bluuebunny/crossref_metadata_2025 huggingface dataset:
Residual Stress Model in Laser Direct Deposition Based on Energy Equation
Authors: Cheng Manping , Zou Xi , Gong Muhong , Chang Tengfei , Cao Qi , Ju Houlai
Published: February 2025

In this paper, 316 L stainless steel deposited samples were fabricated by direct layer deposition (DED) using both continuous-wave (CW) and pulsed-wave (PW) laser modes. Effects of laser modes on residual stress of deposited samples were investigated. On this basis, a mathematical model of thermal stress evolution during DED was established for the first time based on the energy equation. The variation law of thermal stress on the top of the substrate under multi-material and multi-process conditions was qualitatively predicted and the corresponding residual stress reduction mechanism has been studied using this model. Meanwhile, in situ thermal strain evolution is used to prove the correctness of the mathematical model. This model lays the foundation for predicting the thermal stress evolution and the magnitude of the residual stress of deposited samples under multi-material and process conditions during DED.
Coatings for Advanced Devices
Authors: Long Xu
Published: February 2025

The continuous evolution of coating materials is critically important to driving innovative technologies in various domains, especially advanced electrical devices [...]
A Literature Review on Security in the Internet of Things: Identifying and Analysing Critical Categories
Authors: Sebestyen Hannelore , Popescu Daniela Elena , Zmaranda Rodica Doina
Published: February 2025

With the proliferation of IoT-based applications, security requirements are becoming increasingly stringent. Given the diversity of such systems, selecting the most appropriate solutions and technologies to address the challenges is a complex activity. This paper provides an exhaustive evaluation of existing security challenges related to the IoT domain, analysing studies published between 2021 and 2025. This review explores the evolving landscape of IoT security, identifying key focus areas, challenges, and proposed solutions as presented in recent research. Through this analysis, the review categorizes IoT security efforts into six main areas: emerging technologies (35.2% of studies), securing identity management (19.3%), attack detection (17.9%), data management and protection (8.3%), communication and networking (13.8%), and risk management (5.5%). These percentages highlight the research community’s focus and indicate areas requiring further investigation. From leveraging machine learning and blockchain for anomaly detection and real-time threat response to optimising lightweight algorithms for resource-limited devices, researchers propose innovative and adaptive solutions to address emerging threats. The review underscores the integration of advanced technologies to enhance IoT system security, while also highlighting ongoing challenges. The paper concludes with a synthesis of security challenges and threats of each identified category, along with their solutions, aiming to support decision-making during the design approach of IoT-based applications and to guide future research toward comprehensive and efficient IoT frameworks.
Automated Disassembly of Waste Printed Circuit Boards: The Role of Edge Computing and IoT
Authors: Mohsin Muhammad , Rovetta Stefano , Masulli Francesco , Cabri Alberto
Published: February 2025

The ever-growing volume of global electronic waste (e-waste) poses significant environmental and health challenges. Printed circuit boards (PCBs), which form the core of most electronic devices, contain valuable metals as well as hazardous materials. The efficient disassembly and recycling of e-waste is critical for both economic and environmental sustainability. The traditional manual disassembly methods are time-consuming, labor-intensive, and often hazardous. The integration of edge computing and the Internet of Things (IoT) provides a novel approach to automating the disassembly process, potentially transforming the way e-waste is managed. Automated disassembly of WPCBs involves the use of advanced technologies, specifically edge computing and the IoT, to streamline the recycling process. This strategy aims to improve the efficiency and sustainability of e-waste management by leveraging real-time data analytics and intelligent decision-making at the edge of the network. This paper explores the application of edge computing and the IoT in the automated disassembly of WPCBs, discussing the technological framework, benefits, challenges, and future prospects. The experimental results show that the YOLOv10 model achieves 99.9% average precision (AP), enabling accurate real-time detection of electronic components, which greatly facilitates the automated disassembly process.
Correction: Ghashghaei et al. Enhancing the Security of Classical Communication with Post-Quantum Authenticated-Encryption Schemes for the Quantum Key Distribution. Computers 2024, 13, 163
Authors: Ghashghaei Farshad Rahimi , Ahmed Yussuf , Elmrabit Nebrase , Yousefi Mehdi
Published: February 2025

In the original publication [...]
Impact of Nanoparticles on Heat Transfer Enhancement and Thermal Performance Improvement in HTS Power Transformers
Authors: Mahamed Mahdi , Seyyedbarzegar Seyyedmeysam
Published: February 2025

