bluuebunny/crossref_metadata_2025 huggingface dataset:
Digital Diplomacy in Russian International Studies: Research Methods
Authors: Pavlyuchenko A. A.
Published: February 2025

In the last decade, digitalization has had an increasing impact on the world political processes and is turning into an integral element of international life. Diplomatic practice, given the growing importance of the digital diplomacy, is no exception. In this context, the need for continuous improvement of the methods of studying these processes and phenomena is all the more pressing. This paper examines the key methods used in Russian studies on digital diplomacy, and identifies their strengths and weaknesses. The first section of the article covers publications built on the discourse analysis of small texts on international policy or their excerpts. The author concludes that such an approach allows for a correct description of the communication strategies of various political players. However, it is more suitable for linguistic research, since it does not address the issues crucial for political scientists, namely those concerning the evaluation of the effectiveness of digital diplomacy tools. The second section considers the research works based on the content analysis. The author argues that although this method provides an opportunity to process a larger amount of data and to reduce the impact of subjective factors, it has its limitations, particularly, inability to take into account the visual component of publications and to place them within a wider information context. The third section focuses on the scholarly papers drawing on the most time-consuming and demanding method of Big Data (hashtags) processing. The author stresses that its application is fraught with a whole range of quite specific methodological and purely technical problems, in particular, screening out bots and other ways of traffic manipulation. Against this backdrop, the author advocates further improvement in the methods of studying digital diplomacy by fostering a culture of data analytics and making a more active use of developments from other disciplines (philology, sociology, cultural studies, etc.).
The EU Sanctions Policy: A Thorny Path of Transformation
Authors: Oganisyan L. D.
Published: February 2025

In recent years, the rising international tensions have raised for the EU leadership pressing questions on the prospects for both the internal development of the union and its global positioning. Against this backdrop, the analysis of the EU sanctions policy as a key tool of its foreign policy strategy is of particular interest. Such an analysis can not only help identify the specific features of the European Union as a subject of restrictions, but also reach a better understanding of the current evolutionary trends of this integration association on the whole. The first section examines the key institutions responsible for the development of the EU sanctions policy, as well as the principles and mechanisms underlying the adoption and implementation of the relevant measures. The author emphasizes the targeted and value-based character of the EU sanctions policy, its commitment to multilateralism and concerns about possible adverse humanitarian effects. The second section traces the evolution of the EU sanctions policy over the period from 1992 to 2022. It shows that it was as early as the 2010s that the EU countries started to switch from using sanctions primarily as a means of conveying an addressee a certain ‘political message’ designed to ensure human rights and democratic values, to harnessing them as an economic leverage. At the same time, evident disagreements emerged between the EU and the United States on secondary sanctions. All this led the EU leadership to realize the need to strengthen its sanctions policy, as well as its strategic autonomy in general. The third section analyzes the key aspects of the qualitative transformation of the EU sanctions policy that began in 2022. The author notes that along with a sharp increase in the number of sanctions against Russia provoked by the beginning of the special military operation in Ukraine, the most important element of this transformation was the intensification of the EU’s efforts to combat sanctions evasion. All this indicates the growing readiness of the EU, which previously condemned the practice of extraterritorial sanctions, to apply sanctions measures beyond its borders. At the same time, the European Commission plays an increasingly active role in the development of restrictive measures. This may significantly change the institutional balance within the EU in its favor, contributing to strengthening the supranational principle. The prospects for the implementation of these tendencies, however, will largely depend on the international context and the ability of the EU leadership to reconcile competing interests of the member states.
Reducing an Indonesian Auto Part Production Cycle Time using the PDCA Approach: A Case Study
Authors: Jonny .
Published: February 2025

Constituting the biggest challenge in any manufacturing company’s product delivery, operational excellence can be accomplished by obtaining the best Quality, Cost, and Delivery (QCD). The delivery parameter has been an issue for the ABC Company, an Indonesian auto part manufacturer, having automotive manufacturers as customers, since its cycle time has been found to be lower than its takt time. This could jeopardize the company’s status in that it could degrade its customer service level. Thus, the company immediately initiated a quality control circle, especially when it was revealed that its production cycle time in Core Assy Line 3 was 49 s more than its targeted 41.7 s takt time. The team was then committed to reducing it to 39.2 s, accounting for a 20% reduction. A method named 8 steps and 7 tools was deployed under the Plan-Do-Check-Act (PDCA) approach to solve the specific problem. This method guided the team to find five root causes of the problem, while five solutions were equivalently provided. As a result, the achieved cycle time reduction was from 49 to 38.9 s, that is, a 21% decrease, capable of securing the company’s relationship with its customers and granting an order increase.
DeepMelaNet: Advancing Melanoma Stage Classification in Skin Cancer Diagnosis
Authors: Al Huda Md Sadi , Shrestha Tahmid Enam , Hossain Asmaul , Sharif Nissan Bin , Ali Md Asraf , Erdei Timotei Istvan
Published: February 2025

