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24 pages, 23907 KiB  
Article
Optimizing Data Pipelines for Green AI: A Comparative Analysis of Pandas, Polars, and PySpark for CO2 Emission Prediction
by Youssef Mekouar, Mohammed Lahmer and Mohammed Karim
Computers 2025, 14(8), 319; https://doi.org/10.3390/computers14080319 (registering DOI) - 7 Aug 2025
Abstract
This study evaluates the performance and energy trade-offs of three popular data processing libraries—Pandas, PySpark, and Polars—applied to GreenNav, a CO2 emission prediction pipeline for urban traffic. GreenNav is an eco-friendly navigation app designed to predict CO2 emissions and determine low-carbon [...] Read more.
This study evaluates the performance and energy trade-offs of three popular data processing libraries—Pandas, PySpark, and Polars—applied to GreenNav, a CO2 emission prediction pipeline for urban traffic. GreenNav is an eco-friendly navigation app designed to predict CO2 emissions and determine low-carbon routes using a hybrid CNN-LSTM model integrated into a complete pipeline for the ingestion and processing of large, heterogeneous geospatial and road data. Our study quantifies the end-to-end execution time, cumulative CPU load, and maximum RAM consumption for each library when applied to the GreenNav pipeline; it then converts these metrics into energy consumption and CO2 equivalents. Experiments conducted on datasets ranging from 100 MB to 8 GB demonstrate that Polars in lazy mode offers substantial gains, reducing the processing time by a factor of more than twenty, memory consumption by about two-thirds, and energy consumption by about 60%, while maintaining the predictive accuracy of the model (R2 ≈ 0.91). These results clearly show that the careful selection of data processing libraries can reconcile high computing performance and environmental sustainability in large-scale machine learning applications. Full article
(This article belongs to the Section Internet of Things (IoT) and Industrial IoT)
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18 pages, 860 KiB  
Article
Disruption in Southern Africa’s Money Laundering Activity by Artificial Intelligence Technologies
by Michael Masunda and Haresh Barot
J. Risk Financial Manag. 2025, 18(8), 441; https://doi.org/10.3390/jrfm18080441 - 7 Aug 2025
Abstract
The rise in illicit financial activities across the South Africa–Zimbabwe corridor, with an estimated annual loss of $3.1 billion demands advanced AI solutions to augment traditional detection methods. This study introduces FALCON, a groundbreaking hybrid transformer–GNN model that integrates temporal transaction analysis (TimeGAN) [...] Read more.
The rise in illicit financial activities across the South Africa–Zimbabwe corridor, with an estimated annual loss of $3.1 billion demands advanced AI solutions to augment traditional detection methods. This study introduces FALCON, a groundbreaking hybrid transformer–GNN model that integrates temporal transaction analysis (TimeGAN) and graph-based entity mapping (GraphSAGE) to detect illicit financial flows with unprecedented precision. By leveraging data from South Africa’s FIC, Zimbabwe’s RBZ, and SWIFT, FALCON achieved 98.7%, surpassing Random Forest (72.1%) and human auditors (64.5%), while reducing false positives to 1.2% (AUC-ROC: 0.992). Tested on 1.8 million transactions, including falsified CTRs, STRs, and Ethereum blockchain data, FALCON uncovered $450 million laundered by 23 shell companies with a cross-border detection precision of 94%, directly mitigating illicit financial flows in Southern Africa. For regulators, FALCON met FAFT standards, yielding 92% court admissibility, and its GDPR-compliant design (ε = 1.2 differential privacy) met stringent legal standards. Deployed on AWS Graviton3, FALCON processed 2 million transactions/second at $0.002 per 1000 transactions, demonstrating real-time scalability, making it cost-effective for financial institutions in emerging markets. As the first AI framework tailored for Southern Africa’s financial ecosystems, FALCON sets a new benchmark for ethical AML solutions in emerging economies with immediate applicability to CBDC supervision. The transparent validation of publicly available data underscores its potential to transform global financial crime detection. Full article
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24 pages, 1966 KiB  
Article
A Hybrid Bayesian Machine Learning Framework for Simultaneous Job Title Classification and Salary Estimation
by Wail Zita, Sami Abou El Faouz, Mohanad Alayedi and Ebrahim E. Elsayed
Symmetry 2025, 17(8), 1261; https://doi.org/10.3390/sym17081261 - 7 Aug 2025
Abstract
In today’s fast-paced and evolving job market, salary continues to play a critical role in career decision-making. The ability to accurately classify job titles and predict corresponding salary ranges is increasingly vital for organizations seeking to attract and retain top talent. This paper [...] Read more.
