Volume 13 | Issue 9 |

 Abstract

Pitta Dosha, as described in Ayurveda, is characterized by strong digestion (Tikshnagni), sharp intellect (Medha), moderate build, and intolerance to heat (Ushna) .While traditional assessment relies on observation (Darshana), enquiry (Prashna), and palpation (Sparshana), these methods lack reproducibility1.This study aims to enhance Prakruta karma, vrddhi & kshaya assessment by integrating subjective Ayurvedic indicators with objective biomedical tools5.Classical signs were systematically mapped to quantifiable parameters such as gastric pH, thermal imaging, ghrelin and leptin levels, cognitive function tests, and metabolic markers6.The model also addresses Pitta imbalances: Vriddhi (aggravation) was associated with elevated bile pigments, gastric hyperacidity, and liver function markers; Kshaya (depletion) correlated with low BMR, cold sensitivity, and reduced skin hydration8 .This integrative approach strengthens clinical practice by improving diagnostic precision, enabling early detection of Dosha imbalances, and supporting personalized treatment strategies9.It also facilitates standardization and opens avenues for interdisciplinary research, including Ayurgenomics and digital Prakriti profiling11.

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 Keywords

: Pitta Dosha, Prakruti assessment, Vriddhi and Kshaya, Ayurvedic diagnostics, Biomedical integration, Metabolic markers, Ayur genomics, Personalized medicine

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The impact of technology on various parts of our lives, notably among the youth and college students around the globe. The emergence of digital technologies has revolutionized the way we learn, communicate, and socialize. Technology has created new opportunities for education and personal growth by increasing access to educational resources and allowing students to collaborate in real time with their peers and instructors. However, technology has also introduced a number of issues, including addiction, social isolation, and distractions, all of which can have serious consequences for mental health and wellbeing. The impact of technology on college students is enormous, with online learning platforms and distant coursework becoming more common. While this provides flexibility and convenience, it may also result in feelings of loneliness and isolation. Additionally, the requirement for ongoing connectivity and the possibility of distraction can have a negative impact on academic achievement. It is critical to understand the possible benefits and drawbacks of technology use, as well as to establish healthy habits and boundaries. Practicing digital literacy, self-awareness, and striking a balance between online and offline activities might help us avoid the negative effects of technology while maximizing its benefits. To summarize, it is critical to embrace technology's potential to improve our lives while staying aware of its possible disadvantages. Many college students use technology for academic objectives such as online study, research, and communication with professors and classmates. In addition, technology is commonly utilized for social networking, entertainment, and gaming. While technology has clearly improved our lives, there are concerns about its impact on our mental health, particularly among college students. This review research paper investigates the confluence of technology and college students' psychology, including the positive and negative effects of technology on their mental health.

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 Keywords

Technology; College students, Psychology, Positive effects, Negative effects

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Cloud computing has transformed the global digital ecosystem by offering scalable, cost-effective, and collaborative solutions across industries. Its service models--Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS)--and varied deployment modes have created unprecedented opportunities for innovation and operational agility. However, the decentralised and third-party-dependent nature of cloud systems introduces significant security vulnerabilities. Data breaches, unauthorised access, interoperability gaps, and weaknesses in identity management remain pressing concerns for stakeholders in sectors such as healthcare, finance, education, and government. This paper examines these security challenges through a comprehensive review of literature and insights from practitioners. It argues that the absence of standardised, context-specific frameworks exacerbates risks and undermines the confidentiality, integrity, and availability of data. Employing a mixed-methods approach, the study integrates theoretical perspectives with empirical observations to identify gaps in existing solutions. The discussion proposes a multi-layered security strategy encompassing advanced encryption, robust identity management, compliance-driven governance, and global interoperability standards. By bridging the divide between technical measures and policy frameworks, this research contributes to building resilient cloud ecosystems capable of mitigating emerging threats while maintaining trust and reliability. The findings hold value for researchers, industry professionals, and policymakers seeking sustainable and secure cloud adoption.

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 Keywords

cloud computing security, data breaches, interoperability, identity management, global standards, risk mitigation

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 Abstract

This paper presents the design and simulation of a hybrid-shaped microstrip patch antenna (MPA) that combines the benefits of U-shaped cutouts and a hexagonal shape to achieve enhanced gain and bandwidth at 2.45 GHz. The antenna is designed by incorporating U-shaped cutouts within a hexagonal structure to improve its performance compared to conventional U-shaped and hexagonal antennas. The antenna was designed and simulated on an FR4 substrate with a dielectric constant of 4.4 and a thickness of 1.6 mm. The design and simulations were carried out using High-Frequency Structure Simulator (HFSS). The simulation results indicate that the hybrid-shaped antenna achieved a gain of 5.4 dB and a bandwidth of 1.96 GHz, which are improved values relative to the individual designs. These enhanced characteristics make the antenna well-suited for wireless communication applications such as Wi-Fi, Bluetooth, and Zigbee. The combination of higher gain and wider bandwidth highlights the potential of the proposed antenna for integration into modern wireless systems.

