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: SCARLET PUBLISHING SOLUTIONS CO. L.L.C. UAE.
SCARLET PUBLISHING SOLUTIONS CO. L.L.C. UAE.
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
Seasonal Ultrastructural Modulations of the Molting gland -Y organ in the fresh water crab “Barytelphusa Guerini”
The Y-organ in crustaceans plays a crucial role in regulating molting through the synthesis and secretion of ecdysteroid hormones. The present study investigates seasonal ultrastructural variations in the Y-organ of the freshwater crab Barytelphusa guerini to understand its functional adaptations under changing environmental conditions. Specimens were collected across different seasons, and Y-organs were processed for transmission electron microscopy to examine cellular and subcellular alterations. Ultrastructural observations revealed significant seasonal modulation in organelle organization, including variations in mitochondrial density, smooth endoplasmic reticulum proliferation, nuclear morphology, and secretory vesicle abundance. Enhanced development of mitochondria and smooth endoplasmic reticulum during pre-reproductive and reproductive period indicates elevated steroidogenic activity associated with active molting phases. Conversely, reduced organelle complexity and increased cytoplasmic inclusions during post reproductive period suggest lowered metabolic and endocrine activity. These findings demonstrate that the Y-organ exhibits pronounced environmental fluctuations and molting physiology. The study provides insights into endocrine regulation and adaptive strategies of freshwater crabs, contributing to a better understanding of crustacean reproductive and growth cycles in fluctuating freshwater ecosystems.
Barytelphusa guerini, Y organ, Ultrastructure, Steroidal nature. Moulting
Evaluating the Influence of Performance Measurement Systems on Quality of Care in Chinese Public Hospitals
Quality of care in hospitals is shaped not only by clinical expertise, but also by the systems used to monitor performance, measure compliance, and drive service accountability. In China’s public hospitals, performance measurement frameworks—ranging from patient safety indicators to response-time benchmarks and documentation audits—have become central to healthcare governance. This study investigates how such measurement systems influence the quality of care delivered in large-scale public institutions. A sample of 458 healthcare professionals from four Chinese provinces was analysed through structured surveys, descriptive statistics, correlation mapping and regression modelling. Results reveal that performance metrics significantly improve treatment standardisation, handover accuracy, and patient safety outcomes when used supportively and complemented with digital tracking tools. However, excessive emphasis on quantitative evaluation can lead to documentation burden, task overload and reduced patient interaction time. Findings emphasise that performance systems must balance quantitative scoring with qualitative care values to achieve real clinical excellence. Quality of care improves not only when it is measured, but when systems allow measurement to guide improvement rather than overshadow compassion.
Performance measurement systems | Quality of care | Public hospitals China | Clinical indicators | Healthcare governance
AI and Human Relationships In Kazuo Ishiguro’s Klara And The Sun
AI and human relationships have been a hot topic of the decade now. With time AI and human relationships are being stronger and this can be said as humans are using AI a lot in their daily lives and AI is now an important part of our daily lives. And this paper is going to deal with the complex relations between humans and AI robot in Kazuo Ishiguro’s Klara and the Sun. The book is focussed on an Artificial Friend (AF), named Klara; she has been designed to provide companionship to a child. Thus, through this the book provides a point to question the boundaries which are drawn between humans and machines. Using Klara, Kazuo Ishiguro has tried to humanise AI by showing that they can also have feelings like humans including empathy, loyalty, and self-sacrifice. And this blurs the difference between humans and artificial intelligence. In this paper we will discuss how the Klara and the Sun challenges notions of humanity, empathy, and moral obligations. We will make use of post humanist theory and psychology to analyse how Ishiguro has humanised AI and review anthropocentrism to question the boundaries of connection, consciousness and emotional labour. The study explores the future ethical requirements surrounding intelligent machines and shows how AI, like Klara, can both satisfy and undermine human needs for attachment and purpose.