In today’s fast-paced and evolving job market, salary continues to play a critical role in career decision-making. The ability to accurately classify job titles and predict corresponding salary ranges is increasingly vital for organizations seeking to attract and retain top talent. This paper proposes a novel approach, the Hybrid Bayesian Model (HBM), which combines Bayesian classification with advanced regression techniques to jointly address job title identification and salary prediction. HBM is designed to capture the inherent complexity and variability of real-world job market data. The model was evaluated against established machine learning (ML) algorithms, including Random Forests (RF), Support Vector Machines (SVM), Decision Trees (DT), and multinomial naïve Bayes classifiers. Experimental results show that HBM outperforms these benchmarks, achieving 99.80% accuracy, 99.85% precision, 100% recall, and an F1 score of 98.8%. These findings highlight the potential of hybrid ML frameworks to improve labor market analytics and support data-driven decision-making in global recruitment strategies. Consequently, the suggested HBM algorithm provides high accuracy and handles the dual tasks of job title classification and salary estimation in a symmetric way. It does this by learning from class structures and mirrored decision limits in feature space. Full article
(This article belongs to the Special Issue Mathematics: Feature Papers 2025)
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10 pages, 1801 KiB  
Article
Strong Radiative Cooling Coating Containing In Situ Grown TiO2/CNT Hybrids and Polyacrylic Acid Matrix
by Jiaziyi Wang, Yong Liu, Dapeng Liu, Yong Mu and Xilai Jia
Coatings 2025, 15(8), 921; https://doi.org/10.3390/coatings15080921 (registering DOI) - 7 Aug 2025
Abstract
Traditional forced-air cooling systems suffer from excessive energy consumption and noise pollution. This study proposes an innovative passive cooling strategy through developing aqueous radiative cooling coatings made from a combination of TiO2-decorated carbon nanotube (TiO2-CNT) hybrids and polyacrylic acid [...] Read more.
Traditional forced-air cooling systems suffer from excessive energy consumption and noise pollution. This study proposes an innovative passive cooling strategy through developing aqueous radiative cooling coatings made from a combination of TiO2-decorated carbon nanotube (TiO2-CNT) hybrids and polyacrylic acid (PAA), designed to simultaneously enhance the heat dissipation and improve the mechanical strength of the coating films. Based on CNTs’ exceptional thermal conductivity and record-high infrared emissivity, bead-like TiO2-CNT architectures have been prepared as the filler in PAA. The TiO2 nanoparticles were in situ grown on CNTs, forming a rough surface that can produce asperity contacts and enhance the strength of the TiO2-CNT/PAA composite. Moreover, this composite enhanced heat dissipation and achieved remarkable cooling efficiency at a small fraction of the filler (0.1 wt%). The optimized coating demonstrated a temperature reduction of 23.8 °C at an operation temperature of 180.7 °C, coupled with obvious mechanical reinforcement (tensile strength from 13.7 MPa of pure PAA to 17.1 MPa). This work achieves the combination of CNT and TiO2 nanoparticles for strong radiative cooling coating, important for energy-efficient thermal management. Full article
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29 pages, 1494 KiB  
Article
Advanced and Robust Numerical Framework for Transient Electrohydrodynamic Discharges in Gas Insulation Systems
by Philipp Huber, Julian Hanusrichter, Paul Freden and Frank Jenau
Eng 2025, 6(8), 194; https://doi.org/10.3390/eng6080194 - 6 Aug 2025
Abstract
For the precise description of gas physical processes in high-voltage direct current (HVDC) transmission, an advanced and robust numerical framework for the simulation of transient particle densities in the course of corona discharges is developed in this work. The aim is the scalable [...] Read more.
