bluuebunny/crossref_metadata_2025 huggingface dataset:
château d’or pour horizon.
Authors: Macé Laurent
Published: February 2025

L’épigraphie offre un support documentaire qui peut se révéler riche dans le domaine de l’héraldique, notamment aux premiers temps de diffusion de celle-ci, à la charnière du XIIIe siècle. Certes, les traces de polychromie font souvent défaut en raison des séquelles du temps, mais le recoupement avec d’autres sources de l’histoire permet de proposer une identification du commanditaire de l’inscription lapidaire. Quand le dossier est constitué de plusieurs témoins vivant dans un intervalle chronologique resserré, certaines conclusions peuvent être déduites sur les pratiques et les usages héraldiques dans une zone donnée. Le cas d’étude proposé concerne un lignage chevaleresque résidant à Toulouse, les Castelnau, dont certains membres font partie de l’entourage de la dynastie comtale des Raimondins. Cette proximité rend compte de l’influence héraldique qui émane des autorités princières et qui touche des familles de chevaliers urbains de haut rang dans les années 1180. Dans le cas présent, le rôle des épouses et des mères mérite d’être amplement souligné.
The State Symbols of Bosnia and Herzegovina during the Twentieth Century
Authors: Filipovic Emir
Published: February 2025

This paper explores the relatively frequent changes of symbols that had been used to represent the land, province, territory and state of Bosnia and Herzegovina during the twentieth century. Throughout this time the small multi-ethnic Balkan country had mostly been a part or unit of larger state structures, such as the Austro-Hungarian Monarchy (1878-1918), Royal (1918-1941) and Socialist Yugoslavia (1943-1992). In 1992 it managed to gain its independence which was then followed by a three-year-long brutal military aggression and armed conflict that ended in 1995. Seeing as each regime change also required an adjustment of symbols which would have reflected the newly established political realities in Bosnia and Herzegovina, this case study can serve as an appropriate illustration of the significant role symbols can play in modern state-building processes and construction of collective identities. In analysing these changes, the author examined a number of legal texts and contemporary discussions, revealing that the political elites responsible for designing Bosnia and Herzegovina’s symbols often oscillated between traditional heraldic inspirations from the Middle Ages and the unconventional modern desire for neutral solutions.
Writing through the Lens of Anthropology and Neurosciences
Authors: Gerdzhikova Nina
Published: February 2025

Anthropology and neuroscience are two independent scientific fields that study learning, respectively, and writing as part of it as an activity in the broad sociocultural context. Anthropology directs interest to the social and cultural dimensions of the writing process. Neuroscience views learning through writing as dynamic multisensory processes related to the functioning of neural networks. The processing of information acquired in experience and its memory storage defines the neuroscience discourse in the research writing process. The purpose of the paper presented here is to attempt to connect the perspectives of anthropology and neuroscience and to develop the hypothesis of possible research on writing through a mixed-methods design.

In the first part of the paper, an anthropological qualitative study conducted in a Bulgarian compulsory education school will be presented. Its purpose is to describe how the teacher’s actions influence the students’ identity in the writing process. Using photographs representing fragments of one lesson conducted in a mother tongue (Bulgarian language), I will analyze the interaction between the teacher and the students, who write simultaneously. The study clearly shows how pupil’s actions correspond with the identity of their teacher during the work. This inquiry is significant because teachers undoubtedly have a lasting effect on the learning behavior and outcomes of pupils. In the second part of the presentation, I will present data from scientific journal publications that describe the neurophysiological indicators measuring the writing process. They are a complex code that marks the inner reactions that happen in students’ brains. Thankfully, of these markers, we can suppose how teaching should be adapted according to students’ information processing style.

In conclusion, I will try to explain how connecting the epistemology of qualitative research which is socially and content-oriented – from one side and neuroscience’s approach to synaptic plasticity as the biological basis for learning – from the other side, could deepen research on writing in the broad social context. Using a mixed-methods design would contribute to the study of writing at a social and biological level. This will expand the possibilities for improving teaching in pedagogical practice.
Contribution of Skin Biopsy in the Diagnosis of Restless Legs Syndrome
Authors: Yangatimbi Emmanuel , Grenaba Duval Lewis , Ndouellet Caprice Vivien , Kinima Josué Pierre , Kpengougna Larissa , Tchebemou Jacqueline , Mbelesso Pascal
Published: February 2025

Introduction: The diagnosis of restless legs syndrome (RLS) is an exclusively clinical diagnosis which is based on questioning the patient. Furthermore, several pathologies can mimic the symptoms of RLS. It should also be emphasized that small fiber neuropathies (SNF) escape electroneuromyography (ENMG) since this examination essentially studies large diameter myelinated fibers. The objective of this study is to assess the contribution of skin biopsy on the one hand in the differential diagnosis of RLS when an associated pathology may be responsible for NPF and on the other hand in the diagnosis of RLS with tone very painful.

