bluuebunny/crossref_metadata_2025 huggingface dataset:
Humanitarian Food Logistics: Optimizing the Inventory Model of Corn Soya Blend (CSB) and Deterioration Rate by the World Food Program (WFP) Under the Conflict and Insurgency Controlled Areas
Authors: Buba M.T. Hambagda
Published: February 2025

The paper carefully examines malnutrition food supplements, the corn soya blend  (CSB) distribution among children, and breastfeeding mothers in areas engulfed by conflict attacks. The vulnerable situation created by the Boko Haram Jihadists cannot be easily quantified or overemphasized. The deterioration rate of corn soya blend (CSB) is high due to the impact of climate, temperature, and storage facility. This situation also leads to a substantial rise in holding and ordering costs and the safety of WFP humanitarian workers, CBS consignments, and other essential items is crucial. This study attempts to develop an optimized inventory model for the distribution of corn soya blend (CSB) to enhance the performance of the WFP.  An inventory model is developed to identify the optimality, a numerical analysis is conducted to assess the model's effectiveness, and a sensitivity analysis is performed to evaluate how changes in key parameters affect the optimal solution. This analysis helps demonstrate the model's reliability and highlights which parameters are most influential to the objective. The model can assist relief organizations in efficiently managing the distribution of supplies to conflict-controlled areas.
Improved Medical Image Enhancement by Optimized Features and Parameters using Convolution with Grey Wolves Approach
Authors: Monika
Published: February 2025

Ultrasound imaging is a cornerstone in medical diagnostics, valued for its non-invasive nature, cost-effectiveness, and widespread accessibility. This imaging modality is critical in the early detection of a variety of conditions, particularly amidst the rising global prevalence of chronic diseases. Despite its numerous advantages, ultrasound imaging is frequently compromised by speckle noise, which can obscure crucial anatomical details and potentially lead to diagnostic inaccuracies. Addressing this issue, the paper introduces an innovative approach combining a Convolutional Neural Network (CNN) with Grey Wolves Optimization to enhance ultrasound image quality by optimizing features and parameters for noise reduction. This method leverages the strengths of both advanced machine learning algorithms and nature-inspired optimization techniques to efficiently and effectively reduce speckle noise without sacrificing critical image details. Our experiments, conducted across various types of ultrasound images, demonstrate that the proposed method not only significantly outperforms traditional denoising techniques like Kuan-NLM, Anisotropic Diffusion, and Wavelet-based methods in terms of PSNR, SSIM, NCC, and UQI metrics but also maintains a competitive processing time. The optimized NLM filter, in particular, showcases remarkable noise suppression capabilities while preserving essential features, thereby greatly enhancing the diagnostic utility of ultrasound images. The implementation of this method in clinical settings could potentially lead to quicker, more accurate diagnoses without the need for repeated scans, thereby reducing both patient discomfort and procedural costs. The results affirm the efficacy of combining CNNs with Grey Wolves Optimization, highlighting its potential to set a new standard in medical imaging enhancements. This study paves the way for further research into hybrid approaches that could refine diagnostic processes across various imaging modalities.
Fabrication And Design Of Sustainable Natural, Artificial And Hybrid Fiber Composites For Automotive And Aerospace Applications Using Coconut, Banana, And Jute Fibers
Authors: Manoj Shalikrao Patil
Published: February 2025

The aim of this study is to provide a comprehensive overview of the fabrication, design, and characteristics of sustainable composites based on natural, artificial, and hybrid fibers, particularly with coconut, banana, and jute fibers. It emphasizes these materials as resources that are very friendly to the environment for application in the automotive and aerospace sectors. This discusses the influences of mixing together natural fibers of banana and jute with synthetic fibers in hybrid composites with an objective to increase the shelf life and the performance of the epoxy-based composites. Key techniques in the manufacture of fiber composites include fiber choice, surface modifications, and composition of the epoxy matrix; processing techniques like compression molding, vacuum infusion, and traditional lay-up all became important contributors. Mechanical tests return tensile strengths from 12 MPa (abaca) to 1627 MPa (fruit-based composites), and densities from 295 kg/m³ for kenaf up to 1560 kg/m³ for pineapple, so these are versatile materials. The study underlines that mechanical performance, tensile strength, thermal conductivity, and impact resistance are highly influenced by fiber-matrix interaction, fiber orientation, and interfacial adhesion. Hybrid composites show immense potential for use in structural and non-structural applications in automotive and aerospace industries, especially in load-bearing and interior components. The research concludes that the integration of coconut, banana, and jute fibers in epoxy-based composites offers a promising solution to reduce environmental impact while maintaining durability and performance, supporting sustainable advancements in high-performance sectors.
A Testing Design Approach to the Gap in People's Business Credit Recipients, in Micro Small Business in Depok, West Java, Indonesia
Authors: Irma Setyawati
Published: February 2025

