E-ISSN: 2458-942X FishTaxa - Journal of Fish Taxonomy is a peer-reviewed international journal to support free exchange of ideas and information in systematics, phylogeny and biogeography of fishes with a preference for large taxonomic works such as monographs, revisions and checklists. FishTaxa considers papers on all freshwater and marine fishes both living and fossil, and especially encourages descriptions of new fish taxa. All types of papers are considered, including theories and methods of systematics and phylogeny, taxonomic monographs, revisions and reviews, catalogues/checklists, biographies and bibliographies, identification guides, analysis of characters, phylogenetic relationships and zoogeographical patterns of distribution, descriptions of taxa, and nomenclature. FishTaxa publishes original research articles, high quality reviews and technical notes/short communication covering all aspects of fish systematics. The members of the FishTaxa scientific board are from well-known universities and research institutes to ensure high-quality publications. Manuscripts are processed using an online system, ensuring efficient and timely evaluations. Type of Journal: Academic/Scholarly Format: Print & Online E-ISSN: 2458-942X Website: www.fishtaxa.com Frequency: annually Language: English Article Processing Charges: 1500 USD Indexed and Abstracted: Yes Contact: editor@fishtaxa.com Scopes of FishTaxa includes: Biology, systematic ichthyology, phylogeny and biogeography of fishes both freshwater and marine (living and fossil fishes) Taxonomic monographs, revisions and reviews, catalogues/checklists Descriptions of taxa, and nomenclature Systematics and phylogeny Biodiversity Biogeography/phylogeography Population genetics Invasive species Morphology and osteology Publisher name: Global Edge Printers and Publishers, India
The Historicity of the Bhartiya Economic Reforms in the post 190-91 era
The paper of this nature is a challenging academic exercise as it delivers a narratives on the most significant but the most commonly known structural economic reforms in the Indian economy in 1990-91. The lingo of Liberalisation, Privatization and Globalization as being a Bhartiya response to the Global torrents of Convergence, amalgamation and Interdependence have been both enumerated and discussed in the argumentative and history imbued narrative in the present paper. The [paper further highlights the initiative and pioneer decision taken up by Prime Minister Manmohan Singh in the face of an Indian economic crisis. The paper too draws an interregnum between the classical economic reforms of 1990-91, Modi led GST decisions and demonetization, sthus drawing out and eking a framework for the Indian economic reforms across Governments and ideologies.
Intelligent Reliability Enhancement of Solar-Integrated Distribution Networks: A Probabilistic and Optimization-Based Approach Utilizing Battery Energy Storage Systems
The rapid deployment of solar photovoltaic (PV) generation in modern power distribution systems is both a challenge and an opportunity. While solar energy is the basis for decarbonisation and sustainability goals, the intermittent nature of solar energy raises reliability concerns for distribution networks. This paper presents a comprehensive probabilistic reliability assessment of solar-integrated distribution networks with the focus on advanced optimisation of battery energy storage system (BESS) operations. Using state-of-the-art modelling, simulation and optimisation techniques, we assess impacts of solar variability and BESS intervention on the important reliability indices, SAIDI, SAIFI and CAIDI. The extensive scenario analysis and comparative studies with conventional systems demonstrate that the intelligent BESS operation not only alleviates the negative effects of renewable intermittency but also allows the reliability indices to exceed the traditional benchmarks. This work provides a strong framework to future proof grid infrastructure in the era of high renewable energy integration.
Smart Grid, Battery Energy Storage System, Distribution System Reliability, Optimisation, Probabilistic Modelling, Renewable Integration.
Substitution of fishmeal with groundnut meal and its effect on growth, feed conversionefficiency, carcass composition and digestive enzyme activity of fingerling Labeo rohita
The effects of replacing fishmeal (FM) with groundnut meal (GNM) in the diets of Labeo rohita fingerling (6.32 ± 0.38 cm; 2.36 ± 0.02 g) were evaluated by conducting an 8-week feeding trial. The diets with 18 kJ g⁻¹ gross energy and 35% crude protein were prepared by gradually replacing FM with GNM on a protein-to-protein basis at levels of 0%, 20%, 40%, 60%, 80% and 100%. Diets were fed to different groups near apparent satiation. No evident effect was noted by the replacement of FM with GNM up to 40% on live weight gain, specific growth rate, feed conversion ratio, protein retention efficiency and whole-body carcass composition. Digestive enzyme activities were not affected up to 40% FM replacement (P > 0.05) but declined significantly at 60%, 80% and 100% replacement (P < 0.05). The findings suggest that FM can be substituted by GNM up to 40% in formulating nutritionally balanced, cost-effective commercial feeds for Labeo rohita fingerling without hampering growth performance, feed utilization and carcass quality.
