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By. D. Dharani, Ateetha Santhosh, D. Hemavadhana, N. Nachammai
Wildlife monitoring plays a crucial role in understanding and conserving biodiversity, but traditional methods often have limitations in scope, accuracy, and ethical impact. Advances in multimodal technologies such as drones, acoustic sensors, environmental DNA (eDNA), and camera traps offer new avenues for gathering rich, non-invasive data. However, the integration of these technologies comes with a range of challenges, particularly in terms of ethical concerns related to animal welfare, data management, and environmental impact. This paper explores how combining these advanced technologies with ethical guidelines can address these challenges, creating a balanced approach to wildlife monitoring. By emphasizing the use of non-invasive techniques, sustainability, and ethical principles, this paper aims to propose practical solutions for modern wildlife monitoring and conservation efforts, ensuring that technological advancements support both scientific progress and ethical responsibility. Poaching, species extinction, habitat loss, and climate change are just a few of the many issues facing wildlife monitoring today. Data collection and analysis have been transformed by the integration of multimodal technologies including unmanned aerial vehicles (UAVs), bioacoustics, artificial intelligence (AI), and remote sensing. However, a clear ethical framework is required due to the ethical implications of new technologies, which include privacy issues, data ownership, and the possible damage of natural environments. In order to promote responsible innovation that strengthens conservation efforts while maintaining ecological and ethical integrity, this paper examines the relationship between multimodal technology and ethical considerations in wildlife monitoring.
Keywords: Wildlife Monitoring, Multimodal Technologies, Ethical Frameworks, Environmental DNA, Acoustic Sensors, Drones, Camera Traps, Non-invasive Monitoring, Conservation, Data Privacy.
By. Bhargav Rajyagor, Prof. Paresh Vora
Weeds présent a major challenge to crop productivité by compétent with crops for vital resources, including water, sunlight, and nutrients, often resulting in significant yield reductions. On a global scale, weeds are responsible for approximately 13.2% of annual crop losses, a quantity sufficient to feed nearly one billion people. These invasive plants disrupt agricultural systems and adversely impact crop yields. Given their uneven distribution in fields, ground or aerial robots are commonly used for targeted herbicide application, utilizing computer vision algorithms to detect weeds before treatment. In cotton cultivation, prevalent weeds such as horse purslane (Trianthema portulacastrum L.) and purple nutsedge (Cyperus rotundus L.) pose significant threats. Despite increasing interest in deep learning-based weed detection, progress remains limited due to the scarcity of extensive datasets.
To address this challenge, we introduce CottonWeeds, a curated dataset featuring 7,000 images of horse purslane and purple nutsedge captured under varied conditions in Indian cotton fields. This dataset is designed to facilitate the development of real-time weed recognition models. Weed control strategies typically involve physical, mechanical, biological, and chemical methods. However, recent advancements in Unmanned Aerial Vehicles (UAVs) and deep learning technologies, particularly Convolutional Neural Networks (CNNs), have enabled precise weed detection, reducing costs and promoting sustainable agricultural practices. This study explores the integration of UAVs and CNNs to improve weed detection accuracy, address herbicide-resistant species, and advance sustainable farming solutions. Future research aims to enhance these approaches by incorporating advanced image processing and deep learning algorithms for automated feature extraction, thus tackling complex challenges in cotton weed management. Model performance will be evaluated using metrics such as precision, recall, and F1 score to ensure a comprehensive assessment and minimize false positives and false negatives.This version maintains the original intent and detail while rephrasing for uniqueness and improved readability.
Keywords: Weeds, Computer Vision, Deep learning, UAVs (Unmanned Aerial Vehicles)
By. Mohd. Wasiullah, Piyush Yadav, Sushil Yadav, Roshan Yadav
The creation of safe and efficient medications depends heavily on the art of drug design and process chemistry. This multidisciplinary discipline designs and optimizes drug candidates for therapeutic uses by fusing the concepts of biology, chemistry, and engineering. Researchers can develop compounds with particular pharmacological activity by using logical drug design techniques if they have a thorough understanding of the molecular targets implicated in disease pathways. By making it easier to predict molecular interactions and properties, computational techniques significantly improve the drug discovery process. These theoretical concepts are then translated into workable synthesis pathways, purification techniques, and scale-up procedures that are necessary to produce pharmaceuticals by process chemistry. This article delves into the complexities of process chemistry and drug design, examining the core ideas and real- world uses that spur innovation in Pharmaceutical Development.
