bluuebunny/crossref_metadata_2025 huggingface dataset:
‘Trumpism’ as a Dominant Movement in the U.S. Republican Party in the 2020s
Authors: Sharikov P. A.
Published: February 2025

Donald Trump’s decisive victory in the 2024 U.S. presidential election offers a fresh perspective on the nature of ‘Trumpism’, which has now firmly established itself not as a temporary or accidental fluctuation in U.S. political life, but as a stable and powerful social phenomenon. This article attempts to identify the origins and key ideological components of this trend, examining it in the context of contemporary political processes within the Republican Party. The first section of the article traces the changes in Trump’s approach to building relationships with the Republican establishment. It highlights that there were favorable domestic political conditions and an updated campaign strategy that allowed Trump to solidify his electoral position at both regional and federal levels in 2024. This marks a sharp contrast with the 2016 campaign, where he was viewed as an ‘outsider’ and a disruptive actor. The second section delves into the ideology of ‘Trumpism’ in the context of the 2024 presidential election. It notes that while Trump distanced himself from the most radical initiatives of the far-right faction of the Republican establishment during the campaign, his rhetoric was distinctly conservative and combative, especially when contrasted with the conciliatory platform of the Democrats. In conclusion, the article argues that ‘Trumpism’ has evolved into a powerful, independent movement within the Republican Party, one that is likely to maintain its influence in the years to come. At the same time, it suggests that Trump’s and the Republican Party’s victory in the U.S. reflects a broader global trend towards strengthening rightwing ideologies. In this regard, the case of the European Union is particularly noteworthy. It raises the question of how Trump’s victory will impact the positions of conservative and right-populist parties and movements across Europe.
History Politics of Belarus: Peripeteias of Evolution
Authors: Ponamareva A. M.
Published: February 2025

Amidst the sharp escalation of great-power rivalry on the world stage, almost all spheres of public life are facing increasing politicization and even securitization. Historical studies are no exception, as they are becoming an essential element of governments’ efforts to strengthen the ideological cohesion of their societies by revisiting history and constructing new historical narratives. Given the significance of the strategic partnership with the Republic of Belarus for the Russian Federation, the study of history politics of the former is of particular interest and relevance. This paper attempts to identify the logic of the Belarus official historical narratives’ transformation since independence up to and including 2024. The research provides a discourse analysis of a wide range of primary sources, i.e. legal acts, media materials, political speeches, reports from official institutions in charge of the implementation of Belarus’s politics of memory. Its evolution falls into several stages, each being a result of a complex intermix of domestic, foreign policy and economic factors, as well as attempts by the republic’s leadership to maneuver between its eastern and western neighbors. The author concludes that the corpus of scholarly texts, documents and ‘places of memory’ created over the years of the republic’s independence contains various ‘entry points’ allowing the government to change its historical policy in almost any way to its liking. This situation persists despite the noticeable decrease of the Republic of Belarus leadership interest in accentuating the European component of national memory after the events of August 2020, and, on the contrary, a pronounced rapprochement with the Russian historical narrative (primarily with regard to the World War II). The author claims that in these conditions, an uncritical perception of common historical memory as an unshakable cornerstone underlying the unity of the Belarusian and Russian peoples may prevent Russia from adequately predicting possible changes in the geostrategic aspirations of its partner.
Discharge Coefficient of a Compound Weir with a Triangular underneath Gate for Different Geometric and Hydraulic Conditions
Authors: Almalki Abdulrahman Seraj
Published: February 2025

It is common practice in the field of irrigation systems to use composite hydraulic structures, which are constituted of two distinct parts. The initial component, represented by two rectangles, is responsible for the overflow regime, while the subsequent component, represented by a triangular gate, is responsible for the underflow regime. In order to measure, direct, and control the flow, both components are required. The present study investigates the flow through a combined two-rectangle weir with a below-triangular gate across the channel, which serves as a control structure. The weir's upper rectangle has a constant width, designated as b1 and measuring 20 cm, while the lower rectangle has a variable width, designated as b2 and comprising the values of 8 cm, 10 cm, and 12 cm. The depth of the lower rectangle, z, is also a variable with values of 6 cm, 9 cm, and 11 cm. The dimensions of the triangular gate are 15 cm in height and 0, 60, 90, or 120 degrees in vertex angle. The aforementioned dimensions were employed interchangeably as geometric conditions and the disparate water heads h2 as hydraulic conditions. Additionally, the compound weir devoid of a gate (θ = 0) was used for varying water heads. The results demonstrated that the dimensions of the weir and gate had an impact on the discharge that went through the two rectangular weirs and gates. In terms of discharge capacity, the combined structure was observed to be more effective than a classic weir, with the ability to convey a discharge that was two to ten times greater. An empirical formula was developed to predict the discharge coefficient, Cd, for the combined structure, based on the given geometric and hydraulic conditions. It should be noted that the results and analysis of this study were limited to the tested dataset.
An Energy-Efficient Hybrid LEACH Protocol that Enhances the Lifetime of Wireless Sensor Networks
Authors: Jaleel Malik Adnan , Khan Muhammad Amir , Mazhar Tehseen , Khan Jawad , Uz Zaman Sardar Khaliq , Khattak Umar Farooq , Batool Sahar
Published: February 2025