Groundnut meal, fishmeal, Labeo rohita, cost-effective.
Effects of a Six-Week Breath-Only Pranayama Intervention on Pulmonary Function and Anxiety Among College Students: A Randomized Controlled Trial
Pranayama, a disciplined system of breath regulation rooted in yogic tradition, has demonstrated promise as a non-pharmacological modality for enhancing physiological resilience and psychological well-being. This randomized controlled trial aimed to isolate the effects of a breath-only Pranayama protocol-specifically Anulom Vilom and Bhramari-on pulmonary function and anxiety in healthy young adults. A total of 100 college students aged 18–25 were randomized into control and experimental groups. The experimental group received supervised Pranayama training six days per week over six weeks, while the control group maintained routine activity without intervention. Lung function was assessed using digital spirometry (FVC, FEV₁, FEV₁/FVC), and anxiety was measured using Sinha’s Comprehensive Anxiety Test (SCAT). Results showed a statistically significant increase in lung capacity in the experimental group (mean Δ = 612 mL, p < 0.001), while no significant change occurred in controls. Likewise, anxiety scores in the experimental group decreased markedly (p < 0.001), whereas control scores remained unchanged. Graphical and statistical analyses confirmed the robustness of these effects, suggesting improved respiratory mechanics and vagal modulation. These findings align with previous neurophysiological and psychometric literature underscoring Pranayama’s ability to enhance autonomic balance and emotional regulation. This study substantiates the standalone efficacy of breath-based Pranayama techniques, offering a cost-effective intervention model for both mental and physical health promotion in academic populations.
Pranayama; Pulmonary Function; Anxiety Reduction; Breath Regulation; College Students.
Phytochemical Analysis and Antioxidant Activity of Linum usitatissimum Seed Extracts with Potential Applications in Aquaculture
The present study aimed to investigate the phytochemical composition and antioxidant potential of Linum usitatissimum (flaxseed) seed extracts using gas chromatography–mass spectrometry (GC–MS) and 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging assay. Extracts were prepared using suitable organic solvents and subjected to GC–MS analysis, which revealed the presence of diverse bioactive compounds, including fatty acids, sterols, phenolic compounds, and tocopherols. Major constituents identified included α-linolenic acid, hexadecanoic acid, and phytosterols, which are known for their antioxidant and biological properties. The antioxidant activity of the extracts was evaluated using the DPPH assay, demonstrating significant free radical scavenging activity in a concentration-dependent manner. The IC₅₀ value indicated strong antioxidant potential when compared with the standard ascorbic acid. The observed activity may be attributed to the synergistic effect of the identified phytochemicals. These findings suggest that Linum usitatissimum seed extracts are a rich source of natural antioxidants and bioactive compounds. Furthermore, the presence of such phytoconstituents highlights their potential application in aquaculture systems as natural feed additives to enhance fish health, improve oxidative stress resistance, and support overall physiological performance.
Linum usitatissimum ,GC–MS ,DPPH assay ,Antioxidant activity Aquaculture ,Fish health
Sindhu Govindaraj, Gnanamoorthy Kumaran, Gomathi kannayiram
Anitha.M, Varshini G, Kalidass Nagupillai
AI-Powered Oral Cancer Detection Using Machine Learning and Deep Learning
Globally, oral cancer remains a major problem. Late diagnosis is often associated with lower chance of survival. Although established techniques such as a biopsy are essential, they have limitations, especially in communities with no specialists. This study investigates how deep learning can improve the diagnostic process by developing and accessing convolutional neural network (CNN) models on images of oral lesions. Three architectures were trained and evaluated which are MobileNet, a modified VGG16, and a VGG16 baseline. Through thoughtful image processing and accepting greater dataset of 655 instances, they were assessed. The modified VGG16 achieved particularly strong performance under fresh test data with accuracy up to 98%. In addition, it had a great F1-score, recall, and precision. It reduced missed cancers and false alarms considerably because it achieves perfect recall of healthy tissue and perfect precision of cancerous lesions. The findings suggest that AI models can quickly and accurately diagnose oral cancer, particularly in resource-limited areas or places with less expertise.
VGG16, MobileNet, ResNet, CNN, Deep Learning, Machine Learning.
Virtual Cancer Biopsy: An Integrated Machine Learning Framework for Tumor Malignancy Prediction, Surgical Operability Assessment, and Prognosis Estimation
Cancer is one of the major causes of morbidity and mortality in the world, and therefore, early cancer detection is a crucial factor when it comes to increasing the survival period. The traditional diagnostic methods are often time-consuming, costly and invasive, thus putting some patients in pre-treatment risks. We are introducing what we call the Virtual Cancer Biopsy (VCB) which is a web-based application that uses machine learning instead of needles that are invasive. VCB uses logistic regression to predict the country of tumors as being malignant or not and uses k nearest neighbor modelling as a prognosis predictor of cancerous tumors. Instead of using simple ML code that is custom-built, the system is built on the Flask library and the front-end is integrated with JavaScript, which is compatible with other browsers. All the workflow is contained in a Streamlit system providing a user-friendly interface that suits clinical needs. In addition to speeding up the diagnostics process, VCB provides doctors with readable visuals and interactive tools, which are changing the way things are categorized.
