Apr 2025
Viewed by 151
Abstract
Code-to-code translation, a critical domain in software engineering, increasingly utilizes trans-compilers to translate between high-level languages. Traditionally, the fidelity of such translations has been evaluated using the BLEU score, which predominantly measures token similarity between the generated output and the ground truth. However, [...] Read more.
Code-to-code translation, a critical domain in software engineering, increasingly utilizes trans-compilers to translate between high-level languages. Traditionally, the fidelity of such translations has been evaluated using the BLEU score, which predominantly measures token similarity between the generated output and the ground truth. However, this metric falls short of assessing the methodologies underlying the translation processes and only evaluates the translations that are tested. To bridge this gap, this paper introduces an innovative architecture, “TC-Verifier”, to formally employ the Uppaal Model-checker to verify trans-compiler-based code translators. We applied the proposed architecture to a trans-compiler translating between Swift and Java, providing insights into the verified and unverified aspects of the translation process. Our findings illuminate the strengths and limitations of using Model-checking for formal verification in code translation. Notably, the examined trans-compiler reached a verification success rate of 50.74% for the grammar rules and productions modeled. This study underscores the gaps in trans-compiler-based translations and suggests that these gaps could potentially be addressed by integrating Large Language Models (LLMs) in future work. Full article
Show Figures

Figure 1

15 pages, 1266 KiB  
Article
Enhancing Genomic Prediction Accuracy with a Single-Step Genomic Best Linear Unbiased Prediction Model Integrating Genome-Wide Association Study Results
by Zhixu Pang, Wannian Wang, Pu Huang, Hongzhi Zhang, Siying Zhang, Pengkun Yang, Liying Qiao, Jianhua Liu, Yangyang Pan, Kaijie Yang and Wenzhong Liu
Animals 2025, 15(9), 1268; https://doi.org/10.3390/ani15091268 - 29 Apr 2025
Viewed by 124
Abstract
Genomic selection (GS) is a genetic breeding method that uses genome-wide marker information to improve the accuracy of the prediction of complex traits. The single-step GBLUP (ssGBLUP) model, which integrates pedigree, phenotypic, and genomic data, has improved genomic prediction. However, ssGBLUP assumes that [...] Read more.
Genomic selection (GS) is a genetic breeding method that uses genome-wide marker information to improve the accuracy of the prediction of complex traits. The single-step GBLUP (ssGBLUP) model, which integrates pedigree, phenotypic, and genomic data, has improved genomic prediction. However, ssGBLUP assumes that all markers contribute equally to genetic variance, which can limit its predictive accuracy, especially for traits controlled by major genes. To overcome this limitation, we integrate results from genome-wide association studies (GWAS) into an enhanced ssGBLUP framework, termed single-step genome-wide association assisted BLUP (ssGWABLUP). Our approach assigns differential weights to markers on the basis of their GWAS results, thereby increasing the contribution of effective markers while diminishing the influence of ineffective ones during the construction of the genomic relationship matrix. By incorporating pseudo quantitative trait nucleotides (pQTNs) as covariates, we aim to capture the effects of markers closely associated with major causal variants, leading to the development of the ssGWABLUP_pQTNs. Compared with weighted ssGBLUP (WssGBLUP), the ssGWABLUP model demonstrated superior accuracy and dispersion across different genetic architectures. We then compared the performance of our proposed ssGWABLUP_pQTNs model against both ssGBLUP and ssGWABLUP across various genetic scenarios. Our results demonstrate that ssGWABLUP_pQTNs outperforms other models in terms of prediction accuracy, particularly in scenarios with simpler genetic architectures. Additionally, evaluation using pig dataset confirmed the effectiveness of ssGWABLUP_pQTNs, highlighting its potential for practical breeding applications. The incorporation of pQTNs and a weighted genomic relationship matrix presents a promising and potentially scalable approach to further enhance genomic prediction, with potential implications for improving the accuracy of genomic selection in breeding programs. Full article
(This article belongs to the Section Animal Genetics and Genomics)
Show Figures