For the precise description of gas physical processes in high-voltage direct current (HVDC) transmission, an advanced and robust numerical framework for the simulation of transient particle densities in the course of corona discharges is developed in this work. The aim is the scalable and consistent modeling of the space charge density under realistic conditions. The core component of the framework is a discontinuous Galerkin method that ensures the conservative properties of the underlying hyperbolic problem. The space charge density at the electrode surface is imposed as a dynamic boundary condition via Lagrange multipliers. To increase the numerical stability and convergence rate, a homotopy approach is also integrated. For the experimental validation, a measurement concept was realised that uses a subtraction method to specifically remove the displacement current component in the signal and thus enables an isolated recording of the transient ion current with superimposed voltage stresses. The experimental results on a small scale agree with the numerical predictions and prove the quality of the model. On this basis, the framework is transferred to hybrid HVDC overhead line systems with a bipolar design. In the event of a fault, significant transient space charge densities can be seen there, especially when superimposed with new types of voltage waveforms. The framework thus provides a reliable contribution to insulation coordination in complex HVDC systems and enables the realistic analysis of electrohydrodynamic coupling effects on an industrial scale. Full article
(This article belongs to the Section Electrical and Electronic Engineering)
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29 pages, 2960 KiB  
Article
(H-DIR)2: A Scalable Entropy-Based Framework for Anomaly Detection and Cybersecurity in Cloud IoT Data Centers
by Davide Tosi and Roberto Pazzi
Sensors 2025, 25(15), 4841; https://doi.org/10.3390/s25154841 - 6 Aug 2025
Abstract
Modern cloud-based Internet of Things (IoT) infrastructures face increasingly sophisticated and diverse cyber threats that challenge traditional detection systems in terms of scalability, adaptability, and explainability. In this paper, we present (H-DIR)2, a hybrid entropy-based framework designed to detect and mitigate [...] Read more.
Modern cloud-based Internet of Things (IoT) infrastructures face increasingly sophisticated and diverse cyber threats that challenge traditional detection systems in terms of scalability, adaptability, and explainability. In this paper, we present (H-DIR)2, a hybrid entropy-based framework designed to detect and mitigate anomalies in large-scale heterogeneous networks. The framework combines Shannon entropy analysis with Associated Random Neural Networks (ARNNs) and integrates semantic reasoning through RDF/SPARQL, all embedded within a distributed Apache Spark 3.5.0 pipeline. We validate (H-DIR)2 across three critical attack scenarios—SYN Flood (TCP), DAO-DIO (RPL), and NTP amplification (UDP)—using real-world datasets. The system achieves a mean detection latency of 247 ms and an AUC of 0.978 for SYN floods. For DAO-DIO manipulations, it increases the packet delivery ratio from 81.2% to 96.4% (p < 0.01), and for NTP amplification, it reduces the peak load by 88%. The framework achieves vertical scalability across millions of endpoints and horizontal scalability on datasets exceeding 10 TB. All code, datasets, and Docker images are provided to ensure full reproducibility. By coupling adaptive neural inference with semantic explainability, (H-DIR)2 offers a transparent and scalable solution for cloud–IoT cybersecurity, establishing a robust baseline for future developments in edge-aware and zero-day threat detection. Full article
(This article belongs to the Special Issue Privacy and Cybersecurity in IoT-Based Applications)
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31 pages, 336 KiB  
Article
Enhancing Discoverability: A Metadata Framework for Empirical Research in Theses
by Giannis Vassiliou, George Tsamis, Stavroula Chatzinikolaou, Thomas Nipurakis and Nikos Papadakis
Algorithms 2025, 18(8), 490; https://doi.org/10.3390/a18080490 - 6 Aug 2025
Abstract
Despite the significant volume of empirical research found in student-authored academic theses—particularly in the social sciences—these works are often poorly documented and difficult to discover within institutional repositories. A key reason for this is the lack of appropriate metadata frameworks that balance descriptive [...] Read more.