Patients and Methods: We carried out a prospective study covering the medical files of 2 patients followed in consultation at the Limoge University Hospital for RLS, one of whom had a systemic disease and the other had RLS evolving since then through attacks. unbearably painful. Each patient underwent a clinical evaluation based on the diagnostic criteria for RLS, an ENMG and a skin biopsy.

Results: For each patient, the diagnosis of RLS was confirmed by the presence of four diagnostic criteria. Patient 1 had an IRLS score of 30/40, a normal ENMG and a normal skin biopsy with normal FNIE density. Patient 2 had RLS with a very painful tone, a normal ENMG and a skin biopsy showing rarefaction of FNIE proximally and distally, and concomitant rarefaction of subepidermal fibers.

Conclusion: This study, considered preliminary with a limited sample, does not allow us to achieve the objective. A longitudinal study with a satisfactory sample could allow us to elucidate the question.
Decentralization of Mental Health Care as One of the Key Pillars of the Republic of Serbia Health Reform: The Importance of the Information Integration
Authors: Periša Simonović  , Snežana Svetozarević  , Knezevic Snezana pavle , Stefan Milojević  , Nebojša Zdravković
Published: February 2025

Healthcare system reforms are ongoing and are considered globally. However, Serbia's social, political, and economic crises have slowed progress, which started in 2002. Decentralization is regarded as critical for improving healthcare services and ensuring financial sustainability. The development of Community Mental Health Centers is considered the cornerstone of the decentralization strategy. A comparative analysis of Big Data on national public health highlights critical issues. According to our findings, it is essential to rethink the models and procedures of a review of the existing healthcare facility network and examine the local population's mental health status and health service usage. One of the appropriate procedures may be the unification and centralization of data collection and storage processes for mental health centers in the national network, both private and public. However, the decentralization processes must be balanced with centralization principles, as they are sensitive to social, historical, political, and economic influences.
Taekyun Kim $\alpha$-Index of graphs
Authors: Gowramma H. M. , Reddy Polaepalli Siva Kota , Kim Taekyun , Rajendra R.
Published: February 2025

In this study, we introduce the Taekyun Kim $\alpha$-index, a novel topological index for graphs, where $\alpha\in \mathbb{R}$, which involves stresses and degrees of nodes. We calculate this index for a few common graphs and prove a few results. Further, a QSPR analysis is carried for Taekyun Kim $2$-index and physical properties of lower alkanes and linear regression models have been provided.
Adrenocortical tumor in children: About three pediatric cases
Authors:   Department of Pediatrics B, Hopital Mere-Enfant, CHU Mohammed VI, Marrakech, Morocco., Fahri J
Published: February 2025

Adrenocortical tumor is a malignant and rare tumor in children, and are sometimes integrated within the framework of predisposition syndromes such as Li and Fraumeni or Beckwith-Wiedemann syndromes.
Bottleneck emergency: A case of penile strangulation
Authors:   House Surgeon, Lady Reading Hospital, MTI, Peshawar, Pakistan., Rehman Muhammad Zohaib
Published: February 2025

This case report describes a 60-year-old male who was referred to our hospital due to penile strangulation by a plastic bottle neck with marked oedema and ischemia. The patient diagnosed with Bipolar affective disorder which is characterized by impulsivity and episodes of mania
Historical cutaneous squamous cell carcinoma with pulmonary metastasis
Authors:   Cheikh Khalifa International University Hospital, Mohammed VI University of Health Sciences, Department of Hematology, Casablanca, Morocco., Nejjari Sara
Published: February 2025

Objectives: Squamous cell carcinoma is the second most common form of skin cancer. We report the case of a patient with a history of untreated HPV who presented with a historical and metastatic form of squamous cell carcinoma.
Impact of manual physiotherapy technics with ultra sound therapy on treatment of chronic PLID patient: A clinical case study of Ashulia Women and Children Hospital
Authors:   Consultant and Head of Physiotherapy Department, Ashulia Women and Children Hospital, Dhaka, Bangladesh., Das Bijoy
Published: February 2025

Low Back Pain (LBP) is a common complaint in Bangladesh and has many possible causes. Multiple Physical therapy interventions, including electrotherapy, traction, and lumbar stabilization exercises such as bridging and planking variations, have been shown to be very effective in reducing LBP.
Chemo-induced diabetes during acute lymphoblastic leukemia: About a pediatric case
Authors:   Faculty of Medicine and Pharmacy, Marrakech Sciences, Morocco., Mghazli Hajar
Published: February 2025