Virtual Cancer Biopsy, Machine Learning, Logistic Regression, K-Nearest Neighbor, Tumor Malignancy, Surgical Operability, Prognosis, Clinical Decision Support
Ecological and anthropogenic factors shaping fish survival in Kashmir streams
The Himalayas harbor one of the most diverse assemblages of freshwater hill stream fishes, shaped by steep environmental gradients such as altitude, temperature, and flow rate. This review synthesizes current knowledge on how these gradients influence fish diversity, distribution, and physiological adaptations. Unlike earlier descriptive accounts, this paper critically examines the ecological and physiological mechanisms underlying species’ responses to environmental stressors, with attention to recent advances in climate and conservation research. The review also highlights urgent conservation challenges—hydropower development, habitat fragmentation, pollution, and climate change—and evaluates ongoing initiatives in the region. Specific recommendations are proposed, including maintaining environmental flows, restoring riparian zones, and developing community-led conservation models. A systematic literature review framework was employed to identify relevant studies from 2000–2025, ensuring comprehensive coverage of both classical and recent works. This study emphasizes the need for integrated research on diversity, physiology, and conservation to safeguard Himalayan hill stream fishes. One of the most diverse assemblages of freshwater hill stream fishes inhabits the Himalayas, shaped in steep environmental gradients of altitude, temperature, and flow rate. This review synthesizes the knowledge on how those gradients shape fish diversity, distribution, and physiological adaptations. Unlike earlier accounts that were largely descriptive, this paper places ecological and physiological mechanisms of species responses to environmental stressors into focus, with due attention to recent advances in climate and conservation research. It highlights urgent conservation challenges-hydropower development, habitat fragmentation, pollution, and climate change-and assesses ongoing initiatives in the region. It puts forward specific recommendations: maintenance of environmental flows, restoration of riparian zones, and community-led models for conservation. For this purpose, a systematic literature review framework was used to identify relevant studies from both classical and recent works between the years 2000-2025. The study argues for an integrated research approach on diversity, physiology, and conservation to safeguard Himalayan hill stream fishes.
Hill stream fish, water quality, stream ecology, biodiversity, pollution, climate change
Consumer Perception and Adaptability towards the New Concepts and Technology in Agriculture Industry
Consumer behavior in the Agriculture Industry is the basic instinct to be studied, when appropriately linked with the Consumer Perception. Because the behavior of purchase is directly linked with the Perception of the consumers. Consumer perception plays a very vital role. Farmers do the purchase of Agricultural products under the influence. The Influence may be again subdivided into many factors again like the influence of own experience whilst using the products, influence of the retails channel in the purchase model, influence from opinion leaders, influence from the key market share holding organizations. The Purchase influence defines the consumer behavior in the sector. This affects the usage of new technology in the fields. The pesticides which have a major market share are the Red triangle and Yellow triangle molecules. Green and Blue Triangle molecules are gaining space in the purchase bucket but only in the crops which are produced for exports, where the pesticide residual obligation exists for the fruits in order to be exported. The research intends to identify the consumer behavior linked to the above all mentioned factors. On prima facie the research tries to interpret the link between Consumer Awareness and Consumer Perception and behavior in Agriculture sector.
Agriculture, Consumer Behavior, Consumer Perception, Pesticides, Organic Farming.
Ethical Considerations in Data-Driven Marketing: Balancing Personalization And Privacy
In the evolving landscape of digital marketing, the ethical tension between personalized advertising and consumer privacy has become a central concern. The study aimed to critically examine the dual imperatives of personalization and privacy, assessing how data-driven marketing practices influence consumer trust, consent behaviors, and perceptions of transparency. A mixed-methods approach was employed, combining quantitative survey data from 300 digital consumers with qualitative thematic analysis of 50 scholarly articles, regulatory frameworks, and industry white papers. Quantitative data were analyzed using descriptive statistics and inferential methods, including correlation and regression analysis, while qualitative data underwent thematic coding to identify recurring ethical patterns. Results revealed that while 82% of participants appreciated personalized advertisements, only 31% trusted companies to handle their data ethically, and just 28% felt informed about how their data were used. There was a notable gap between the preference for opt-in consent (71%) and actual usage of privacy controls (19%). Thematic findings emphasized issues such as algorithmic opacity, re-identification of anonymized data, consent misalignment, and the erosion of digital trust. The study concludes that existing regulatory frameworks, while necessary, are insufficient on their own. Ethical, consumer-centric models that prioritize transparency, informed consent, and trust-building are essential for sustainable digital marketing practices. Recommendations include ethics-by-design strategies, simplified privacy interfaces, and greater consumer education to bridge the gap between personalization benefits and privacy protection.