Melanoma skin cancer is a global public health threat due to its increasing rates and the possibility of severe outcomes if not adequately addressed. Melanoma is caused by ultraviolet radiation and, among its two stages, malignant is more dangerous than benign. The diagnosis of melanoma is typically based on visual inspection and manual methods carried out by experienced physicians. However, this method is usually slow and has a high probability of error. Deep-learning-based models can offer better and low-cost treatments for people with melanoma. This study aimed to develop a deep-learning model to classify melanoma skin cancer in its early stages. This study presents a modified deep-learning model, named DeepMelaNet, to correctly classify skin cancer images as benign or malignant. The proposed classifier achieved an accuracy of 93.40%, a precision of 98%, a recall of 94%, and an F1 score of 93% on a dataset of 10,000 melanoma skin cancer images, offering a practical solution that can help healthcare professionals in early skin cancer prediction.
Sustainable Transportation Solutions in Remote Areas: Static Analysis of Vertical Axis Wind Turbines for Enhanced Efficiency of Wind-Powered Cars
Authors: Kassem Youssef , Camur Huseyin , Mosbah Almonsef Alhadi Salem
Published: February 2025

It is imperative that a sustainable transportation system, powered by renewable energy resources, be implemented in order to mitigate the impacts of climate change and enhance living standards. A Wind-Powered Car (WPC) is a vehicle that employs a connection between the vehicle and wind turbine blades, thereby leveraging the advantages of wind kinetic energy. The energy is then conveyed directly to the car's wheels via a system of mechanical connections and gears, enabling the vehicle to move without the use of fossil fuels. The absence of an internal combustion engine results in the generation of negligible emissions. The primary objective of this study is to examine the static aerodynamic drag of nine WPC designs with diverse blade configurations of Vertical Axis Wind Turbines (VAWT). To achieve this objective, Autodesk Computational Fluid Dynamics (CFD) was employed to model the aerodynamic drag of WPC designs at varying wind speeds of 4 m/s, 6 m/s, and 8 m/s. The comparative analysis revealed that model 8, featuring a 3-blade Savonius wind turbine without a circular end plate, demonstrated superior efficiency among all car models. This is evident in its ability to generate the highest mechanical power compared to other blade designs. These findings contribute to the understanding of aerodynamic performance in VAWT cars, offering valuable insights for further design optimization. Furthermore, the results highlight model 8 as a promising solution for sustainable transportation, aligned with SDG 7 and SDG 11, through the development of clean and efficient wind-powered vehicles.
CuLOA-based Data Encryption with Tuned Key for Privacy Preservation in the Cloud
Authors: Patil Rajkumar , HimaBindu Gottumukkala
Published: February 2025

Preservation of data privacy in cloud computing involves securing sensitive data during analysis and storage. Conventional approaches often use techniques such as encryption and differential privacy, but they can be computationally intensive and may still risk data leakage through indirect inferences. These limitations necessitate advanced methods to balance efficiency and robust privacy. To address this, this study proposes a novel approach using DL-based fine-tuned keys for encrypting the data, aimed at preserving data privacy in the cloud through the Coati Lyrebird Optimization Algorithm (CuLOA) approach. Initially, sensitive data is randomly chosen from the database. Then, the optimal key is derived using the CuLOA approach. This key, along with the sensitive data is input into the SqueezeNet model, which generates a fine-tuned optimal key. Subsequently, the sensitive data are encrypted and stored in cloud storage. Finally, the encrypted data and the optimally tuned key are employed in the data decryption process to recover the original. A comparative experimental analysis showed that the proposed CuLOA approach was better than previous schemes.
Using a Fuzzy Model to Evaluate Risks caused by Variation Orders in Construction Projects
Authors: Abu Mwais Allam , Mahamid Ibrahim
Published: February 2025

The construction industry is the main driving sector for any country's economy. However, it is too risky due to the many risks and challenges in this industry. One of the main challenges in construction projects is variation orders, as they lead to conflicts between project parties and cause poor project performance. This study aims to evaluate the risks associated with variation orders in construction projects using fuzzy risk assessment. The targeted population is the construction contractors in Palestine. A questionnaire survey was used to collect data on the severity and frequency of the 22 identified factors of variation orders. Then, a fuzzy logic system was developed to rate these factors. The factor risk level was determined by connecting the relationship between the severity and frequency indices using If-Then rules. The determination of risk level is a critical task in the proposed fuzzy system, which depends on the complex combinations of all If-Then rules. Risk is determined not only by the If-Then rules but also by the weight of each rule. This study shows that the factors with the highest risk levels are scope change by the client, client financial problems, unavailability of required materials, poor design documents, and modification of specifications. The results can serve as guidelines for project participants who need to prepare and implement a comprehensive and effective risk management plan to meet project goals. To improve project performance, construction parties are recommended to implement the proposed method to assess risks related to construction projects. Finally, the proposed risk model demonstrates the ability to evaluate risk levels by aggregating rules and combining the project risk factors using a fuzzy logic model and MATLAB.
Smart Dustbins: Real-Time Monitoring and Optimization for Waste Management in Smart Cities through IoT Devices
Authors: Maturi Sreerama Murty , Dhanikonda Srinivasa Rao , Giddaluru Somasekhar
Published: February 2025