Figure 1

45 pages, 9372 KiB  
Article
Low-Carbon Optimization Operation of Rural Energy System Considering High-Level Water Tower and Diverse Load Characteristics
by Gang Zhang, Jiazhe Liu, Tuo Xie and Kaoshe Zhang
Processes 2025, 13(5), 1366; https://doi.org/10.3390/pr13051366 - 29 Apr 2025
Viewed by 124
Abstract
Against the backdrop of the steady advancement of the national rural revitalization strategy and the dual-carbon goals, the low-carbon transformation of rural energy systems is of critical importance. This study first proposes a comprehensive architecture for rural energy supply systems, incorporating four key [...] Read more.
Against the backdrop of the steady advancement of the national rural revitalization strategy and the dual-carbon goals, the low-carbon transformation of rural energy systems is of critical importance. This study first proposes a comprehensive architecture for rural energy supply systems, incorporating four key dimensions: investment, system configuration, user demand, and policy support. Leveraging the abundant wind, solar, and biomass resources available in rural areas, a low-carbon optimization model for rural energy system operation is developed. The model accounts for diverse load characteristics and the integration of elevated water towers, which serve both energy storage and agricultural functions. The optimization framework targets the multi-energy demands of rural production and daily life—including electricity, heating, cooling, and gas—and incorporates the stochastic nature of wind and solar generation. To address renewable energy uncertainty, the Fisher optimal segmentation method is employed to extract representative scenarios. A representative rural region in China is used as the case study, and the system’s performance is evaluated across multiple scenarios using the Gurobi solver. The objective functions include maximizing clean energy benefits and minimizing carbon emissions. Within the system, flexible resources participate in demand response based on their specific response characteristics, thereby enhancing the overall decarbonization level. The energy storage aggregator improves renewable energy utilization and gains economic returns by charging and discharging surplus wind and solar power. The elevated water tower contributes to renewable energy absorption by storing and releasing water, while also supporting irrigation via a drip system. The simulation results demonstrate that the proposed clean energy system and its associated operational strategy significantly enhance the low-carbon performance of rural energy consumption while improving the economic efficiency of the energy system. Full article
(This article belongs to the Section Energy Systems)
Show Figures

Figure 1

31 pages, 1997 KiB  
Article
Leveraging Blockchain Technology for Secure 5G Offloading Processes
by Cristina Regueiro, Santiago de Diego and Borja Urkizu
Future Internet 2025, 17(5), 197; https://doi.org/10.3390/fi17050197 - 29 Apr 2025
Viewed by 271
Abstract
This paper presents a secure 5G offloading mechanism leveraging Blockchain technology and Self-Sovereign Identity (SSI). The advent of 5G has significantly enhanced the capabilities of all sectors, enabling innovative applications and improving security and efficiency. However, challenges such as limited infrastructure, signal interference, [...] Read more.
This paper presents a secure 5G offloading mechanism leveraging Blockchain technology and Self-Sovereign Identity (SSI). The advent of 5G has significantly enhanced the capabilities of all sectors, enabling innovative applications and improving security and efficiency. However, challenges such as limited infrastructure, signal interference, and high upgrade costs persist. Offloading processes already address these issues but they require more transparency and security. This paper proposes a Blockchain-based marketplace using Hyperledger Fabric to optimize resource allocation and enhance security. This marketplace facilitates the exchange of services and resources among operators, promoting competition and flexibility. Additionally, the paper introduces an SSI-based authentication system to ensure privacy and security during the offloading process. The architecture and components of the marketplace and authentication system are detailed, along with their data models and operations. Performance evaluations indicate that the proposed solutions do not significantly degrade offloading times, making them suitable for everyday applications. As a result, the integration of Blockchain and SSI technologies enhances the security and efficiency of 5G offloading. Full article
(This article belongs to the Special Issue 5G Security: Challenges, Opportunities, and the Road Ahead)
Show Figures

