Volume 13 | Issue 9 |

 Abstract

This study develops a hybrid machine learning framework to analyze and forecast surface ocean dynamics in the Arabian Sea. The pipeline integrates preprocessing, anomaly detection, and forecasting within a cycle-wise modular design that separates carbon, nutrient, phytoplankton, oxygen, and physical drivers into independent modeling workflows. Classical ensemble methods such as support vector machines and clustering were employed for anomaly detection, while hybrid CNN-LSTM architectures were trained for temporal forecasting, supported by additional ensemble models (ARIMA, Prophet). Evaluation demonstrated that the modular design is computationally feasible and ecologically interpretable, successfully reproducing key surface-layer dynamics including carbonate variability and nutrient-chlorophyll coupling. Carbon cycle forecasts achieved the strongest performance, while oxygen predictions were more uncertain due to gradient complexity and data sparsity. Although constrained by reduced training and absence of in-situ validation, the framework establishes a baseline methodology for hybrid ML-DL applications in marine forecasting, offering a scalable approach for future integration with physical ocean models.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Hybrid machine learning; Deep learning; CNN-LSTM; Ensemble models; Anomaly detection; Surface ocean forecasting; Arabian Sea; Biogeochemical cycles; Carbon system; Time-series prediction

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

It is a comparative study conducted among adolescent girls of age 18-23yrs. A sample size of 100 was selected to analyze their dietary habits and corresponding hemoglobin levels. The objective is to evaluate the intake of whole foods versus processed foods in relation to individual hemoglobin levels. Additionally, the study aims to examine the correlation between the consumption of whole foods and processed foods and their impact on hemoglobin levels.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Whole Foods, Processed Foods, Hemoglobin

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Lifestyle disorders, including obesity, diabetes, hypertension, and cardiovascular disease, are major global health concerns closely linked to diet. Modern biomedical science attributes these conditions to chronic inflammation, oxidative stress, and hormonal dysregulation, whereas Unani medicine explains them through disturbances in Miz?j (temperament) and the production of abnormal Akhl?? (humours). This paper undertakes a comparative analysis of diet in the causation and management of lifestyle disorders from both perspectives. Classical Unani texts such as Al-Q?n?n f? al-?ibb (Ibn S?n?) and Kit?b al-??w? (al-R?z?) were examined alongside contemporary biomedical literature. Despite theoretical differences, both systems converge on the principles of moderation, balanced nutrition, proper meal timing, and personalization of diet. Unani concepts like Il?j bi'l-Ghidh? (dietotherapy), temperament-based food selection, and avoidance of harmful combinations resonate with modern preventive nutrition and chrono-nutrition. The integration of these approaches offers a holistic, sustainable model for preventing and managing lifestyle disorders, reaffirming the timeless view of diet as both a cause of disease and a cornerstone of therapy.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Lifestyle disorders; Unani medicine; Diet (Ghidh?'); Miz?j; Integrative nutrition

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The Eastern Ghats of India, a biodiversity hotspot, are increasingly threatened by forest fires, which significantly impact vegetation and ecosystem stability. This study conducts a statistical analysis of forest fire occurrences and their effects on vegetation in the Eastern Ghats. The present study has focused on the techniques on the remote sensing, GIS, and statistical models to understand the fire frequency, intensity, and their correlation with vegetation degradation. The review has demonstrated a rising trend in fire frequency, with notable impacts on tropical dry deciduous forests, leading to reduced species diversity and soil degradation. The Forest Survey of India states that nearly 36 per cent of India's forests are prone to frequent fires. The forest fire season in India lasts from November to June. Development of statistical models, including principal component analysis (PCA) and regression, reveal strong relationships between fire events, climatic factors, and vegetation loss are essential to continuous monitoring and locating the fire prone areas. We also explore the causes of forest fires, their broader ecological impacts, and prevention strategies, emphasizing the need for integrated fire management to conserve the Eastern Ghats' ecosystems.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Forest fires, Eastern-Ghats, impact on vegetation and biodiversity, mitigation measures

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Abstract :- Time series analysis has emerged as a critical tool for understanding dynamic patterns in financial and environmental systems. Both domains exhibit complex behaviours, such as non-stationarity, seasonality, volatility, and long-term trends, making advanced statistical and machine learning methods indispensable. This paper explores the methodologies, applications, and comparative analysis of time series models in finance, (stock prices, exchange rates, volatility) and environment (climate change indicators, air quality, temperature forecasting). The integration of computational methods, particularly machine learning and deep learning, is discussed as a way to enhance predictive performance and policy implications.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

KEYWORDS: Time Series Analysis, Financial Forecasting, Environmental Data, ARIMA, GARCH, Machine Learning, Deep Learning

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Wound management is an integral part of healthcare and holds significant importance in Ayurvedic practice. This case study highlights the role of Jalaukavacharan (leech therapy) in managing a chronic wound. A 24-year-old female presented with wounds over the ankle region, associated with pain, itching, and inflammation for two months. Conventional symptomatic care provided little relief. Ayurvedic management was initiated with leech therapy, accompanied by internal medicines such as Gandhak Rasayan, Triphala Guggul, Punarnava Guggul, and Sariva-Manjishta-Nimsaal Kadha. The therapy demonstrated marked improvement within five days--complete relief in pain, reduction in erythema, absence of discharge, and signs of progressive wound healing (Ruhyamanavastha). This study concludes that Ayurvedic leech therapy, along with internal medicines, is highly effective in wound healing, providing rapid relief without adverse effects.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Ayurvedic wound management Jalaukavacharan Leech therapy Chronic wound Case report Tridoshaja wound Rakta dushti Shalyatantra Pain and inflammation relief Ayurvedic parasurgical procedure Ruhyamanavastha (healing stage) Natural wound healing