The Internet of Things (IoT) provides a technological foundation for the development of smart cities.  The technology has yielded novel possibilities for urban governance. The IoT offers a practical and cost-effective means of gathering vast quantities of data, which can be applied across a range of industries with the aim of increasing production. One of the most pressing challenges currently facing the development of smart cities is the issue of waste collection beyond the municipal corporation. The IoT has the potential to streamline the waste collection process across a smart city. The proposed model is designed to facilitate more efficient location determination of smart dustbins through the use of IoT enabling technologies. The primary objective of the proposed models is to monitor the status of the smart dustbin. The smart dustbins are equipped with a GPS module, a GSM module, ultrasonic sensors, and a Liquid-Crystal Display (LCD). The primary function of the proposed model is to ascertain whether the smart dustbin is full, medium, or empty.  In the event that the smart dustbin is full, the GPS location and status of the smart dustbin are automatically transmitted via SMS by GSM module. This SMS is sent to the relevant authorities of the Municipal Corporation, who are then responsible for collecting the waste from the smart dustbin. The SMS contains longitude and latitude values, which are used to identify the correct route map for the smart dustbins. Ultimately, the city can be transformed into a smart city by using IoT-enabled smart dustbins.
An Improved Laxity based Cost Efficient Task Scheduling Approach for Cloud-Fog Environment
Authors: Mishra Praveen Kumar , Chaturvedi Amit Kumar
Published: February 2025

Task scheduling is critical in fog computing, as it has to assign workloads to fog nodes to save costs and execution times. This study emphasizes the allocation of jobs received from clients to suitable nodes through a proposed scheduling technique, which is deployed on layer 2 servers within a cloud-fog environment. Laxity-based Cost-efficient Task Scheduling (LCTS) is proposed for contemporary task scheduling difficulties, such as balancing cost and delay with optimal energy utilization. The results show that the proposed strategy decreased execution time and cost more than Round Robin (RR) and Genetic Algorithm (GA). Furthermore, the proposed method was less expensive than cloud-based IoT solutions. Compared to GA and RR, the simulation results showed that cost and execution time were reduced by 6.99%-17.36% and 4.58%-9.09%, respectively.
Comprehensive Learning Salp Swarm Algorithm with Ensemble Deep Learning-based ECG Signal Classification on Internet of Things Environment
Authors: Tounsi Mohamed , Ali Haider , Azar Ahmad Taher , Al-Khayyat Ahmed , Ibraheem Ibraheem Kasim
Published: February 2025

The Internet of Things (IoT) in healthcare relates to implementing interconnected devices and systems for collecting and sharing healthcare information in real time. The integration of IoT in healthcare has the potential to enhance patient outcomes, reduce healthcare costs, and improve the efficacy of medical services. Electrocardiogram (ECG) is a non-invasive heart monitoring method that has become widely accessible due to user-friendly, low-cost, and lead-free wearable heart monitors. However, relying on overworked caregivers for manual monitoring is inefficient. This study develops a Comprehensive Learning Salp Swarm Algorithm with Ensemble Deep Learning (CLSSA-EDL) technique for ECG signal classification in IoT healthcare. The objective of CLSSA-EDL is to detect and classify ECG signals to support decision-making in the IoT healthcare environment. The CLSSA-EDL approach employs the DenseNet201 feature extraction method, with hyperparameters optimally selected by the CLSSA system. For ECG signal detection and classification, an ensemble model using a Stacked Autoencoder (SAE), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) is utilized. The CLSSA-EDL technique was evaluated on a benchmark ECG dataset, achieving an accuracy of 98.7%, sensitivity of 97.5%, and specificity of 99.1%, demonstrating superior performance compared to recent algorithms.
A Comprehensive Review on Biomedical Image Classification using Deep Learning Models
Authors: Tounsi Mohamed , Aram Erahid , Azar Ahmad Taher , Al-Khayyat Ahmed , Ibraheem Ibraheem Kasim
Published: February 2025

Medical imaging is one of the most efficient tools for visualizing the interior organs of the body and its associated diseases. Medical imaging is used to diagnose diseases and offer treatment. Since the manual examination of a massive number of Medical Images (MI) is a laborious and erroneous task, automated MI analysis approaches have been developed for computer-aided diagnostic solutions to reduce time and enhance diagnostic quality. Deep Learning (DL) models have exhibited excellent performance in the MI segmentation, classification, and detection process. This article presents a comprehensive review of the recently developed DL-based MIK classification models for various diseases. The current review aims to assist researchers and physicians of biomedical imaging in understanding the basic concepts and recent DL models. It explores recent MI classification techniques developed for various diseases. A thorough discussion on Computer Vision (CV) and DL models is also carried out.
Analysis of the Excess Deaths in Ecuador caused by the COVID-19 during 2020 and 2021
Authors: Benalcazar Marco E. , Cifuentes Cesar Israel Leon , Ona Jose Miguel Munoz , Caraguay Angel Leonardo Valdivieso , Lopez Lorena Isabel Barona
Published: February 2025