This research investigates the challenges encountered by micro and small enterprises (MSEs) in Depok, Indonesia, in accessing the Kredit Usaha Rakyat (KUR) program and explores how these obstacles impact poverty reduction and income distribution. Employing an experimental design, the study focuses on key factors affecting financial access, including MSEs' comprehension of KUR, business age, financial reporting practices, and the adoption of technology in marketing. A total of 147 MSEs were divided into treatment and control groups, where the treatment group received targeted interventions through Focus Group Discussions (FGDs). Data was collected both before and after the intervention to assess its impact on KUR access. The findings reveal a marked improvement in KUR access for the treatment group, particularly in terms of enhanced understanding of KUR procedures and significantly improved financial reporting capabilities. Additionally, the increased adoption of technology in marketing was found to positively influence financial access. Regression analysis demonstrates that factors such as KUR understanding and financial reporting practices have a statistically significant effect on financial accessibility, while business age exerts minimal influence. Conversely, the control group did not exhibit any substantial changes in KUR access, highlighting the efficacy of the intervention. In conclusion, this study emphasizes that structured interventions, such as FGDs, are effective in strengthening MSEs' ability to secure financing through KUR, which is pivotal for poverty alleviation and economic inclusion. The results underscore the necessity for ongoing training and support programs to sustain MSEs' growth and financial stability.
Construct Post Competency Model Based on the Big Data Technology
Authors: Feng Jing
Published: February 2025

This study aimed to explore one construction method of competency model by big data technology. In the first stage, The data were collected from the recruitment website by Web Crawling. The data analysis was done through text mining using Python to obtain the characteristics of competency elements. In the second stage, part of the data came from the questionnaire survey. The data were analyzed by factor analysis, analytic hierarchy process, and k-means cluster analysis using SPSS, which was to give weight through subjective and objective combination. The results of this study revealed that the competency model of the company’s software project manager is constructed. The model included five dimensions: professional ability, self-improvement, personal charm, personal characteristics, and work practice. The weights account for 28.59%, 15.36%, 15.17%, 18.58% and 22.31% respectively. The five dimensions contain 34 indicators, among which the top ones are organization and coordination ability of 7.9%, systematic thinking ability of 6.99%, problem analysis and problem-solving ability of 4.65%, correct values of 4.84%, unity, and cooperation of 4.81% and forward-looking ability of 4.13%. Traditional methods lack a quantitative way in the data collection and analysis stage, resulting in the lack of objectivity of the results. The study improved this situation. The method of a building competency model is enriched by big data technology. At the same time, the subjective and objective combination weighting method effectively avoided the limitations of being too subjective or too objective.
Mathematical Modeling and analysis of the Relationship Between Educational Achievements and Income
Authors: Archana Ratnaparkhi
Published: February 2025

The correlation between income and education has been studied in many fields and literature. But, in socio-economic context, other factors like demographic aspects, work experience, industry, and job satisfaction are necessary. In this study, we created a synthetic dataset that contains educational levels, work experience, industries, sex, marital status, and work satisfaction. We performed supplementary data analysis to show how em- pirical data of different educational levels obtained increased income did increased income. The findings obtained suggest that education and work experience greatly affect income, which shows that higher earnings are linked to the level of schooling completed. In particular, a very weak negative relationship was established between influence and income, but this effect was noted among men. The change in the influence that was observed differed marginally by marital status. From this evidence, one can infer that any affirmative policy should focus on the subsidizing of barriers to accessibility of higher education because it is said to resolve income inequality gaps in the country.
A Cross-Layer-Routing Protocol with Malicious node detection using BiRNN in WSN
Authors: S.Saritha
Published: February 2025

A wireless sensor network (WSN) is a standalone device that consists of a discrete group of sensor nodes (SN) for information gathering, device monitoring, and environmental position sensing. The main challenge is to present an energy-efficient framework and conserve energy while building a route path alongside each sensor node at SN due to the limited energy resources available. Nonetheless, a great deal of energy-efficient methods concentrated heavily on energy harvesting and decreased energy usage, but they were unable to enable energy-efficient routing in WSNs with low energy usage. The research work uses Kinetic Gas Molecules Optimization (KGMO) based Modified Reptile Search Algorithm for cluster head selection to minimise the energy consumption in order to address this issue. Utilising a modified version of the Reptile search algorithm, the inertia weight of the KGMO is determined by evaluating trust (direct and indirect) based on energy, distance, probability of risk, delay, and Received Signal Strength Indicator (RSSI), with the optimal CH being chosen. Lastly, to find the shortest path, use an energy-efficient cross-layer-based expedient routing protocol (E-CERP), which lowers network overhead dynamically. Assuring security for the massive amounts of data being generated by sensors is the most difficult task. It must balance the trade-offs with a number of other factors, including power consumption, delay, latency, and data aggregation, in order to ensure security and make room for different types of research. The work's concept is to use a Bidirectional Recurrent Neural Network (BiRNN) model to identify the malicious nodes. When compared to previous methods, the proposed model demonstrated exceptional results by achieving a Packet Delivery Ratio (PDR) of 0.99, Throughput of 0.98, Packet Delay reduced to 0.05, Power Consumption minimized to 1.52, and an outstanding Accuracy of 99.21%. These findings underscore its efficacy in enhancing both network performance.
A Study on Comparative Analysis of Energy-Efficient Cluster-Based Algorithms for Wireless Sensor Networks
Authors: M. V. Sudhamani
Published: February 2025