Hyperglycemia is a common side effect of treatment for Acute Lymphoblastic Leukemia (ALL). It has long been recognized as a consequence of corticosteroids (prednisone or dexamethasone) and L-asparaginase, chemotherapeutic agents essential for the treatment of ALL.
Congenital hydrocephalus: A case report
Authors:   Pediatric Emergency Department, Mohammed VI University Hospital, Morocco., Fahri J
Published: February 2025

Hydrocephalus is the accumulation of excessive quantities of cerebrospinal fluid, causing dilation of the cerebral ventricles and/or an increase in intracranial pressure. Manifestations may include an enlarged head, bulging fontanelle, irritability, lethargy, vomiting and convulsions.
Gastric outlet obstruction secondary to giant traumatic pancreatic pseudocyst
Authors:   International Islamic University of Malaysia (IIUM), Malaysia., Bin Yahya M Zakwan
Published: February 2025

Traumatic pancreatic injury is rare due to its retroperitoneal location and relative protection to the organ. Pancreatic pseudocyst formation may occur following pancreatic trauma, but giant pseudocyst rarely develops. Early management is recommended for giant pancreatic pseudocysts before clinical deterioration
Ictal asystole as primary presentation of seizure: A case report
Authors:   Jinnah Sindh Medical University, Karachi, Pakistan., Nasir Ayman
Published: February 2025

Ictal Asystole (IA) is a rare but serious condition associated with epilepsy, characterized by a cessation of ventricular complexes for more than 4 seconds during a seizure.
A rare case report of perineal hernia after abdominal perineal resection
Authors:   Department of General Surgery, Local Health Unit of Tras-Os-Montes and Alto Douro ULSTMAD, Vila Real, Portugal., Dupont Margarida
Published: February 2025

This report describes a case of a 65-year-old man with a perineal hernia after an abdominal perineal resection for low rectal cancer. A contrast-enhanced computed tomography scan revealed significant prolapse of digestive loops at the level of the posterior pelvic floor. Surgery via perineal access using a synthetic mesh was performed.
Silent trauma leading to necrotizing fasciitis in a diabetic patient: A case report
Authors:   Medical Doctor, Batumi Shota Rustaveli State University, Adjara, Georgia., Jakasaniya Jaykumar
Published: February 2025

Necrotizing Fasciitis (NF) is a severe, rapidly spreading infection of deep soft tissues that can result in significant morbidity and mortality if not treated promptly. This condition is more common in men and primarily affects the lower limbs.
Intracranial solitary fibrous tumor: A rare case report and literature review
Authors:   Junior Resident, Radiation Oncology Department, Sarojini Naidu Medical College, Agra, Uttar Pradesh 282002, India., Malik Shivani
Published: February 2025

Solitary Fibrous Tumors (SFT) are very rare, particularly those of the Central Nervous System (CNS), and they have mesenchymal origin. Here, we present the case of a 46-year-old man who, a year after suffering a head injury, lost consciousness for an hour.
Clinical image report of patient with non-signet gastric adenocarcinoma presented with MAHA
Authors:   Department of Internal Medicine, Hamad Medical Corporation, Qatar., Aboujabal Khaled
Published: February 2025

Gastric Adenocarcinoma is the fifth most common malignant tumor worldwide. The presenting symptoms are usually related to the local invasion or distant metastasis.
The impact of cumulative smoking volume and length of smoking cessation on brachial artery reactivity
Authors:   Department of Psychology, University of Quebec at Montreal (UQAM), CP 8888, Succursale Centre-Ville, Montreal, Quebec, Canada., Lavoie Kim L
Published: February 2025

Cigarette smoking has been shown to cause endothelial abnormalities. Though the endothelium has shown the ability to recover function after smoking cessation, it is unclear how the intensity of lifetime cigarette consumption and the length of smoking cessation influence this recovery.
The Value of Aquatic Food for the Human Metabolic Functions
Authors:   Department of Biology, Cukurova University, Adana, Turkey., Yesilbudak Burcu
Published: February 2025

Since organic food products derived from terrestrial ecosystems are negatively affected by global climate changes, it is predicted that aquatic ecosystems may be the first and most important source that will compensate for the food resources coming from this area
Cybercrime Analysis and Online Fraud Prevention Using Advanced Machine Learning Models
Authors: Shammi L, C Emilin Shyni
Published: February 2025

With the rise of e-commerce on a global scale, numerous companies have been able to provide goods and services through cutting-edge, time-saving, and budget-friendly business strategies. A sizable portion of the global economy is supported by the ever-expanding digital economy. Due to the simplicity of committing these crimes, the attractive returns, and the slowness of prevention measures, cyber-crimes have also increased at an even quicker rate than this relatively rapid expansion. The necessity to efficiently manage the expansion of cybercrime is critical, since it poses an existential danger to online trade. Businesses should implement creative preventive measures because laws and police resources aren't keeping up with the dynamic nature of crimes. How big and small businesses alike are implementing cyber-crime prevention techniques is the main topic of this special issue.
Customer Perspective of using Digital Payment Systems for Online Buying
Authors: Lalitha
Published: February 2025