Keywords: Drug design, Rational drug design, De novo design, Pharmacophore
By. Aastha Parekh, Richa Shah, Foram Shah, Viddhi Bhatt, Dr. Vishwa Mehta
The integration of genomic and precision medicine into cardiovascular healthcare represents a transformative advancement in addressing the global burden of cardiovascular diseases (CVDs). Precision medicine leverages genomic, proteomic, and metabolomic data to provide personalized care, optimizing treatment outcomes while minimizing adverse effects. Over the past decade, extensive research has highlighted its potential to revolutionize the management of major CVDs, including myocardial infarction, hypertension, and heart failure, which significantly contribute to global morbidity and mortality. Genomic tools, such as polygenic risk scores (PRS) and single nucleotide polymorphisms (SNPs), enhance cardiovascular risk prediction, enabling early interventions and targeted therapies. For example, PRS has demonstrated utility in stratifying individuals with a heightened genetic predisposition to coronary artery disease. Advances in the identification of monogenic variants have also enabled personalized interventions for familial hypercholesterolemia. Furthermore, biomarkers like cardiac troponins, copeptin, and genetic variants in CYP2C19 have refined diagnostic and therapeutic strategies for acute myocardial infarction. Genomic insights into hypertension have unveiled critical pathways, offering potential targets for innovative treatments. Despite the promise, challenges such as ensuring diverse study populations and addressing ethical considerations remain. This evolving paradigm underscores the transformative impact of genomic and precision medicine, heralding a new era of individualized cardiovascular care.
Keywords: Genomic Medicine, Precision Medicine, Cardiovascular Diseases (CVDs), Polygenic Risk Scores (PRS), Single Nucleotide Polymorphisms (SNPs), Coronary Artery Disease, Familial Hypercholesterolemia, Genetic Variants, Genomic Insights.
By. Vikash Yadav, Mohd. Wasiullah, Piyush Yadav, Rahul Nishad
The pharmaceutical sector is progressively adopting software solutions to enhance the drug development process and optimize clinical research results. Drug development is a time-consuming, expensive, and intricate process that traditionally requires extensive laboratory research, preclinical testing, and several stages of clinical trials. Software tools are revolutionizing these stages by improving efficiency, minimizing errors, and speeding up timelines. During preclinical testing, predictive software tools are used to model toxicological effects and assess the safety profiles of drug candidates, reducing the risk of adverse outcomes in human trials. These tools contribute to making early-stage testing more efficient and focused on viable candidates. In clinical research, software solutions play a critical role in trial design, patient recruitment, data collection, and monitoring. Clinical trial management systems (CTMS), electronic data capture (EDC) platforms, and data analytics tools facilitate real-time data collection and integration, simplifying the tracking of progress, identification of trends, and ensuring adherence to regulatory standards. The use of artificial intelligence (AI) and machine learning (ML) is becoming increasingly prevalent in clinical research, as these technologies allow for advanced data analysis and predictive modelling. AI and ML can uncover patterns in patient responses, predict drug outcomes, and optimize treatment plans, thus improving the precision of clinical trials. Software tools also support regulatory compliance, ensuring that clinical data is accurate, traceable, and consistent with health authority guidelines.