Despite the significant volume of empirical research found in student-authored academic theses—particularly in the social sciences—these works are often poorly documented and difficult to discover within institutional repositories. A key reason for this is the lack of appropriate metadata frameworks that balance descriptive richness with usability. General standards such as Dublin Core are too simplistic to capture critical research details, while more robust models like the Data Documentation Initiative (DDI) are too complex for non-specialist users and not designed for use with student theses. This paper presents the design and validation of a lightweight, web-based metadata framework specifically tailored to document empirical research in academic theses. We are the first to adapt existing hybrid Dublin Core–DDI approaches specifically for thesis documentation, with a novel focus on cross-methodological research and non-expert usability. The model was developed through a structured analysis of actual student theses and refined to support intuitive, structured metadata entry without requiring technical expertise. The resulting system enhances the discoverability, classification, and reuse of empirical theses within institutional repositories, offering a scalable solution to elevate the visibility of the gray literature in higher education. Full article
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50 pages, 10020 KiB  
Article
A Bio-Inspired Adaptive Probability IVYPSO Algorithm with Adaptive Strategy for Backpropagation Neural Network Optimization in Predicting High-Performance Concrete Strength
by Kaifan Zhang, Xiangyu Li, Songsong Zhang and Shuo Zhang
Biomimetics 2025, 10(8), 515; https://doi.org/10.3390/biomimetics10080515 (registering DOI) - 6 Aug 2025
Abstract
Accurately predicting the compressive strength of high-performance concrete (HPC) is critical for ensuring structural integrity and promoting sustainable construction practices. However, HPC exhibits highly complex, nonlinear, and multi-factorial interactions among its constituents (such as cement, aggregates, admixtures, and curing conditions), which pose significant [...] Read more.
Accurately predicting the compressive strength of high-performance concrete (HPC) is critical for ensuring structural integrity and promoting sustainable construction practices. However, HPC exhibits highly complex, nonlinear, and multi-factorial interactions among its constituents (such as cement, aggregates, admixtures, and curing conditions), which pose significant challenges to conventional predictive models. Traditional approaches often fail to adequately capture these intricate relationships, resulting in limited prediction accuracy and poor generalization. Moreover, the high dimensionality and noisy nature of HPC mix data increase the risk of model overfitting and convergence to local optima during optimization. To address these challenges, this study proposes a novel bio-inspired hybrid optimization model, AP-IVYPSO-BP, which is specifically designed to handle the nonlinear and complex nature of HPC strength prediction. The model integrates the ivy algorithm (IVYA) with particle swarm optimization (PSO) and incorporates an adaptive probability strategy based on fitness improvement to dynamically balance global exploration and local exploitation. This design effectively mitigates common issues such as premature convergence, slow convergence speed, and weak robustness in traditional metaheuristic algorithms when applied to complex engineering data. The AP-IVYPSO is employed to optimize the weights and biases of a backpropagation neural network (BPNN), thereby enhancing its predictive accuracy and robustness. The model was trained and validated on a dataset comprising 1030 HPC mix samples. Experimental results show that AP-IVYPSO-BP significantly outperforms traditional BPNN, PSO-BP, GA-BP, and IVY-BP models across multiple evaluation metrics. Specifically, it achieved an R2 of 0.9542, MAE of 3.0404, and RMSE of 3.7991 on the test set, demonstrating its high accuracy and reliability. These results confirm the potential of the proposed bio-inspired model in the prediction and optimization of concrete strength, offering practical value in civil engineering and materials design. Full article
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31 pages, 18795 KiB  
Review
Timber Architecture for Sustainable Futures: A Critical Review of Design and Research Challenges in the Era of Environmental and Social Transition
by Agnieszka Starzyk, Nuno D. Cortiços, Carlos C. Duarte and Przemysław Łacek
Buildings 2025, 15(15), 2774; https://doi.org/10.3390/buildings15152774 - 6 Aug 2025
Abstract
This article provides a critical review of the current design and research challenges in contemporary timber architecture. Conducted from the perspective of a designer-researcher, the review focuses on the role of wood as a material at the intersection of environmental performance, cultural meaning, [...] Read more.