A Wireless Sensor Networks (WSN) comprises of little, low-power sensors, which have a low battery limit and are often utilized in unfavorable conditions. These sensors are data processing and networking devices. The battery life, processing power, communication range, and memory of sensors, which are tiny, self-contained devices, are just a few of their limits. They also have transceivers, which gather environmental data and transmit it to a base station. There are various strategies accessible to expand the existence of a WSN and diminish energy utilization. LEACH (Low Energy Adaptive Clustering Hierarchy) is a successful and widely used method. The protocol clusters the network in order to conserve energy The cluster head, which is a single-region indicative node, receives fusion cluster data once the sensor nodes are divided into clusters. In our study, we provide a novel method for reducing sensor node energy consumption and extending network lifetime. Our research aims to increase network lifespan by reducing energy consumption based on the restrictions of the LEACH and its related algorithms.
Enhancing Free Space Optical System Performance through Fog and Atmospheric Turbulence using Power Optimization
Authors: Belgaonkar Vijayashri V. , Sundaraguru Ramakrishnan , Poongothai C.
Published: February 2025

Free Space Optical (FSO) communication is gaining traction as a pivotal technology for next-generation communication systems, offering extremely high data rates, unlicensed bandwidth, and rapid transmission capabilities. However, its performance is significantly hindered by atmospheric factors such as turbulence and fog. This paper presents a comprehensive model for FSO communication designed to optimize performance under various atmospheric conditions. We assess channel capacity across a spectrum of weak to strong atmospheric turbulence using a Gamma-Gamma channel distribution. To mitigate channel losses, our system employs Wavelength Division Multiplexing (WDM) and multi-beam Multiple Input Multiple Output (MIMO) technologies. Results indicate that the integration of diversity techniques and WDM substantially enhances system performance in adverse weather conditions. Furthermore, power optimization is achieved through the implementation of optical amplifiers and feedback mechanisms from the receiver to the transmitter to adjust the transmitter power in accordance with received Bit Error Rate (BER). The proposed power-optimized WDM MIMO system demonstrates a remarkable BER of 1.48884e-15, while extending the transmission link distance to 2500 meters with a Q factor of 21.5 even under strong atmospheric turbulence conditions.
Residual Stress and Distortion Analysis for TMCP Steel Grade EH36 Butt Welding Parts in GTAW-SMAW Hybrid Welding Process using Finite Element Method
Authors: Nuchim Sumeth , Bunyawanichakul Phacha , Angsuseranee Natchanun , Boonmag Visanu
Published: February 2025

The present work evaluates residual stress and distortion in Thermo-Mechanical Control Process (TMCP) grade EH-36 steel plates subjected to hybrid Gas Tungsten Arc Welding (GTAW) and Shielded Metal Arc Welding (SMAW) processes Specimens measuring 650 × 170 mm with a thickness of 12 mm were utilized. Finite Element Method (FEM) analysis was employed to model residual stress and distortion, a critical step in optimizing the manufacturing process of mechanical structures and parts in shipbuilding. The FE model was developed using ANSYS software incorporating a heat source model with a user-defined subroutine to represent an ellipsoidal moving weld torch with front and rear power density distribution. Heat losses due to radiation and convection were accounted for, while mechanical boundary conditions were applied to restrict rotation and displacement but allow material deformation. Thermal analysis demonstrated close agreement between experimental thermocouple data and numerical simulations, with a temperature deviation of only 5%. Residual stress analysis using X-Ray Diffraction (XRD) revealed that ultrasonic stress relief reduced the maximum residual stress from an average of 193.4 MPa to 39.1 MPa Distortion analysis showed that the maximum FEM deformation was 0.2873 mm, with a 12% deviation from coordinate measuring machine (CMM) results, while the minimum FEM deformation was 0.031922 mm, differing by 3%. The larger deviation occurred in areas with peak distortion, attributed to variations in mechanical restraint positioning, which significantly influence material deformation during cooling.
Vernacular Materials for Thermal Comfort
Authors: Kalla Ruma , Kumar Ravish
Published: February 2025

This study proposes Dulmera sandstone cooling tiles as a novel solution to the prevalent heat absorption problems in building construction, particularly in hot climates, such as that of Bikaner, Rajasthan. The objective is to enhance building energy efficiency and indoor comfort while promoting sustainable practices. The development of Dulmera slim tiles uses the natural cooling properties of Dulmera sandstone, with research focusing on its heat absorption and emission characteristics. This approach integrates traditional knowledge with modern manufacturing techniques, addressing heat-related building challenges. The findings indicate that these tiles significantly reduce heat absorption and emission, leading to decreased reliance on energy-intensive cooling systems, lower electricity consumption, and reduced greenhouse gas emissions. Additionally, the tiles enhance indoor comfort, boost occupant well-being, and improve productivity. The innovation's distinctiveness stems from its incorporation of locally available materials and state-of-the-art manufacturing methodologies, providing a culturally sensitive and sustainable solution that safeguards Rajasthan's architectural heritage. Consequently, Dulmera sandstone cooling tiles emerge as a valuable and scalable solution for analogous climates worldwide, fostering resilience and environmental consciousness in the built environment.
Causes and Effects of Change Orders for Construction Projects in Palestine
Authors: Mahamid Ibrahim , Abdelaal Ahmad
Published: February 2025