Ecuador became one of the most affected countries in the world from the COVID-19 pandemic in 2020: the number of deaths during March and April of 2020 suggests that the pandemic in this country was much worse than the studies reported by the Ecuadorian national institutions. A number of studies concerning the number of excess deaths have been conducted, but they used a limited amount of data because of the time they were done. Additionally, these studies do not provide a way of comparing results with those of other countries since they use the raw number of excess deaths, and not a relative measure. This study fills all these gaps by presenting an analysis of the excess deaths (raw and per 100000 inhabitants). For this analysis, Long Short-Term Memory (LSTM) Artificial Neural Network (ANN) and Autoregressive Integrated Moving Average (ARIMA) were used to do a forecasting. These methods were trained using the data of deaths in Ecuador before the pandemic, over a period of 5 years. The methodology used for this work takes steps from recognized guidelines from CDC and the University of Melbourne to compute the excess deaths. In 2020, Ecuador had an excess death of 42009 ± 5823 people, which means an excess death within 207.14 to 273.81 people per 100,000 inhabitants. The decrease of deaths due to land traffic accidents, congenital malformations, deformities, chromosomal abnormalities and HIV in 2020 was 435. Additionally, the causes with the highest excess deaths were respiratory insufficiency, influenza and pneumonia, ischemic heart diseases, hypertensive diseases and mellitus diabetes. In order to contrast these numbers, a computation of the excess (decrease) of deaths was computed.
Digital Health Transformation in Saudi Arabia: Examining the Impact of Health Information Seeking on M-Health Adoption during the COVID-19 Pandemic
Authors: Aljohani Nasser
Published: February 2025

This study investigates the intention of Saudi Arabian users to adopt mobile health (m-health) applications through the lens of the United Theory of Acceptance and Use of Technology (UTAUT) framework. The research highlights the growing importance of m-health solutions in Saudi Arabia, especially in the context of the country's Vision 2030 development agenda and the accelerated adoption of e-health/m-health technologies during the COVID-19 pandemic. The key findings indicate that health information seeking, and social influence are significant factors driving users' intentions to adopt m-health applications, while Performance Expectancy (PE) is not a primary driver. Additionally, Effort Expectancy (EE) positively influences users' behavioral intentions. Improving health information features in these applications could facilitate broader adoption. This research contributes to the existing literature on information systems and m-health adoption by shedding light on the critical factors influencing user intentions in a developing context. However, the study does not account for all potential technological and external variables that could affect adoption behavior. Future research, particularly qualitative or mixed-method studies, should explore the impact of age on m-health adoption, as the current findings primarily reflect younger users.
Enhancing Data Streaming Clustering Algorithms for AutoML in Cloud Environments: A Novel Design Approach
Authors: Parekh Madhuri H. , Shukla Madhu Shambhu
Published: February 2025

The objective of this revision is to enhance existing AutoCloud clustering technology, which demonstrates optimal performance when dealing with clusters of specific dimensions and arrangements. AutoCloud uses the TEDA framework to break down the clustering challenge into two smaller problems, called micro cluster and macro cluster. AutoCloud is an innovative method that eliminates the requirement for any pre-existing understanding of datasets, where clusters can develop and combine when new information and explanations are presented. This study proposes an experimental configuration to generate microclusters and data clouds without imposing a certain topology on static datasets. MLAutoCloud uses a modified distance-based technique, utilizing the big data framework and incorporating the adjusted random index value with the TEDA framework for streaming data. The MLAutoCloud technique yielded optimal cluster numbers and achieved excellent data collection results, as seen in the test results on different datasets. Estimating thickness despite changes in the underlying assumptions is a process that could modify the variables used to provide data. The MLAutoCloud method is an effective way to generate a cloud clustering algorithm in the data streaming section.
Clustering Commuter Behavior based on Automated Fare Collection (AFC)
Authors: Purbohadi Dwijoko , Marifatul Azizah Laila , Kurniasari Lilis , Diah Wulandari Novi , Pratiwi Nurna , Hastuti Puji
Published: February 2025

This paper examines the application of the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) method to cluster Automated Fare Collection (AFC) transaction data from train travelers in Jakarta, Bogor, Depok, Tangerang, and Bekasi (Jabodetabek) in Indonesia. To enhance the clustering process, the Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction and the DenseClus library are employed. In this study, different combinations of hyperparameters are used to identify the optimal configuration for producing distinct clusters with a high concentration and noticeable distinction. The results demonstrate that the utilization of HDBSCAN on UMAP-reduced data effectively, discerning unique trip patterns and emphasizing notable disparities in travel distance, time, and length among various clusters. The UMAP intersection method showed notable efficacy in maintaining the local structure of the data, resulting in the development of distinct and meaningful clusters. In addition, categorical data were transformed into numerical formats using hashing techniques, efficiently tackling the difficulties posed by a high number of categories and assuring efficient data processing. The results reveal vital insights into the application of density-based clustering to intricate transportation data, with major implications for enhancing route planning and capacity management for Jabodetabek commuters.
A GPS - Integrated Slide Controller Application for Quadrotor Tracking in Straight Trajectory
Authors: Hoang Anh Le , Huu Toan Le , Duc Thuan Tran
Published: February 2025

