bluuebunny/crossref_metadata_2025 huggingface dataset:
Supervisory Regulation and Performance of Insurance Industry
Authors: L. Wahua, , G. F. Kenea,
Published: February 2025

Claims settlement by insurance companies is a hydra-headed issue in many third-world-and-emerging economies. Therefore, the need to increase the confidence of the insured in insurance coverage necessitated this study which is based on resource-capability theory, quantitative paradigm, and descriptive research design. Secondary data for 2010 – 2019 was collected from the 2019 edition of Nigerian Insurers Associations’ Digest. The independent variable is insurance regulations while performance is the dependent variable with two (proxies insurance claims settlement and insurance awareness). Based on relevant works, insurance regulation was measured with approved investments made by insurance companies; insurance awareness was measured with premium income paid by policyholders; and claims settlement was measured with insurance claims paid by insurance companies. Using inferential statistics, the work established that insurance regulation has significant positive association with claims settlement and insurance awareness; and insurance awareness equally has statistical positive association with insurance claims settlements. The study calls for increased insurance regulation by the Supervisors/Regulators; and aggressive awareness campaigns by insurance companies in order to reduce the hydra-headed problem of non-claims settlements by insurance companies in emerging and third world countries.
Awareness and Perception of the Use of Artificial Intelligence for Learning Among Select Communication Undergraduates in Nigeria
Authors: G. E. Nwodu,
Published: February 2025

Artificial Intelligence has come to life and is part of our everyday lives. It has disrupted and will continue to have tremendous impacts across sectors, including education. To meet the recent demands, undergraduate students in Nigerian universities need to be at home with ICTs. Thus, this study examined awareness and perception of the use of artificial intelligence for learning among select communication undergraduates in Nigeria. The study was anchored on the Technology Adoption Model (TAM). The researcher adopted a survey research method and communication undergraduates were selected from two universities in Anambra State (private and government owned universities). The researcher found that the majority of the respondents have a low level of awareness on the use of artificial intelligence for learning and 82% of the respondents do not have access to artificial intelligence for learning. Further findings showed that most of the respondents are not competent in the use of artificial intelligence for learning. The researcher concluded that competency in technological innovation is dependent on the knowledge of, availability, and access to the technological innovations. Thus, the study recommends that efforts should be made by the school authorities to create awareness to students on the use of artificial intelligence for academic purpose and also, the universities should consider including AI courses in the syllabus to encourage the students to know and explore more about AI.
Communication Students’ Perception of AI in Planning, Creating and Disseminating Advertisement
Authors: G. E. Nwodu, , A. V. Obiora, , P. I. Agbachukwu,
Published: February 2025

Artificial intelligence has been an intrinsic part of the creative industry in the recent era; its exceptional contribution to development and technological advancement is apt and can never be undermined in the process and practice of advertising. The essence of advertising is to promote a cause, goods, and services. Thus, AI helps in the improvement and creative tactics advancement of advertising. The study, amongst its objectives, sought to ascertain the use of artificial intelligence in creating and disseminating advertisements. Also, it tends to analyse the perception of mass communication undergraduate students of Chukwuemeka Odumegwu Ojukwu University on the contributions of AI towards the planning and creating of advertisements. The study is anchored on the technology determinism theory of mass communication. The census method of study was employed, adopting focus group discussion as the technique of data collection. The findings of this study will help in understanding the contributions and usability of artificial intelligence in the terrain of advertising, both digital and mainstream advertising.
Some characterizations of translation surface generated by a non-null curve and a curve on a spacelike surface in Minkowski 3-space
Authors: Yadav Akhilesh , Yadav Ajay Kumar
Published: February 2025

In this paper, we study translation surfaces generated by a non-null curve in Minkowski 3-space and a curve lying on a spacelike surface in Minkowski $3$-space and obtain necessary and sufficient conditions for such surfaces to be flat or minimal. Further we obtain normal curvature, geodesic curvature and geodesic torsion of the generating curves and find necessary and sufficient conditions for these curves to be geodesic, asymptotic line and line of curvature. Finally we study some special cases of such translation surfaces according to the angle between the normal of the translation surface and the normals of the generating curves.
A new structure of random approach vector space
Authors: Hussein Boushra Y. , Kream Alaa A.
Published: February 2025