Data-driven marketing, Personalization, Privacy, Ethics, Algorithmic transparency, Consumer trust
Biphasic Neurostimulatory Effects of Caffeine on Dopamine-Mediated Locomotor Circuits in Drosophila melanogaster
This study aimed to examine how caffeine affects the movement of Drosophila melanogaster in a dose-dependent manner. It also aimed to evaluate the use of Drosophila as an ethical and inexpensive model for studying the neurobehavioral effects of stimulants, which are relevant to human health. Experimental groups were given freshly prepared caffeine solutions from instant coffee (a stock solution of about 3.5 mg/mL). These solutions were diluted to concentrations of 50% (1.75 mg/mL), 75% (2.63 mg/mL), and 100% (3.50 mg/mL). A control group was not treated. Locomotor activity was measured using the negative geotaxis assay over six hours. The average climbing height (in centimeters) and percent of active flies were recorded for each group. The 75% group demonstrated peak locomotor performance (mean climbing height: 10.1 ± 0.7 cm; 9/10 flies active), significantly exceeding the control (6.2 ± 0.5 cm; 5/10 active) and confirming caffeine's stimulatory effect at moderate doses. The 100% concentration produced a decline in activity (7.5 ± 0.8 cm; 6/10 active) relative to the 75% group, indicating physiological stress and impaired neuromuscular coordination at excessive doses, consistent with a concentration-dependent biphasic response. This study provides direct quantitative evidence of a biphasic locomotor response across a graded, ecologically relevant caffeine concentration series in wild-type Drosophila under standardised laboratory conditions, establishing that neural stimulation occurs only within an optimal physiological range. The results underscore the utility of Drosophila melanogaster as a reproducible, and accessible model for neuropharmacological investigation and highlight the public health relevance of dosage regulation among young caffeine consumers.
Caffeine; Drosophila melanogaster; locomotor activity; negative geotaxis; biphasic response; adenosine receptor antagonism; dopamine; neurostimulant; dose-dependent.
A Multidimensional Capital-Based Framework for Entry-Level Management Graduate Employability: Integrating Foundational, Behavioural, Social, Signaling, and Critical Capital Dimensions
Current models of graduate employability tend to prioritize technical skills and immediate job placement, often overlooking the complex psychosocial and market dynamics required for long-term success. To address this gap, this paper presents a multidimensional capital-based framework tailored specifically for entry-level management graduates. Drawing on a synthesis of multidisciplinary literature, we propose a comprehensive model that integrates five core dimensions: foundational human capital, behavioural and psychological capital, social and relational capital, signaling capital, and market alignment capital. Additionally, we introduce the concept of critical capital as a transformative element that equips graduates with the systems thinking and ethical reasoning necessary to navigate volatile business environments. By distinguishing between the performative actions of acquiring a job and the transformative process of sustaining a career, this framework provides a robust lens for understanding graduate readiness. The paper concludes with a series of testable propositions, laying the groundwork for future empirical research and offering strategic guidance for the evolution of management education curricula.
Graduate employability, Management education, Capital-based framework, Critical capital, Career sustainability, Social justice awareness.
The Many Faces of Draupadi: Comparative Depictions by Koral DasGupta and Devdutt Pattanaik
This study explores the comparative depictions of Draupadi, the central female character from the Mahabharata, in the works of two prominent authors: Karal Das Gupta and Devdutt Pattanaik. Through a feminist lens and a divine framework, respectively, both authors offer distinct interpretations of Draupadi’s character, revealing the complexity and timeless relevance of her portrayal. Gupta’s retelling focuses on Draupadi’s agency, resilience, and role as a symbol of empowerment within a patriarchal society. His feminist perspective underscores her humanity, emphasizing her strength in the face of adversity. In contrast, Pattanaik presents Draupadi as a divine figure, an agent of justice within a cosmic order, whose actions are predestined by fate. This study analyzes the themes of strength, agency, divinity, and justice as portrayed by both authors, shedding light on how their cultural and philosophical contexts influence their respective retellings. By comparing these two perspectives, the study highlights the evolving nature of Draupadi’s characterization in contemporary literature, providing insights into the broader discourse on gender, power, and morality in mythological narratives.
