Volume 14 | Issue 5 |

 Abstract

ABSTRACT: Background: Medial Tibial Stress Syndrome (TSS), commonly known as shin splints, is a frequent overuse injury affecting the lower extremities, particularly among athletes and individuals engaged in repetitive weight bearing activities. Bowling, though perceived as a low-impact sport, involves repetitive biomechanical stresses that may predispose recreational bowlers to tibial stress injury. Because of all these factors can cause TSS in future, So Prevent future impairments Proper diagnosis is important. The Shin Palpation Test (SPT) is a simple and reliable clinical tool for identifying TSS. Methodology: This observational study includes 58 recreational bowlers aged 18-30 years, selected through convenience sampling. Participants with a minimum of 2 years of playing experience will be screened according to inclusion and exclusion criteria. After obtaining informed consent, demographic data will be recorded, and the Shin Palpation Test will be administered bilaterally. Any report of pain during palpation will be noted as a positive finding. Result: Data will be analysed to calculate the prevalence of TSS among recreational bowlers. The findings will be presented in terms of prevalence rate and distribution according to demographic and pain intensity. Conclusion: The study is expected to provide evidence regarding the prevalence of Tibial Stress Syndrome in recreational bowlers. The study concluded that 88% shows the prevalence of Tibial Stress Syndrome in recreational bowlers. Findings may help increase awareness of injury risks in non-professional sports participants and guide preventive and management strategies.

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Tibial Stress Syndrome, Shin Splints, Recreational Bowlers, Shin Palpation Test

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This extensive research document is an extensive review of current literature concerning the field of Earthquake Monitoring and Alert systems, both of which are part of traditional Earthquake Monitoring using seismological methods, and the newest technological innovations. Based on our work, major trends are precisely analyzed using seismic monitoring technologies, machine learning applications, artificial intelligence integration, early warning systems, Internet of Things (IoT) based monitoring networks, and preventive methodologies that are deployed and implemented all over the globe. Through a rigorous study of the current research by 2020-2025, this paper reveals prominent developments in real- time seismic instruments, deep learning approaches in earthquake detection, transformer-based models in heterogeneous environments, and community-based earthquake early warning systems. Key findings include breakthroughs in the development of real-time monitoring capabilities with detection accuracies of more than 95%, predictive modeling capabilities that employ advanced neural networks with more than 700,000 labeled training examples, and community-based, early warning systems with practical implementation in sixteen countries worldwide. The study highlights ongoing issues with the ability to accurately predict earthquakes long enough ahead, system interoperability with different types of monitoring networks in heterogeneous networks, false alarm handling in real-time warning systems, and fair coverage across the globe, especially in the developing world. The paper further focuses on the global growth of earthquake early warning (EEW) systems using diverse communication technologies, the science and societal impact of aftershocks, emerging technologies for aftershock monitoring and loss reduction, and the important relationship between earthquakes and tsunamis with the integrated multi- hazard warning approaches. Issues and challenges related to these advancements are systematically addressed.

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Earthquake Early Warning, Seismic Monitoring, Machine Learning, IoT Sensor Networks, Aftershock Forecasting, Tsunami Warning, Deep Learning, Edge Computing, Real-time Detection, Hazard Mitigation

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Background: Benign paroxysmal positional vertigo (BPPV) is one of the most common vestibular disorders, characterized by brief episodes of vertigo triggered by changes in head position. Canalith repositioning and habituation exercises are commonly used conservative treatment approaches. Among these, Epley's Manoeuvre and Brandt-Daroff exercises are widely practiced, yet their comparative effectiveness requires further evaluation. Objective: To compare the effectiveness of Epley's Manoeuvre and Brandt Daroff exercises in reducing symptoms of vertigo and improving functional outcomes in patients with benign paroxysmal positional vertigo. Methodology: A comparative experimental study was conducted on patients clinically diagnosed with posterior canal BPPV. Participants were randomly divided into two groups. Group A received Epley's Manoeuvre, while Group B was treated with Brandt-Daroff exercises. Outcome measures included vertigo severity, frequency of dizziness episodes, and balance performance, assessed at baseline and after the intervention period. Results: Both treatment groups demonstrated significant improvement in vertigo symptoms and functional balance. However, patients treated with Epley's Manoeuvre showed faster and greater reduction in vertigo severity and symptom resolution compared to those performing Brandt-Daroff exercises. Conclusion: Both Epley's Manoeuvre and Brandt-Daroff exercises are effective in the management of BPPV. However, Epley's Manoeuvre is more effective in achieving rapid symptom relief, making it the preferred first-line treatment, while Brandt-Daroff exercises serve as a useful adjunct or home-based management option.