Figure 1

21 pages, 9110 KiB  
Article
SwinTCS: A Swin Transformer Approach to Compressive Sensing with Non-Local Denoising
by Xiuying Li, Haoze Li, Hongwei Liao, Zhufeng Suo, Xuesong Chen and Jiameng Han
J. Imaging 2025, 11(5), 139; https://doi.org/10.3390/jimaging11050139 - 29 Apr 2025
Viewed by 144
Abstract
In the era of the Internet of Things (IoT), the rapid growth of interconnected devices has intensified the demand for efficient data acquisition and processing techniques. Compressive Sensing (CS) has emerged as a promising approach for simultaneous signal acquisition and dimensionality reduction, particularly [...] Read more.
In the era of the Internet of Things (IoT), the rapid growth of interconnected devices has intensified the demand for efficient data acquisition and processing techniques. Compressive Sensing (CS) has emerged as a promising approach for simultaneous signal acquisition and dimensionality reduction, particularly in multimedia applications. In response to the challenges presented by traditional CS reconstruction methods, such as boundary artifacts and limited robustness, we propose a novel hierarchical deep learning framework, SwinTCS, for CS-aware image reconstruction. Leveraging the Swin Transformer architecture, SwinTCS integrates a hierarchical feature representation strategy to enhance global contextual modeling while maintaining computational efficiency. Moreover, to better capture local features of images, we introduce an auxiliary convolutional neural network (CNN). Additionally, for suppressing noise and improving reconstruction quality in high-compression scenarios, we incorporate a Non-Local Means Denoising module. The experimental results on multiple public benchmark datasets indicate that SwinTCS surpasses State-of-the-Art (SOTA) methods across various evaluation metrics, thereby confirming its superior performance. Full article
(This article belongs to the Topic Intelligent Image Processing Technology)
Show Figures

Figure 1

20 pages, 3179 KiB  
Article
Estimation of Lithium-Ion Battery State of Health-Based Multi-Feature Analysis and Convolutional Neural Network–Long Short-Term Memory
by Xin Ma, Xingke Ding, Chongyi Tian, Changbin Tian and Rui Zhu
Sustainability 2025, 17(9), 4014; https://doi.org/10.3390/su17094014 - 29 Apr 2025
Viewed by 155
Abstract
Accurate estimation of battery state of health (SOH) is critical to the efficient operation of energy storage battery systems. Furthermore, precise SOH estimation methods can significantly reduce resource waste by extending the battery service life and optimizing retirement strategies, which is compatible with [...] Read more.
Accurate estimation of battery state of health (SOH) is critical to the efficient operation of energy storage battery systems. Furthermore, precise SOH estimation methods can significantly reduce resource waste by extending the battery service life and optimizing retirement strategies, which is compatible with the sustainable development of energy systems under carbon neutrality goals. Conventional methods struggle to comprehensively characterize the health degradation properties of batteries. To address that limitation, this study proposes a data-driven model based on multi-feature analysis using a hybrid convolutional neural network and long short-term memory (CNN-LSTM) architecture, which synergistically extracts multi-dimensional degradation features to enhance SOH estimation accuracy. The framework begins by systematically collecting the voltage, current, and other parameters during charge–discharge cycles to construct a temporally resolved multi-dimensional feature matrix. A correlation analysis employing Pearson correlation coefficients subsequently identifies key health indicators strongly correlated with SOH degradation. At the same time, the K-means clustering method was adopted to identify and process the outliers of CALCE data, which ensures the high quality of data and the stability of the model. Then, CNN-LSTM hybrid neural network architecture was constructed. The experimental results demonstrated that the absolute value of MBE for the dataset provided by CALCE was less than 0.2%. The MAE was less than 0.3%, and the RMSE was less than 0.4%. Furthermore, the proposed method demonstrated a strong performance on the dataset provided by NASA PCoE. The experimental results indicated that the proposed method significantly reduced the estimation error of SOH across the entire battery lifecycle, and they fully verified the superiority and engineering applicability of the algorithm in battery SOH estimation. Full article
Show Figures