Despite the numerous benefits of high-temperature superconducting (HTS) power transformers, they are highly sensitive and vulnerable from a thermal perspective, particularly under fault current conditions due to their fault current tolerance properties. Ensuring the proper operation of the cooling system can enhance the transformer’s performance during fault and overload conditions. To improve the thermal management of this transformer in both convective heat transfer and nucleate boiling conditions, utilizing liquid nitrogen (LN2) nanofluid instead of conventional LN2 is a promising solution. In this study, a two-phase Eulerian model using ANSYS Fluent software is employed to analyze the impact of different volume fractions (VFs) of Al2O3 nanoparticles with a 40 nm diameter on the cooling performance of a power HTS transformer. The numerical simulations are conducted using the Ranz–Marshal method for heat transfer and the finite element method for solving the governing equations. Nanoparticle concentrations ranging from 0 to 1% are evaluated under various fault conditions. Additionally, the influence of nanoparticles on bubble behavior is examined, partially mitigating the blockage of cooler microchannels. The simulation reveals that adding nanoparticles to the fluid reduces the temperature of the hotspot by 29% in steady state and by 34–52% under different fault currents as a result of 0–46% enhancement of nucleate boiling heat transfer, thereby improving the cooling efficiency of the transformer.
Taxonomic and Genomic Characterization of Niveibacterium terrae sp. nov., a New Species from Riparian Soil in the Seomjin River, Republic of Korea
Authors: Choi Ahyoung , Jang Sumin , Lee Soo-Yeong , Goh Jaeduk
Published: February 2025

A novel Gram stain-negative, rod-shaped, non-motile bacterium, designated strain 24SJ04S-2T, was isolated from riparian soil near the Seomjin River, Republic of Korea. Phylogenetic analysis based on the 16S rRNA gene sequence placed strain 24SJ04S-2T within the genus Niveibacterium in the family Zoogloeaceae, with high similarity to Niveibacterium umoris (97.3%) and Niveibacterium microcysteis (96.7%). Whole-genome sequencing revealed a genome size of 3.98 Mbp with a G+C content of 63.9 mol%, and genomic relatedness metrics confirmed that strain 24SJ04S-2T represents a novel species. The strain demonstrated unique growth characteristics, including tolerance to low salinity and a broad pH range (5.0 to 10.0), suggesting ecological adaptability to fluctuating riparian environments. Chemotaxonomic analysis identified ubiquinone-8 as the major quinone, with diphosphatidylglycerol, phosphatidylglycerol, and phosphatidylethanolamine as dominant polar lipids. Fatty acid profiling showed that summed feature 3 (C16:1ω7c and/or C16:1ω6c) and C16:0 were predominant. Genome analysis identified several biosynthetic gene clusters, including those potentially associated with antimicrobial and antioxidant compounds. Based on its distinct genomic and phenotypic characteristics, we propose the designation Niveibacterium terrae sp. nov. This discovery highlights the potential ecological and biotechnological roles of Niveibacterium terrae in nutrient cycling and environmental remediation in freshwater ecosystems. The type strain is 24SJ04S-2T (=KACC 23837T =FBCC-B16161T).
Prescribed Total Daily Insulin Dose and Predictors of Insulin Dose for Adults with Type 2 Diabetes on Multiple Daily Injections of Insulin: A Retrospective Cohort Study
Authors: Wright, Jr. Eugene E. , Shah Viral N. , Miller Eden , Thach Andrew , Javadi Pasha , Davies Shawn , Sieradzan Ray
Published: February 2025

Background/Objectives: Limited evidence is available regarding insulin total daily dose (TDD), or the factors associated with TDD, among adults with type 2 diabetes (T2D) using multiple daily injections of insulin (MDI). Our aim was to determine the percentage of adults in the United States (US) with T2D who are prescribed MDI, their prescribed insulin TDD, and potential factors associated with TDD. Methods: This retrospective cohort study used deidentified data from the US IQVIA ambulatory electronic medical record database to study adults (≥18 years) with T2D initiating MDI (≥3 daily basal-plus-prandial insulin injections) from 1 January 2017 to 1 July 2022. The TDD was calculated from first evidence of MDI (index date). We used a generalized linear model regression analysis to model the relationship between TDD and clinically relevant factors associated with TDD. Results: During the study period, of 3,339,663 adults with T2D, 451,769 (13.5%) had ≥1 basal insulin prescriptions, 206,000 (6.2%) had both basal and prandial insulin prescriptions, and 41,215 (1.2%) were prescribed MDI (mean age, 58 years; 52% women; 62% White/Caucasian, 14% African American; mean body mass index [BMI], 34 kg/m2). Mean TDD was 96 units (1.0 units/kg/day); median TDD was 80 units (interquartile range, 54–124). In the regression analysis (model R2, 0.14), factors predicting lower TDD included female sex, African American race, and prior 6-month (pre-index) prescriptions of sulfonylurea, metformin, or 2–3 noninsulin glucose-lowering medications. Predictors of greater TDD included increasing BMI, age 30–64 years, and pre-index SGLT2 inhibitor or GLP-1 RA prescription. Conclusions: Among US adults with T2D, 1.2% were prescribed MDI, with a wide range of TDD and median TDD of 80 units. Further research in other populations and using other data sources is warranted to explore prescribed insulin TDD for T2D and to examine other potentially relevant predictors of TDD.
Automated Imaging of Cataract Surgery Using Artificial Intelligence
Authors: Kim Young Jae , Hwang Sung Ha , Kim Kwang Gi , Nam Dong Heun
Published: February 2025