Wireless Sensor Networks (WSNs) have revolutionized applications in environmental monitoring, healthcare, and military operations by enabling efficient and real-time data acquisition. However, the limited power resources of sensor nodes pose a significant challenge to the long-term sustainability of these networks. Addressing this critical issue, cluster-based algorithms have emerged as an effective solution for optimizing energy consumption by organizing sensor nodes into energy-efficient clusters.
This paper explores the latest advancements in energy-efficient cluster-based algorithms for WSNs, focusing on their design and implementation across various scenarios. The algorithms are systematically categorized based on key metrics, including clustering techniques, energy optimization strategies, and scalability. By highlighting innovative methodologies and their potential impact, this study provides valuable insights into overcoming energy limitations while ensuring the reliability and longevity of WSNs in diverse application domains.
A Study on Signless Laplacian Energy of an Intuitionistic Fuzzy Graphs with Applications to Group Decision Making
Authors: Obbu Ramesh,
Published: February 2025

Abstract:Introduction: Group decision-making shows a critical job while designating with dynamic issues of quick development of society. Decision make is the manner about discovering the beneficial option among the possible alternatives. In first-rate multiple-criteria choice construction methods, the rankings or the weights of the standards are recognized precisely. However, if selection makers are now not capable in conformity with involve doubt within the defining on linguistic variables based concerning fuzzy sets (FSs) and the intuitionistic fuzzy set (IFS) theory is useful to this job well. The first assurance of this paper is to show the sensibility of some Group decision-making on the Signless Laplacian energy of intuitionistic fuzzy diagrams. We present numerical models, flexibly chain associations in the construction business problem based on extended VIKOR method and Evaluation of the schemes for development of agriculture and farmers’ welfare problem based on MULTIMOORA method to light up the introductions of our arranged ideas in result making to rank the best one. Choice makes is the way about finding the valuable choice among the potential other options. In top mark numerous rules decision development techniques, the rankings or the weights of the guidelines are perceived exactly. In any case, on the off chance that determination creators are currently not skilled in congruity with include question inside the characterizing on semantic factors based concerning fuzzy sets (FSs) and the intuitionistic fuzzy set (IFS) hypothesis is valuable to this work well.
Conclusions: An Intuitionistic fuzzy model is pushed off in PC innovation, correspondence, organizing, when the idea of indeterminacy is current. In this paper, we have acquainted persuaded original thoughts requesting in group decision-making dependent on IFIRs is introduced to delineate the relevance of the proposed ideas of Intuitionistic Fuzzy Graphs. This paper covers the EXTENDED VIKOR method and MULTIMOORA method to resolve the problem of evaluation and ranking the potential alternatives based on Signless laplacian energy of Intuitionistic fuzzy graphs. At long last, mathematical models show a use of the VIKOR technique, EXTENDED VIKOR strategy and MULTIMOORA strategy. These insights are furthermore shown with genuine stage outline. Furthermore, we see the situation with the best one. Furthermore, it gives another aspect in the gathering assessment processes and these strategies can be applied to different circumstances too. Despite the fact that our proposed techniques give another aspect to the calculation viewpoint, the issue on how genuine the contentions are permitted along the judgment cycle actually needs further examination. This issue is left for additional exploration sooner rather than later.
Quasinormed Cones and Bicompletion Isometries
Authors: Manal Yagoub Ahmed Juma
Published: February 2025

Since (X, e_(p_j )) is an extended quasi-metric cone, we demonstrate how any quasi-norm p_jon an actual cancellative cone X naturally implies an extended quasi-metric e_(p_j ) on that cone. We demonstrate that bicompletion respects the structure of a quasi-normalized cone under bijective isometries. In fact, we find that isometries are not generally injective in this case. In addition, a few situations are shown.
Two Warehouse Inventory Model for Deteriorating Items with Trade Credit Under Inflation
Authors: Monika Rani,
Published: February 2025