Digital transactions now account for the majority of all transactions worldwide, including those in India. Studies show that effective payment systems improve an economy's liquidity flow. Using technology for transactions is the most effective approach to be flexible and provide superior customer service in the digital age. As a result, the researchers wished to investigate customer attitudes about adopting digital payment systems for online purchases. The study's goal is to learn about the future level of digital payment usage among customers. For this study, the researcher used cross tabulation, correlation, and ANOVA as statistical tools. According to the study, there are no variations in respondents' opinions about buying products using digital payments and promoting them. As stated in the report, customers prefer digital payment systems for future purposes.
Employee Well-Being Programs and Their Effect on Organizational Commitment: An Analytical Study with Special Reference to Private Bank Employees
Authors: Irshad Nazeer
Published: February 2025

Employee well-being programs are critical for fostering a productive and committed workforce. Employee well-being initiatives are crucial for cultivating a productive and motivated staff, particularly in high-stress sectors such as private banking. Workers in this sector frequently endure extended working hours, elevated client expectations, and stringent performance standards, resulting in stress and job unhappiness. Well-being initiatives, such as mental health support, financial aid, flexible work arrangements, and career development programs, are essential for sustaining employee morale and organizational commitment. This study examines the effects of such initiatives on staff retention and satisfaction in private banks, utilizing empirical data from 122 respondents. Diverse statistical methodologies, such as factor analysis, correlation, regression, and hypothesis testing, are utilized to assess the efficacy of these activities. The findings reveal a robust positive correlation between well-being programs and organizational commitment, indicating that employees who receive organized support have increased job satisfaction and reduced turnover intentions. The report emphasizes that financial assistance and healthcare benefits substantially improve employee loyalty, whereas work-life balance programs foster long-term involvement. The findings highlight the imperative for private banks to invest in comprehensive well-being programs to enhance workforce stability and organizational performance. Through the implementation of comprehensive well-being initiatives, private banks can diminish employee turnover while fostering a more dedicated, engaged, and high-performing team.
Improving the Scalability of Blockchain Based on Trust Practical Byzantine Fault Tolerance Algorithm (tPBFT)
Authors: Noor Sabah
Published: February 2025

Since Bitcoin's inception, the non-custodial financial and programmable aspects of blockchain technology have garnered significant global attention, akin to other groundbreaking innovations. Blockchain is a transparent and decentralized ledger that prioritizes immutability, transparency, decentralization, and privacy. Its potential extends to large-scale, beneficial applications across various sectors, encompassing healthcare, supply chain management, logistics, the Internet of Things (IoT), and more. Many industrial applications make use of permissioned blockchains. However, blockchain still faces inherent and external challenges. While permissioned blockchains are suitable for many use cases, they do have limitations, especially in terms of scalability and throughput. In this paper, the authors propose implementing the proposed method by Swathi and Venkatesan using tPBFT (trust Practical Byzantine Fault Tolerance) instead of PBFT (Practical Byzantine Fault Tolerance) to improve the scalability and performance of the blockchain. The Hyperledger Fabric framework is conducted for the proposed method within the assessment of scalability, considering varying transaction volumes, and it demonstrates an enhancement in scalability. This work addresses the need for blockchain systems to evolve and adapt to meet the demands of various industries and applications, acknowledging that scalability is a key factor in their success.
Development of STEM Integrated Project Based Learning Model in Vocational Education
Authors: Akmal
Published: February 2025

The purpose of this research is to develop a Project-Based Learning model integrated with STEM and to measure its validity and practicality levels. This research uses the development model from Borg & Gall, by reducing the development stages into four phases: 1) Data collection phase, 2) Planning and design phase, 3) Development phase, 4) Validation and trial phase. The sample involves experts for the evaluation, as well as students from class XI of the Light Vehicle Engineering Power Transmission System subject, and also includes teachers. The data collection tool used is a questionnaire, and the data analysis technique is conducted descriptively. The findings show that the product produced consists of a model book, teaching module, teacher guide, and student guide. This model produces 7 steps, which include: 1) Providing the problem to be faced, 2) Giving students freedom, 3) Planning and developing the project, 4) Organizing the schedule and monitoring the project, 5) Evaluating the project, 6) Communication and reflection, 7) sentence incomplete, please provide the next part for translation. Providing rewards to students within the group. The developed learning model has also been proven to be valid and practical, making it usable. The suggestion from this finding is that it is expected to be applied to other subjects, and there is a need to stimulate and motivate students in implementing this learning model, so that the learning process can be focused, effective, and efficient.
A Novel Classifier for Plant Health Monitoring: A Focus on Banana Leaf Disease Detection Using Deep Learning
Authors: Ravi Kumar Tirandasu
Published: February 2025