Objectives: This study proposes a state-of-the-art technology to estimate a set of parameters to automatically display an optimized image on a screen during cataract surgery. Methods: We constructed an architecture comprising two stages to estimate the parameters for realizing the optimized image. The Pix2Pix approach was first introduced to generate fake images that mimic the optimal image. This part can be considered a preliminary step; it uses training datasets comprising both an original microscopy image as the input data and an optimally tuned image by ophthalmologists as the label data. The second part of the architecture was inspired by ensemble learning, in which two ResNet-50 models were trained in parallel using fake images obtained in the previous step and unprocessed images. Each set of features extracted by the ensemble-like scheme was exploited for the regression of the optimal parameters. Results: The fidelity of our method was confirmed through relevant quantitative assessments (NMSE 121.052 ± 181.227, PSNR 29.887 ± 4.682, SSIM 0.965 ± 0.047). Conclusions: Subsequently, surgeons reassured that the objects to be highlighted on the screen for cataract surgery were faithfully visualized by the automatically estimated parameters.
Data-Based Parametrization for Affine GARCH Models Across Multiple Time Scales—Roughness Implications
Authors: Escobar-Anel Marcos , Ferrando Sebastian , Li Fuyu , Xu Ke
Published: February 2025

This paper revisits the topic of time-scale parameterizations of the Heston–Nandi GARCH (1,1) model to create a new, theoretically valid setting compatible with real financial data. We first estimate parameters using three US market indices and six frequencies to let data reveal the correct, data-implied, time-scale parameterizations. We compared the data-implied parametrization to two popular candidates in the literature, demonstrating structurally different continuous-time limits, i.e., the data favor fractional Brownian motion (fBM)—instead of the standard Brownian motion (BM)-based parametrization. We then propose a theoretically flexible time-scale parameterization compatible with this fBM behavior. In this context, a fractional derivative analysis of our empirically based parametrization is performed, confirming an anomalous diffusion in the continuous-time limit. Such a finding is yet another endorsement of the recent and popular stylized fact known as rough volatility.
Renewable Energy Transition on Employment Dynamics in BRICS Nations
Authors: Hlongwane Nyiko Worship , Khobai Hlalefang
Published: February 2025

As the world transitions towards a low-carbon economy, understanding the employment implications of renewable energy growth is crucial, particularly in emerging economies like the BRICS nations, where energy demand and employment pressures are rapidly evolving. The justification for this study lies in the critical need to understand the employment effects of renewable energy growth in emerging economies, particularly in the BRICS nations, which account for a significant share of global energy demand and are poised to drive the next wave of renewable energy adoption. As these countries navigate the challenges of energy transition, employment creation, and sustainable development, this research aims to provide timely and actionable insights for policymakers, industry stakeholders, and researchers seeking to optimize the employment benefits of renewable energy growth in these regions. The purpose of this study is to investigate the impact of disaggregated renewable energy (solar, hydro, wind, nuclear, and other renewables including bioenergy) on employment dynamics in BRICS nations, so as to provide empirical evidence on the employment effects of renewable energy growth in these regions. The key findings from the study are summarized as follows: Hydro contributes positively to employment creation in BRICS nations, with FMOLS (0.78%), DOLS (2.06%), and PCSE (0.61%) results showing significant positive effects. Solar contributes positively to employment creation in BRICS nations, with FMOLS (1.99%) and DOLS (9.60%) results showing significant positive effects, although country-specific results are mixed. Economic growth contributes positively to employment creation in BRICS nations, with FMOLS (32.93%), DOLS (36.86%), and PCSE (27.68%) results showing significant positive effects. Wind contributes negatively to employment creation in BRICS nations at the aggregate level (FMOLS, −0.66%), but has positive effects in some countries (Brazil, China, Russia, and South Africa). Nuclear contributes negatively to employment creation in BRICS nations at the aggregate level (FMOLS, −0.47%; PCSE, −1.04%), but has positive effects in some countries (Russia, India, China, and South Africa). Other Renewables contribute negatively to employment creation in BRICS nations, with FMOLS (−2.57%) and PCSE (−4.77%) results showing significant negative effects. Policymakers in BRICS nations should prioritize investments in hydropower, solar power, and wind power to leverage their job creation potential and promote sustainable economic growth. Additionally, governments should implement policies to support the development of other renewable energy sources, such as bioenergy, geothermal, and tidal power, to increase their job creation potential. Furthermore, policymakers should promote economic growth through green investments and sustainable development initiatives to maximize employment creation in the renewable energy sector.
Interrelationships Among Government Participation, Population and Growth of per Capita Income: Inquiry on Top Twenty Income-Holding Countries in the World
Authors: Das Ramesh Chandra
Published: February 2025