This article provides a critical review of the current design and research challenges in contemporary timber architecture. Conducted from the perspective of a designer-researcher, the review focuses on the role of wood as a material at the intersection of environmental performance, cultural meaning, and spatial practice. The study adopts a conceptual, problem-oriented approach, eschewing the conventional systematic aggregation of existing data. The objective of this study is to identify, interpret and categorise the key issues that are shaping the evolving discourse on timber architecture. The analysis is based on peer-reviewed literature published between 2020 and 2025, sourced from the Scopus and Web of Science Core Collection databases. Fifteen thematic challenges have been identified and classified according to their recognition level in academic and design contexts. The subjects under discussion include well-established topics, such as life cycle assessment and carbon storage, as well as less commonly explored areas, such as symbolic durability, social acceptance, traceability, and the upcycling of low-grade wood. The review under consideration places significant emphasis on the importance of integrating technical, cultural, and perceptual dimensions when evaluating timber architecture. The article proposes an interpretive framework combining design thinking and transdisciplinary insights. This framework aims to bridge disciplinary gaps and provide a coherent structure for understanding the complexity of timber-related challenges. The framework under discussion here encourages a broader understanding of wood as not only a sustainable building material but also a vehicle for systemic transformation in architectural culture and practice. The study’s insights may support designers, educators, and policymakers in identifying strategic priorities for the development of future-proof timber-based design practices. Full article
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12 pages, 2135 KiB  
Article
Development of Yellow Rust-Resistant and High-Yielding Bread Wheat (Triticum aestivum L.) Lines Using Marker-Assisted Backcrossing Strategies
by Bekhruz O. Ochilov, Khurshid S. Turakulov, Sodir K. Meliev, Fazliddin A. Melikuziev, Ilkham S. Aytenov, Sojida M. Murodova, Gavkhar O. Khalillaeva, Bakhodir Kh. Chinikulov, Laylo A. Azimova, Alisher M. Urinov, Ozod S. Turaev, Fakhriddin N. Kushanov, Ilkhom B. Salakhutdinov, Jinbiao Ma, Muhammad Awais and Tohir A. Bozorov
Int. J. Mol. Sci. 2025, 26(15), 7603; https://doi.org/10.3390/ijms26157603 - 6 Aug 2025
Abstract
The fungal pathogen Puccinia striiformis f. sp. tritici, which causes yellow rust disease, poses a significant economic threat to wheat production not only in Uzbekistan but also globally, leading to substantial reductions in grain yield. This study aimed to develop yellow rust-resistance [...] Read more.
The fungal pathogen Puccinia striiformis f. sp. tritici, which causes yellow rust disease, poses a significant economic threat to wheat production not only in Uzbekistan but also globally, leading to substantial reductions in grain yield. This study aimed to develop yellow rust-resistance wheat lines by introgressing Yr10 and Yr15 genes into high-yielding cultivar Grom using the marker-assisted backcrossing (MABC) method. Grom was crossed with donor genotypes Yr10/6*Avocet S and Yr15/6*Avocet S, resulting in the development of F1 generations. In the following years, the F1 hybrids were advanced to the BC2F1 and BC2F2 generations using the MABC approach. Foreground and background selection using microsatellite markers (Xpsp3000 and Barc008) were employed to identify homozygous Yr10- and Yr15-containing genotypes. The resulting BC2F2 lines, designated as Grom-Yr10 and Grom-Yr15, retained key agronomic traits of the recurrent parent cv. Grom, such as spike length (13.0–11.9 cm) and spike weight (3.23–2.92 g). Under artificial infection conditions, the selected lines showed complete resistance to yellow rust (infection type 0). The most promising BC2F2 plants were subsequently advanced to homozygous BC2F3 lines harboring the introgressed resistance genes through marker-assisted selection. This study demonstrates the effectiveness of integrating molecular marker-assisted selection with conventional breeding methods to enhance disease resistance while preserving high-yielding traits. The newly developed lines offer valuable material for future wheat improvement and contribute to sustainable agriculture and food security. Full article
(This article belongs to the Special Issue Molecular Advances in Understanding Plant-Microbe Interactions)
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24 pages, 5022 KiB  
Article
Aging-Invariant Sheep Face Recognition Through Feature Decoupling
by Suhui Liu, Chuanzhong Xuan, Zhaohui Tang, Guangpu Wang, Xinyu Gao and Zhipan Wang
Animals 2025, 15(15), 2299; https://doi.org/10.3390/ani15152299 - 6 Aug 2025
Abstract
Precise recognition of individual ovine specimens plays a pivotal role in implementing smart agricultural platforms and optimizing herd management systems. With the development of deep learning technology, sheep face recognition provides an efficient and contactless solution for individual sheep identification. However, with the [...] Read more.