Crop productivity and food security in modern agriculture are largely dependent on efficient monitoring and early identification of plant diseases. With a focus on the identification of leaf diseases, this study investigates the use of machine learning algorithms in the context of plant health monitoring. To extract pertinent characteristics from photos of plant leaves, the study makes use of sophisticated image processing techniques. This results in a large dataset that can be used to train and assess machine learning models. The proposed approach makes use of a comparison study to assess how well different machine learning algorithms recognise and categorise distinct leaf disease kinds. High-resolution photos of plant leaves displaying disease signs are collected as part of the approach, which also include preprocessing the data to improve feature extraction and utilising labelled datasets to train machine learning models. After that, the models are evaluated on hypothetical data to see how well they generalise and perform in actual situations. In this paper an Ensembled CNN Leaf Disease Detection Classifier (ECNNLDD) used to predict the healthiness of a leaf which classifies whether is healthy or unhealthy which is a 2-class problem and compared with the existing classifier like Decision Tree and SVM algorithm in which the proposed classifier Ensembled CNN Leaf Disease Detection Classifier outperformed when compared with the existing classifiers. The findings of proposed classifier gave the best accuracy in agriculture by offering an intelligent and automated solution for early detection and diagnosis of plant leaf diseases. The integration of Deep learning into plant health monitoring systems holds the potential to revolutionize farming practices, enabling farmers to adopt timely and targeted interventions, thereby minimizing crop losses, and promoting sustainable agriculture.
Multi-Dataset Evaluation of Hybrid Models for Brain Tumor Diagnosis
Authors: Prachiti Pimple
Published: February 2025

Brain tumor detection is the identification and categorization of aberrant brain tissues using methods like MRI for tumor diagnosis and tracking. This sophisticated technique uses deep learning to analyze images, resulting in precise early detection and treatment. This study uses hybrid architectures for different deep-learning applications to offer a comprehensive hybridization strategy with promising prospects for improving the diagnostic precision of images obtained for medical diagnostics. In this study, it employs three separate datasets, previously known as Brain MRI images, Br35H and BraTS, to assess several architectures, including ResNet, VGG, Inception, EfficientNet, DenseNet121, MobileNetV2, Xception, NASNetMobile, and InceptionResNetV2. For the Brain MRI dataset, the findings demonstrated that the VGG16 model had a training accuracy of 99.93% with the lowest train loss of 0.0238; on all three datasets, the InceptionV3 showed exceptional robustness, with an accuracy of 99.78%. Although hybrid models that combined architectures such as Xception, NASNetMobile, and InceptionResNetV2 performed effectively, they also appeared to overfit, with validation and test losses being comparatively larger than training accuracy. The hybrid model hybrid model (EfficientNet, DenseNet121, MobileNetV2) achieved 99.87% training accuracy using the BraTS dataset. These findings indicate the possibility of applying deep learning architectures more effectively to better diagnose brain tumors, in addition to rigorous model optimization and selection to reduce the tendency for overfitting. This study encourages hybrid DL models' application in the medical area.
Predictive Analytics in Mental Health: A Machine Learning Approach to Assessing Depression Severity
Authors: Satya Kiranmai Tadepalli
Published: February 2025

Contemporary healthcare reforms are being significantly shaped by the ongoing advance-ments in technology. One area where these advancements are proving crucial is in the un-derstanding and treatment of depression, which is increasingly emerging as a substantial public health concern. To address this issue, there is a growing interest in leveraging novel research methods and therapeutic approaches to identify the contributing factors to de-pression. This study adopts an innovative approach, utilizing machine learning techniques to carry out an exhaustive examination of diverse data sources. The primary aim is to gain a profound comprehension of the complex interplay between various facets of individuals' quality of life and the presence of depression. To undertake this investigation, the research-ers have harnessed the National Health and Nutrition Examination Survey (NHANES) Da-taset provided by the Centers for Disease Control and Prevention, a rich source of valuable health-related information.In this study, the focus is on exploring the behavioral and social dimensions of numerous subjects and their intricate connections to depression. To achieve this, a diverse array of machine learning classifiers has been deployed, including the Decision Tree Classifier, Ran-dom Forest Classifier, Gaussian Naive Bayes, KNN Classifier, Logistic Regression, Support Vector Machine Classifier, and Multilayer Perceptrons. By applying these classifiers, the re-searchers aim to assess their performance across various metrics, providing valuable insights into which models are best suited to discern the connection between depression and as-pects of quality of life.
Optimizing Resource Allocation in Cloud-Based Information Systems through Machine Learning Algorithms
Authors: Yashwant Dongre
Published: February 2025