The literature on growth in economics encompasses two main facets of thinking: the applicability of diminishing productivity of capital, as has been in the neoclassical growth model with exogenous technological progress, and the applicability of non-diminishing productivity of capital, as has been in the endogenous growth models. The main conclusion of the former is the cross-country convergence to a common steady state while that of the latter is non-convergence. The tremendous history of the growth of the world’s so-called developed economies in the 1980s, diverging with the so-called backward economies, has nullified the applicability of the neoclassical growth model and justified non-steady state positive per capita growth of income and consumption through endogenous technological progress in terms of knowledge capital, human capital, good public institutions, etc. The present study aims to examine whether per capita income growth is explained by the size of government intervention coupled with the working population size in the world’s top twenty countries in terms of aggregate income. With the theoretical setup of the model and using empirical tools, such as cointegration, error correction and causality in a vector autoregression structure, this study reveals that eighteen countries maintain long-run relationships among per capita income growth, government participation, population and the interaction factors between government intervention and population, excepting Germany and Canada. Further, in the short run, for eleven countries on the list, there are instances in which public institutions associated with the population and the interaction term have a causal influence on the growth of per capita income. The empirical results relating to income growth, thus, have sustainability implications.
Semantic Segmentation of Key Categories in Transmission Line Corridor Point Clouds Based on EMAFL-PTv3
Authors: Lu Li , Wang Linong , Wu Shaocheng , Zu Shengxuan , Ai Yuhao , Song Bin
Published: February 2025

Accurate and efficient segmentation of key categories of transmission line corridor point clouds is one of the prerequisite technologies for the application of transmission line drone inspection. However, current semantic segmentation methods are limited to a few categories, involve cumbersome processes, and exhibit low accuracy. To address these issues, this paper proposes EMAFL-PTv3, a deep learning model for semantic segmentation of transmission line corridor point clouds. Built upon Point Transformer v3 (PTv3), EMAFL-PTv3 integrates Efficient Multi-Scale Attention (EMA) to enhance feature extraction at different scales, incorporates Focal Loss to mitigate class imbalance, and achieves accurate segmentation into five categories: ground, ground wire, insulator string, pylon, and transmission line. EMAFL-PTv3 is evaluated on a dataset of 40 spans of transmission line corridor point clouds collected by a drone in Wuhan and Xiangyang, Hubei Province. Experimental results demonstrate that EMAFL-PTv3 outperforms PTv3 in all categories, with notable improvements in the more challenging categories: insulator string (IoU 67.25%) and Pylon (IoU 91.77%), showing increases of 7.06% and 11.39%, respectively. The mIoU, mA, and OA scores reach 90.46%, 92.86%, and 98.07%, representing increases of 5.49%, 2.75%, and 2.44% over PTv3, respectively, proving its superior performance.
VCONV: A Convolutional Neural Network Accelerator for FPGAs
Authors: Neelam Srikanth , Amalin Prince A.
Published: February 2025

Field Programmable Gate Arrays (FPGAs), with their wide portfolio of configurable resources such as Look-Up Tables (LUTs), Block Random Access Memory (BRAM), and Digital Signal Processing (DSP) blocks, are the best option for custom hardware designs. Their low power consumption and cost-effectiveness give them an advantage over Graphics Processing Units (GPUs) and Central Processing Units (CPUs) in providing efficient accelerator solutions for compute-intensive Convolutional Neural Network (CNN) models. CNN accelerators are dedicated hardware modules capable of performing compute operations such as convolution, activation, normalization, and pooling with minimal intervention from a host. Designing accelerators for deeper CNN models requires FPGAs with vast resources, which impact its advantages in terms of power and price. In this paper, we propose the VCONV Intellectual Property (IP), an efficient and scalable CNN accelerator architecture for applications where power and cost are constraints. VCONV, with its configurable design, can be deployed across multiple smaller FPGAs instead of a single large FPGA to provide better control over cost and parallel processing. VCONV can be deployed across heterogeneous FPGAs, depending on the performance requirements of each layer. The IP’s performance can be evaluated using embedded monitors to ensure that the accelerator is configured to achieve the best performance. VCONV can be configured for data type format, convolution engine (CE) and convolution unit (CU) configurations, as well as the sequence of operations based on the CNN model and layer. VCONV can be interfaced through the Advanced Peripheral Bus (APB) for configuration and the Advanced eXtensible Interface (AXI) stream for data transfers. The IP was implemented and validated on the Avnet Zedboard and tested on the first layer of AlexNet, VGG16, and ResNet18 with multiple CE configurations, demonstrating 100% performance from MAC units with no idle time. We also synthesized multiple VCONV instances required for AlexNet, achieving the lowest BRAM utilization of just 1.64 Mb and deriving a performance of 56GOPs.
A Dynamic-Selection-Based, Retrieval-Augmented Generation Framework: Enhancing Multi-Document Question-Answering for Commercial Applications
Authors: Kwon Mincheol , Bang Jimin , Hwang Seyoung , Jang Junghoon , Lee Woosin
Published: February 2025