The construction sector is vital to Palestinian economy, contributing significantly to its growth and development. Its complex nature encompasses human, non-human, and other elements and often necessitates change orders, which are inevitable, regardless of the project size, type, or characteristics. Change orders lead to massive delays and cost overruns impacting project timeline and profitability. The ccurrent study explores and ranks the causes and impacts of change orders in Palestinian construction projects from contractors' and consultants' perspectives. The findings revealed that internal factors related to the owner were the primary source of change orders. The major five causes were ranked based on the Relative Importance Index as follows: "use of duplicated documents from previous projects," "change in plan and scope by owner," "owner's financial difficulties," "poor site investigation before the design stage," and "errors and omissions in design." Similarly, the study presents the top five impacts of change orders as follows: "time overruns," "cost overruns," "rework and demolition," "delay in payment by the owner," and "disputes between contract parties." This study holds particular importance for the construction sector, offering valuable insights into managing the change orders to meet the projects objectives in terms of schedule, budget, and quality.
Control Design for Electronic Voltage Stabilizer
Authors: Diep Nguyen Thi , Quan Doan Hong , Minh Nguyen Huu , Trung Nguyen Kien
Published: February 2025

Vietnam's electrical infrastructure has undergone notable advancements in recent times. Nevertheless, concerns pertaining to under-voltage, over-voltage, and voltage fluctuations persist in rural, mountainous, and industrial regions. This results in the inefficient operation of electrical equipment, disruptions in industrial production processes, data loss, and equipment damage. This paper puts forth a novel control design methodology for electronic voltage stabilizers, with the objective of mitigating the detrimental effects of grid voltage fluctuations on electrical apparatus. A compact and cost-effective AC-AC converter is proposed as a means of regulating the compensation voltage. The paper puts forth the use of the state-space averaging method for system modeling and proposes the combination of feedback and feed-forward controllers to achieve high accuracy and rapid response times. Furthermore, the moving average method is proposed for measuring the Root Mean Square (RMS) voltage, which significantly enhances the control response speed and accuracy. A 10 kVA single-phase electronic voltage stabilizer was constructed and tested in a laboratory setting under a range of grid voltage conditions, from 150V to 290V, and with varying loads. The results demonstrate that the electronic voltage stabilizer is effective in maintaining the load voltage within the desired range. The maximum response time recorded was found to be half a cycle of the grid voltage in the simulation and one cycle in the experiments, which is a significantly faster response time than that of similar designs. Furthermore, the low total harmonic distortion provides additional confirmation of the effectiveness of the designed electronic voltage stabilizer.
Enhanced Real-Time Object Detection using YOLOv7 and MobileNetv3
Authors: Ennaama Sara , Silkan Hassan , Bentajer Ahmed , Tahiri Abderrahim
Published: February 2025

Object detection serves as a crucial element in computer vision, increasingly relying on deep learning techniques. Among various methods, the YOLO series has gained recognition as an effective solution. This research enhances object detection by merging YOLOv7 with MobileNetv3, known for its efficiency and feature extraction. The integrated model was tested using the COCO dataset, which contains over 164,000 images across 80 categories, achieving a mAP score of 0.61. Additionally, confusion matrix analysis confirmed its accuracy, especially in detecting common objects such as 'person' and 'car' with minimal misclassifications. The results demonstrate the potential of the proposed model to address the complexities of real-world scenarios, highlighting its applicability in various scientific and industrial domains.
Α Solar-Integrated Wireless Charging System for Electric Vehicles
Authors: Channi Harpreet Kaur , Malik Meena , Chen Chin-Ling , Chen Hsing-Chung , Sandhu Ramandeep , Prabha Chander
Published: February 2025

This paper presents a well-integrated system combining photovoltaic (PV) energy harvesting and Wireless Power Transfer (WPT) technology to develop a Solar Wireless Electric Vehicle Charging System (SWEVCS). With the growing adoption of Electric Vehicles (EVs), the demand for efficient and sustainable charging infrastructure has become a critical issue. The proposed system utilizes photovoltaic panels as a clean renewable energy source to charge EVs, eliminating the need for physical cables. The system performance is evaluated using MATLAB simulations, considering key parameters, such as solar irradiance, power output, battery State of Charge (SOC), charging current, and voltage. The results indicate a peak power output of approximately 500 W during midday, and a high SOC of up to 100% by late afternoon. The charging current reaches almost 5 A, demonstrating the high system’s efficiency in wireless energy transfer/WPT. The concerns of this study are the prospects of SWEVCS in minimizing reliance on the power grid while promoting Renewable Energy Solutions (RES) for EV charging. Future work will address scalability challenges and further improvement of WPT efficiency to advance this innovative charging technology.
Digital Transformation in Higher Education Obstacle Assessment and Development of Strategies against Cybersecurity Threats: The Case of Moroccan Universities
Authors: Chahid Abdelilah , Ahriz Souad , Guemmat Kamal El , Mansouri Khalifa
Published: February 2025