Efficient resource sharing has become a must for improving system performance and lowering running costs as cloud-based information systems continue to serve a wide range of large-scale apps. Traditional ways of allocating resources don't always work well when tasks change, which wastes time and money. This article suggests a high-tech structure based on machine learning that can help make the best use of cloud resources. It focuses on features that can predict and adapt to changes in task in real time. Our method uses both controlled and unstructured learning to correctly predict resource needs and to find the best way to distribute resources across virtual machines with the least amount of delay and the highest cost-effectiveness. The study used real-world cloud task data to run a lot of models that compared standard heuristic methods to machine learning-based distribution. According to the results, the system is much more efficient now, with up to 35% less wasted resources and 25% faster response times. We talk about how choosing the right model (decision trees, neural networks, and support vector machines) affects the accuracy of predictions and the amount of work that needs to be done. The study also talks about how the machine learning system can be scaled up or down, showing that it can work with different cloud platforms and types of applications. The suggested method lowers the need for human work by automating resource sharing. This lets cloud companies better handle resources, which makes users happier overall. This study adds to the growing field of optimizing cloud resources and shows how important machine learning methods will be in designing future cloud infrastructure. The results show that machine learning is a good, scalable way to handle resources in cloud settings that are getting more complicated all the time.
Harnessing Machine Learning for Accurate Prediction of Mental Health Conditions based on DASS-42 scores
Authors: C. G. Patil
Published: February 2025

The increasing global prevalence of mental health conditions, coupled with their profound impact on individual well-being, necessitates the development of efficient diagnostic tools. Traditional methods of mental health assessment, such as the Depression, Anxiety, and Stress Scales (DASS-42), rely on manual interpretation of questionnaire scores, which may be limited in detecting complex patterns. This study proposes harnessing the power of machine learning (ML) to improve the accuracy and efficiency of mental health condition predictions based on DASS-42 scores. By leveraging various ML algorithms, including Random Forest, Decision Trees, and Nearest Neighbour, we aim to predict mental health outcomes such as depression, anxiety, and stress with greater precision. The DASS-42 dataset used in this study contains responses from individuals across diverse demographic backgrounds. Feature engineering is applied to extract meaningful attributes, while the models are trained and evaluated on labelled data indicating the presence or absence of mental health conditions. Cross-validation and hyperparameter tuning are employed to optimize the performance of the models, and metrics such as accuracy, precision, recall, and F1 score are used to assess their predictive capabilities. Initial results demonstrate that machine learning models, particularly ensemble methods like random forests, outperform traditional statistical methods in predicting mental health outcomes. The incorporation of ML not only improves diagnostic accuracy but also has the potential to streamline mental health screening processes in clinical and non-clinical settings. This research highlights the significant role that machine learning can play in enhancing the identification and management of mental health conditions, thereby contributing to more effective interventions and personalized treatment plans. Future work will focus on refining models by integrating additional psychological and physiological data to further increase predictive power.
Secure Data Aggregation and data Transmission using HMAC Protocol in Cluster base UAV Communication Network
Authors: Reshma C Sonawane
Published: February 2025

In remote or hard-to-reach areas, deploying unmanned aerial vehicle (UAV’s) significantly increases the likelihood of various attacks, making security a critical concern. Given the limited computing power, memory, bandwidth, and energy resources of sensors, traditional security methods designed for resource-heavy systems like WLANs are unsuitable for UAV’s. Therefore, it is crucial to develop specialized security protocols tailored to UAV’s resource constraints. The use of public key cryptography in UAVs faces several challenges, such as high computational costs, lengthy key generation, susceptibility to brute-force attacks, and complexities in key distribution and management. In contrast, symmetric key cryptography, which requires minimal computation and storage, is a more suitable choice for securing data transmission in UAVs. This research focuses on existing and emerging security mechanisms for unmanned aerial vehicle (UAVs), specifically those enhancing the 802.11 standard with additional security layers. The research follows a four-phase methodology. In the first phase, a genetic algorithm is proposed for optimizing the selection and number of cluster heads (CHs) using a clustering approach. This method balances intra-cluster communication, CH distance from the base station (BS), and the remaining energy of nodes to maximize network efficiency. In phase two, a secure data aggregation technique employing the Hash Mandatory Access Control Protocol (HMAC) is introduced to reduce network data overhead and protect against multiple threats. Phase three presents a pairwise key management strategy for UAVs, utilizing a one-way hash function to minimize the impact of compromised nodes while maintaining secure links between neighbouring nodes. The fourth phase introduces a Broadcast Tree Construction method to reduce communication overhead by identifying the shortest paths, integrating data aggregation, and enhancing network connectivity. This approach emphasizes reducing power consumption to extend network lifespan while ensuring the timely detection and removal of rogue nodes before they can transmit harmful data..
Financial Sustainability Challenges and Opportunities for SMEs in Andhra Pradesh: An Empirical Analysis
Authors: K Ravi Kiran Yasaswi
Published: February 2025