Commercial multi-document question-answering (QA) applications require a high multi-document retrieval performance, while simultaneously minimizing Application Programming Interface (API) usage costs of large language models (LLMs) and system complexity. To address this need, we designed the Dynamic-Selection-based, Retrieval-Augmented Generation (DS-RAG) framework, which consists of two key modules: an Entity-Preserving Question Decomposition (EPQD) module that effectively decomposes questions while preserving the entities of the original user’s question to reduce unnecessary retrieval and enhance performance, and a Dynamic Input Context Selection (DICS) module that optimizes the LLM input context based on the content of the user’s question, thereby minimizing API usage. We evaluated the proposed framework on a newly constructed dataset containing questions that require up to four multi-document retrievals. Experimental results demonstrated the new framework’s superior performance in terms of retrieval quality, input context optimization, and final answer generation compared to existing approaches. Consequently, the DS-RAG framework can be leveraged to develop domain-specific commercial QA applications in the future.
DeepGuard: Identification and Attribution of AI-Generated Synthetic Images
Authors: Namani Yasmine , Reghioua Ikram , Bendiab Gueltoum , Labiod Mohamed Aymen , Shiaeles Stavros
Published: February 2025

Text-to-image (T2I) synthesis, driven by advancements in deep learning and generative models, has seen significant improvements, enabling the creation of highly realistic images from textual descriptions. However, this rapid development brings challenges in distinguishing synthetic images from genuine ones, raising concerns in critical areas such as security, privacy, and digital forensics. To address these concerns and ensure the reliability and authenticity of data, this paper conducts a systematic study on detecting fake images generated by text-to-image synthesis models. Specifically, it evaluates the effectiveness of deep learning methods that leverage ensemble learning for detecting fake images. Additionally, it introduces a multi-classification technique to attribute fake images to their source models, thereby enabling accountability for model misuse. The effectiveness of these methods is assessed through extensive simulations and proof-of-concept experiments. The results reveal that these methods can effectively detect fake images and associate them with their respective generation models, achieving impressive accuracy rates ranging from 98.00% to 99.87% on our custom dataset, “DeepGuardDB”. These findings highlight the potential of the proposed techniques to mitigate synthetic media risks, ensuring a safer digital space with preserved authenticity across various domains, including journalism, legal forensics, and public safety.
An Optimal-Transport-Based Multimodal Big Data Clustering
Authors: Yang Zheng , Shi Chongyang , Guan Ying
Published: February 2025

Multimodal clustering achieves outstanding performance in various applications by aggregating information from heterogeneous devices. However, previous methods rely on strong-notion distances to fuse crossmodal complementary knowledge, established on a fragile assumption about the existence of a ubiquitous non-negligible intersection between heterogeneous manifolds of modalities. Due to this unstable theoretical basis, previous methods are essentially challenged by limited performance on general multimodal data. To address this challenge, an optimal-transport-based multimodal clustering (OTMC) method is defined as the optimal transport (OT) from multimodal data distributions to clustering distributions, which leverages a weak-topology measure to capture complementary knowledge with clear discriminative structures. OTMC consists of a modality-specific OT delivering private structures and a modality-common OT delivering shared structures, which transports category structures scattered in manifolds of each modality and all modalities to common prototypes, respectively. Furthermore, variational solutions to OTMC are derived by matching the data-prototype joint distribution, which induces the multimodal OT clustering network, to capture discriminative structures. Finally, the experimental results from four real-world datasets demonstrate the superiority of OTMC, helped by never relying on the phantom of heterogeneous manifold intersections. In particular, OTMC obtains 92.15% ACC, 84.96% NMI, and 83.35% ARI on Handwritten, improving by 2.25%, 2.82%, and 3.28%, respectively.
State of the Art in Parallel and Distributed Systems: Emerging Trends and Challenges
Authors: Dai Fei , Hossain Md Akbar , Wang Yi
Published: February 2025

Driven by rapid advancements in interconnection, packaging, integration, and computing technologies, parallel and distributed systems have significantly evolved in recent years. These systems have become essential for addressing modern computational demands, offering enhanced processing power, scalability, and resource efficiency. This paper provides a comprehensive overview of parallel and distributed systems, exploring their interrelationships, their key distinctions, and the emerging trends shaping their evolution. We analyse four parallel computing paradigms—heterogeneous computing, quantum computing, neuromorphic computing, and optical computing—and examine emerging distributed systems such as blockchain, serverless computing, and cloud-native architectures. The associated challenges are highlighted, and potential future directions are outlined. This work serves as a valuable resource for researchers and practitioners aiming to stay informed about trends in parallel and distributed computing while understanding the challenges and future developments in the field.
Lattice-Based Group Signature with VLR for Anonymous Medical Service Evaluation System
Authors: Gao Wen , Ren Simeng , Liu Zhaoyang , Qin Baodong , Dong Xiaoli , Zhao Zhen
Published: February 2025