Unmanned Aerial Vehicle (UAV) trajectory guidance is an important area in modern aviation and automatic control, requiring the UAV to maintain precise position and velocity along the trajectory despite environmental fluctuations. This article presents the quadrotor hardware and method for developing a trajectory tracking control algorithm, using a sliding mode controller combined with GPS data. The controller' design is based on the nonlinear model of the system, integrating the nonlinear sliding mechanism with information from GPS to ensure that the system follows the target trajectory. The stability of the proposed method is proven through Lyapunov's theorem. The controller is verified through simulation and experiments. The results show that the proposed algorithm helps the quadrotor stabilize the tilt angle and track the trajectory with small errors.
Enhanced Prediction of Intensive Care Unit Length of Stay using a Stack Ensemble of Machine Learning Models
Authors: Tella Ashok Kumar , Balasundaram S. R.
Published: February 2025

The Length of Stay (LoS) refers to the time between a patient's hospital admission and discharge. LoS is considered to increase as the complexity of the disease increases. A prolonged stay in the Intensive Care Unit (ICU) can consume clinical resources and be labor intensive. Models that correctly predict LoS are needed to help medical experts make better decisions. To define an ideal process system, healthcare models must consider the patient's condition, availability of beds, resources, etc. These predictions can also help insurance companies manage their budgets. Existing models deploy machines and deep learning techniques to predict LoS. However, there is a need for improvement, considering the features associated with the process. This study presents machine learning algorithms, such as SVM and a stack ensemble, with improved accuracy over existing models. Experiments were carried out on a benchmark dataset, MIMIC-III, specific to ICU patients. The SVM model achieved an accuracy of 93.88%, while the stack ensemble model showed an improved accuracy of 94.70%. The results show that combining machine learning models achieves better prediction rates, which helps healthcare professionals make better decisions.
An Intuitive Approach on Transfer Learning with an IBF+IHP Model for Stroke Classification and Prediction
Authors: Rohini Talekar , Praveen P.
Published: February 2025

A cerebral stroke can have significant health ramifications. Efficient stroke prevention requires precise prevention and prompt detection of risk factors. This study introduces a novel predictive modeling technique that uses uncomplicated spatial filter maps and ensemble approaches to enhance stroke risk prediction. The proposed approach utilizes ensemble approaches along with comprehensible spatial filter maps to uncover significant spatial patterns in brain imaging data. The ensemble approach employs a multitude of prediction models to enhance the accuracy of stroke risk forecasts. The experimental findings demonstrate that spatial filter maps and ensemble techniques surpass traditional models in predicting performance. This study showcases the potential of spatial filters to include several patient data to accurately predict stroke risk with a 98% success rate.
Robust Direction-of-Arrival Estimation using improved Coprime Array for Wireless Communication Applications
Authors: Shashidhara K. S. , Venu K. N. , Saritha I. G. , Jayaramu Raghu , Dakulagi Veerendra
Published: February 2025

In wireless communication systems, robust and accurate Direction-of-Arrival (DOA) estimation is essential for tasks such as beamforming and interference suppression. This research presents advancements in the Multiple Signal Classification (MUSIC) algorithm leveraging enhanced coprime sensor arrays for DOA estimation. Coprime arrays, characterized by their non-uniform spacing derived from coprime integers, offer superior angular resolution compared to traditional uniform arrays. By exploiting this unique array geometry, the proposed method enhances the spatial localization capabilities of the MUSIC algorithm, thereby improving signal detection and mitigation of interference. Experimental validation demonstrates the efficacy of the approach in various signal environments, highlighting its potential to enhance the performance and reliability of wireless communication systems.
An Interpretation of the Cumulative Impact of FASTag on the Reduction of Environmental Pollution and Traffic Delays at Toll Booths: A Case Study
Authors: Chhotu Anil Kumar , Kumar Anil , Priyadarshee Akash , Ansari Ghausul Azam , Kumar Niraj
Published: February 2025