This paper is explaining the fundamental goal of A-Random approach on nonempty set if it meets the condition. A duo (, R) is dubbed A-Random approach space, the relationship between approach space and A-Random approach space is clarified. We define the R-contraction function and debate some of its properties. We introduce the definition of A-Random approach semi group, A-Random approach group, A-Random approach vector space and we will also discuss solve various problems.
Decentralized AI for Medical Emergency Response Using Blockchain and Computer Vision
Authors: Md Solaiman Ahamed
Published: February 2025

This paper explores the integration of blockchain, artificial intelligence (AI), and the Internet of Things (IoT) to revolutionize healthcare data management, storage, access, and analysis, aiming to enhance data security, diagnostic accuracy, and healthcare accessibility. By leveraging blockchain’s decentralized and immutable nature, AI’s diagnostic capabilities, and IoT’s real-time monitoring, the study highlights how these technologies can address challenges exposed during the COVID-19 pandemic, such as data breaches, supply chain integrity, and remote patient care. The research emphasizes the potential of these innovations to improve emergency response, optimize clinical workflows, and ensure patient privacy while overcoming technical, security, and ethical hurdles. Through frameworks like decentralized emergency intelligence (D-EI) and blockchain-protected medical imaging systems, the study demonstrates the transformative impact of these technologies on healthcare efficiency and patient outcomes. Future directions focus on enhancing interoperability, scalability, and regulatory compliance to fully realize their potential in global healthcare systems.
Current Dynamics and Obstacles Facing Small and Medium-Sized Businesses in Ukraine Amidst the War
Authors: Olena Korohod
Published: February 2025

Introduction: This study’s relevance is driven by the need to develop scientifically sound economic instruments to create a favourable business environment that will help restore and increase the resilience of small and medium-sized businesses in Ukraine during the war and post-war reconstruction.
Objectives: This research article aims to analyse the economic instruments for supporting small and medium-sized businesses in Ukraine during the war and post-war periods by analysing the current barriers to their functioning, assessing the status and main factors affecting the productivity of SMEs, and formulating ways to improve efficiency, optimise processes and recover from the war.
Methods: The study used methods of information synthesis, statistical method, forecasting method, correlation analysis, and method of generalisation and systematisation, which allowed for a comprehensive analysis of current problems and barriers to the functioning of small and medium-sized businesses, identification of key trends, and substantiation of the prospects for the development of this sector in the context of the war and post-war recovery of Ukraine.
Results: The correlation analysis revealed a correlation between the sales volumes of medium-sized enterprises, the innovation index (r = 0.587), and the business confidence index (r = 0.667). However, the lack of statistical significance (p > 0.05) indicates the need for innovative development and increased investment in technology. At the same time, the correlation between small business sales and the business confidence index (r = 0.806 at p = 0.05) shows the importance of ensuring a stable external environment for SMEs. In turn, micro-enterprises also demonstrate a high correlation with business confidence (r = 0.912 at p = 0.016), emphasising the dependence of their functioning on economic stability.
Conclusions: The results of the study indicate the need to reduce regulatory pressure, eliminate corruption and bureaucracy, expand financing programmes and create a mechanism for preserving human resources to ensure the prompt recovery of the SME sector both during the war and post-war periods, as well as to ensure sustainable economic growth by creating favourable conditions for economic activity and stimulating the development of critical sectors of the economy.
Analysis of PI and Hysteresis Controlled Cascaded Dual loop Quadratic -Boost-Multilevel Inverter System
Authors: Neelashetty Kashappa
Published: February 2025

Quadratic Boost Converter (QBC) finds a place between PV and single phase Multi Level Inverter (MLI) with motor load. In this work, we devise a new QBC between DC source and nine-levels MLI. A Hysteresis Controller (HC) and PI have been considered for the closed loop response of QBC-MLI system. The key finding of proposed system is evaluating and differentiate dynamic response of closed two-loop PI-PI & PI-HC controlled QBC-MLI systems and output current THDs. The computer simulation using MATLAB software demonstrates efficient and dynamic performance of the HC controlled QBC-MLI system. Efficacy of proposed system is authenticated through the simulation outcomes.
Maximizing Solar PV Performance with Deep Learning Optimization and Advanced Power Regulation
Authors: Umasankar L, Nithyananthan K, Umaeswari P, Bindu K V
Published: February 2025