Digital Transformation(DT) in higher education has become essential in improving both educational delivery and operational efficiency. However, this transition also exposes institutions to increasing cybersecurity threats, often associated with various barriers reported in the literature. Although these barriers have been widely studied, no research has yet systematically prioritized them in the academic context. This study, conducted within the framework of DT in Morocco, addresses this gap by classifying and prioritizing these barriers to better understand how they contribute to the spread of cybersecurity threats. Using methodologies such as the Analytic Hierarchy Process (AHP) and the Analytic Network Process (ANP), we not only prioritized the major barriers but also developed specific strategies to counter the resulting threats, revealing significant variations in the prioritization of cybersecurity strategies. These differences arise from the complex interactions between the barriers identified by the ANP, highlighting the importance of considering interdependencies when developing effective cybersecurity strategies.
Smart Contract-Enhanced Residual GRU with Merkle-Damgard Cryptography for IoT Attack Detection
Authors: Nishitha T. , Khare Akhil
Published: February 2025

As IoT continues to expand, the security of connected devices remains a critical concern, particularly in the face of DDoS attacks. This study introduces a novel approach that leverages blockchain technology through smart contracts integrated with an advanced attack detection mechanism. Central to this approach is the Enhanced Residual Gated Recurrent Unit (ERGRU) architecture, designed to effectively identify and mitigate DDoS attacks within IoT networks. The Adaptive Coati Optimization Algorithm (ACOA) was used to adjust the hyperparameters of the ERGRU model, such as the learning rate and the number of GRU neurons, to further improve detection accuracy. In addition, the proposed framework uses a one-way compression function to generate secure hashes for input data, utilizing the Merkle-Damgård cryptography technique to ensure data integrity and confidentiality. The proposed solution was tested through a rigorous process using a DDoS dataset. Performance was assessed by focusing on metrics such as processing time, data integrity rate, and confidentially rate. The results demonstrate the effectiveness of the proposed smart contract-based framework in providing a durable and efficient protection mechanism against DDoS attacks in IoT environments.
An Estimation Method for the Overthrowing Moment of Vehicles on the Car Deck of the Ro-Ro Ferry
Authors: Alamsyah . , Suardi . , Setiawan Wira , Pawara M. Uswah , Hariyono . , Nurbaya . , Paroka Daeng , Ardianti Andi
Published: February 2025

Ensuring passenger safety and comfort is crucial for maintaining trust in sea transportation, especially as ship accidents remain a concern. One critical area for improvement is the vehicle fastening systems on Roll-On-/Roll-Off (Ro-Ro) ferries, which must account for fastening techniques and vehicle positioning to prevent shifting or overturning during adverse conditions. This study calculates the shear and overturning moments acting on vehicles when a ferry experiences rolling motion due to beam waves, using numerical simulations to evaluate the impact of vehicle placement. Wave heights were adjusted to reflect operational area conditions, and vertical and lateral accelerations were derived based on vehicle dimensions, type, and enter of gravity. A case study of a Ro-Ro ferry operating on the Bira-Pamatata route revealed that Bus 2, located at the rear centreline, exhibited the highest rolling moment of 14,370 N·m, while Ayla 21, positioned at the front centreline, had the lowest moment of 1,141 N m. These results demonstrate that vehicle mass, dimension, and vertical and horizontal center of gravity significantly influence rolling and share moments.
Hierarchical Deep Learning for Robust Cybersecurity in Multi-Cloud Healthcare Infrastructures
Authors: Ali Tariq Emad , Zoltan Alwahab Dhulfiqar
Published: February 2025

Patient safety is in danger because healthcare networks are more susceptible to cyberattacks as they become more intricate and linked. By altering data transmitted between various system components, malicious actors can hack into these networks. As cloud, edge, and IoT technologies become more widely used in contemporary healthcare systems, this difficulty is predicted to increase. This study presents a Combined Hybrid Deep Learning Framework with Layer Reuse for Cybersecurity (CHDLCY) to address this issue. This system is built to detect malicious actions that modify the metadata or payload of data flows across IoT gateways, edge, and core clouds quickly and precisely. The CHDLCY's is a unique design demanding less training time, while bigger models at the core cloud profit from a cutting-edge layer-merging method. The core cloud model is partially pre-trained by reusing layers from trained edge cloud models, which drastically reduces the number of training epochs required from 35 to 40 to just 6 to 8. Thorough tests demonstrated that CHDLCY not only accelerates the training phase but also achieves remarkable accuracy rates, ranging from 98% to 100%, in identifying cyber threats. The proposed approach offers a significant improvement over previous models in terms of training efficiency and generalizability to new datasets.
Comprehensive Analysis of a YOLO-based Deep Learning Model for Cotton Plant Leaf Disease Detection
Authors: Madhu Sailaja , RaviSankar V.
Published: February 2025