The medical industry has made significant advancements in recent years. However, the lack of accountability in medical management has resulted in systemic deficiencies, which have adversely affected patient trust and contributed to an increase in medical disputes. As a result, there is a growing emphasis on managing the quality of medical services, particularly in enhancing patient experience. To address these challenges, we propose a new system for evaluating health services. This system will allow patients to anonymously rate the services they receive while also providing doctors the opportunity to appeal specific reviews. The hospital handles the evaluations and appeals through the management of the cloud platform. We propose a new scheme to assist the work of the platform, which is a lattice-based group signature with verifier-local revocation (VLR-GS). Most of the work on VLR-GS has focused on the random oracle model (ROM) or using non-interactive zero-knowledge proofs (NIZKs). Our construction is anonymous and traceable in the standard model under the hardness of the learning with errors problem and short integer solution problem. Furthermore, theoretically analyzing it has practical significance in both security and efficiency. In conclusion, the proposed scheme establishes a secure and privacy-oriented platform for an anonymous medical service evaluation system, with the goal of fostering patient trust and improving hospital service quality within the healthcare sector.
Aspect-Based Sentiment Analysis with Enhanced Opinion Tree Parsing and Parameter-Efficient Fine-Tuning for Edge AI
Authors: Liao Shih-wei , Wang Ching-Shun , Yeh Chun-Chao , Lin Jeng-Wei
Published: February 2025

Understanding user opinions from user comments or reviews in social media text mining is essential for marketing campaigns and many other applications. However, analyzing social media user comments presents significant challenges due to the complexity of discerning relationships between opinions and aspects, particularly when comments vary greatly in length. To effectively explore aspects and opinions in the sentences, techniques based on mining opinion sentiment of the referred aspects (implicitly or explicitly) in the user comments with ACOS (aspect-category-opinion-sentiment) quadruple extraction have been proposed. Among many others, the opinion tree parsing (OTP) scheme has been shown to be effective and efficient for the ACOS quadruple extraction task in aspect-based sentiment analysis (ABAS). In this study, we continue the efforts to design an efficient ABSA scheme. We extend the original OTP scheme further with richer context parsing rules, utilizing conjunctions and semantic modifiers to provide more context information in the sentence and thus effectively improving the accuracy of the analysis. Meanwhile, regarding the limitations of computation resources for edge devices in edge computing scenario, we also investigate the trade-off between computation saving (in terms of the percentage of model parameters to be updated) and the model’s performance (in terms of inference accuracy) on the proposed scheme under PEFT (parameter-efficient fine-tuning). We evaluate the proposed scheme on publicly available ACOS datasets. Experiment results show that the proposed enhanced OTP (eOTP) model improves the OTP scheme both in precision and recall measurements on the public ACOS datasets. Meanwhile, in the design trade-off evaluation for resource-constrained devices, the experiment results show that, in model training, eOTP requires very limited parameters (less than 1%) to be retrained by keeping most of the parameters frozen (not modified) in the fine-tuning process, at the cost of a slight performance drop (around 4%) in F1-score compared with the case of full fine-tuning. These demonstrate that the proposed scheme is efficient and feasible for resource-constrained scenarios such as for mobile edge/fog computing services.
The Effectiveness of Generative Adversarial Network-Based Oversampling Methods for Imbalanced Multi-Class Credit Score Classification
Authors: Adiputra I Nyoman Mahayasa , Lin Pei-Chun , Wanchai Paweena
Published: February 2025

Credit score models are essential tools for evaluating creditworthiness and mitigating financial risks. However, the imbalanced nature of multi-class credit score datasets poses significant challenges for traditional classification algorithms, leading to poor performance in minority classes. This study explores the effectiveness of Generative Adversarial Network (GAN)-based oversampling methods, including CTGAN, CopulaGAN, WGAN-GP, and DraGAN, in addressing this issue. By synthesizing realistic data for minority classes and integrating it with majority class data, the study benchmarks these GAN-based methods across classical (KNN, Decision Tree, Logistic Regression) and ensemble machine learning models (XGBoost, Random Forest, LightGBM). Evaluation metrics such as accuracy and F1-score reveal that WGAN-GP consistently achieves superior performance, especially when combined with Random Forest, outperforming other methods in balancing dataset representation and enhancing classification accuracy. The results showed that WGAN-GP + RF achieved 0.873 in accuracy, 0.936 F1-score in the “good” class, 0.806 F1-score in the “poor” class, and 0.816 F1-score in the “standard” class. The findings underscore the potential of GAN-based oversampling in improving multi-class credit score classification and highlight future directions, including hybrid sampling and cost-sensitive learning, to address remaining challenges.
Smart Tuning of Control System Parameters for a Grid Connected Converter: A Robust Artificial Neural Network Approach
Authors: Rehimi Sharara , Bevrani Hassan , Urabe Chiyori T. , Kato Takeyoshi , Kato Toshiji
Published: February 2025