Small and Medium Enterprises (SMEs) play a vital role in Andhra Pradesh's economy, yet they face persistent challenges in achieving financial sustainability. This study uniquely explores the relationship between Environmental, Social, and Governance (ESG) practices and financial performance, addressing a critical gap in the existing research. By analyzing a dataset of 200 SMEs using descriptive statistics and correlation analysis, this study uncovered significant variability in financial performance and ESG compliance. The findings reveal that effective ESG integration positively affects financial outcomes, highlighting its importance for long-term growth. Additionally, the study identifies barriers, such as limited access to affordable credit and insufficient awareness of ESG benefits. Policy recommendations include creating tailored financial products, offering training programs on ESG adoption, and incentivizing sustainable practices through tax benefits and grants. These strategies aim to enhance SME resilience, enabling them to balance financial growth with sustainability goals, while contributing to regional economic stability and development.
Communication Strategies in the Social Sphere: Tactics and Information and Communication Technologies
Authors: Yaroslava Martyniuk
Published: February 2025

Introduction: Digital technologies are transforming communication and social interaction, and information and communication technologies are becoming vital tools for solving social problems and improving access to information. They promote effective interaction between the state and citizens, improving the quality of social services and governance.
Objectives: The study aims to analyse and develop effective communication strategies and tactics to improve social interaction. It also aims to study the role of information and communication technologies (ICT) in access to social services, engaging citizens in initiatives, and optimising information exchange between government agencies and the public.
Methods: The study uses analytical and systematic methods and statistical analysis to assess the effectiveness of strategies. The analysis of social media and official websites revealed the main communication topics and areas of application of social and psychological technologies in forming communication strategies in the social sphere.
Results: It was found that developing new communication models is essential for improving information exchange, engaging citizens in social initiatives, and adapting policies based on feedback. Social media and participatory approaches allow for more effective audience engagement, while artificial intelligence and data analysis help manage communications. Media literacy training stimulates critical thinking, and ICTs significantly impact social group interaction, especially in low-income countries.  
Conclusions: The study confirmed that effective communication strategies are vital in improving interaction between social groups and organisations. The use of ICTs and participatory methods contributes to transparency, accessibility, and engagement of citizens in social initiatives. Strategic planning, adaptation of messages for different audiences, personal stories and data increase the credibility of initiatives.
Evaluating Information Security Strategies in the Public Sector: Efficiency and Cybersecurity Risks
Authors: Serhii Shtantsel
Published: February 2025

Introduction: Given the steady growth of the importance of information and communication technologies in developing socio-economic and managerial activities, the issue of cybersecurity is becoming particularly relevant. The problem is exacerbated by the military aggression of the Russian Federation in Ukraine, where cybersecurity is positioned as an integral part of national security.
Objectives: The purpose of the article is to analyse modern methods of information protection in the public sector.
Methods: The research methodology is based on several modern scientific methods, including the following: a systemic method, a method of retrospective analysis, comparative analysis.
Results: The author examines the NCSI (National Cyber Security Index) cybersecurity rating and the correspondence of the level of digital development of several countries, particularly Ukraine. Current risks and challenges in the cybersecurity sector are identified. The most effective approaches to the information security strategy, mainly using artificial intelligence and innovative technologies, cooperation with international institutions and monitoring, are studied. The specifics of creating modern integrated information protection systems (IPS), which use artificial intelligence and innovative technologies today, are investigated. It is substantiated that cybersecurity in the public sector should be based on the principle of effective implementation of the functionality of the relevant public authorities.
Conclusions: Thus, it is established that in the cyber defence system, the priority role of protecting information resources and the state’s digital infrastructure is assigned to public authorities. It is substantiated that against the background of active digitalisation of all spheres of public life, active interaction between government agencies, the business sector, research institutions and the public in the context of effective prevention of cyber threats and mitigation of their consequences is becoming a priority.
Implementing Digital Solutions to Safeguard the Economic and Legal Stability of Industrial Enterprises
Authors: Viktoriia Derhachova
Published: February 2025