Diagnosis of cotton plant diseases is essential to maintain agricultural sustainability and output. This study proposes a YOLO-based deep learning model for leaf disease detection to maximize cotton plant leaf disease detection accuracy. This method ensures a comprehensive evaluation of cotton plant health by combining various image processing techniques, improving the accuracy of disease identification. This study provides a viable path to improve crop health monitoring and management in cotton farming systems and emphasizes the importance of utilizing cutting-edge image processing techniques in agricultural activities. ROC curve performance and classification metrics were better for YOLOv5 than for VGG16 and ResNet50, as it had the highest F1 score (99.21%), recall, and precision. Consistent performance in classification tests was demonstrated by all models, which showed balanced precision, recall, and F1 scores. ResNet50 marginally outperformed VGG16 in terms of true positive rates, F1 score (98.88% vs. 98.65%), recall, and precision. More sophisticated models, such as YOLOv5 and ResNet50, showed higher efficiency and accuracy than VGG16, which makes them more appropriate for applications demanding low false positive rates and high precision. The proposed YOLO-based method improves the accuracy of disease identification, ensuring a thorough assessment of cotton plant health using image processing techniques. The results show that the proposed approach is quite successful in correctly detecting and classifying a variety of diseases that affect cotton plants.
The Impact of Hexane on the Dynamic Viscosity of Kazakhstans’s Heavy Oil
Authors: Logvinenko Alexandr
Published: February 2025

The Republic of Kazakhstan has significant amount of heavy oil and natural bitumen reserves. Some of that reserves have high viscous oil more than 1000 mPa*s at shallow depth. The main recovery technique is thermal methods using steam or hot water injection. One of the variants to improve thermal methods is to add liquid or gaseous solvent. In our research we consider hexane as an additive solvent. The purpose of this study is to determine how hexane affects to dead heavy oil of Sarybulak field within the context of dynamic viscosity reduction. Mix of heavy oil and hexane in various proportions are obtained. Also, dependences of heavy oil dynamic viscosity on the hexane content are considered.
Revolutionizing Diagnostic Insights: Exploring Advanced Image Processing Techniques and Neural Networks in Traditional Indian Medicine
Authors: Srinivasan R. , Korah Reeba , Ravichandran M.
Published: February 2025

The Siddha and Ayurveda traditional Indian medicine practices utilize non-invasive diagnostic methods, such as Neikuri and Taila Bindu Pariksha, for patient diagnosis through urine analysis. While these methods have proven effective for centuries, their accuracy highly depends on the subjective experience of practitioners. To address this limitation, this study explores the use of advanced image processing techniques and deep learning, specifically Convolutional Neural Networks (CNNs), to automate and enhance diagnostic image analysis. This study utilized five pre-trained CNN models, namely DenseNet, ResNet, VGG-19, Inception, and EfficientNet, on a dataset of Neikuri images acquired from a Siddha medical institute, to standardize and improve the accuracy of patient diagnosis. The comparative evaluation revealed DenseNet as the best-performing model, achieving a classification accuracy of 93.33%, while Inception v3 followed with 90.5%. This study highlights the potential of integrating modern neural networks with traditional diagnostic practices, paving the way for more objective, efficient, and accessible healthcare solutions in traditional Indian medicine.
A Stacking Ensemble Model with Enhanced Feature Selection for Distributed Denial-of-Service Detection in Software-Defined Networks
Authors: Ali Tariq Emad , Chong Yung-Wey , Manickam Selvakumar , Yusoff Mohd Najwadi , Yau Kok-Lim Alvin , Zoltan Alwahab Dhulfiqar
Published: February 2025

The proliferation of Distributed Denial of Service (DDoS) attacks poses a significant threat to network accessibility and performance. Traditional feature selection methods struggle with the complexity of network traffic data, leading to poor detection performance. To address this issue, a Genetic Algorithm Wrapper Feature Selection (GAWFS) is proposed, integrating Chi-squared and Genetic Algorithm (GA) approaches with a correlation method to select the most correlated features. GAWFS effectively reduces feature dimensions, eliminates redundancy, and identifies crucial and correlated features for classification. Detection accuracy is further improved by employing a stacking ensemble model, combining Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM) as base models, with Random Forest (RF) as the metamodel. The proposed classifier achieves impressive accuracies of 99.86% for training data and 98.89% for test data, representing improvements of approximately 5% and 40%, respectively, over previous studies. The training time was also reduced to 2,593 s, a substantial improvement of approximately 29.92%. Validation on various benchmark datasets confirmed the efficacy of the proposed approach, underscoring the importance of the enhanced feature selection method and the stacking ensemble model against DDoS attacks.
Performance Analysis of Effective Retrieval of Kannada Translations in Code-Mixed Sentences using BERT and MPnet
Authors: Rohith H. P. , Kumar Lava , Kavitha Sooda , Karunakara Rai B. , Inchara K. P.
Published: February 2025