The reliable and effective operation of modern power grids is highly dependent on accurately adjusting the control system parameters of power converters. Traditional approaches to parameter tuning often depend on analytical models and offline optimization, which may not fully describe the intricate dynamics and nonlinearities seen in real-world modern power grids. This paper presents an innovative method for intelligently adjusting the control system settings of converters in a modern power grid. The proposed approach utilizes machine learning methods, particularly robust artificial neural networks, to tune the converter control parameters and improve the overall modern power grid performance. This intelligent tuning system can obtain ideal parameters for stable, economical, and resilient modern power grid operation under different operating circumstances and disturbances by training the neural network models robustly using detailed simulation data and real-time measurements. This study provides a comprehensive description of the intricate structure of the intelligent tuning framework, including the neural network models and the robust methods. The proposed approach’s usefulness in enhancing the modern power grid frequency control, active power regulation, and transient response is validated via comprehensive case studies in comparison to existing parameter tuning approaches. The performed simulation and laboratory real-time experiments indicate that the smart tuning system is adaptable and resilient, making it a potential alternative for improving the stability and performance of modern power grids.
Modular DC-DC Converter with Adaptable Fast Controller for Supercapacitor Energy Storage Integration into DC Microgrid
Authors: Kroičs Kaspars , Gaspersons Kristiāns , Zdanowski Mariusz
Published: February 2025

Supercapacitors are well suited for braking energy recovery in electrical drive applications and for voltage sag compensation. For voltage-sensitive devices, only a small voltage deviation can be acceptable, and therefore the voltage controller should be fast. This paper analyzes the design of such a controller for DC bus voltage stabilization considering a GaN transistor-based converter with a 400 kHz switching frequency. Inner and outer loop controller design considering delays caused by the digital nature of the controllers has been analyzed in the paper. This paper proposes to use adaptable controller for the outer voltage loop, thus increasing the stability and reducing the calculation time. The 2p2z controller has been developed for the current loop, achieving a 20 kHz bandwidth and a response time of less than 0.2 ms. A 2p2z controller with adaptable coefficients has been developed for the outer voltage loop, achieving a 2 kHz bandwidth and a response time of 2 ms. Voltage controller digital implementation is split into two switching cycles, thus decreasing the required time for current and voltage control loops implementation from 2.1 µs to 1.5 µs. This allows calculating three current control loops for three-phase interleaved converter control. By combining such three-phase converters in parallel, it is possible to develop modular six-, nine-, or even more-phase interleaved DC-DC converters for high-current applications.
Real-Time Gesture-Based Hand Landmark Detection for Optimized Mobile Photo Capture and Synchronization
Authors: Marques Pedro , Váz Paulo , Silva José , Martins Pedro , Abbasi Maryam
Published: February 2025

Gesture recognition technology has emerged as a transformative solution for natural and intuitive human–computer interaction (HCI), offering touch-free operation across diverse fields such as healthcare, gaming, and smart home systems. In mobile contexts, where hygiene, convenience, and the ability to operate under resource constraints are critical, hand gesture recognition provides a compelling alternative to traditional touch-based interfaces. However, implementing effective gesture recognition in real-world mobile settings involves challenges such as limited computational power, varying environmental conditions, and the requirement for robust offline–online data management. In this study, we introduce ThumbsUp, which is a gesture-driven system, and employ a partially systematic literature review approach (inspired by core PRISMA guidelines) to identify the key research gaps in mobile gesture recognition. By incorporating insights from deep learning–based methods (e.g., CNNs and Transformers) while focusing on low resource consumption, we leverage Google’s MediaPipe in our framework for real-time detection of 21 hand landmarks and adaptive lighting pre-processing, enabling accurate recognition of a “thumbs-up” gesture. The system features a secure queue-based offline–cloud synchronization model, which ensures that the captured images and metadata (encrypted with AES-GCM) remain consistent and accessible even with intermittent connectivity. Experimental results under dynamic lighting, distance variations, and partially cluttered environments confirm the system’s superior low-light performance and decreased resource consumption compared to baseline camera applications. Additionally, we highlight the feasibility of extending ThumbsUp to incorporate AI-driven enhancements for abrupt lighting changes and, in the future, electromyographic (EMG) signals for users with motor impairments. Our comprehensive evaluation demonstrates that ThumbsUp maintains robust performance on typical mobile hardware, showing resilience to unstable network conditions and minimal reliance on high-end GPUs. These findings offer new perspectives for deploying gesture-based interfaces in the broader IoT ecosystem, thus paving the way toward secure, efficient, and inclusive mobile HCI solutions.
Revolutionizing Urban Mobility: A Systematic Review of AI, IoT, and Predictive Analytics in Adaptive Traffic Control Systems for Road Networks
Authors: Gheorghe Carmen , Soica Adrian
Published: February 2025

Urban mobility has undergone and continues to undergo a profound transformation driven by the convergence of artificial intelligence (AI), the Internet of Things (IoT), and predictive analytics in recent years. These technologies are redefining adaptive traffic control systems, enabling real-time decision-making and increasing the efficiency and safety of road networks. The main questions addressed in the review explore how the integration of advanced technologies such as IoT, AI in traffic systems, are useful in optimizing traffic flows, vehicle coordination and infrastructure adaptability in increasingly complex traffic environments. The integration of IoT-enabled devices and AI-based algorithms has been essential to enable data-driven approaches to urban traffic control. Predictive analytics improves emergency response mechanisms, improves traffic signal operations, and supports the deployment of autonomous and connected vehicles. Among the various methodologies evaluated, AI-based models combined with IoT sensors demonstrated superior performance, reducing average traffic delays by up to 30% and improving safety metrics in various urban environments. This systematic review underscores the transformative potential of integrating AI, IoT, and predictive analytics into urban traffic management, offering a blueprint for smarter, more sustainable urban transportation solutions.
Li-Ion Batteries for Electric Vehicle Applications: An Overview of Accurate State of Charge/State of Health Estimation Methods
Authors: Dannier Adolfo , Brando Gianluca , Ribera Mattia , Spina Ivan
Published: February 2025