Introduction: In today’s technological world, digital transformation has become a prerequisite for the successful operation of any enterprise. This digital transformation is fundamentally changing approaches to doing business, setting new standards of efficiency and competitiveness in the global market.
Objectives: The study aims to qualitatively assess the current level of use of digital tools for integrated management of industrial enterprises’ economic and legal security, identify potential areas for developing digital security in this area, and justify the need to introduce cyber defence in the current business environment.
Methods: The study was conducted using a set of methods, including theoretical analysis, empirical research, and regulatory and legal analysis of the problem of using digital tools to ensure industrial enterprises’ economic and legal security.
Results: The article presents a study that covers both the impact of digital transformation on business and the analysis of current trends and challenges in this area. It has been proved that digital transformation involves introducing innovative technologies that automate processes, optimise resource management, and facilitate the adoption of sound management decisions. The international indices that assess countries’ digital development level, particularly the quality of communication, availability of digital services and cybersecurity, are analysed.
Conclusions: Thus, the current business challenges associated with digital transformation are identified. The author identifies typical obstacles in managing industrial enterprises with the introduction of digital transformation, areas of implementation and several digital tools to ensure their digital security, and provides proposals for the introduction of legal mechanisms for digital transformation.
Social Transfers and Public Finance Efficiency: Navigating Digital Transformation Challenges
Authors: Oleksandr Bulavynets
Published: February 2025

Introduction: The paper analyses the experience and challenges of digital transformations in terms of the impact of social transfers on the efficiency of public finances. The significance of the study is that social transfers as payments to persons not engaged in economic activity are currently receiving particular attention in the context of the Russian-Ukrainian war. Social transfers constitute a significant part of public finances and, therefore, require public scrutiny.
Objectives: The study aims to analyse the experience and challenges of digital transformation in terms of the impact of social transfers on the efficiency of public finances.
Methods: The study used the following research methods: description, comparison, analysis and synthesis, and literature analysis.
Results: The study revealed that, at the present stage, there are problems with introducing digitalisation in public finance. This is due to both insufficient material support and the lack of an established system of public access to e-government. This suggests that it is essential to accelerate the introduction of information technology in the area of social transfers.
Conclusions: The research identifies trends in social transfers in Ukraine. In particular, an increasing amount of transfers is allocated to help war veterans and their families, as well as persons in difficult life circumstances due to martial law. It is emphasised that social expenditures are realised mainly from external sources and are spent on the needs of citizens of retirement age. It is established that social transfers to combatants are currently insufficient.
The Ecosystem of Human Resource Management in the Ukrainian Public Service: Development in the Context of Digital Transformation of Society
Authors: Nataliia Goncharuk
Published: February 2025

Introduction: The study is dedicated to analysing the modern ecosystem of human resource management in Ukraine's public service amid global digitalisation.
Objectives: The study aims to investigate modern approaches to human resource management in the civil service of Ukraine in global digitalisation and to identify the main ways of introducing digital technologies in civil service HR.
Methods: General scientific methods were used in the course of the study, including the structural-logical method, analysis and synthesis and concretisation.
Results: The article elucidates the essence of contemporary trends in digital technologies within the human capital management system (human resources – HR), which include the automation of HR processes, HR analytics and Big Data, the use of chatbots and mobile applications, digital integration with cloud systems, and the application of artificial intelligence and VR technologies. The specifics of transitioning HR processes to a digital format have been examined, particularly in actualising the digital platform Career.gov.ua and its integration with popular resources Work.ua and Robota.ua, as well as creating a HELP DESK for candidates.
Conclusions: Thus, it has been proven that implementing global digital trends in the development of the human resource management ecosystem of Ukraine's public service is an effective tool for increasing the performance of public service in Ukraine, its efficiency, and its alignment with the country's European integration progress.
Educational Leadership in Vocational Education: A 10-Year Bibliometric Analysis
Authors: Suneel Kumar
Published: February 2025

Introduction: Educational leadership in vocational education is a complex and important topic. Nevertheless, there is still a deficiency in academic discourse regarding the bibliometric analysis of this body of knowledge.
Objectives: This study aims to give a summary of the development of this topic by locating publications indexed in the Scopus database between 2014 and 2024.
Methods: Educational leadership in vocational education-related research has garnered significant scholarly attention for more than fifty years, yielding a large corpus of literature on the domain. Using keywords like "vocational education" and "educational leadership," the search produced a total of 15315 articles at first. After applying exclusion and inclusion criteria, 2089 studies were kept for final analysis. The research includes the most prolific researchers, top 10 journals, yearly article frequency, composition of articles, top 10 contributing countries, the most influential articles, and the most frequently used keywords.
Results: The study's main contribution is to highlight the bibliometric insights gained through in-depth analysis regarding educational leadership in vocational education. Additionally, universities, trends, and nations are displayed and analyzed.
Conclusions: The conclusions provide useful insights for academics into the precise growth of educational leadership in vocational education through organizing the field's body of existing literature.