Precise recognition of individual ovine specimens plays a pivotal role in implementing smart agricultural platforms and optimizing herd management systems. With the development of deep learning technology, sheep face recognition provides an efficient and contactless solution for individual sheep identification. However, with the growth of sheep, their facial features keep changing, which poses challenges for existing sheep face recognition models to maintain accuracy across the dynamic changes in facial features over time, making it difficult to meet practical needs. To address this limitation, we propose the lifelong biometric learning of the sheep face network (LBL-SheepNet), a feature decoupling network designed for continuous adaptation to ovine facial changes, and constructed a dataset of 31,200 images from 55 sheep tracked monthly from 1 to 12 months of age. The LBL-SheepNet model addresses dynamic variations in facial features during sheep growth through a multi-module architectural framework. Firstly, a Squeeze-and-Excitation (SE) module enhances discriminative feature representation through adaptive channel-wise recalibration. Then, a nonlinear feature decoupling module employs a hybrid channel-batch attention mechanism to separate age-related features from identity-specific characteristics. Finally, a correlation analysis module utilizes adversarial learning to suppress age-biased feature interference, ensuring focus on age-invariant identifiers. Experimental results demonstrate that LBL-SheepNet achieves 95.5% identification accuracy and 95.3% average precision on the sheep face dataset. This study introduces a lifelong biometric learning (LBL) mechanism to mitigate recognition accuracy degradation caused by dynamic facial feature variations in growing sheep. By designing a feature decoupling network integrated with adversarial age-invariant learning, the proposed method addresses the performance limitations of existing models in long-term individual identification. Full article
(This article belongs to the Section Animal System and Management)
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22 pages, 7705 KiB  
Article
Implementation of SLAM-Based Online Mapping and Autonomous Trajectory Execution in Software and Hardware on the Research Platform Nimbulus-e
by Thomas Schmitz, Marcel Mayer, Theo Nonnenmacher and Matthias Schmitz
Sensors 2025, 25(15), 4830; https://doi.org/10.3390/s25154830 - 6 Aug 2025
Abstract
This paper presents the design and implementation of a SLAM-based online mapping and autonomous trajectory execution system for the Nimbulus-e, a concept vehicle designed for agile maneuvering in confined spaces. The Nimbulus-e uses individual steer-by-wire corner modules with in-wheel motors at all four [...] Read more.
This paper presents the design and implementation of a SLAM-based online mapping and autonomous trajectory execution system for the Nimbulus-e, a concept vehicle designed for agile maneuvering in confined spaces. The Nimbulus-e uses individual steer-by-wire corner modules with in-wheel motors at all four corners. The associated eight joint variables serve as control inputs, allowing precise trajectory following. These control inputs can be derived from the vehicle’s trajectory using nonholonomic constraints. A LiDAR sensor is used to map the environment and detect obstacles. The system processes LiDAR data in real time, continuously updating the environment map and enabling localization within the environment. The inclusion of vehicle odometry data significantly reduces computation time and improves accuracy compared to a purely visual approach. The A* and Hybrid A* algorithms are used for trajectory planning and optimization, ensuring smooth vehicle movement. The implementation is validated through both full vehicle simulations using an ADAMS Car—MATLABco-simulation and a scaled physical prototype, demonstrating the effectiveness of the system in navigating complex environments. This work contributes to the field of autonomous systems by demonstrating the potential of combining advanced sensor technologies with innovative control algorithms to achieve reliable and efficient navigation. Future developments will focus on improving the robustness of the system by implementing a robust closed-loop controller and exploring additional applications in dense urban traffic and agricultural operations. Full article
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31 pages, 34013 KiB  
Article
Vision-Based 6D Pose Analytics Solution for High-Precision Industrial Robot Pick-and-Place Applications
by Balamurugan Balasubramanian and Kamil Cetin
Sensors 2025, 25(15), 4824; https://doi.org/10.3390/s25154824 - 6 Aug 2025
Abstract
High-precision 6D pose estimation for pick-and-place operations remains a critical problem for industrial robot arms in manufacturing. This study introduces an analytics-based solution for 6D pose estimation designed for a real-world industrial application: it enables the Staubli TX2-60L (manufactured by Stäubli International AG, [...] Read more.