Traffic congestion has been identified as a significant contributor to the environmental degradation and elevated fuel consumption. The causes of traffic delays are multifaceted, with toll booths positioned on highways being a notable factor. This study aims to analyze the impact of toll booths on traffic congestion, with a particular focus on the case study of the highway toll booth in question. The analysis will include a comparison of the delay times before and after the implementation of the FASTag system. The study will also undertake an economic evaluation to assess the cost implications of delay. The findings of this study indicate that congestion is more pronounced in the presence of manual toll booths compared to automated systems. The present study uses the 'Chakiya Toll Plaza on National Highway 27A, located between Motihari and Muzaffarpur, as a case study. This toll booth has been initially operated with a manual toll collection system, but has since transitioned to the FASTag implementation. A comprehensive analysis encompasses both traffic volume and delay studies, with a focus on their environmental and economic ramifications. The analysis reveals that for each Passenger Car Unit (PCU), the environmental cost is 8.3 rupees and the fuel cost is 15.34 rupees. The idle time cost is calculated as 8.34 rupees. The overall cost of the manual toll collection delay, including all the aforementioned factors, is found to be 52.3 rupees. However, the implementation of FASTag significantly reduces this cost to. 8.20 rupees.
Advanced Soil Moisture Predictive Methodology in the Maize Cultivation Region
Authors: Vimalkumar S. , Latha R.
Published: February 2025

Soil moisture is a critical determinant of the maize crop health and productivity. With over 60% of India's maize cultivation concentrated in South Indian states, accurately forecasting soil moisture is essential for optimizing irrigation and enhancing agricultural output. This study introduces an Improved Hybrid Machine Learning (IHML) model that integrates and optimizes Machine Learning (ML) models to deliver superior predictive performance. By leveraging data from key maize-growing districts in South India, the IHML model demonstrates enhanced convergence rates and accuracy compared to traditional ML approaches. The research framework is grounded in comprehensive correlation evaluations, which inform parameter selection and model architecture. Extensive comparisons reveal that the IHML model significantly outperforms individual ML models in forecasting soil moisture with higher precision. These findings highlight the potential of IHML models to advance smart farming practices and enable precise irrigation management, paving the way for improved crop yield and sustainable agriculture.
Optimization of PLA 3D Printing Parameters using a Combined SMART-MOORA Multi-Criteria Decision-Making Approach
Authors: Linh Pham Ngoc , Tu Ngo Quang , Nguyen Van-Canh , Pham Viet-Thanh
Published: February 2025

This paper presents an optimization study of 3D printing parameters for Polylactic Acid (PLA) using a combined SMART-MOORA multi-criteria decision-making approach. The research focused on three key performance characteristics: tensile strength, strain, and modulus. By employing the Taguchi L27 orthogonal array, the authors conducted 27 experimental trials, varying the printing temperature, print speed, layer height, and bed temperature. The Simple Multi-Attribute Rating Technique (SMART) method was utilized to assign weights to the criteria, emphasizing tensile strength due to its significance in structural applications. Subsequently, the Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) method was applied to rank the experiments based on the weighted criteria. The findings demonstrated that experiments with high tensile strength and strain values were ranked the highest, underscoring the importance of balancing strength and flexibility in optimizing 3D-printed parts. The sensitivity analysis confirmed the robustness of the optimization results, as the rankings remained stable even when the importance of the criteria was adjusted. This study showcases the effectiveness of the SMART-MOORA approach in optimizing 3D printing parameters, providing a framework to enhance the mechanical performance of PLA parts.
Temperature Dependence Evaluation of CO2 Adsorption on Eagle Ford Shale using Isothermal Models: A Comparative Study
Authors: Zardari Zaheer Hussain , Mohshim Dzeti Farhah
Published: February 2025

This study investigates the CO2 adsorption capacity of the Eagle Ford (EF) shale under varying temperatures, utilizing six isothermal adsorption models: Langmuir, Freundlich, Dubinin-Radushkevich (D-R), Sips, Toth, and Brunauer-Emmett-Teller (BET). The shale sample was characterized through Total Organic Carbon (TOC) analysis, X-ray diffraction (XRD), BET surface area analysis, and Field Emission Scanning Electron Microscopy (FESEM) to assess its organic content, mineral composition, pore structure and elemental composition. CO2 adsorption experiments were conducted using a volumetric method at pressures up to 12 MPa and temperatures of 35°C, 55°C, and 70°C. The results revealed that the adsorption capacity increased with pressure but decreased with rising temperature, which is consistent with the exothermic nature of CO2 adsorption. Among the models, Freundlich and Sips provided the best fit for most temperature conditions, highlighting the heterogeneous nature of the shale surface, while the Langmuir, Toth, and D-R models performed well but with slight deviations. The BET model exhibited the poorest fit. Overall, the findings suggest that the EF shale has significant potential for CO2 storage, especially at lower temperatures, with Freundlich and Sips models being the most reliable for predicting adsorption behavior in EF shale formations.
Reducing the Error of Measurement of the Potentiometric Method of the Conductive Liquid Level Meter
Authors: Smirnov Andrey , Ritter Ekaterina , Savostin Alexey , Ritter Dmitry , Lengard Andrey
Published: February 2025