Figure 1

33 pages, 20540 KiB  
Article
SG-ResNet: Spatially Adaptive Gabor Residual Networks with Density-Peak Guidance for Joint Image Steganalysis and Payload Location
by Zhengliang Lai, Chenyi Wu, Xishun Zhu, Jianhua Wu and Guiqin Duan
Mathematics 2025, 13(9), 1460; https://doi.org/10.3390/math13091460 - 29 Apr 2025
Viewed by 118
Abstract
Image steganalysis detects hidden information in digital images by identifying statistical anomalies, serving as a forensic tool to reveal potential covert communication. The field of deep learning-based image steganography has relatively scarce effective steganalysis methods, particularly those designed to extract hidden information. This [...] Read more.
Image steganalysis detects hidden information in digital images by identifying statistical anomalies, serving as a forensic tool to reveal potential covert communication. The field of deep learning-based image steganography has relatively scarce effective steganalysis methods, particularly those designed to extract hidden information. This paper introduces an innovative image steganalysis method based on generative adaptive Gabor residual networks with density-peak guidance (SG-ResNet). SG-ResNet employs a dual-stream collaborative architecture to achieve precise detection and reconstruction of steganographic information. The classification subnet utilizes dual-frequency adaptive Gabor convolutional kernels to decouple high-frequency texture and low-frequency contour components in images. It combines a density peak clustering with three quantization and transformation-enhanced convolutional blocks to generate steganographic covariance matrices, enhancing the weak steganographic signals. The reconstruction subnet synchronously constructs multi-scale features, preserves steganographic spatial fingerprints with channel-separated residual spatial rich model and pixel reorganization operators, and achieves sub-pixel-level steganographic localization via iterative optimization mechanism of feedback residual modules. Experimental results obtained with datasets generated by several public steganography algorithms demonstrate that SG-ResNet achieves State-of-the-Art results in terms of detection accuracy, with 0.94, and with a PSNR of 29 between reconstructed and original secret images. Full article
(This article belongs to the Special Issue New Solutions for Multimedia and Artificial Intelligence Security)
Show Figures

Figure 1

29 pages, 4529 KiB  
Article
Smart Buildings and Digital Twin to Monitoring the Efficiency and Wellness of Working Environments: A Case Study on IoT Integration and Data-Driven Management
by Giuseppe Piras, Sofia Agostinelli and Francesco Muzi
Appl. Sci. 2025, 15(9), 4939; https://doi.org/10.3390/app15094939 - 29 Apr 2025
Viewed by 169
Abstract
Quality and efficiency of the work environment are essential to the well-being, health and productivity of employees. Despite the increasing focus on these aspects, many workplaces currently do not fully meet the needs and expectations of employees, with negative consequences for their well-being [...] Read more.
Quality and efficiency of the work environment are essential to the well-being, health and productivity of employees. Despite the increasing focus on these aspects, many workplaces currently do not fully meet the needs and expectations of employees, with negative consequences for their well-being and productivity. The research aims to develop a system based on the Smart Building and Digital Twin paradigm, focusing on the implementation of various IoT components, the creation of automation flows for energy-efficient lighting, HVAC and indoor air quality control systems, and decision support through real-time data visualization enabled by user interfaces and dashboards integrating the geometric and information model (BIM). The system also aims to provide a tool for both monitoring and simulation/planning/decision support through the processing and development of machine learning (ML) algorithms. In relation to emergency management, real-time data can be acquired, allowing information to be shared with users and building managers through the creation of dashboards and visual analysis. After defining the functional requirements and identifying all3 the monitorable quantities that can be translated into requirements, the system architecture is described, the implementation of the case study is illustrated and the preliminary results of the first data collection campaign and initial estimates of future forecasts are shown. Full article
Show Figures