Establishing warehouses is essential in societies where commercial activities have expanded significantly, making efficient storage a prerequisite for smooth exchange. Variable holding costs play a crucial role in determining warehouse expenses, as these costs typically increase over time. This study examines an inventory model for perishable goods stored in two types of warehouses (rented and owned) under conditions of partial backlogging and inflation. Along with exploring sustainable marketing strategies, the article investigates the connections between product pricing, advertising, and demand, highlight the considerable impact of price reductions and promotions by advertisement. Additionally, this study addresses a trade credit scheme where the supplier grants the retailer a fixed time frame to finalize the account. Top of FormBottom of FormThe research aims to enhance operational efficiency and reduce overall costs by optimizing key decision factors such as storage capacity, credit period, and replenishment time period. To explore the effects on the system’s optimal total cost, along with managerial insights, numerical examples and sensitivity analyses are provided. The model’s outcomes are validated through sensitivity analysis using Mathematica 13.0 software, ensuring their robustness and reliability.
Some Special Structures of〖 δ〗_1 Near-Rings
Authors: S.Sivanthi
Published: February 2025

If, in a ring (N, +, ∙) we ignore the commutativity of ‘+’ and one of the distributive laws, (N, +, ∙) becomes a Near-Ring. If we do not stipulate the left distributive law, (N, +, ∙) is a right near-ring. This research aims to introduce the concept of  δ_1 Near-Ring. For every x,y in N,  xNy=Nx^2 y^2 is called δ_1Near-Ring. The element wise characterization for〖 δ〗_1 Near-Ring will be investigated and shall establish theorems and properties in this Near-Ring.
Examining Ukraine's Approaches and Obstacles in Addressing Cyber-Financial Fraud Amid Armed Conflict
Authors: Oleg Reznik
Published: February 2025

This article provides an in-depth examination of contemporary methods of financial fraud in the digital realm, highlighting strategies for addressing this pervasive issue through the lens of Ukraine's experiences. The authors employ an interdisciplinary methodology that combines methods from legal science, economics, and information technology. Financial fraud is continuously evolving, manifesting in new forms and expanding in scope. Cybercriminals utilize various tactics, including phishing schemes, hacking into banking systems, stealing payment information, and deploying malware to unlawfully access the financial assets of individuals and organizations. Additionally, fraudulent activities are increasingly prevalent on cryptocurrency exchanges, where criminals exploit digital assets. The study underscores that an effective response to financial cybercrime necessitates a holistic approach, which includes monitoring financial transactions to detect anomalous activities, analyzing cyber threats to understand the landscape of financial cybercrime, and enhancing cybersecurity measures to protect against potential threats. Furthermore, user education is emphasized as a critical component, focusing on raising awareness about risks and protective strategies, as many fraudulent activities stem from negligence and a lack of awareness. Throughout the investigation, the authors identify key forms of financial cyber fraud and formulate effective strategies for investigating, preventing, and analyzing these socially harmful acts.
Modi Script Handwritten Character Classification using Drop-out Induced Incremental Learning Approach
Authors: Chaitali Chandankhede
Published: February 2025

Most of genuine information based on healthcare, food habits and Ayurveda is imbibed in Modi script and very few people are remaining who understand Modi script reading and writing. This motivates us to work with Modi script. After going through the deep literature review, we found InceptionV3 and Residual framework (ResNet152) need to experiment on Modi script dataset. The main objective of the system is to develop an incremental learning model which starts identifying from individual characters with acceptable accuracy, which further trained and tested for words followed by sentences, group of sentences. We are the first one to experiment with whole Modi script character set which covers 360 class labels comprising 35 consonants and 10 vowels which was collected using different people. The individual characters are cut, labelled manually and pre-processed using Otsu, Savaula and To-zero binarization techniques. The model is further explored by adding InceptionResNetV2 framework for classification. Initially the model shows overfitting behaviour as discussed in section 5.2. To regularize the model, first experiment is done using augmented dataset which also not able to show satisfactory results and took un-conventional time for training. Further, drop-out approach is induced which shows good hyper-parameter tuning as shown in table 6. Due to drop-out layers, training is extended till 300 epochs to get better results. After training using drop-out technique, the model is showing proportionate increase in training and validation accuracy. At the same time, it is showing considerable consistent decrease in training and validation loss. This gives us intuition that the newly developed hybrid model helps to reduce overfitting and learns appropriately. The developed incremental model is tested on Modi words where all words are mostly classified correctly except the character “sa” which is in-correctly classified as banacha “na” sinch both the characters look similar.
The Role of Artificial Intelligence in Enhancing Business Efficiency and Supply Chain Management
Authors: Irshadullah Asim Mohammed
Published: February 2025