Artificial Intelligence (AI), Humans, Relationships, Kazuo Ishiguro, Artificial Friend (AF)
Himanshi Rathi, Prof Parul Mishra, Dr Kaushal Kishore Sharma
A new species of genus Zenion Jordan & Evermann, 1896 (Zeiformes: Zeniontidae), with comments on its congeners sensu lato
Zenion Jordan & Evermann, 1896 is currently an understudied genus. A cosmopolitan species with multiple records as “Zenion japonicum” is reidentified as a new species, named Zenion retrojaponicum n. sp., based on both morphological and molecular evidence. This new species can be morphologically distinguished from other species in terms of the lower margin of the orbit below the lower end of the pectoral-fin base, ventral-fin origin posterior to dorsal-fin origin, a thinner caudal peduncle, branchiostegal rays completely hidden in the inner side of or above the preopercular, and pored lateral-line scales less than 50. Molecularly, its mitochondrial marker, cytochrome C Oxidase subunit I gene sequence, exhibits a minimum interspecific Kimura-Two-Parameter genetic distance of 9.88% with its sister group. The real Zenion japonicum Kamohara, 1934, described by Kamohara (1934), is herein referred to as a junior synonym of Zenion hololepis (Goode & Bean, 1896). In addtion, all species that belong to or belonged to Zenion Jordan & Evermann, 1896 are discussed. Among them, for a rarely mentioned species, which was originally described as Cyttula macropus Weber, 1913 and has been reduced to junior synonym of Zenion hololepis (Goode & Bean, 1896), a new subfamily, Cyttulinae, is established with its redetermination to be under the original genus Cyttula Weber, 1913, based on both its morphological characters, including, two functional lateral lines, long ventral-fin soft rays, mouth almost vertical, profile of spinous dorsal-fin a positively skewed parabola, absent spiny ridges and absent locking mechanism, and molecular character of a minimum interspecific Kimura-Two-Parameter genetic distance of 7.56 % in multigene phylogeny among all the members under Parazenidae McAllister, 1968, and its monophyly does not disturb any other subfamilies and families, with available genetic information.
Dory, Undescribed, New Subfamily, Redescription, Marine Fish, China
Thought Control Strategies and Psychological Flexibility among the Patients with Generalized Anxiety Disorder, Obsessive and Compulsive Disorder, And Healthy Controls
Objective: The present study aimed to explore thought control and psychological flexibility among the patients with generalized anxiety disorder, obsessive-compulsive disorder and healthy peoples and other objective was to determine the relationship among the variables thought control and psychological flexibility. Methods: Purposive sampling technique was used to select 60 participants from different hospital settings. For this, purpose 20 GAD , 20 OCD and 20 healthy peoples from different hospital settings of Delhi NCR were availability selected and they were administered Generalized Anxiety Disorder – 7, Yale Brown Obsessive Compulsive Scale, Thought Control Questionnaire (TCQ) and The Acceptance and Action Questionnaire II (AAQ II). The ANOVA and Post hoc were applied to analyze the data. The results as follows: Patients with OCD obtained significantly greater mean score on psychological flexibility than other two groups. The study aims in making the people aware of the various anxiety disorder and their psychological symptom and different coping strategies that can help them deal with the anxiety disorder in a better way, and thus maintaining their emotion. The review concludes with a summary of major research findings, as well as a consideration of future directions and implications for practice and policy.
Thought Control Strategies and Psychological Flexibility, Generalized Anxiety Disorder, Obsessive and Compulsive Disorder, And Healthy Controls
Dr. Feroz Ahmad, Dr. Ramuday Singh, Dr. Dinesh Ballabh, Dr. Rajeev Kumar Gupta, Dr. Barishter yadav, Dr. Awadh Kishor Singh
Status and diversity of Avian fauna of Kot dam (Shakambari) – A conservation reserves of Sikar and Jhunjhunu District of Rajasthan
As a feeding area for migratory and resident bird species as well as other faunal diversity, kot dam is significant. It makes room for other creatures because it serves as a breeding site for large numbers of birds. One bioindicator of the ecological health of wetlands is a healthy population. The biodiversity of Shakambhari's fauna and flowers is directly linked to the reserve's ecological value. Variations in the distribution of species, as well as species richness and abundance, are sources of future information that can be further quantified in the reserve to determine its ecological value and thereby support its conservation. All wetlands, however, are under danger because of growing human activity. For this reason, it's critical to designate this region as a rich biodiversity hotspot and urge the state government to take quick action to protect it. With minimal adjustments, the location has enormous potential to grow into a popular tourist destination. This research offers the first thorough analysis of the bird fauna in the area and explores how it may be exploited as a tourism destination and refuge for avian enthusiasts.