This study presents a two-stage; three-phase grid-connected solar photovoltaic (PV) system that leverages advanced deep learning techniques to optimize Maximum Power Point Tracking (MPPT), significantly enhancing energy extraction and power quality. The system architecture integrates a Landsman Converter with an Artificial Neural Network (ANN)-based MPPT controller, which dynamically adjusts the duty cycle to respond to fluctuations in irradiance and temperature, ensuring efficient DC-DC conversion. Building on this, a deep learning layer continuously refines the MPPT algorithm in real time, boosting tracking accuracy and response speed. In the first stage, the Landsman Converter, optimized with deep learning, enables rapid, precise power tracking, achieving an impressive MPPT efficiency of 86.38%. The second stage incorporates a Phase-Locked Loop (PLL)-controlled DC-AC inverter, providing stable grid synchronization and reducing Total Harmonic Distortion (THD) to -40.27%, which enhances overall power quality and supports grid stability under variable conditions. MATLAB simulations demonstrate that this deep learning-enhanced system consistently outperforms conventional approaches, establishing its potential as a scalable, high-efficiency solution for grid-connected PV systems. The proposed framework not only improves real-time energy yield but also supports stable renewable energy integration into the grid, promoting sustainable energy adoption. By combining deep learning with traditional ANN-based MPPT in a robust, two-stage control system, this research offers a promising approach to maximizing solar PV performance, making a valuable contribution to the field of renewable energy and intelligent power regulation..
Implementing a Hybrid Deep Learning Model to Identify Critical Factors for Energy Efficiency in Smart Grids
Authors: Tushar V. Deokar
Published: February 2025

The creation and use of a mixed deep learning model in the smart grid system to increase energy efficiency and environmental friendliness is discussed in this paper Considering how complex energy consumption patterns might be, the model is designed to forecast simultaneously heating and cooling demands. It employs cutting-edge deep learning approaches like attention processes to simplify things, recurrent layers for temporal relationships, and convolutional layers for feature extraction. Combining these elements not only opens doors to stakeholders but also enables the mix model to provide reliable forecasts. For applications in the smart grid, this makes it a valuable instrument. Part of this approach involves designing a multi-output neural network, cleaning up energy data ahead of time, and verifying the model's effectiveness using significant criteria such Mean Absolute Error (MAE) and loss. The hybrid model performs better than conventional neural networks according the findings. On the practice and test environments, it greatly reduces errors. By focussing on the little details, one may get crucial knowledge about the elements influencing energy pricing. To accommodate various smart grid configurations, the model may also be raised or lowered. This facilitates quick judgements and most effective use of resources. Although the proposed mixed model represents a significant advance in smart grid analytics, many issues still exist like the need for increased processing capability and minor proof adjustments. This research aids to improve smart energy systems in keeping with the objectives of sustainable energy management. Modern grids therefore become more flexible and resilient as well as stronger. This study prepares the basis for further studies with fresh elements like employment trends and weather. Future projections will therefore be increasingly more reliable and accurate.
Integrated Intrusion Detection and Mitigation Framework for SDN-Based IIoT Networks Using Lightweight and Adaptive AI Techniques
Authors: Mamatha Maddu
Published: February 2025

Advanced and scalable intrusion detection frameworks are in great demand for the rapid proliferation of Software-Defined Networking (SDN) in Industrial Internet of Things (IIoT) environments. Traditional methods for network anomaly detection fail to adapt to dynamic traffic patterns, handle resource-constrained edge deployments, and utilize vast amounts of unlabeled data samples. To address these limitations, we propose an integrated framework combining state-of-the-art techniques for accurate, efficient, and scalable intrusion detection in SDN-based IIoT networks. Our framework starts with domain-adapted feature extraction by the use of EfficientNet-B0, a lightweight yet powerful architecture, fine-tuned on IIoT-specific traffic data samples. Incremental learning with Elastic Weight Consolidation ensures adaptability to new intrusion patterns while preserving previously learned knowledge. SimCLR is applied to generate robust embeddings through self-supervised learning in environments where labeled data are scarce. Autoencoders detect novel patterns in anomaly detection, while XGBoost conducts precise classification of known threats. Furthermore, DQN optimizes the mitigation strategy of either flow rerouting or rate limiting in real time based on the network state. In case of edge-based deployment, Tiny-YOLO presents a lightweight model for anomaly detection that performs low latency with high accuracy. This holistic framework achieves a detection accuracy of ~96%, with a false positive rate below 3% and a latency of under 15 ms, supporting the large-scale IIoT networks of more than 10,000 nodes. Our methodology pushes forward scalability, adaptability, and robustness by unifying feature extraction, anomaly detection, classification, and mitigation process
Development of Advanced Deep Learning Models for Enhanced Object Classification in Satellite Imagery
Authors: Gyanendra Singh Tomar
Published: February 2025