Artificial Intelligence (AI) is revolutionizing business operations and supply chain management by enhancing efficiency, reducing costs, and optimizing decision-making. This paper explores the transformative impact of AI in streamlining supply chain processes, improving demand forecasting, and enhancing inventory management. AI-driven automation, predictive analytics, and real-time data processing are enabling businesses to minimize inefficiencies, mitigate risks, and enhance overall productivity.
The study highlights key AI technologies, including machine learning, natural language processing, and robotic process automation, which are reshaping logistics, procurement, and distribution networks. AI-powered solutions enable organizations to make data-driven decisions, enhance supplier collaboration, and improve customer satisfaction. Additionally, AI facilitates real-time monitoring of supply chains, providing actionable insights that help businesses proactively address disruptions and optimize resource allocation.
Furthermore, this paper examines the role of AI in fostering sustainability by reducing waste, improving energy efficiency, and supporting ethical sourcing practices. While AI offers numerous benefits, its implementation also presents challenges such as data security concerns, high initial investment costs, and the need for skilled professionals. Addressing these challenges requires strategic integration, continuous innovation, and adherence to ethical guidelines.
Through an extensive review of existing literature and case studies, this paper provides insights into the growing significance of AI in enhancing business efficiency and supply chain resilience. The findings emphasize the necessity for organizations to embrace AI-driven strategies to maintain a competitive edge in an increasingly dynamic and digitalized marketplace. Future research should focus on overcoming implementation barriers and exploring emerging AI applications that can further revolutionize supply chain management.
Transforming Narratives and Engagement of AI and its Related Technology in Different Domains of Journalism - A Bibliometric Analysis
Authors: Prabhat Dixit
Published: February 2025

Introduction: This research paper explores the profound impact of Artificial Intelligence (AI) on the print media landscape, highlighting its transformative effects on narrative construction and audience engagement. As AI technologies advance, Indian media organizations are actively integrating these innovations to enhance content creation and audience interaction.
Objectives: Citation analysis employs a full counting method, while timeline and burst detection analyses uncover significant topic trends and recent citations.
Methods: The paper conducts an extensive bibliometric analysis of AI utilization in media research, drawing from publications in the Scopus database over the years. The analysis encompasses yearly publications, types of publications, and trends across various domains, including content creation and audience engagement.
Results: The research highlights bibliometric findings related to authors, organizations, publication types, and documents with the strongest collaborative linkages within the context of AI in Indian media.
Conclusions: The paper provides valuable insights into how AI is reshaping the print media sector in India, emphasizing the key trends and collaborative efforts driving this transformation.
Blockchain for the Next Decade: Future Trends and Predictions in Healthcare
Authors: Kavithamani M
Published: February 2025

The digital era healthcare everywhere is leveraging towards implementing blockchain technology as a fundamental element to enable secure, transparent, and decentralised solutions for the worldwide. In this chapter we will explore where blockchain in healthcare is likely to go over the next decade, with a particular emphasis on how it may be used to address the challenges of patient data management, interoperability and supply chain security. In the world of decentralized health care ecosystems fuelled by blockchain to help data integrity, privacy and access this chapter looks at where blockchain intersects with artificial intelligence (AI), the Internet of Things (IoT), and machine learning, and how these intersections are expected to evolve personalized medicine, real-time patient monitoring and predictive analytics. We also discuss the regulations issues, ethical and privacy concerns as well as prerequisite blockchain infrastructure enhancements needed to facilitate DCT utilization and provide recommendations for healthcare stakeholders based on our review.
The Influence of Accountability and “Berakhlak” Organizational Culture on Local Government Performance: Evidence from South Sumatera Province, Indonesia
Authors: Mahdi Hendrich
Published: February 2025

Accountability and Organizational Culture can affect the performance of Local Government. Through the framework of the New Public Management theory, the South Sumatra Provincial Government, Indonesia, is considered to have been able to implement modifications of accountability and organizational culture from the old pattern to a more professional and less rigid pattern, which has an impact on changes in performance. This study attempts to analyze the magnitude of the influence of Accountability and Organizational Culture “BerAKHLAK” on the Performance of Local Government in South Sumatra Province, Indonesia. The results of the study found that Accountability and Organizational Culture “BerAKHLAK” have a positive and significant effect on Local Government Performance.
Statistical and Mathematical Approaches to Disease Identification in Pennisetum glaucum Using Hybrid Optimization Techniques
Authors: Chiti Nigam
Published: February 2025