Translating Kannada-English (Kn-En) code-mixed text is a challenging task due to the limited availability of Kannada language resources and the inherent complexity of the dataset. This study evaluates the effectiveness of the sentence transformer model, utilizing pre-trained multilingual MPNet and Bidirectional Encoder Representations from Transformers (BERT) architectures, in generating sentence embeddings to enhance translation accuracy. It encodes both code-mixed sentences and their corresponding Kannada translations into high-dimensional embeddings. By employing cosine similarity, it maps input sentences to their closest translations, encoding 2000 code-mixed sentences and their translations using both the MPNet and BERT models. The findings indicate that the MPNet model proved to be more effective, achieving a model accuracy of 98%, compared to BERT's 88%. Moreover, MPNet outperformed BERT in terms of Bilingual Evaluation Understudy (BLEU) and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) scores, attaining 85.0 and 80.0, respectively, while BERT scored 65.3 and 58.7. These results highlight the advanced capabilities of MPNet in translating code-mixed languages and its potential applicability to a broader range of multilingual Natural Language Processing (NLP) tasks.
A Comparative Study of Fine-Tuning Deep Learning Models for Leaf Disease Identification and Classification
Authors: Prashanthi Bh. , Praveen Krishna Anne Venkata , Mallikarjuna Rao Ch.
Published: February 2025

Innovative agricultural solutions are needed to detect and classify leaf diseases early across crop species and environments. This study compares deep learning approaches, focusing on Convolutional Neural Networks (CNN) and Vision Transformers (VTs), to identify leaf diseases early and accurately for scalable crop management and productivity. Optimizing CNNs, Explainable Transfer Learning (ETPLDNet) using ResNet50 architecture, and LEViT leaf disease diagnosis are compared. The CNN model, optimized with dynamic hyperparameters, achieved an impressive 99.58% accuracy for leaf disease classification, demonstrating its effectiveness in feature extraction and classification precision. On the other hand, the VT-based LEViT model, which leverages self-attention mechanisms and Explainable AI (XAI), achieved 95.22% accuracy but offers enhanced interpretability and generalization capabilities due to its transformer-based architecture. This distinction illustrates that while CNNs excel in accuracy, VTs provide a more transparent decision-making process and better handle the complex variances in plant leaf diseases, making them ideal for precision agriculture. The combined use of CNNs and VTs showcases the strengths of each model, with CNN focusing on high classification precision and VTs offering improved interpretability and adaptability for various leaf disease conditions. The use of XAI enables the models to highlight important areas in plant leaf images that influence the model's decisions, offering a transparent and interpretable decision-making process that allows researchers and farmers to understand why a particular diagnosis or classification was made. This ability to visualize and explain the reasoning behind the model predictions is crucial to increasing trust in AI-driven solutions in agriculture. By combining the high precision of CNN and the interpretability of VT with XAI, this study offers a robust approach to improving crop disease management and precision agriculture.
Enhancing Colorectal Polyps Detection using Transfer Learning on DICOM Metadata
Authors: Hicham Khadija , Laghmati Sara , Cherradi Bouchaib , Hamida Soufiane , Tmiri Amal
Published: February 2025

Colorectal and Rectum Cancer (CRC) presents significant global health challenges, necessitating early detection and precise diagnosis to achieve effective treatment and better patient outcomes. Transfer learning techniques have shown considerable promise, especially in cancer detection. This study presents a CRC prevention system based on a fusion of a pre-trained VGG16 model with dense layers for metadata processing. Experiments were performed using the CT Colonography dataset from The Cancer Imaging Archive (TCIA), applying preprocessing and class weighting to address class imbalance. The system was evaluated using accuracy, loss, recall, precision, F1-score, and AUC. This study investigated the impact of integrating DICOM patient metadata to enhance the proposed CRC prevention system. The findings indicate that the proposed MetaVGGNet model outperformed the standard VGG16, achieving greater accuracy (82%) and a marginally lower loss. This successful application has the potential to enhance CRC diagnosis and treatment and underscores the importance of incorporating metadata into deep learning classification systems, offering avenues for more effective and dependable diagnostic tools in CRC management.
Performance of Asphalt Concrete Mixtures Containing Nickel Slag
Authors: Azizah Nurul , Tjaronge Muhammad Wihardi , Amiruddin Andi Arwin , Lando Asiyanthi Tabran
Published: February 2025

Recent advancements in material technology have led to an increased interest in using alternative materials in the asphalt mixtures. One such material is Nickel Slag (SN), a byproduct of nickel ore smelting. With the growing volume of slag produced during nickel smelting, research has focused on using SN as a component in pavement materials to reduce the steel waste accumulation. The primary objective of this study is to explore the optimal use of SN as a coarse aggregate in asphalt concrete mixtures, aiming to achieve the maximum asphalt content. The study also evaluates the impact of SN on the stability, volumetric characteristics of the asphalt mixtures, and Ultrasonic Pulse Velocity (UPV) wave patterns. The research involved Marshall testing using a Universal Testing Machine (UTM) and UPV testing. The results indicated that SN mixtures reached maximum stability at 5.8% asphalt content and demonstrated higher stability values than conventional mixtures. As a coarse aggregate replacement, SN enhances the resistance to permanent deformation due to its hardness, interlocking properties, and the silica content that improves adhesion to the asphalt. Incorporating SN into asphalt mixtures improves mix stability, reduces industrial waste, conserves natural resources, and enhances road infrastructure quality. This method supports the principles of sustainable development.
Underwater Image Enhancement using Convolution Denoising Network and Blind Convolution
Authors: Adagale-Vairagar Shubhangi , Gupta Praveen , Sharma R. P.
Published: February 2025