Road transport significantly contributes to greenhouse gas emissions in all places where it is used and therefore also in Europe, prompting the EU to set ambitious objectives for CO2 reduction. In order to reach these objectives, the automotive industry is transitioning to electric vehicles, utilizing electric powertrains powered by battery packs. However, the longevity and reliability of these batteries are critical concerns. This review paper focuses on the advanced diagnostic techniques for effective battery State of Charge (SoC) and State of Health (SoH) monitoring. Accurate SoC/SoH estimation is crucial for optimizing battery performance, avoiding premature degradation, and ensuring driver safety. By investigating these areas, this paper aims to contribute to the development of more sustainable and durable electric vehicles, supporting the transition to cleaner transportation systems.
Proposal of a Model for Improving the Management of the Wind Energy Industry in the Context of Sustainable Development Challenges
Authors: Pabian Aleksander , Pabian Barbara , Bilińska Katarzyna
Published: February 2025

The aim of the article is to provide recommendations for the implementation of sustainable management principles into the practice of various companies operating in the onshore wind energy industry. The desk research method was used in the study, as well as free interviews which were conducted with 30 people who are directly concerned with the issues related to the expansion of the wind farm network—they live in or plan to move to the vicinity of wind farms. The results obtained show that onshore wind energy should not be perceived solely through the prism of the sustainable operation of turbines and wind farms. As the interviews with the interlocutors showed, wind energy has also disadvantages and is a source of threats. The authors believe that the full sustainability of the wind energy industry will only occur when all economic entities, both directly and indirectly related to this sector, i.e., those involved in the design, production, transport, assembly, operation, supervision, and decommissioning of wind turbines, implement the goals of sustainable development by implementing systemic management solutions into their practice. This will be facilitated by the model approach to sustainable management presented in this article. In this context, the article contributes to the theory of sustainable development and management. It has also a practical value. There are specific solutions indicated that will facilitate the transformation of companies currently operating in the onshore wind energy industry into their fully sustainable counterparts. Their usage and implementation in the everyday practice of companies will contribute to increased energy security and the protection of the planet’s resources.
Experimental Study on R290 Performance of an Integrated Thermal Management System for Electric Vehicle
Authors: Luo Zihao , Xiong Shusheng , Wen Min , Zhao Jiahao , Zhang Yifei
Published: February 2025

Integrated thermal management system (ITMS) technology for electric vehicles (EV) has become a major industry research direction. However, R290 refrigerants are still not applied on a large scale in EVs. Therefore, we developed a suitable thermal management system for R290 in this study. This architecture adapts an unusual indirect design, which can coordinate the heat between the air conditioner, battery pack, and electric motor. We focused on heat pump air conditioning systems for EV thermal management; thus, we carried out the performance analysis of R290 under the cooling and heating conditions of our ITMS through an experimental approach. The current study explores various aspects affecting the performance of heat-pump air conditioners: refrigerant charge, electronic expansion valve (EXV) opening, compressor speed, and performance between R290 and R134a under different external temperatures. We aim to improve cooling and heating efficiencies. Among these parameters, the EXV opening and compressor speed have the greatest impact on the performance of the ITMS, as evidenced by the optimal EXV opening and lower compressor speed to maximize the coefficient of performance (COP) and increase the heat transfer rate. In addition, this study has shown that, compared to an ITMS equipped with R134a, R290 has a smaller refrigerant charge, better heat transfer rate and COP under heating conditions, and similar performance under cooling conditions.
CAES as a Way for Large-Scale Storage of Surplus Energy in Poland from Renewable Energy Sources
Authors: Polański Krzysztof
Published: February 2025

The ongoing energy transformation and growing share of renewable energy sources (RES) in electricity production force the search for large-scale energy storage facilities as a possibility for storing electricity from RES because its production is not correlated with the current demand in the power grid. This article discusses the use of salt caverns as large-scale energy storage facilities, proposing a combination of the possibilities of storing energy in natural gas and energy stored in compressed air. Based on the selected potential area where such a storage facility could operate in Poland, the optimal operating parameters of storage caverns were estimated. Several possible cavern exploitation scenarios were analyzed to estimate the impact of the convergence phenomenon in salt caverns on the active storage volume over the long term of exploitation. The obtained results showed that even a high frequency of cavern exploitation cycles does not significantly affect the loss of its capacity due to the convergence phenomenon. The results confirmed the possibility of the effective use of this type of installation for storing surpluses from RES.