Keywords: CTMS, Artificial Intelligence, Electronic Data Capture, Machine Learning
By. Mohd. Wasiullah, Piyush Yadav, Azhan Ahmad
As dengue fever continues to surge across tropical and subtropical regions, the exploration of novel therapeutic approaches is crucial. Nutraceuticals, natural bioactive compounds derived from food sources, are emerging as promising adjuncts in the fight against this viral disease. Unlike conventional treatments that primarily target symptoms, nutraceuticals offer a complementary strategy by enhancing the body’s immune defense and addressing key complications. This review highlights the diverse potential of nature’s remedies, such as papaya leaf extract for boosting platelet count, quercetin for its antiviral effects, and curcumin for modulating the inflammatory response. Additionally, the role of coconut water and vitamin C in strengthening immune resilience is examined. These nutraceuticals not only provide symptomatic relief but may also play a pivotal role in reducing disease progression and severity. By leveraging the healing power of nature, nutraceuticals present a unique, holistic approach to dengue management–one that bridges the gap between natural wellness and clinical science, offering new avenues for future treatment strategies.
Keywords: Curcumin, dengue treatment, dietary supplement, nutraceutical, platelets count, quercetin
By. P.C Kathuria, Manisha Rai
Climate change, a global issue, directly and indirectly impacts human health while posing a significant threat to animals and ecosystems. As part of the exposome, it influences various factors like seasonality, production, and the distribution of airborne allergens due to rising carbon dioxide levels and temperatures. These environmental changes also contribute to increased epithelial permeability, microbial imbalances, and heightened allergic reactions to pollen and fungal spores. Earth’s average surface temperature, currently around 14°C, has risen by 1°C over the past century, reflecting the growing urgency of this crisis. This has resulted in change in the environmental pattern and can have serious impact on vulnerable population, such as atopic individuals, elderly, children, or pregnant women. Climate change, air pollution, & reduced biodiversity are inter-related and has led to an increase in respiratory allergy diseases. This review emphasizes the important steps to mitigate the health disparities arising from global warming, climate change, and aeroallergen respiratory allergic diseases.
Keywords: Climate change, Green-house gases (GHGs), particulate matter (PM), volatile organic compounds (VOCs), sand & dust storm (SDS), ozone, carbon dioxide (CO2)
By. vaidik A sharma, N. Madurai Meenachi
This paper provides an in-depth analysis of entanglement entropy (EE) in quantum field theory (QFT), with a particular focus on its computation using the replica trick and its applications to both conformal and non-conformal systems. Beginning with an introduction to the basics of QFT, the study explains how entanglement entropy quantifies the quantum correlations between subsystems in a pure state, represented by the von Neumann entropy of the reduced density matrix. The replica trick is employed to derive the entanglement entropy, involving path integrals over n-sheeted Riemann surfaces. The paper demonstrates the method’s utility in 1+1-dimensional conformal field theory (CFT), where the central charge governs universal properties of entropy. It further extends the analysis to massive field theories, finite systems, and topological phases, exploring how deviations from conformal symmetry affect entanglement. The work also delves into the AdS/CFT correspondence, showcasing how holographic techniques facilitate entanglement entropy calculations in higher-dimensional systems via the Ryu-Takayanagi formula. In addition, the study investigates entanglement entropy in quantum lattice systems, considering the effects of spatial and thermal fluctuations. Numerical methods are used to compute the scaling of entanglement entropy with interval length for varying system masses and temperatures, highlighting the influence of these parameters on entropy behavior. Fractal lattice structures are explored to uncover unique entropy scaling laws and self-similar entanglement patterns. This study advances the under-standing of EE in diverse quantum systems and establishes a foundation for exploring its role in quantum criticality and fractal geometries. The paper also explores multiscale entanglement entropy (MSE), offering a comprehensive framework for future research in quantum critical phenomena and non-equilibrium systems, shedding light on the complex relationship between quantum entanglement and thermal effects. This work serves as a foundational reference for future theoretical and computational studies in quantum entanglement
Keywords: Entanglement Entropy, Quantum Field Theory, Conformal Field Theory, AdS/CFT Correspondence, Holographic Principle, String Theory, Quantum Gravity
By. Rhea, Sangeeta Kakkar
The transmission of trauma effects from one generation to the next is known as intergenerational trauma, and it is particularly common in patriarchal civilisations such as India. Laws, economy, and social conventions are shaped by patriarchy, a societal framework that primarily gives males power, authority, and advantages. This perpetuates gender inequity and marginalises women. Aim: The purpose of this review is to examine how trauma is transmitted throughout generations in India, with a particular emphasis on the interaction between trauma and patriarchal systems. The review included previous research from Google Scholar and PubMed that was published during the previous 15 years. Results: Through the prism of historical trauma, the article analyses both minor traumas, such as emotional abuse within families, and big traumas, such as forced migration during the 1947 Partition. It also looks into how patriarchal standards normalise gender-based violence, which worsens psychological injury and feeds the trauma cycle for decades. The review covers a number of trauma transmission mechanisms, such as genetics, family, and psychological elements. It draws attention to the ways that sociocultural factors fuel psychological suffering, health inequities, and enduring dysfunctional parenting styles. To treat trauma, the review finds culturally sensitive coping mechanisms and healing techniques, such as contemporary psychotherapies and customary rituals. To support healing and resilience among populations impacted by intergenerational trauma, a comprehensive strategy that takes into account individual, family, and societal aspects is crucial.