ResearchOptimizing Ratios for Eco-Friendly Apparel Production- Pineapple and Cotton Blend
This study investigates the mechanical and comfort properties of blended yarns comprising Pineapple Fiber (PAF) and Organic Cotton. As the textile industry shifts toward sustainability, PAF emerges as a high-strength, agro-waste alternative. Using a blended fiber, we optimized blending ratios (20:80, 30:70, and 40:60) and twist factors. Results indicate that a 30:70 PAF/Cotton blend offers the optimal balance between tensile strength, Comfort and moisture management. This study provides a strategic framework for scaling PAF-based organic apparel for kids wear in the global market.
Organic Apparel, exceptional tensile, land-intensive crop, Sustainable Textiles; Tri-blended Yarn.
Himali Pareek, Prof. (Dr.) Smriti Agarwal, Supervisor
The Dilemma of Choice: How Design Shapes Consumer Decisions
Design: What is it about? At its core, it is meant to simplify flows, reduce friction, create experiences that feel effortless and ultimately make our lives easier. But somewhere along the way, design stopped being about usability. It became a smart and meticulously crafted system that shapes, guides and influences user choices, a strategy built to keep users hooked to the service. While that may still align with what good design strives to do, it is equally important to recognise the moment it blurs the line and turns manipulative. A manipulative design takes advantage of its users through subtle trickery, hidden beneath the illusion of a seamless experience. This paper explores dark patterns in UI and UX design, which are deceptive interface choices that manipulate users into performing actions that benefit businesses more than individuals. Every button, colour and word is placed with intent. From Netflix's hidden cancellation options to Amazon's auto-renewals and LinkedIn's contact traps, every element in a design is calculated to serve a business goal. Through secondary qualitative and quantitative research, this study examines how dark patterns influence consumer decisions, increase engagement and retention and at the same time reduce user autonomy. Observations from the reviewed literature and interface analyses suggest that although these tactics may deliver short-term business gains, they also risk undermining long-term user trust and brand credibility. The paper encourages a move towards ethical design that prioritises transparency, empathy and accountability, inviting designers and organisations to reconsider whether their work ultimately serves business objectives or the people who rely on these systems.
Dark Patterns, UI/UX Design, Consumer Decision-Making, Ethical Design, Behavioural Manipulation, Business Strategy, Persuasive Design, Consumer Trust
Administrative Indicators as drivers of Communication Effectiveness in Chinese Public Hospitals
Communication within Chinese public hospitals determines patient management, clinical safety and administrative response efficiency. In recent years, administrative performance indicators have become the primary mechanism for evaluating staff behaviour, reporting speed, documentation clarity and inter-department information flow. These indicators include timeliness KPIs, documentation accuracy scores, digital reporting dashboards, workload tracking systems and patient feedback handling benchmarks. This research examines how administrative indicators influence communication quality, collaboration and information transparency in public hospitals across China. A hospital-based dataset of 442 healthcare professionals was analysed through descriptive distribution, correlation testing and regression modelling. Results show that performance indicators positively improve communication clarity and timeliness when supported by digital reporting infrastructure and non-punitive feedback culture. However, excessive metric pressure increases communication stress and reduces open dialogue among professionals. The paper concludes that administrative indicators must be structured, balanced and technology-integrated to enable smooth communication culture across China’s public healthcare ecosystem.
Administrative performance indicators, Communication effectiveness, Healthcare management, Digital reporting systems
Multimodal Analysis for Early Parkinson’s Disease Detection: Integrating Voice Analysis and Brain MRI Analysis with Web-Based Deployment
Parkinson's disease is a progressive neurodegenerative disorder affecting over 10 million people worldwide, characterized by motor dysfunction and voice alterations that manifest early in disease progression. Traditional diagnostic approaches rely on expensive neuroimaging and subjective clinical evaluation, limiting accessibility and early detection capabilities. This study presents a comprehensive multimodal framework combining voice biomarker analysis and brain MRI imaging for automated PD detection, encompassing nine distinct machine learning approaches and advanced convolutional neural networks. The voice analysis component employs Mel-Frequency Cepstral Coefficients extracted from audio recordings, evaluated across four neural network variants including batch normalization, residual connections, and LSTM architectures, alongside five traditional ML models. The MRI component utilizes a custom CNN architecture with four convolutional blocks, batch normalization, and dropout regularization, trained on brain imaging data from Kaggle repositories. A production-ready Flask web application enables real-time multimodal assessment with voice-only, image-only, and combined prediction modes, featuring risk stratification, patient management, and comprehensive evaluation through confusion matrix analysis. Results demonstrate superior performance of ensemble methods and batch-normalized architectures, achieving high accuracy across stratified cross-validation and robust generalization. The deployed system provides accessible, cost-effective screening tools positioning this framework as a valuable clinical decision support system for early PD detection in telehealth and resource-constrained environments.