To achieve maximum production and food security in the realm of agricultural biotechnology, timely and efficient disease detection in crops such as pearl millet, or Pennisetum glaucum, is crucial. This study explores the application of hybrid optimization methods to enhance the identification of Pennisetum glaucum disease. Traditional sickness classification techniques frequently have low accuracy and efficacy, necessitating research into more advanced techniques. Our approach integrates a variety of optimization techniques, including genetic algorithms (GA), particle swarm optimization (PSO), and artificial neural networks (ANN), to create a dependable hybrid model. The hybrid model enhances classification performance overall by utilizing the benefits of each unique strategy. We collected a sizable dataset of samples of Pennisetum glaucum suffering from various illnesses in order to train and validate our model. The results demonstrate a significant improvement in sickness classification accuracy when compared to conventional methods, with the hybrid model achieving a precision rate of more than 95%. Furthermore, hybrid optimization strategies reduced the computational time required for model training and prediction. The current study demonstrates how hybrid optimization has the potential to transform agricultural disease management techniques by offering a scalable and practical answer to farmers and agronomists.
Deep Learning based Glaucoma Classification Performance Evaluation of Stacking Boosting and CNN Ensembles
Authors: Radheshyam Acholiya
Published: February 2025

Glaucoma remains a major global health concern, leading to irreversible blindness if not detected early. Recent advancements in deep learning have enabled automated glaucoma classification using retinal imaging techniques. This study evaluates four ensemble-based models Stacking (CNN, Transformer, XGBoost), Boosting (CNN Features + LightGBM), Voting CNN Ensemble (EfficientNet, ResNet, DenseNet), and Bagging (Random Forest with CNN Features) to determine the most effective approach for glaucoma detection. Performance analysis based on accuracy, precision, recall, F1-score, and AUC-ROC demonstrates that Voting CNN Ensemble achieved the highest accuracy (99%), outperforming other models. The segmentation results further validate the models' ability to detect the optic disc and cup, essential for glaucoma diagnosis. These findings emphasize the potential of ensemble deep learning models in clinical applications, paving the way for real-world deployment and future enhancements using multimodal data integration.
Deep Learning-Based High-Accurate Violence Detection for Intelligent Video Surveillance Systems
Authors: Sumeet Kothari
Published: February 2025

The rapid increase in violent incidents necessitates the development of intelligent video surveillance systems capable of real-time violence detection. This research introduces a high-accuracy deep learning framework integrating CNN and Bi-LSTM, significantly enhancing the detection of violent activities in surveillance footage. The proposed model outperforms existing approaches, achieving 99% accuracy, surpassing traditional deep learning models such as CNN, VGG16, and ResNet50. Extensive evaluations validate its robustness, precision, and recall, ensuring reliable monitoring with minimal false positives. The study highlights the role of deep learning in revolutionizing crime prevention by enabling automated, real-time security solutions.
OUTPUT WINDOWS IN ACCELERATOR TECHNOLOGY
Authors: Isunts H.A.
Published: February 2025

The study relates to accelerator technology, in particular, the output window unit, which plays a key role in extracting the beam from the accelerator. The task is to obtain a quasi-ideal unit of the accelerator output window, which will maximize the parameters of the beam exiting the accelerator while maintaining the vacuum level present in the chamber. The output windows consist of a metal foil, two different flanges, and an O-ring. Other minor details may also be involved in the unit. Simplifying the connection, we can say that it is a thin foil attached to pipelines that are vacuum tight, and through which the electron beam moves in an electromagnetic field.
According to the research conducted to date, an analysis of the output windows designed for single and multiple use in accelerator technology has been carried out. According to the results of the analysis, the relevant problems are presented in terms of the use. The materials for this connection are of great importance, since, depending on the composition of the connection and the selected materials, the parameters of the resulting beam and the degree of vacuum tightness may vary. Nowadays, titanium foil output windows are used more often because this metal has higher strength and reliability. Also, titanium metal foil has high electrical permeability compared to aluminum and beryllium foils, which are widely used in the manufacture of output windows.
The focus of the work is on two characteristics of the metal foil: the thinner the metal foil, the less loss of parameters of the beam passing through it. But the thinner the metal foil, the higher the probability of its rupture. It can be said that these two characteristics are rivals.
The units of the output window are connected in two ways: in the form of detachable and non-detachable connections. In a detachable or mechanical connection, the assembly is tightening with a bolts and nuts with plastic deformation of the O-ring. While in the other case, the tightening of the joint is ensured at high temperatures.
Overall, the paper analyzes the current state of the output windows, substantiates the initial data for further research related to this topic.
A PREVALENCE STUDY ON THE OCCURRENCE OF WORK ACCIDENTS AMONG THE MANUAL MATERIAL HANDLING WORKERS OF THE CENTRAL MARKET AREA IN AGARTALA CITY, WEST TRIPURA
Authors: Das Swagatam , Ghoshal Dr. Goutam
Published: February 2025