Keywords: Intergenerational trauma, patriarchal societies, epigenetic mechanisms, cultural healing practices, gender inequality
By. Gurusowmyasri Kota
Aim: Rheumatoid arthritis (RA) is a long-term autoimmune condition characterized by the immune system mistakenly attacking the joints, leading to swelling, discomfort, and joint deformities. Current management strategies include anti-rheumatic drugs and biologics, which have limitations. This study aims to explore the therapeutic potential of Ricinus communis (castor bean plant) phytochemicals as plant-based therapies for autoimmune conditions like RA.Methods: Phytochemicals from Ricinus communis were retrieved using the IMPPAT database, identifying 95 compounds. Their drug-like properties were evaluated using the SwissADME tool, resulting in the selection of seven compounds: Ricinine, Apigenin, Glycolic acid, Shikimic acid, Nicotinamide, threo-9, 10-Dihydroxystearic acid, and uric acid. Toxicity assessments were conducted using the protox tool. Molecular docking was performed with pyrx to evaluate interactions was achieved using BIOVIA.Results: This study revealed that Ricinus communis Phytochemicals demonstrated strong binding affinities with autoimmune pathway-related proteins 1A8M and 1TNF, indicating significant anti-inflammatory and immunomodulatory potential. These finding suggest their promise as plant-based therapeutic agents for rheumatoid arthritis.Conclusion: This research underscores the promising anti-inflammatory and immunomodulatory properties of phytochemicals derived from Ricinus communis. The identified compounds exhibit favorable drug-like and safety profiles, with significant binding to autoimmune pathway-related proteins. These findings provide a foundation for experimental studies and the development of plant-based therapies for autoimmune disorders such as RA.
Keywords: Rheumatoid arthritis, Ricinus communis, phytochemicals, autoimmune disorders, molecular docking, anti-inflammatory, immunomodulatory.
By. Nagham Mahmood Aljamali, Saher Mahmood Jwad, Wisam Hassan Ali Alfartosi, Rajaa Abdul Ameerghafil
Among the organic compounds that are commonly used as chemical reagents in the field of pollution, removals of heavy metals from rivers, coordination chemistry and in various fields of life such as industry, medicine and pharmaceutical research are organic reagents such as oxime and imine azo reagents, Fehling’s and Tollen’s reagents and their derivatives. This type of reagent is characterized by high stability and rapid reaction with metal ions, as these ions bond with the organic reagent through donor atoms (S, O, N) to form colored complexes with high sensitivity and selectivity. The reason for the stability of this type of reagent is attributed to the double bond between the two nitrogen atoms of the azo bridge group, and it is also affected by the types of groups attached to the ends of the azo bridge group, whether aliphatic or aromatic. The use of chemical reagents ensures the accuracy and reliability of results, which is essential for scientific research and development. In general, chemical reagents are indispensable tools in laboratory analysis, enabling scientists to conduct experiments. Due to these features, azo compounds have received the attention of a large number of researchers in various fields of science.