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The soil of the Nashik District offers remarkable opportunities for studying the diversity of arbuscular mycorrhizal fungi. A survey was conducted on arbuscular mycorrhizal fungi from the rhizosphere and non-rhizosphere soil of garlic (Allium sativum) across four different locations in the Niphad and Chandwad talukas of the Nasik district in Maharashtra. The highest number of AM propagules, totaling 446 per 100 grams of soil, along with the greatest root infection rate of 82%, was observed in the Panchakeshwar locality. In contrast, the Rahud locality recorded the lowest number of AM propagules at 174 per 100 grams of soil and a root infection rate of just 50%. The other two localities, Palkhed and Dighwad, exhibited intermediate results. From both the rhizosphere and non-rhizosphere soil of garlic, three genera comprising seventeen species were isolated. The genus Glomus was the most prevalent, featuring ten species. The other genera identified included Acaulospora, which had three to four species, and Scutellospora, which had five species. Notably, the number of AM propagules was higher in the rhizosphere soil compared to the non-rhizosphere soil. Soil analysis was performed to assess various parameters such as pH, electrical conductivity (EC), organic carbon (OC %), phosphorus (P2O5 in Kg/ac), potassium (K2O in Kg/ac), and trace elements including zinc (Zn in ppm), copper (Cu in ppm), iron (Fe in ppm), and manganese (Mn in ppm) from both rhizosphere and non-rhizosphere soil of garlic.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Arbuscular Mycorrhizal fungi, Diversity, Garlic (Allium sativum L.)

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Due to excessive loading, construction errors and improper maintenance strengthening of concrete structures becomes critical. Fibre reinforced polymer brings improvement in load bearing capacity, improved ductility and reduces damages due to deterioration. The researchers are seeking new and innovative ways of strengthening of beams as conventional methods of reinforcement encountered certain limitations which are required to overcome. The technique of wrapping a Reinforced Concrete (RC) beam with composite material becomes popular and has been widely adopted in the structural applications due to their superior properties. FRP consumes relatively a less labour cost and also easy way to strengthen the structures to give effective solution. The experimental work has been done for both conventional and CFRP strengthening sheets and calculated compressive, Split-tensile strength & flexural strength sheets with various intervals such as CFRP wrapped with 50mm interval, CFRP wrapped without interval

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Reinforced Concrete (RC) Beams, Carbon Fibre Reinforced Polymer (CFRP) ,Structural Strengthening, Compressive Strength, Split-Tensile Strength, Flexural Strength , FRP Wrapping Technique , Load Bearing Capacity, Ductility Improvement, Interval Wrapping (50mm / Continuous Wrapping)

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Cloud computing combined with big data analytics offers transformative opportunities for healthcare--enabling scalable storage, multimodal data fusion, and advanced machine learning for clinical and operational decision support. This paper presents a comprehensive framework and experimental plan for deploying cloud-based analytics to improve patient care. We describe architectures (centralized Lakehouse, hybrid edge-cloud, and federated setups), data engineering pipelines for electronic health records (EHR), imaging, and device telemetry, and privacy-preserving machine learning strategies (federated learning, differential privacy). We propose a set of experiments using public and partner datasets to evaluate predictive performance, latency, cost, and privacy-utility tradeoffs. We also outline deployment considerations, regulatory compliance, and ethical safeguards for real-world adoption. Our work aims to provide a reproducible blueprint for researchers and healthcare IT teams to build cloud-centered analytics that improve clinical outcomes while minimizing privacy risks.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Cloud Computing, Big Data Analytics, Healthcare, Patient Care, Electronic Health Records, Privacy-Preserving Machine Learning, Telemedicine

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This paper explores the intersection of Thaang-Taa, the martial tradition of the Meiteis, and Lai Haraoba, the ritual festival that enacts cosmological creation, cultural memory, and social practices of the community. While Thaang-Taa is often studied in terms of its martial discipline, this article highlights its ritual embodiment within Lai Haraoba, where tools and weapons such as swords, knives, spears, scissors, and agricultural implements appear not materially but symbolically through codified gestures and movements. Episodes like Nungnao Jagoi, Phisarol, Yumsharol, and Uyanlon reveal how martial and agricultural traditions are preserved in ritual performance. The study argues that the human body serves as the primary site where the spiritual, martial, and cultural converge, transforming material absence into symbolic presence. Drawing on performance studies and cultural analysis, this article demonstrates how Lai Haraoba sustains intangible heritage and positions Thaang-Ta as a living archive of Meitei identity, aesthetics, and philosophy.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Lai Haraoba, Thaang-Ta, Ritual Performance, Cultural Memory, Intangible Heritage

  License

Creative Commons Attribution 4.0 and The Open Definition