Underwater Image Enhancement (UWIE) is essential for improving the quality of Underwater Images (UWIs). However, recent UWIE methods face challenges due to low lighting conditions, contrast issues, color distortion, lower visibility, stability and buoyancy, pressure and temperature, and white balancing problems. Traditional techniques cannot capture the fine changes in UWI texture and cannot learn complex patterns. This study presents a UWIE Network (UWIE-Net) based on a parallel combination of a denoising Deep Convolution Neural Network (DCNN) and blind convolution to improve the overall visual quality of UWIs. The DCNN is used to depict the UWI complex pattern features and focuses on enhancing the image's contrast, color, and texture. Blind convolution is employed in parallel to minimize noise and irregularities in the image texture. Finally, the images obtained at the two parallel layers are fused using wavelet fusion to preserve the edge and texture information of the final enhanced UWI. The effectiveness of UWIE-Net was evaluated on the Underwater Image Enhancement Benchmark Dataset (UIEB), achieving MSE of 23.5, PSNR of 34.42, AG of 13.56, PCQI of 1.23, and UCIQE of 0.83. The UWIE-Net shows notable improvement in the overall visual and structural quality of UWIs compared to existing state-of-the-art methods.
Modeling and Optimization of Hydropower Plant Operations in the Northern Vietnam Power System by 2030
Authors: Giap Luong Ngoc , Le Ngo Phuong , Khanh Nguyen Binh , Trung Bui Tien , An Truong Nguyen Tuong , Kien Pham Ngoc
Published: February 2025

The Northern Vietnam Power System (NVPS) has a long history, with over a century and a quarter of development. The NVPS has experienced a significant expansion in its total power generation capacity. In 1954, the total capacity was 31.5 MW. By the end of 2023, the total capacity of the NVPS reached 29,537 MW, with hydropower accounting for 9,764 MW, representing 33.06%. In 2023, the NVPS generated 90.380 billion kWh of electricity. Over the past decade, the annual growth rate of commercial electricity has averaged between 10% and 12%. Northern Vietnam is endowed with vast hydropower potential, particularly across its four major river basins: the theoretical maximum capacity of the Bang Giang-Ky Cung, Red River, Ma River, and Ca River basins is 20,598 MW. Hydropower plays a significant role in ensuring energy security due to its low production cost, capacity to rapidly meet peak demand, and status as a clean energy source. This study proposes an optimal operational model for hydropower plants in the NVPS for the period from 2025 to 2030. The results demonstrate that hydropower will continue to be crucial for meeting the growing energy demand while minimizing operational costs. Additionally, it supports the integration of renewable energy sources into the power grid, thereby underscoring the strategic importance of hydropower in maintaining system stability and promoting sustainable development in the region.
Blockchain Non-Fungible Token for Effective Drug Traceability System with Optimal Deep Learning on Pharmaceutical Supply Chain Management
Authors: Perumalsamy Shanthi , Kaliyamurthy Venkatesh
Published: February 2025

In recent times, the number of fake drugs has increased dramatically, which has resulted in millions of victims severely affected by poisoning and treatment failures, resulting in a need for Drug Supply Chain (DSC) traceability. The DSC is generally reluctant to share traceability data and includes several parties having heterogeneous interests. Moreover, existing provenance and traceability systems for DSCs need more trust, data sharing transparency, and separated data storage. By realizing decentralized, trustless systems, a decentralized Blockchain (BC)-based solution is proposed to tackle these constraints. BC is an immutable, decentralized, shared network that allows management directly through a peer-to-peer (P2P) network without the necessity of a central authority to check transactions. This study proposes a new Blockchain Non-Fungible Token-based Drug Traceability with Enhanced Pharmaceutical Supply Chain Management (BNFTDT-EPSCM) model. The proposed BNFTDT-EPSCM model presents transparent and more secure reporting of changes in the operating condition of transported pharmaceutical products to prevent drug recalls. The Ethereum BC enables transactions and computational services using the cryptocurrency Ether (ETH). Simultaneously, an enhanced Byzantine fault-tolerant consensus (RB-BFT) leverages a reputation system to address reliability issues of primary nodes and reduce communication complexity inherent in the Practical Byzantine algorithm (PBFT). The BNFTDT-EPSCM model presents a decentralized solution using Non-Fungible Tokens (NFTs) to improve the traceability and tracking capabilities of the standard serialization process. In addition, the BNFTDT-EPSCM model employs a Deep Belief Network (DBN) approach to perform the inbound logistics task prediction process. Finally, the Tasmanian Devil Optimization (TDO) method is utilized to enhance the hyperparameter tuning of the DBN approach. A detailed set of simulations was executed to examine the effectiveness of the BNFTDT-EPSCM approach, demonstrating a higher throughput at the highest user count of 6000 and achieving 551.22 TPS, significantly outperforming existing models.
Context Management Life Cycle for Internet of Things: Tools, Techniques, and Open Issues
Authors: Vijayvargia Kirti , Saxena Preeti , Bhilare D. S.
Published: February 2025