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Benign paroxysmal positional vertigo, Epley's Manoeuvre, Brandt Daroff exercises, vestibular rehabilitation, dizziness.

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The advent of technologies and advancement of artificial intelligence (AI) has raised significant concerns leading to a phenomenon termed AI anxiety. As Artificial Intelligence (AI) has become increasingly integrated into teaching and learning, teaching professionals particularly teachers and trainees need to understand students' attitudes toward artificial intelligence and AI learning anxiety to ensure its appropriate implementation. In this research, attitudes toward artificial intelligence and AI learning anxiety among 195 B.Ed. Trainees [90 Males (Urban 45 & Rural 45); 105 Females (Urban 50 & Rural 55)] is selected from teachers' training institutions namely, Aligarh Teacher's Training College, Aligarh and Jamia Urdu College of Education- JUCE (Aligarh) located in different locations of Aligarh (U.P.). The data was collected using standardized tools; Schepman and Rodway's General Attitudes towards Artificial Intelligence Scale: GAAIS (2020) and Wang & Wang's Artificial Intelligence Anxiety Scale: AIAS (2022). The findings reveal that (i) there is significant relationship between Attitudes toward Artificial Intelligence and AI Learning Anxiety among B.Ed. trainees (ii) there is significant difference between B.Ed. trainees regarding their attitude towards artificial intelligence and AI learning anxiety across gender and locale. Findings highlight the pivotal role of attitudes toward artificial intelligence and associated AI learning anxiety in shaping teacher aspirants' acceptance of AI. Understanding the root causes of AI learning anxiety and implementing strategic interventions are critical steps for mitigating its rise as society enters the era of pervasive AI. Therefore, stakeholders and educators should cultivate students' AI learning attitudes among B.Ed. trainees through comprehensive AI education and guiding them to form correct emotions through scientific and psychological interventions so that they encounter least AI learning anxiety.

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Artificial Intelligence, AI Attitude, AI learning Anxiety, B.Ed. trainees, Integration.

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The advent of technology in the world has changed manifold. Today's schools continue to be challenged by the increased visibility, role and cost of educational technology. Considering current trends in education, a modern classroom would not be complete without computers, software, internet connection, projects and variety of other hi-tech devices. Technology is the key to its development. Technologies are essential tools for teaching and learning. To use these tools effectively and efficiently, teachers need to have a vision of its challenging professions in our society where knowledge is expanding rapidly and much of it is available to students as well as teachers at the same time. Modern development of innovative technologies have provided new possibilities to teaching profession, but at the same time have placed more demands on teachers to learn how to use these technologies in their teaching.

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Educational Technology, computers, software, internet connection.

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Across several recent studies, diffusion models [10] have become leading approaches for generating high-quality synthetic data, especially for complex, limited, or sensitive datasets. These works emphasize the increasing demand for effective data generation and imputation methods in areas such as healthcare, finance, and rare-event detection, where real-world data is often scarce, imbalanced, or constrained by privacy concerns. Diffusion-based frameworks [11][15]--including TabDDPM [17], transformer-based architectures, and privacy-preserving federated diffusion systems [25]--have shown significant advantages over conventional GAN and VAE models [2], providing more stable training, higher-quality sample generation, and better support for mixed-type tabular data. These models are capable of handling tasks such as Rare- event augmentation, missing data imputation. and privacy preservation. Compliant financial data synthesis, and pandemic related medical image generation [8] Surveys and systematic reviews further emphasize diffusion models' growing role in addressing challenges like class imbalance, multimodal feature distributions, missing values, and privacy risks. Overall, the combined body of work shows that diffusion models are reshaping synthetic data generation across modalities--enabling more accurate, fair, and privacy-safe machine learning in situations where data is scarce, sensitive, or difficult to obtain.

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Diffusion models, synthetic data generation, rare event detection, imbalanced datasets, tabular data, data augmentation, differential privacy, federated learning, conditional generation, data imputation, transformer models, privacy- preserving machine learning, financial data, healthcare data, anomaly detection. financial defaults are of disproportionate importance despite representing a tiny fraction of real-world.