High-precision 6D pose estimation for pick-and-place operations remains a critical problem for industrial robot arms in manufacturing. This study introduces an analytics-based solution for 6D pose estimation designed for a real-world industrial application: it enables the Staubli TX2-60L (manufactured by Stäubli International AG, Horgen, Switzerland) robot arm to pick up metal plates from various locations and place them into a precisely defined slot on a brake pad production line. The system uses a fixed eye-to-hand Intel RealSense D435 RGB-D camera (manufactured by Intel Corporation, Santa Clara, California, USA) to capture color and depth data. A robust software infrastructure developed in LabVIEW (ver.2019) integrated with the NI Vision (ver.2019) library processes the images through a series of steps, including particle filtering, equalization, and pattern matching, to determine the X-Y positions and Z-axis rotation of the object. The Z-position of the object is calculated from the camera’s intensity data, while the remaining X-Y rotation angles are determined using the angle-of-inclination analytics method. It is experimentally verified that the proposed analytical solution outperforms the hybrid-based method (YOLO-v8 combined with PnP/RANSAC algorithms). Experimental results across four distinct picking scenarios demonstrate the proposed solution’s superior accuracy, with position errors under 2 mm, orientation errors below 1°, and a perfect success rate in pick-and-place tasks. Full article
(This article belongs to the Section Sensors and Robotics)
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41 pages, 7308 KiB  
Review
Challenges and Opportunities for Extending Battery Pack Life Using New Algorithms and Techniques for Battery Electric Vehicles
by Pedro S. Gonzalez-Rodriguez, Jorge de J. Lozoya-Santos, Hugo G. Gonzalez-Hernandez, Luis C. Felix-Herran and Juan C. Tudon-Martinez
World Electr. Veh. J. 2025, 16(8), 442; https://doi.org/10.3390/wevj16080442 - 5 Aug 2025
Abstract
The shift from Internal Combustion Engine Vehicles (ICEVs) to Battery Electric Vehicles (BEVs) has accelerated global efforts to decarbonize transportation. However, battery degradation, high costs, and limited lifespan remain critical barriers. This review synthesizes recent innovations to extend Li-ion battery life in BEVs [...] Read more.
The shift from Internal Combustion Engine Vehicles (ICEVs) to Battery Electric Vehicles (BEVs) has accelerated global efforts to decarbonize transportation. However, battery degradation, high costs, and limited lifespan remain critical barriers. This review synthesizes recent innovations to extend Li-ion battery life in BEVs by exploring advances in degradation modeling, adaptive Battery Management Systems (BMSs), electronic component simulations, and real-world usage profiling. The authors have systematically analyzed over 80 recent studies using a PRISMA-guided review protocol. A novel comparative framework highlights gaps in current literature, particularly regarding real-world driving impacts, ripple current effects, and second-life battery applications. This review article critically compares model-driven, data-driven, and hybrid model approaches, emphasizing trade-offs in interpretability, accuracy, and deployment feasibility. Finally, the review links battery life extension to broader sustainability metrics, including circular economy models and predictive maintenance algorithms. This review offers actionable insights for researchers, engineers, and policymakers aiming to design longer-lasting and more sustainable electric mobility systems. Full article
(This article belongs to the Special Issue Electric Vehicle Battery Pack and Electric Motor Sizing Methods)
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21 pages, 1209 KiB  
Article
Sustainable Membrane-Based Acoustic Metamaterials Using Cork and Honeycomb Structures: Experimental and Numerical Characterization
by Giuseppe Ciaburro and Virginia Puyana-Romero
Buildings 2025, 15(15), 2763; https://doi.org/10.3390/buildings15152763 - 5 Aug 2025
Abstract
This work presents the experimental and numerical investigation of a novel acoustic metamaterial based on sustainable and biodegradable components: cork membranes and honeycomb cores made from treated aramid paper. The design exploits the principle of localized resonance induced by tensioned membranes coupled with [...] Read more.
This work presents the experimental and numerical investigation of a novel acoustic metamaterial based on sustainable and biodegradable components: cork membranes and honeycomb cores made from treated aramid paper. The design exploits the principle of localized resonance induced by tensioned membranes coupled with subwavelength cavities, aiming to achieve high sound absorption at low (250–500 Hz) and mid frequencies (500–1400 Hz) with minimal thickness and environmental impact. Three configurations were analyzed, varying the number of membranes (one, two, and three) while keeping a constant core structure composed of three stacked honeycomb layers. Acoustic performance was measured using an impedance tube (Kundt’s tube), focusing on the normal-incidence sound absorption coefficient in the frequency range of 250–1400 Hz. The results demonstrate that increasing the number of membranes introduces multiple resonances and broadens the effective absorption bandwidth. Numerical simulations were performed to predict pressure field distributions. The numerical model showed good agreement with the experimental data, validating the underlying physical model of coupled mass–spring resonators. The proposed metamaterial offers a low-cost, modular, and fully recyclable solution for indoor sound control, combining acoustic performance and environmental sustainability. These findings offer promising perspectives for the application of bio-based metamaterials in architecture and eco-design. Further developments will address durability, high-frequency absorption, and integration in hybrid soundproofing systems. Full article
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