This article examines the linearization of a nonlinear transfer characteristic in a two-component sensor, with the potentiometric level meter serving as a case study. The conducted analysis employs a method of interpolation involving two interrelated variables: the liquid level readings are contingent upon both the liquid level and the liquid conductivity, while the liquid conductivity readings are influenced by both the liquid conductivity and the liquid level. The objective of the current paper is to identify a mathematical approach that enhances the precision of the measurement. A methodology for linearizing the nonlinear transfer characteristic of a conductivity level meter was established through the integration of two conversion correction tables and piecewise quadratic interpolation with iteration in the form of a table algorithm. This approach resulted in a reduction in the measurement error compared to the interpolation methods without iteration.
Comparative Evaluation of YOLO Models on an African Road Obstacles Dataset for Real-Time Obstacle Detection
Authors: Mutabarura Pison , Muchuka Nicasio , Segera Davies
Published: February 2025

Public datasets are used to train road obstacle detection models, but they lack diverse and rare object classes found on African roads, negatively impacting the performance of models trained on them. Although attempts have been made to create custom datasets to train road obstacle detection models, they lack the unique challenges posed by African wildlife and livestock commonly encountered on African roads. This leads to poor performance of road obstacle detection systems in the African context. This study presents a custom dataset with rare African object classes and compares the performance of three YOLO models on it using mean Average Precision (mAP). The images were collected from multiple sources to ensure a wide range of scenarios. Offline data augmentation was applied to increase dataset diversity and simulate real-world road scenarios. The models were trained and evaluated, with YOLOv5 demonstrating superiority over the other two models, with an object detection accuracy of 94.68% mAP at an Intersection over Union (IoU) threshold of 0.5 with data augmentation. Offline data augmentation significantly improved all models' object detection accuracy, especially for YOLOv3. The results reveal the effectiveness of the custom dataset and highlight the importance of data augmentation in improving object detection.
Torque Measurement System Design for Step Motor 23HS30-3004S
Authors: Minh Tran Vu , Anh Nguyen Thi , Tung Tran Thanh
Published: February 2025

This article describes the methodology employed in the development of a prototype system designed for torque measurement. The system consists of two components. Α rotating component, the transmitter, and a fixed component, the receiver. The torque data, obtained from a strain gauge bridge, are processed and transmitted to the receiver through a transformer-operated device. Rotary transformers are deployed to supply power to electronics that are mounted on shafts. This ensures continuous and stable data transmission without requiring physical connections. Additionally, the accuracy of the design model is verified through design equations and simulation results, reinforcing the system’s feasibility and reliability for real-world applications.
The Effect of Adding Waste Tire Rubber on Compressive Strength, Impact Resistance, and Damping Ratio of Fiber-Reinforced Foamed Concrete
Authors: Abd Oday Asaad , Hilal Ameer A. , Khaleel Tareq A.
Published: February 2025

Research was conducted to investigate the effects of incorporating optimal proportions of Waste Tire Rubber (WTR) on the compressive strength, impact resistance, and damping of fiber-reinforced Foamed Concrete (FC) modified with a Super-Plasticizer (SP). In this study, four FC types with a density of 1100 kg/m3 were produced: conventional FC, modified FC with SP, polypropylene (PP) fiber-reinforced FC, and fiber-reinforced rubberized FC (containing SP, PP, and WTR). To evaluate the effect of density on the FC properties, two additional fiber-reinforced rubberized FC mixtures were produced with densities of 800 and 1400 kg/m3. The sand in the FC was partially replaced with WTR at optimum ratios of 50% for coarse WTR (4.75–10 mm) and 34% for fine WTR (≤ 2.36 mm). Additionally, 53 kg/m3 of cement was substituted with fly ash. The results indicated that the addition of SP enhanced the properties of the fresh and hardened FC. For a given density of 1100 kg/m3, adding WTR led to decreased consistency and strength while increased the impact and damping compared to the reference containing only SP and PP. However, the fiber-reinforced rubberized FC mix with SP showed improvements of 79.5%, 3700%, and 21.45% in compressive strength, impact resistance, and damping, respectively compared to conventional FC (without SP and PP). With the exception of the damping ratio, the compressive strength and impact resistance increased when the rubberized FC density was elevated.
A Positive Output Buck-Boost Converter with Extensive Linear Conversion Ratio Adjustment Range
Authors: Ban Nguyen Van , Nghi Pham Vo Hong , Hai Quach Thanh , Anh Truong Viet
Published: February 2025

The article introduces a non-isolated positive output buck-boost converter that is capable of operating across a wide duty cycle range. The converter exhibits low nonlinearity in its voltage gain relative to D, thereby enhancing its capability to regulate output voltage or effectively regulate current over a broad duty cycle spectrum. A comparison of the performance of the proposed converter with existing converters is presented, with a particular consideration of the DC gain, voltage and current stresses on switches and diodes, and inductor currents. The comprehensive calculations include ideal and practical voltage gains, current assessments, stress analysis on components, parameter design, efficiency, and brief discussions on the discontinuous conduction mode and boundary conditions. The converter exhibits peak efficiencies of approximately 98.98% in boost mode and 99.16% in buck mode, which represent a notable advancement in power electronics. It is noteworthy that the converter displays minimal current and voltage overshoot in buck mode with a conversion ratio below 3. The overshoot relative to the normalized current is less than 1, reaching a minimum of 0.21, while the voltage overshoot is as low as 0.51. This contributes to superior buck mode efficiency. A laboratory model was developed with great care to validate the converter's experimental outcomes and theoretical evaluations. This provides reassurance about the reliability of the research.
Vehicle Body Vibration and Noise Impact on Driver and Passengers Analysis: The Case of the 29-Seat Thaco Garden TB79s Bus
Authors: Quan Vu Hai , Uy Duong Quang
Published: February 2025