Keywords: Reagent, detection, lab identification, synthesis, organic
By. Manoj Sharma
This study investigates the effects of varying weight percentages of Silicon Carbide and Alumina particles in epoxy-based composites, fabricated through compression molding, on their physical and mechanical properties. Comparing different studies on carbon fiber-reinforced polymer (CFRP) composites, as well as glass fiber-reinforced polymer (GFRP) composites, especially with the reinforcement of nanoparticles such as silicon carbide (SiC), alumina (Al2O3) and graphene. The investigations cover fabrication methods, like vacuum infusion and compression molding and their impact on tensile, flexural, hardness, and impact properties, highlighting the effects of varying filler types, particle sizes, and weight percentages. To understand the characteristics of ceramic-reinforced hybrid composites in accordance with ASTM standards, the research evaluates the density, moisture content, dimensional stability, tensile strength, flexural strength, impact resistance and hardness of the ceramic-reinforced hybrid composites in accordance with ASTM standards. Fractographic analysis of tensile and flexural specimens was conducted using SEM. The study highlights a reduction in composite density and examines how immersion time in water and the varying ceramic content influence dimensional stability. The composites exhibited optimal tensile strength (172.36±8.028 MPa), flexural strength (164.493±9.918 MPa), impact resistance (24.2±1.643 J/m) and hardness (77.3±1.702 HSN) at 4 wt% SiC/Al2O3, and thus turned out to be highly promising for applications in automotive parts and sports tools.
Keywords: About Glass Fiber, Epoxy resin, SiC, Al2O3, Compression molding, Flammability.
By. Meghna Chaudhary, Monika
Plastics are high molecular weight materials composed of repeating polymer units and can be classified based on factors like chemical composition, structure, stiffness, application types, and processing methods. A key environmental issue with plastics is their non-biodegradability, causing them to persist in the environment. Pre-consumer plastic waste includes manufacturing defects, rejected items, and excess materials produced during virgin plastic manufacturing. In India, around 15,342 tons of plastic waste are collected daily, highlighting the urgency of the problem. Recycling remains a common but largely ineffective method due to the challenges of cleaning and segregating plastics. Globally, over 100 million tons of plastic are produced annually, with plastic waste filling landfills and acting as carbon sinks, trapping CO₂. Disposal methods like incineration and gasification are criticized for releasing toxic gases and being costly. Pyrolysis stands out as a promising solution, involving the thermal decomposition of plastics into fuel-range hydrocarbons like petrol, diesel, and kerosene. The process involves catalytic cracking at high temperatures, where the resulting gases are condensed to produce liquid fuels. The efficiency of this method depends on the type of plastic used, with some yielding better fuel conversion rates. Compared to traditional disposal methods, pyrolysis offers significant environmental advantages by reducing plastic waste and conserving petroleum resources. As this technology advances, it holds the potential to become a pivotal solution for managing the global plastic waste crisis.
Keywords: PVC, PET, HDPE, LDFE, PP, OD, CFD
By. pradeep Kumar keer, Dr Geeta Agarwal
Partial order structures play a crucial role in understanding the intricate relationships within mathematical spaces. In this paper, we delve into the realm of Menger spaces and investigate their properties through the lens of partial orders. Menger spaces, a generalization of metric spaces, possess unique characteristics that can be further elucidated by considering partial order structures. Through rigorous analysis, we explore various properties of partial order Menger spaces, including topological properties, convergence concepts, and structural characterizations. Additionally, we investigate the applications of partial order Menger spaces in diverse mathematical contexts, such as optimization problems, graph theory, and mathematical modeling. Our study not only contributes to the theoretical understanding of Menger spaces but also sheds light on the practical implications of incorporating partial order structures in mathematical analysis and problem-solving.
Keywords: Partial order, Menger spaces, Topological properties, Convergence
By. R prabakaran
This paper delves into the intricate connection between traditional tribal agricultural practices and astronomical observations, showcasing how indigenous communities have historically relied on celestial movements to guide essential farming activities. By observing the phases of the moon, solar cycles, and the positions of star constellations, tribes have developed precise methods for determining the optimal timing for planting, harvesting, and seasonal planning. These astronomical practices are deeply embedded in their cultural and spiritual beliefs, reflecting a harmonious relationship between human activity and the natural world.