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Over the last thirty years, the Indian banking landscape has undergone a paradigm shift in service delivery, characterised by an increased strategic emphasis on marketing, branding, and Customer Relationship Management (CRM). As catalysts for economic stability and growth, financial institutions have increasingly leveraged brand-building practices to cultivate a distinct market identity and communicate their Unique Selling Propositions (USPs) within a highly competitive sector. This paper investigates the multidimensionality of customer perception regarding brand-building practices within Kerala's commercial banking sector. By evaluating key determinants, including advertising, customer relationship management (CRM), social media, corporate social responsibility (CSR), internal branding, and promotional incentives, the study examines the effects of socio-demographic variables and organizational attributes. The empirical findings reveals that brand-building initiatives exert a moderate influence on consumer behaviour, CSR emerges as the primary driver of positive perception. Conversely, the marginal impact of social media highlights a strategic gap in leveraging digital platforms for personalised engagement. Furthermore, the data reveal significant variance in customer perception, dictated by the interplay between socio-demographic characteristics and organizational attributes.

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Customers Perception, Brand Building Practices, Advertising, Customer Relationship Management, Social Media Engagement, Corporate Social Responsibility, Internal Branding Framework, Promotional Incentives, Organisational Attributes

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Abstract: This study examines the complex difference between financial literacy, behavior finance and financial welfare in the context of emerging economies. While traditional financial education emphasizes the spread of knowledge, this letter examines to what extent behavioral bias convenes relationships between knowledge and real financial results. Using survey data from 80 individuals in diverse demographic background, the research applies statistical analysis to measure financial literacy, identify practical prejudices and assess financial results. The results indicate that while financial literacy is moderately high, prejudices such as overconfidence and mental accounting are prevalent, often reduce rational decision making. However, individuals who combine high financial literacy with low behavioral deformation display better financial results, including more savings, diverse investment and long -term financial planning. The study emphasizes the requirement of integrated financial education approaches that include behavior coaching.

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Financial Literacy, Behavioral Finance, Financial Well-being, Emerging Economies, Cognitive Biases, Financial Education

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The Hotel Management System is a web-based application developed to automate and manage hotel operations efficiently. Traditional systems are manual, time-consuming, and error-prone. This system provides a secure, scalable, and user-friendly platform for managing room allocation, student records, complaints, and leave requests. To enhance security, modern encryption techniques and authentication mechanisms are incorporated to protect sensitive data. The system uses cloud-based storage for better performance, availability, and scalability. It also ensures data confidentiality and integrity through secure login and access control mechanisms. The proposed system reduces manual work, improves efficiency, and provides a reliable solution for both administrators and users.

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Index Terms: Hotel Management, Web Application, Cloud Computing, Security, Automation

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Abstract: Background: The simultaneous occurrence of diabetic ketoacidosis (DKA), hypertriglyceridemia-induced acute pancreatitis (HIAP), and Gram-negative bacteraemia represents an exceptionally rare and clinically challenging constellation in young adults. The triad demands meticulous diagnostic reasoning because each condition can mask or mimic the others. Case Summary: A 25-year-old male with no prior documented diabetes mellitus presented to the emergency department with a three-day history of progressive altered sensorium, breathlessness at rest (MMRC Grade IV), diffuse chest pain, low-grade fever, inability to ambulate, and bowel and bladder incontinence. Investigations confirmed DKA, markedly elevated pancreatic enzymes (serum amylase 597 U/L; serum lipase 235 U/L), bilateral pleural effusions, ascites, and blood culture positivity for Serratia marcescens. A critically depressed C-peptide level (0.14 ng/mL) was consistent with new-onset Type 1 Diabetes Mellitus. The patient was managed in the medical intensive care unit (MICU) with aggressive intravenous fluid resuscitation, insulin infusion, electrolyte replacement, and targeted antimicrobial therapy. Conclusion: This case underscores the importance of a broad diagnostic approach in DKA, particularly the measurement of serum triglycerides, lipase, and amylase, as HIAP may perpetuate DKA in a vicious cycle. Concurrent Gram-negative bacteraemia further compounds morbidity and mandates early blood cultures with targeted therapy. Awareness of this triad can significantly alter the management strategy and improve outcomes.

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Diabetic Ketoacidosis; Hypertriglyceridemia; Pancreatitis, Acute; Bacteremia; Diabetes Mellitus Type 1; C-Peptide; Serratia marcescens; SPICE organisms

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