Feminist Interpretation, Divine Justice, Mythology, Female Agency, Empowerment, Cosmic Order
Reimagining Sita: A Feminist Transmutation in the Works of Devdutt Pattanaik and Amish Tripathi
This study explores the feminist reinterpretations of the character of Sita in the works of contemporary Indian authors Devdutt Pattanaik and Amish Tripathi. Traditionally depicted as the epitome of virtue, purity, and obedience in the Ramayana, Sita has often been portrayed as a passive figure. However, Pattanaik and Tripathi challenge this conventional portrayal by reimagining her character as one of strength, resilience, and agency. Through a comparative analysis of Pattanaik’s Sita: An Illustrated Retelling of the Ramayana and Tripathi’s Sita: Warrior of Mithila, this research examines how both authors present a more dynamic, empowered version of Sita. While Pattanaik emphasizes introspection and emotional depth, portraying Sita as a woman of silent strength navigating societal constraints, Tripathi presents her as a fierce warrior and leader, actively shaping her own destiny. The study highlights the authors' efforts to break away from traditional gender roles and empower female characters within mythology, offering new perspectives on Sita’s identity. This feminist transmutation provides a space for a rethinking of female agency, resilience, and autonomy in ancient texts, suggesting a broader discourse on the role of women in mythology. By reinterpreting Sita, Pattanaik and Tripathi not only transform her character but also contribute to the larger conversation about gender and power in literature and mythology.
Female Agency, Mythology, Gender Roles, Empowerment, Strength, Autonomy
Despite the various laws, regulations, and safety measures in place to prevent fires and protect citizens and property, the incidence of fires continues to rise. In Mongolia, there have been 19,668 fire incidents in the past 5 years, resulting in the loss of 258 lives. This equates to an average of 1.3 fatalities per 100 fires. Conducting thorough research and analysis on the different types of fires in Mongolia and implementing the findings will be crucial in reducing fire risks and improving safety measures.
Fire safety, fire statistics, fire hazards, fire risk, and various types of risks in residential buildings and structures.
Agentic AI-Based Learning Management System
The article reports on the design of an AI, powered educational platform that aims to help students and teachers. By means of smart AI assistants, the platform is able to analyze each learner's educational progress, pinpoint their strengths and weaknesses, and serve them with tailored learning solutions. It is capable of generating and grading tests, homework, and educational resources automatically, which is a significant time, saving for educators. The platform updates the learning materials instantaneously, provides constructive feedback, and assists students in achieving their academic objectives.[1] By presenting instant feedback and intelligent suggestions, the system keeps students motivated, allows for individualized learning pace, and, thus, produces a more efficient and interactive learning experience.
AI-enabled learning, Personalized education, Intelligent agents, Adaptive learning, Automated assessment, Student engagement.
An AI Powered Passive Reconnaissance Kit To Identifies Potential Vulnerabilities By Correlating Open Source Intelligence (OSINT)
Passive reconnaissance is a significant step in the field of cybersecurity that assists in the detection of possible security risks without actively engaging with the target systems. This paper introduces AI-powered passive reconnaissance kit aimed at gathering and correlating OSI or open-source intelligence for determining potential vulnerabilities in an ethical and non-invasive way. The proposed system is based on a Raspberry Pi platform and combines passive service discovery, the collection of data using OSINT and an intelligent risk scoring engine. the kit automates the reconnaissance process with python-based modules and uses rule-based artificial intelligence to process exposed services and normalize risk scores on a standardized risk score scale. A single button interface makes it simple for the user and a real-time result is available on an embedded OLED screen. Detailed reports are created in local environment to support further analysis without having to rely on cloud services or web dashboards.By combining portability, intelligent analysis, and passive operation, the proposed solution reduces manual effort and improves accessibility for cybersecurity education, ethical hacking, and preliminary security assessments for resource-constrained environments.
Passive Reconnaissance, Open-Source Intelligence (OSINT), Artificial Intelligence Risk Scoring, Raspberry Pi, Ethical Hacking, Cybersecurity Assessment, Embedded Security System, Rule-Based Decision Engine, Lightweight Security Platform.
The Impact of AI-Driven Services on Customer Perceptions and Loyalty in The Mobile Telecommunications Sector
Customer service and business operations have significantly changed as a result of the introduction of artificial intelligence in the mobile telecoms industry. The purpose of this study is to investigate how customers perceive artificial intelligence. The primary connection between artificial intelligence technologies and their effects on consumer satisfaction is established by using the secondary research approach, which involves reviewing current literature. The results demonstrate how artificial intelligence improves customer service effectiveness and personalisation, which in turn influences consumer perception. The purpose of this study is to emphasise how crucial it is to strike a balance between ethical concerns and artificial intelligence in order to guarantee client loyalty.