Figure 1

19 pages, 2033 KiB  
Article
DeepStego: Privacy-Preserving Natural Language Steganography Using Large Language Models and Advanced Neural Architectures
by Oleksandr Kuznetsov, Kyrylo Chernov, Aigul Shaikhanova, Kainizhamal Iklassova and Dinara Kozhakhmetova
Computers 2025, 14(5), 165; https://doi.org/10.3390/computers14050165 - 29 Apr 2025
Viewed by 184
Abstract
Modern linguistic steganography faces the fundamental challenge of balancing embedding capacity with detection resistance, particularly against advanced AI-based steganalysis. This paper presents DeepStego, a novel steganographic system leveraging GPT-4-omni’s language modeling capabilities for secure information hiding in text. Our approach combines dynamic synonym [...] Read more.
Modern linguistic steganography faces the fundamental challenge of balancing embedding capacity with detection resistance, particularly against advanced AI-based steganalysis. This paper presents DeepStego, a novel steganographic system leveraging GPT-4-omni’s language modeling capabilities for secure information hiding in text. Our approach combines dynamic synonym generation with semantic-aware embedding to achieve superior detection resistance while maintaining text naturalness. Through comprehensive experimentation, DeepStego demonstrates significantly lower detection rates compared to existing methods across multiple state-of-the-art steganalysis techniques. DeepStego supports higher embedding capacities while maintaining strong detection resistance and semantic coherence. The system shows superior scalability compared to existing methods. Our evaluation demonstrates perfect message recovery accuracy and significant improvements in text quality preservation compared to competing approaches. These results establish DeepStego as a significant advancement in practical steganographic applications, particularly suitable for scenarios requiring secure covert communication with high embedding capacity. Full article
(This article belongs to the Special Issue Natural Language Processing (NLP) and Large Language Modelling)
Show Figures

Figure 1

13 pages, 5193 KiB  
Article
Deep-Subwavelength Composite Metamaterial Unit for Concurrent Ventilation and Broadband Acoustic Insulation
by Xiaodong Zhang, Jinhong He, Jing Nie, Yang Liu, Huiyong Yu, Qi Chen and Jianxing Yang
Materials 2025, 18(9), 2029; https://doi.org/10.3390/ma18092029 - 29 Apr 2025
Viewed by 171
Abstract
Balancing ventilation and broadband sound insulation remains a significant challenge in noise control engineering, particularly when simultaneous airflow and broadband noise reduction are required. Conventional porous absorbers and membrane-type metamaterials remain fundamentally constrained by ventilation-blocking configurations or narrow operational bandwidths. This study presents [...] Read more.
Balancing ventilation and broadband sound insulation remains a significant challenge in noise control engineering, particularly when simultaneous airflow and broadband noise reduction are required. Conventional porous absorbers and membrane-type metamaterials remain fundamentally constrained by ventilation-blocking configurations or narrow operational bandwidths. This study presents a ventilated composite metamaterial unit (VCMU) co-integrating optimized labyrinth channels and the Helmholtz resonators within a single-plane architecture. This design achieves exceptional ventilation efficiency through a central flow channel while maintaining sub-λ/30 thickness (λ/31 at 860 Hz). Coupled transfer matrix modeling and finite-element simulations reveal that Fano–Helmholtz resonance mechanisms synergistically generate broadband transmission loss (STL) spanning 860–1634 Hz, with six STL peaks in the 860 and 1634 Hz bands (mean 18.4 dB). Experimental validation via impedance tube testing confirmed excellent agreement with theoretical and simulation results. The geometric scalability allows customizable acoustic bandgaps through parametric control. This work provides a promising solution for integrated ventilation and noise reduction, with potential applications in building ventilation systems, industrial pipelines, and other noise-sensitive environments. Full article
Show Figures

Graphical abstract

Back to TopTop