One of the most important central market areas of Agartala city is the Maharajganj Bazaar (Gol Bazar) where material handling, loading, and unloading work is a continuous process. Manual material handling (MMH), being a strenuous work often leads to injuries, accidents, and even death. Fifty MMH workers of the central market area in Agartala city were selected to identify the risk factors contributing to accidents. A standardized modified Nordic questionnaire was performed. Analysis of working postures by OWAS method was done and history of accident cases was collected. It was revealed that most accidents, being struck by objects and slipping and falling, have occurred during lifting and carrying loads. Accident records suggested that few accident cases have reached the hospitals indicating a general negligence of attending the hospitals.
Developing Intercultural Sensitivity through Literature Education
Authors: Damyanova Adriana
Published: February 2025

The text uses Milton Bennett‘s Developmental Model of Intercultural Sensitivity (DMIS) to problematize some educational stereotypes and reveal some of the possibilities of reading interpretation, or of understanding reading literature at school to contribute to the development of intercultural sensitivity of students. For this purpose, the works of Hr. Botev, Iv. Vazov, A. Konstantinov, P. P. Slaveykov, Y. Radichkov, and others.
Assessing Reading literacy – Science Meets Education
Authors: Kukova Hristina , Budinova Gergana
Published: February 2025

The paper aims to present a study conducted among Bulgarian students. The study was organized in the framework of the Assessing Reading Literacy and Comprehension of Early Graders in Bulgaria and Italy project intended to compare and analyze the results of primary school students in Italy and Bulgaria. The paper discusses the objectives of the study, the preparation stage by both researchers and teachers, the methods of conducting the study, the platform used, and the preliminary results. A brief outline of the future work on data processing and the parameters of the text to be analysed is given.
Possibilities and Limitations when Using Artificial Intelligence in Project-oriented Learning
Authors: Chakarova Stoyka
Published: February 2025

The article analyzes the possibilities and limitations of using artificial intelligence (AI) in education. Some deficits in the development of the student‘s personality, which may arise as a consequence of its unbalanced use in education, are commented on. A methodological model of project-oriented learning is proposed, in which the use of AI, in particular ChatGPT, as a tool and elements of project work is envisaged. It has in mind its possible negative influence on the motivation for reading, learning, critical thinking, on aspects of the young person‘s mental and moral development, etc. Some pedagogical principles in the use of AI in education are derived, including the use of critical thinking towards the products generated by it and the formation of models for working with AI, in which it is perceived as an auxiliary tool for reaching one‘s own solutions, and not as the author of such or as an authority.
Artificial intelligence and its application in the preparation for the National External Assessment in the 7th grade to compose a detailed free-form answer
Authors: Ivanova Katya
Published: February 2025

The research carried out on the topic: “Artificial intelligence and its application in the preparation for the National External Assessment in the 7th grade to compose a detailed free-form answer” aims to support the work of teachers and students in junior high school. It examines the current topic of artificial intelligence as an opportunity to improve national external assessment results in one of its problem areas. Within the framework of the study, specific steps to help students and evaluation criteria based on the most frequent mistakes made by them were identified. The students participating in the study evaluate their responses and the responses given by ChatGPT on a specific quotation from the work of Ivan Vazov. Based on the comparison, the students bring out and understand the problems and difficulties they have. Through the innovative approach, their research inquiry and critical thinking is stimulated.
Design and assessment of microencapsulation systems for biologically active agents in homemade sparkling winemaking
Authors: M Assilova , B Mutaliyeva , A Tleukeyeva
Published: February 2025

The purpose of this study is to design and assess the use of microencapsulation systems for delivering yeast during secondary fermentation in sparkling winemaking. Microencapsulation has gained prominence in the food and beverage industry for enhancing product quality and process efficiency. Yeast microcapsules were prepared using polysaccharide matrices specifically alginate and chitosan hydrogels. The process involved utilized injecting a cell suspension into a 1% CaCl2 solution with an optimal yeast-to-alginate ratio of 1:5. The findings demonstrated microencapsulation efficiency of 97.21±1.65% for alginate microcapsules and 95.28 ± 2.31% for chitosan microcapsules. Gas chromatography-mass spectrometry (GC-MS) analysis highlighted improvements in the volatile and non-volatile compounds contributing to an enhanced flavor and aroma profile. Furthermore, the use of natural-origin materials in the microcapsules was found to be non-toxic to yeast cells, supporting their viability and fermentation efficiency. The results suggest that yeast microencapsulation offers significant benefits for winemaking including improved process control and product quality. The practical implication of this study lies in the potential application of microencapsulation technology to improve the efficiency and consistency of fermentation processes, offering a reliable and innovative approach for producing sparkling wine of superior quality.
The assessment on the use of artificial intelligence writing tools as a confidant of business administration student-researchers using technology acceptance model
Authors: Jesus Fhrizz S De , Co Annalene Grace E
Published: February 2025