Noise, Vibration, and Harshness (NVH) are critical factors influencing the comfort and satisfaction of vehicle occupants. NVH encompasses the sound levels generated during vehicle operation, the vibrations transmitted through the vehicle structure, and the perceived harshness of the ride quality. This study focuses on the NVH analysis of the Thaco Garden TB79S, a 29-seat passenger vehicle. The Finite Element Method (FEM) is applied to simulate the impact on the vehicle as it traverses two types of road surfaces, characteristic of Vietnam: rumble strips with varying frequencies and uneven roads with random excitation. The results reveal that the Sound Pressure Level (SPL) is within the frequency range from 0 to 200 Hz while the vehicle navigates on rumble strips. Further analysis of the vehicle's body and the air mass within the cabin identifies a resonance phenomenon at the driver's ear location at 176 Hz, resulting in a maximum SPL of 166.92 dB. Additionally, the study examines the noise formation within the cabin during a 25-second period as the vehicle travels over random road surfaces. The maximum sound pressure at the driver's position reaches 0.24 psi, with the SPL fluctuating primarily between 130 and 155 dB.
Dynamic Assessment of a Railway Bridge using Operational Modal Analysis and Fast Fourier Transform: A Comparative Study with Finite Element Analysis
Authors: Suwondo Riza , Hidayat Irpan , Suangga Made , Keintjem Militia , Walewangko Justitia
Published: February 2025

This research investigates the dynamic behavior of a railway bridge using both experimental and numerical methods. Field tests were conducted to capture the bridge response to live loading conditions with acceleration data collected via uniaxial accelerometers placed at critical locations along the structure. The dynamic characteristics, including the natural frequencies and mode shapes, were determined using two analytical techniques: Fast Fourier Transform (FFT) and Operational Modal Analysis (OMA). While FFT provides a frequency domain analysis, OMA enables the estimation of modal parameters, such as natural frequencies, mode shapes, and damping ratios, using the bridge's response to operational forces. The results revealed that the fundamental frequency obtained from the OMA (2.163 Hz) was higher than that obtained from the FFT (1.95 Hz) and the Finite Element Analysis (FEA) model (1.65 Hz). Additionally, the OMA produced mode shapes that were closely aligned with those predicted by the FEA, validating the accuracy of the numerical model. This study highlights the advantages of OMA over FFT, particularly the ability to capture mode shapes, and underscores the importance of integrating OMA with FEA for a comprehensive dynamic assessment of bridge structures. These findings contribute to the growing body of knowledge on structural monitoring and provide practical insights into improving bridge safety and performance.
Enhancing the RC4 Algorithm by Eliminating the Initiative Vector (IV) Transmission
Authors: Mohammed Waleed Abdelrahman Yousif , Fattah Salmah , Saeed Khalid Mohammed Osman , Ibrahim Ashraf Osman , Eltahier Safaa
Published: February 2025

The Rivest Cipher RC4 encryption algorithm is commonly utilized to generate keys of varying lengths. Despite its rapid processing speed, vulnerabilities within the algorithm have made it susceptible to exploitation, allowing attackers to compromise it within a matter of minutes. This paper introduces an innovative approach to address the vulnerabilities of the RC4 encryption algorithm by employing an Initiative Vector (IV). The proposed method incorporates a lengthy random text without transmitting an initialization vector. The proposed solution was rigorously validated, demonstrating performance comparable to existing solutions while simultaneously expanding the range of potential solutions and mitigating security threats. Further exploration into the use of a complex equation is recommended for calculating the swapping value j while maintaining the same high level of performance.
A Novel Lucas-based Clustering Optimization for Enhancing Survivability in Smart Home Design
Authors: Raju Rajana Kanaka , Amiripalli Shanmuk Srinivas
Published: February 2025

This study presents a novel Lucas-based topology optimization framework to enhance network survivability in smart homes, particularly against random node failures. As the proliferation of interconnected devices in the Internet of Things (IoT) environments increases, so does the vulnerability of these networks to node failures, which can significantly disrupt connectivity and functionality. By integrating the mathematical properties of Lucas numbers with advanced graph theory concepts, specifically the Trimet Graph Optimization (TGO) model, this framework systematically addresses the challenges posed by random node failures. The proposed model optimizes network topologies to ensure robust connectivity and resilience, allowing smart home networks to maintain operational integrity even under adverse conditions. Simulations and theoretical analyses demonstrate the effectiveness of this approach, highlighting its potential to improve the reliability of smart home networks.