Artificial Intelligence, Customer perception, Customer loyalty, Telecommunications, Chatbots, Predictive analysis]
Student Engagement As A Mediator Between Curriculum Design, Instructional Resource Quality, And Student Performance
Student performance is influenced not only by curriculum structure and learning materials but also by how actively students engage with the learning process. This study examines student engagement as a mechanism through which curriculum design and instructional resource quality influence academic performance. Using survey data from 412 secondary and higher education students, structural equation modeling was applied to analyze the relationships among curriculum coherence, instructional resource quality, student engagement, and academic performance. Results indicate that both curriculum design and instructional resource quality are significant predictors of student performance, with student engagement acting as a partial mediator. The findings highlight the importance of designing learning environments that actively involve students cognitively, emotionally, and behaviorally. The study contributes to contemporary educational research by clarifying how instructional quality translates into learning outcomes and offers practical implications for curriculum developers and educators.
Student Engagement, Curriculum Design, Instructional Resources, Academic Performance, Mediation
Pedagogical Practices and Challenges in Teaching Language Macro Skills: A Case Study of Filipino Elementary Educators
This study examined the strategies, challenges, and coping mechanisms used by elementary public-school educators when teaching the four main language skills—reading, writing, speaking, and listening. A qualitative approach was taken, involving thematic analysis of teacher responses. The study found that teachers employed various instructional strategies, such as using short stories, story maps, process writing, pronunciation drills, and role-playing. However, they often ran into major challenges, particularly the lack of suitable teaching materials and students’ low level of interest. To overcome these obstacles, teachers showed creativity by working together with colleagues, repurposing available resources, and incorporating technology and multimedia to make lessons more engaging. The study highlights the critical need for the development of context-specific, open-access teaching materials and professional development programs focused on resourcefulness and technology integration that could significantly enhance pedagogical effectiveness.
Pedagogical practices, strategies, challenges, macro-skills, Filipino, elementary educators
Synthesis and Antimicrobial Activity of Transition Metal Trimesate Complexes with Hydrazine
Novel complexes of Mn(II), Co(II), Ni(II), Cu(II), and Zn(II) with trimesic acid (benzene-1,3,5-tricarboxylic acid) and hydrazine were synthesized and characterized. The complexes exhibited octahedral geometry, confirmed through electronic, FTIR, Raman, and EPR spectroscopic analyses. Thermal studies showed stepwise decomposition to form stable metal oxides, highlighting their potential as precursors for nanoparticle synthesis. Antibacterial and antifungal activity evaluations revealed that Zn(II) and Cu(II) complexes exhibited significant bioactivity against tested microbes, surpassing the free ligand. The study demonstrates the potential of these metal complexes for pharmaceutical and nanomaterial applications.
Trimesic acid, hydrazine, transition metals, antimicrobial activity, thermal decomposition, nanoparticle precursors, EPR spectra.
Integrated Limnological Assessment of Gangasamudra Lake (Karnataka): Physicochemical Characteristics, Plankton Dynamics, and Fish Diversity
The present study provides an integrated limnological assessment of Gangasamudra Lake, a semi-arid freshwater ecosystem in Karnataka, India, by evaluating physicochemical characteristics, plankton community dynamics, and ichthyofaunal diversity across five sampling sites during 2024. Monthly analysis of water quality parameters revealed consistently alkaline pH, high turbidity, elevated total dissolved solids, conductivity, biochemical oxygen demand, chemical oxygen demand, magnesium, and iron, indicating meso-eutrophic to eutrophic conditions. Although most toxic heavy metals were absent or below detection limits, elevated iron and occasional lead concentrations suggested localized anthropogenic influences, primarily from agricultural runoff, catchment erosion, and internal nutrient loading. Phytoplankton assemblages comprised 23 genera belonging to Bacillariophyceae, Chlorophyceae, and Cyanophyceae, with diatoms dominating during pre-monsoon and monsoon periods, followed by increased representation of green algae and cyanobacteria during post-monsoon months. Zooplankton communities were represented by Rotifera, Protozoa, Copepoda, and Ostracoda, with rotifers showing marked dominance and seasonal fluctuations, reflecting nutrient enrichment and dynamic trophic interactions. Diversity indices indicated moderate to high plankton diversity during monsoon and post-monsoon periods, while reduced diversity and increased dominance during summer months reflected environmental stress. Ichthyofaunal assessment recorded six fish species belonging to three families (Cichlidae, Cyprinidae, and Clariidae), dominated by hardy and omnivorous taxa such as Oreochromis spp. and Clarias spp. The presence of the endangered Clarias batrachus highlights the conservation significance of the lake despite anthropogenic pressures. Overall, the combined physicochemical and biological evidence suggests that Gangasamudra Lake is a nutrient-enriched but moderately resilient ecosystem, emphasizing the need for regular monitoring, nutrient management, and sustainable fishery practices to preserve its ecological integrity and biodiversity
Gangasamudra Lake; Physicochemical parameters; Phytoplankton diversity; Zooplankton dynamics; Ichthyofaunal diversity; Eutrophication; Semi-arid freshwater lake.