Parkinson’s Disease, Voice Analysis, Brain MRI, Deep Learning, CNN, MFCC
Rashmi Pandey, Priusha Narwaria,Diya Khatri, Devraj Singh Yadav,Dr. Pallavi Khatri,Dr Anand Kr. Pandey
Generative AI For Automated Design of High-Intensity Interval Training Regimens: A Comparative Study Of Model-Based And Data-Driven Approaches
High-Intensity Interval Training (HIIT) is widely recognized as an efficient exercise modality for improving cardiovascular fitness, metabolic health, and overall physical performance. However, the effectiveness and safety of HIIT depend heavily on individualized prescription of intensity, interval duration, recovery structure, and progression. Recent advances in Generative Artificial Intelligence (AI) offer new possibilities for automating and personalizing HIIT program design, yet systematic comparisons between different AI-driven approaches remain limited. This study presents a comparative framework examining model-based and data-driven generative AI approaches for automated HIIT regimen design. The model-based approach relies on physiological principles and constrained optimization to generate safe and interpretable training prescriptions, whereas the data-driven approach leverages wearable sensor data, machine learning, and reinforcement learning to adaptively personalize training sessions. A unified representation of HIIT regimens is proposed, integrating session-level structure and program-level progression while enforcing safety and physiological constraints. Comparative evaluation criteria include safety compliance, physiological training stimulus, personalization capability, and predicted adherence. The study highlights that while model-based approaches offer superior interpretability and safety assurance, data-driven generative models demonstrate greater adaptability and personalization potential. The findings suggest that hybrid systems combining generative AI with physiological constraint layers may represent the most promising direction for scalable, safe, and effective automated HIIT prescription. This research contributes to the growing field of AI-driven exercise prescription by offering a structured methodological framework suitable for digital health, sports science, and human-centered AI applications.
Generative Artificial Intelligence; High-Intensity Interval Training; Exercise Prescription; Wearable Sensors; Reinforcement Learning; Digital Health; Personalized Training
Min Fan, Mariusz Lipowski, Xinghao Wang, Taofeng Liu
Reimagining India through Literature: Cultural Narratives Towards Vision 2047
India’s Vision 2047 is working towards a nation that is innovative, inclusive, culturally rooted, and transformative. Literature becomes a key tool in imagining and shaping this future, both as a mirror and a catalyst for societal transformation. Indian English literature and regional writings portray the country’s historical experiences, cultural richness, social inequalities and emerging aspirations, creating a cultural foundation for imagining India’s future. By exploring themes such as nationalism, environmental sustainability, gender justice, technological change, and subaltern identities, literary narratives offer frameworks for reimagining a more impartial and progressive India. Furthermore, literature provides a platform for dialogue between tradition and modernity, allowing for critical reflection on India’s rich cultural, political, and ethical trajectories. As the nation approaches its centenary of independence, literature presents the moral imagination and intellectual tools necessary for forming a visionary and resilient India. This paper presents that literary narratives - past, present, and digital-play a central role in conceptualizing Vision 2047 and empowering India to make a future grounded in justice, diversity, and creative nation-building. By integrating literary insights into education, policy making, and public discourse, India can cultivate a citizenry capable of thoughtful, ethical, and innovative decision-making, indispensable for the realization of Vision 2047. In this way, literature becomes not only a record of India’s journey but also a dynamic force that leads national transformation and inspires all-around development.
Vision 2047, Indian Literature, Nation-Building, Gender and Social Justice, Cultural Imagination.