The study examines the mathematical frameworks inherent in these indigenous systems of knowledge, revealing a sophisticated understanding of ecological cycles and seasonal variations. For instance, lunar phases often determine planting schedules, as they influence soil moisture levels, while solar patterns are used to track the progression of seasons and plan long-term agricultural strategies. The use of constellations, such as those visible during particular times of the year, provides additional cues for agricultural timing and ecological management. These practices demonstrate an intuitive yet profound grasp of natural rhythms and sustainability principles.
By analyzing these traditions, the study identifies their continued relevance in addressing modern sustainability challenges, such as climate change and resource management. The research argues that integrating tribal astronomical methods with contemporary agricultural techniques could offer significant benefits, including improved crop productivity and ecological balance. Such integration could help modern agriculture move toward more sustainable and resilient systems by leveraging traditional wisdom alongside advanced technologies.
The findings emphasize the importance of preserving and studying indigenous knowledge systems, as they contain valuable insights that can complement scientific approaches. Ultimately, the paper advocates for a collaborative framework where traditional and modern practices coexist, fostering innovation and sustainability in agriculture while respecting and revitalizing cultural heritage.
Keywords: Mathematical modeling, tribal astronomy, agricultural cycles, lunar calendar, indigenous knowledge, sustainability
By. R chetana, N. Raja
In this study, statistical methods must be integrated with fuzzy mathematics to solve complex data. Statistical methods offer clear, unbiased, and computationally feasible tools for analysing numerical data. whereas fuzzy mathematics excels in describing the vagueness and ambiguity of human feeling by way of linguistic variables, membership functions, and inference systems. Giving it an apparent advantage when modelling complex conditions. Hybrid frameworks offer fine-grained decision-making and resilient adaptability to real-world problems by integrating these two approaches.
Through a case-study it was demonstrated which illustrates the application of this hybrid approach on the dataset with quantitative scores and qualitative risk levels. The mean, variance, and standard deviation are all statistical measures that are used in numerical analysis. Providing solutions is a study for the numeric features of the data After assigning, and using logical conditions, all can be balanced to reach the desired DE fuzzified score. First, we explain the joint trend, and second, we show this joint trend using visualizations, such as bar charts and scatter plots.
In the future, further developments, such as the fusion of hybrid statistical-fuzzy frameworks with machine learning and artificial intelligence, will yield greater scalability, efficiency and interpretability. Mitigating computational challenges and extending hybrid models are primary for widespread adoption. Future works shall improve this approach in kinematic application and even in health care, finance, and smart systems.
Keywords: Statistical methods, fuzzy mathematics, data analysis, hybrid frameworks, risk assessment, decision-making, artificial intelligence, quantitative analysis, computational efficiency, qualitative data, defuzzification, membership functions, machine learning, standardization, interdisciplinary applications
By. Vipin Chouhan, Harsh Rathore
This study investigates the impact of incorporating recycled low-density polyethylene (LDPE) and high-density polyethylene (HDPE) in bituminous mixes for potential use in flexible pavement construction. Utilizing a Marshall Stability Test, various proportions of LDPE and HDPE were analyzed to determine optimal stability and flow characteristics. The test maintained a fixed Zycotherm content at 1.5% and varied LDPE content from 2% to 5% by weight, while HDPE content was also tested at 2% to 5%. Results reveal that incorporating 4% HDPE with a constant 1.5% Zycotherm yields a maximum stability of 1752 kg with a flow value of 4.0 mm, achieving an ideal balance between durability and flexibility. This combination also demonstrated improved air voids (3.0%), voids in mineral aggregate (VMA) at 9.48%, and voids filled with bitumen (VFB) at 68.30%. In comparison, mixes with LDPE showed stability improvement but lower binding capacity. The findings indicate that HDPE-modified mixes are not only durable but also provide an effective means of recycling non-biodegradable waste into road construction materials, extending pavement lifespan and reducing environmental impact. This research highlights HDPE as a viable material for enhancing the mechanical properties of bituminous pavements.
Keywords: Bituminous mix, Marshall stability test, recycled polyethylene, LDPE, HDPE, flexible pavement, Zycotherm, environmental sustainability