Lungs Disease Prediction Using Machine Learning And Deep Learning
Lung-related illnesses are rapidly emerging as a major global health challenge, impacting millions each year. The surge in respiratory complications after the COVID-19 pandemic has further emphasized the need for early, precise, and efficient diagnostic techniques. While conventional diagnostic methods are trustworthy, they are often time-consuming and heavily dependent on expert interpretation. To overcome these limitations, Machine Learning (ML) and Deep Learning (DL) have begun playing a crucial role in automating and improving lung disease detection.This research examines the use of various ML and DL models for identifying and classifying different lung conditions. Techniques such as Support Vector Machine (SVM), Random Forest (RF), Logistic Regression, K-Nearest Neighbors (KNN), and Convolutional Neural Networks (CNN) are analyzed based on their performance with medical datasets. Their capabilities are compared using standard evaluation metrics including accuracy, precision, recall, and F1-score.Recent studies consistently show that deep learning methods—especially CNNs—achieve better accuracy than traditional ML algorithms. However, challenges such as limited dataset availability, class imbalance, and reduced interpretability of advanced models still persist. Overall, integrating ML and DL approaches offers a promising pathway toward faster, more accurate lung disease diagnosis and improved patient outcomes.
Lung Disease Prediction, Machine Learning, Deep Learning, CNN, SVM, Feature Selection, Medical Diagnosis, COVID-19
NLP-Based Model for Identifying Misinformation
The massive proliferation of digital media has significantly increased the transmission of fake news and disinformation, which affects the confidence of the population and decision-making. The vast quantity of online content can not be managed through manual methods of fact-checking and requires automated answers. This paper introduces an NLP-based machine learning system that can be used to identify fake information in news stories. The system uses text pre-processing, TF-IDF feature extraction and conventional machine learning classifiers such as Support Vector Machine (SVM) and Logistic Regression. The experimental analysis of the proposed model shows that the model can have an accuracy around 91 per cent, which demonstrates that interpretable and lightweight machine learning methods can effectively detect and identify fake information in academic and real-time prototype settings.
Identification of False News, Disinformation, Natural Language Processing, Machine Learning, and TF-IDF.
Dr.Rakesh Kumar Khare, Nipun Rao Boyana, Simrat Kaur, Sanchita Sharma
Pharma Guide: Medicine Suggestions using ML
The contemporary world is so fast that healthcare professionals are not always readily available and, in this case, the application of machine learning as a method of rapid diagnosis and prescribing treatment can be of great importance in resolving this issue. Covered in the project is a Medicine Suggestions which is a machine learning algorithm that can predict diseases given the symptoms the user has inputted and can offer the correct recommendations of what medication, home remedies and other disease management practices should be used. With the help of the established classifiers, including Support Vector Machine (SVC), Random Forest, and K-Nearest Neighbors (KNN), the system will be able to identify the disease accurately and offer a holistic treatment plan, including medications, diet, exercises, and precautions. It is not only a smart application that will help in the diagnosis but will also offer a comprehensive method in managing health by proposing preventive care, drugs, and household remedies thereby maximizing the overall healthcare experience of the user. Of all the named SVC, KNN, Gradient Boosting, multinomial NB, and Random Forest classifiers were used. The SVC classifier is the most accurate.
Medicine, Machine learning, MultinomialNB, Python, SVC.
Dr. Rakesh Kumar Khare, Astha Pandey, Vanshika Thakur, Vaishnavi Kulkarni
Impact Analysis of Pradhan Mantri Awas Yojana (Urban) in Chhattisgarh: A Division-Wise Performance Evaluation
The Pradhan Mantri Awas Yojana–Urban (PMAY-U) is a national housing initiative aimed at providing affordable and permanent housing to eligible urban households. In Chhattisgarh, the scheme has been implemented across multiple administrative divisions, each showing varying levels of progress in terms of houses sanctioned and constructed. This study presents a division-wise performance evaluation of PMAY-U in the state, focusing on implementation efficiency, construction progress, and patterns of fund disbursement. A custom-developed web-based data analysis portal was utilized to organize beneficiary records, visualize work progress, and identify administrative challenges. The analysis reveals that while Chhattisgarh has progressed significantly in achieving the PMAY-U objectives, the completion rate across all divisions is not uniform. Certain divisions, particularly those with better municipal coordination and faster approval processes, have achieved higher completion levels, whereas others continue to face delays. State-level results indicate that although the state appears to follow a positive trajectory, targeted interventions are required to address delays and ensure the timely completion of remaining units. This study highlights the importance of structured data monitoring and transparent reporting systems to strengthen implementation and support informed decision-making for future urban housing development.