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 Keywords

Analysis System High-frequency structure simulator (ANSYS HFSS), Hybrid-shape antenna, U-cut, hexagon patch, FR4 board, 2.45 GHz, HFSS, signal gain, frequency range, wireless use, Wi-Fi, Bluetooth, Zigbee

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The analysis of body fluids represents one of the most crucial aspects of forensic investigations, providing valuable biological evidence that aids in criminal identification, reconstruction of events, and linking suspects to crime scenes. This review discusses the significance of commonly encountered body fluids such as blood, semen, saliva, urine, sweat, and vaginal secretions, along with emerging sources such as earwax and breast milk. Traditional presumptive and confirmatory methods are compared with recently developed molecular and analytical approaches including mRNA profiling, proteomics, microbiome analysis, and mass spectrometry. The paper also evaluates observations from literature, discusses advantages and challenges of newly developed techniques, and outlines future perspectives. The findings suggest that while traditional methods remain relevant, modern molecular biology and bioinformatics are transforming forensic serology into a multidisciplinary science with greater sensitivity, specificity, and evidentiary value.

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 Keywords

Forensic serology; Body fluids; DNA profiling; RNA profiling; Proteomics; Microbiome analysis; Mass spectrometry; Forensic identification; Biological evidence; Molecular forensics

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Financial inclusion--the process of ensuring access to affordable and appropriate financial services for all--has emerged as a critical pillar for inclusive economic development. With the rise of digital technologies, particularly mobile banking, digital wallets, and fintech platforms, digital banking has become a catalyst for expanding financial access to previously underserved and unbanked populations. This paper explores the interrelationship between financial inclusion and digital banking, examining how technological innovations are reshaping financial systems and contributing to inclusive growth. Drawing on global examples and a detailed review of literature, the study identifies the key drivers, challenges, and impacts of digital financial inclusion. It also evaluates the role of governments, regulators, and the private sector in fostering a robust digital financial ecosystem. The paper concludes with practical policy recommendations to bridge the digital divide and ensure that digital banking contributes meaningfully to equitable development.

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 Keywords

Financial Inclusion, Digital Banking, Fintech, Mobile Money, Unbanked Population, Inclusive Growth, Financial Literacy, Digital Divide

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The convergence of Financial Technology (FinTech) and Agricultural Technology (AgriTech) is revolutionizing the agricultural landscape in both developing and developed economies. Traditionally underserved by formal financial and technological infrastructures, the agricultural sector is now experiencing a wave of innovation that promises to enhance productivity, improve access to markets and finance, and build climate resilience. This research article explores how FinTech and AgriTech are reshaping rural economies by providing solutions for long-standing problems such as lack of access to credit, crop failure risks, market inefficiencies, and low yields. Through case studies, review of global and national initiatives, and evaluation of socio-economic impacts, the paper highlights how digital tools and platforms are empowering farmers, improving food security, and contributing to inclusive and sustainable economic growth. Strategic recommendations are provided to harness the full potential of these technologies in transforming agriculture and empowering rural communities.

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FinTech, AgriTech, Precision Farming, Digital Agriculture, Rural Finance, Agricultural Innovation, Farm-to-Market Solutions, Sustainable Agriculture

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Background: Postpartum coccydynia is a painful and disabling condition that affects women following childbirth. Objective: To compare the effectiveness of Muscle Energy Technique (MET) with phonophoresis versus phonophoresis alone in managing postpartum coccydynia. Methods: A comparative experimental study was conducted on 46 postpartum women clinically diagnosed with coccydynia. Participants were randomly allocated into two groups: Group A (n=23) received MET with phonophoresis and a home exercise program, and Group B (n=23) received phonophoresis only. Pain intensity (NPRS) and disability (ODI) were assessed before and after 4 weeks of intervention. Statistical analysis included paired and unpaired t-tests. Results: Both groups showed significant improvements (p < 0.0001). Group A demonstrated greater reduction in pain intensity, while Group B showed greater improvement in disability. Between-group differences were statistically significant. Conclusion: Both interventions are effective in managing postpartum coccydynia. Combining MET with phonophoresis yields superior pain reduction, while phonophoresis alone showed slightly greater disability improvement. A multimodal approach may provide optimal benefits.

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Coccydynia; Postpartum; Muscle Energy Technique; Phonophoresis; Physiotherapy

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Diabetes, Prevalence, Risk factor, Non- communicable disease.

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This In the digital economy, businesses increasingly rely on data-driven approaches to personalize campaigns and maximize customer engagement. Predicting customer response to marketing campaigns is a crucial challenge in modern business analytics, where organizations seek to optimize targeting and maximize return on investment. This study explores predictive modeling of customer response behavior using machine learning techniques Random Forest and XGBoost with SHAP on a real-world marketing dataset containing demographic, behavioral, and spending attributes. The models are evaluated using classification metrics, confusion matrices, and ROC-AUC scores, ensuring a comprehensive assessment of predictive performance. Results indicate that while Random Forest achieved perfect classification on the dataset, suggesting potential overfitting, the XGBoost model coupled with SHAP offered comparably high predictive performance with the added advantage of robustness and explainability. XGBoost effectively captured complex feature interactions, such as age-gender effects, making it more reliable for real-world marketing applications. The results highlight the potential of explainable artificial intelligence (XAI) in enhancing targeted marketing strategies, while also identifying future directions for scaling the approach with real-world, multi-channel customer data. Furthermore, correlation heatmap analysis revealed significant behavioral patterns, highlighting the role of spending behavior and demographics in influencing engagement. These findings provide actionable insights for businesses to design data-driven, personalized marketing strategies. The research emphasizes the dual importance of predictive accuracy and interpretability, ensuring models are not only effective but also transparent and usable for real-world decision-making.

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 Keywords

Machine Learning, Customer Response Prediction, Random Forest, XGBoost, SHAP, Explainable AI, multi-channel campaigns, Behavioral Modeling.

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