Artificial Intelligence (AI) has steadily made its way into the academic world; its capabilities have expanded, enabling it to play a more integral role in academic research and writing in various fields like business administration. This research aims to assess the use of Artificial Intelligence writing tools by Business Administration Student-Researchers from Region III in terms of perceived usefulness, ease of use of the Technology Acceptance Model (TAM), perceived attitude towards technology, subjective norms, and facilitating conditions. Additionally, it aims to categorize the students in terms of sex, form of AI writing tools used, and frequency of exposure to AI to determine its relation to their perception of using AI writing tools. This study employed a quantitative descriptive methodology, involving a statistical analysis of frequency and percentage, weighted mean, and Pearson r. In gathering data, the instrument used by the researchers was a questionnaire that underwent expert validation and a series of trial testing. As a result, it was found that most of the Business Administration student-researchers were female, who mostly used grammar and style checkers as a form of writing tool, and tended to use it depending on the situation. It was also found that perceptions of usefulness, ease of use, attitude towards technology, and facilitating conditions are major factors affecting the comfort and acceptance of the students in using Artificial Intelligence in research writing. It was also reported that using AI writing tools presented challenges such as inaccurate responses, difficulty of use, fear of potential dependency, lack of institutional support, and limited access to technology. Finally, it is recommended that institutions take action regarding the growing number of AI tools to better integrate their capabilities for the research writing of students. By conducting seminars, webinars, and training, and tailoring the functions of AI, this could help students use AI more effectively.
Cybersecurity awareness among school students: Exploring influencing factors, legal implications, and knowledge gaps
Authors: Altarawneh Mostafa Hussam Mostafa , Althunibat Ahmad , Almajali Mohmmad Husien , Alzriqat Naif , Alazzam Seif
Published: February 2025

In recent times, the widespread use of the Internet by various social groups has led to an increase in cyber risks and threats. Consequently, it has become crucial to educate these groups, particularly younger age groups, on the importance of cybersecurity to prevent them from falling victim to these risks. The study will focus on the comprehension of factors affecting cybersecurity among school students and the prevention of cyber threats and risks targeting the younger generation. A cross-sectional survey was conducted on students from middle school in Jordan, using a standardized questionnaire with 37 questions distributed under three categories: demographic data, cybersecurity awareness, and influencing factors. For this particular research study, the responses gathered from the open-ended questionnaire were analyzed, where multiple regression analysis was conducted, and five major research hypotheses were proposed and verified. The results indicate that it can be deduced that the level of knowledge about passwords, social networking sites, and legal aspects influences the level of cybersecurity awareness among the students. Other significant correlates of awareness were self-perceived awareness and education in cybersecurity; the model accounted for 67.5% of the variance in levels of awareness. The study also establishes that awareness of cybersecurity issues must be based on knowledge and should incorporate an educational process that targets young people. Critical sections to be covered by the specialized programs are password protection and appropriate use of the Internet. It is recommended that within the institutions delivering education, there should be constant cybersecurity training sessions with an emphasis on activities such as simulations and workshops conducted by experts to improve their awareness and subsequent action among learners.
Architecture for detecting advertisement types
Authors: Bello Rotimi-Williams , Owolawi Pius A , Wyk Etienne A Van , Tu Chunling
Published: February 2025

Businesses often encounter difficulties in accurately categorizing advertisements based on their content, which leads to inefficiencies in targeted marketing and data analysis. An automated system is needed to detect the type of advertisement (e.g., food, beverage, clothing, etc.) from the submitted text. This paper aims to develop an automated system leveraging Amazon Web Services (AWS) to categorize advertisement types based on text input. We employed Convolutional Neural Networks (CNNs) in developing the automated system due to the significant performance of CNNs leveraging AWS. The solution aims to boost marketing efficiency and strengthen data analysis capabilities. To achieve this, we utilize Amazon Simple Storage Service (S3), AWS Lambda, Amazon Comprehend, Amazon DynamoDB, Amazon CloudWatch, Amazon CloudFront, and AWS Web Application Firewall (AWS WAF). Moreover, we follow some procedural steps in executing the task by uploading the advertisement text to an S3 bucket, which triggers a Lambda function that forwards the text to Amazon Comprehend for analysis, and the results are stored in DynamoDB, from where the results notification is sent to the user. Magazine image datasets were employed as test datasets for this approach. This work enables automatic advertisement categorization, enhanced marketing effectiveness, better data analysis and reporting functionality, an affordable solution using AWS services, and instant feedback for users. The AWS-based architecture provides a dependable solution for the automatic identification of advertisement types. By leveraging various AWS services, the system ensures efficiency, precision, and scalability, ultimately enhancing marketing strategies and data management.