PMAY-U, Housing for All, Urban Development, Impact Analysis.
Elephants in India: A Comprehensive Zoological Study Based on Distribution, Captivity, Conservation and Protection
India harbors the largest population of wild Asian elephants, estimated at approximately 26,000 to 28,000 individuals, which constitutes nearly 60% of the species' global population. Elephas maximus is classified under Schedule I and Part I of the Indian Wildlife Protection Act (1972), granting it the highest level of legal protection. The elephant holds significant cultural and mythological importance in India's heritage, alongside historical roles in the economy and military, fostering strong public support for its conservation and survival. However, the challenges posed by a burgeoning human population, rapid economic growth, expanding and migrating elephant populations at regional levels, habitat loss and fragmentation, and escalating human-elephant conflicts underscore the critical need for effective long-term policies to manage and safeguard this species.
Wild elephant, zoology, conservation, wildlife protection, habitat loss, fragmentation, population.
A Zoological Study of Edible Insects: With Special Emphasis on Northeast India
A field experiment was conducted in Karbala Governorate/Tawrij District in 2024 on a silty clay soil to investigate the role of the chisel plow in loosening the hardpan layer and promoting crop growth. A New Holland TD80 tractor was used in this experiment. Two components were examined: operating speed (2.80, 4.65, and 6.15 km/h-1), and plowing depth (50 and 65 cm). Mechanical unit technical metrics such drawing force, slippage percentage, fuel usage, and maize plant output were examined. The experiment used a three-replicate Randomized Complete Block Design (RCBD). The lowest slippage percentage was 6.90% and the lowest traction force was 618.11 kg at 2.80 km/h-1. The lowest fuel usage was 9.99 L/ha and the maximum plant output was 8.05 t/ha at 6.15 km/h-1. A 50 cm plowing depth yielded the lowest pulling force (613.21 kg), slippage (7.57%), and fuel usage (11.91 L/ha). The maximum plant output was 6.76 t/ha at 65 cm. All attributes were significantly affected by the speed-plowing plot interaction. Operating speed, traction force, plant productivity, slippage, Chisel Plow.
Edible insect, northeast India, HImalaya, protein, mineral, culture, nutrition.
Understanding Flyways and Hotspots of Migratory Birds in India
Each year, across much of the Northern Hemisphere, a substantial number of bird species migrate southward for winter and return along established routes in spring. This natural phenomenon, known as bird migration, follows specific pathways termed migratory flyways. More than 2000 bird species globally engage in migration, with some undertaking shorter, longitudinal movements or local migrations. However, numerous migratory birds confront multiple threats, leading to declines in species diversity and populations worldwide. To safeguard migratory birds, global initiatives under the United Nations and multilateral treaties have been implemented. India, a participant in the Convention on Migratory Species, actively contributes to the conservation of migratory birds. The convention's appendices list 128 avian species, with 7 species in Appendix I, 103 in Appendix II, and 18 species appearing in both. The Central Asian Flyway, linking Eurasia and the Indian Ocean via India, serves as a critical route for over 180 migratory bird species, including some facing global endangerment. Research conducted in India provides valuable insights into migratory bird behaviors and their habitats.
Migratory bird, species, climate change, flyway, conservation, zoology, science, multilateral treaty.
Exploring the Therapeutic Potential of Sericin in Tissue Regeneration and Wound Healing
Chronic wounds remain a major clinical challenge due to persistent inflammation, impaired angiogenesis, microbial infection, and delayed re-epithelialization, all of which disrupt the normal healing cascade. The increasing global prevalence of chronic wounds highlights the urgent need for safe, effective, and biologically active therapeutic materials. In this context, sericin, a natural hydrophilic protein derived from silkworm cocoons, has emerged as a promising biomaterial for regenerative applications. Owing to its excellent biocompatibility, biodegradability, low immunogenicity, antioxidant capacity, and strong affinity for biomolecules, sericin offers multifunctional benefits that directly address the pathological factors underlying impaired wound healing. This study explores the therapeutic potential of sericin in tissue regeneration and wound healing by examining its biological activities and mechanisms of action. Sericin enhances fibroblast and keratinocyte proliferation, promotes collagen synthesis, supports re-epithelialization, and modulates inflammatory responses through the regulation of cytokine release and macrophage activation. Furthermore, its incorporation into advanced wound dressings and nanotechnology-based delivery systems, including sericin nanoparticles and composite hydrogels, demonstrates improved healing efficiency and controlled therapeutic action. Collectively, the findings highlight sericin as a sustainable and bioactive candidate for developing next-generation regenerative therapies and chronic wound management strategies.
Sericin, Tissue Regeneration, Wound Healing, Collagen Synthesis, Biomaterials