The advent of the Internet of Things (IoT) and the concomitant development of smart systems has rendered context-aware computing an emerging field of research. The IoT facilitates the large-scale integration of Machine-to-Machine (M2M) communication systems, largely independent of human intervention. The context of a situation, encompassing factors, such as mood, location, and activity, is typically taken into account by humans in an implicit manner, influencing their subsequent actions. Similarly, IoT based smart systems require context data acquired through the use of sensors.  The primary challenge lies in the adaptation of context information through the proper modeling and analysis of the vast and heterogeneous sensor data. The phases of context acquisition, modeling, reasoning, and dissemination are collectively referred to as the context management life cycle. The principal aim of this paper is to provide a comprehensive overview of the current state of the art in each phase of the context management life cycle. This study presents a comprehensive review of the tools, techniques, algorithms, and architectures documented in the relevant literature, with a focus on research papers and articles published between 2010 and 2024. The discussion and open issues section at the end of the paper offer insights for future researchers engaged in the study, development, implementation, and evaluation of techniques and approaches for context management in IoT.
Investigation of the Gaussian Process with Various Kernel Functions for the Prediction of the Compressive Strength of Concrete
Authors: Ha Hoang , Trong Hieu Vu , Huyen Trang Le , Nguyen Dam Duc , Prakash Indra , Pham Binh Thai
Published: February 2025

The Compressive Strength of Concrete (CSC) is a critical parameter for evaluating the quality of concrete used in various construction projects, including buildings, bridges, and roads. The primary objective of this study is to examine the efficacy of a Gaussian Process (GP) Machine Learning (ML) model employing two kernel functions: Radial Basis Function (RBF) and Polynomial (POL), for predicting the CSC, considering readily quantifiable parameters. Based on these kernel functions, two models were created for this prediction, GP-RBF and GP-POL. The modeling process employed a total of 369 concrete sample data, including compressive strength values and eleven other physico-mechanical properties, collected from the Cua Luc bridge project in Vietnam. This dataset was partitioned into a training set (70%) and a testing set (30%) for model training and validation. Various validation metrics, including R2, Root Mean Square Error (RMSE), and Mean Absolute Error (MAE), were used to evaluate and compare the models. The findings of this study demonstrated that both models GP-RBF and GP-POL exhibited strong performance in predicting CSC, with GP-POL demonstrating marginal superiority over GP-RBF. Consequently, it can be concluded that POL is more efficacious than RBF in training the GP model for CSC prediction.
Drone Localization using Global Navigation Satellite System and Separated Feature Visual Odometry Data Fusion
Authors: Firmansyah Riza Agung , Muharom Syahri , Masfufiah Ilmiatul , Kisyarangga Ardylan Heri , Rosyad Dzichril Fahimatulloh Mandhia Al Farizi
Published: February 2025

The localization system is the most important part of the overall drone navigation system. The Global Positioning System (GPS) or Global Navigation Satellite System (GNSS) is the main device commonly used in a drone. However, under certain conditions, GPS or GNSS may not function optimally, such as in situations of signal jamming or enclosed environments. This paper implemented a new approach to address this issue by combining GNSS data with Visual Odometry (VO) through Machine Learning (ML) methods. The followed process consists of three main stages. First, performing speed and orientation estimation using VO. Second, performing left and right feature separation on the images to generate a more stable and robust estimation of speed and rotation. Third, refining speed and orientation estimation by integrating GNSS data through ML-based data fusion. The proposed method strives to enhance drone localization accuracy, despite disruptions or unavailability of GNSS signals. The research results indicate that the introduced method significantly reduces Absolute Translation Error (ATE) compared to utilizing VO or GNSS separately. The average ATE produced reached 4.38 m and an orientation of 8.26°, indicating that this data fusion approach provides a significant improvement in drone localization accuracy, making it reliable in operational scenarios with limited GNSS signals.
An Enhanced Random Forest (ERF)-based Machine Learning Framework for Resampling, Prediction, and Classification of Mobile Applications using Textual Features
Authors: Hussain Shahbaz , Sarwar Nadeem , Ali Arshad , Khan Hamayun , Din Irfanud , Alqahtani Abdullah M. , Shabir Mohamed , Ali Aitizaz
Published: February 2025

The amount of mobile applications is increasing rapidly, and it is difficult for software developers to identify the numerous key factors that affect their rating and performance. This study presents a machine-learning framework to improve decisions in adding new features to mobile applications and enhancing overall performance. A dataset of app attributes from the Apple AppStore was used, exploiting NLP techniques to preprocess the textual information and develop an Enhanced Random Forest (ERF) framework to assess and forecast ratings for multifunctional apps and investigate the connections between features and user ratings. The ERF model was compared with other renowned ML methods including Decision Trees (DT), Naive Bayes (NB), CNN, and ANN. The experimental results showed that the proposed model predicts app ratings more effectively compared to other complex models. The proposed model achieved precision, recall, and F1-score of 92.76%, 99.33%, and 95.93%, respectively.