The rapid improvement in satellite imaging technology, the enormous volume and varieties of satellite data have expanded by leaps and bounds. From city planning to environmental monitoring, this information finds great use in many spheres. Standard picture classification techniques, on the other hand, have difficulty organising satellite pictures as they are very complex and include many dimensions. This work addresses these issues by investigating how to create more sophisticated deep learning models that will speedily and with accuracy categorise objects in satellite photos. We propose a fresh approach combining mixed deep learning designs with convolutional neural networks (CNNs) and approaches including attention mechanisms and transfer learning. These sophisticated models are designed to handle the challenges of categorising satellite photos, including their complex patterns, varied sizes, and many kinds of objects. Our models are particularly made to more precisely categorise various types of things, including homes, roads, bodies of water, and plants, more so than other machine learning techniques. We use properly pre-processed high-resolution satellite photos for this work that consider variations in illumination, direction, and noise. Strong assessment systems using techniques like data enrichment and cross-valuation train the proposed models using a broad spectrum of datasets, therefore ensuring their applicability. The model's performance is assessed using accuracy, recall, and F1 score among other metrics. Comparatively to conventional approaches, improvements are shown. Our experiments reveal that the more sophisticated deep learning models outperform the conventional classification techniques. They can manage more intricate satellite scenes and are more precise.
Exploring Face Detection Algorithms: A Comparative Overview
Authors: Arti Deshpande
Published: February 2025

There are diverse approaches in face detection and recognition, highlighting algorithms, datasets, and practical applications. The Haar Cascade Classifier is a popular technique for fast and simple detection, though it is hindered by false positives and reduced accuracy in complex scenes. Advanced methods, such as combining machine learning algorithms with feature extraction techniques like Principal Component Analysis (PCA) and Three-Patch Local Binary Pattern (TPLBP), are examined for their higher recognition rates and improved accuracy. The use of datasets like ORL and Sheffield demonstrates robustness under various conditions, including changes in pose, lighting, and expression. Hybrid methods like Scale-Invariant Feature Transform (SIFT) and Principal Component Analysis integration, achieve remarkable accuracy on multiple datasets despite image variances. Notable contributions include the Dual Shot Face Detector algorithm for addressing challenges like scale variation and occlusion and the Viola-Jones algorithm, which is used for real-time detection with minimum computational overhead. The study also evaluates the performance of different algorithms across datasets and real-world applications, including automated attendance systems and emotion detection. This study conducts a comparative analysis of existing face detection methods, evaluating their accuracy, efficiency, and scalability across diverse environments. By identifying strengths and limitations, the study aims to provide insights and propose potential enhancements to guide the development of more robust and adaptable face detection solutions.
Comprehending Cryptocurrency Investment: An In-Depth Analysis of Technological Impacts and Behavioral Dynamics
Authors: Rashi Chauhan
Published: February 2025

This study seeks to investigate and synthesize existing literature on the cryptocurrency. The researcher adopted for NVIVO 14 software to perform a bibliometric analysis and elaborate a systematic literature review on cryptocurrency with a sample of 52 papers published in reputed journals, considering the different field of knowledge. The researcher explored a growing body of literature on factors affecting the investment behavior in cryptocurrency, which are Age, Gender, Behavioral Intention, Heuristic Factors, Prior Experience, Prospect, Governance, and the Cryptocurrency bubble. The secondary line of variables adopted from the analysis include price, financial literacy, the disposition effect, and investor sentiment, role of Blockchain technology, risks, herding behavior, and uncertainties related to cryptocurrency. Additionally, a dearth of literature has been observed on challenges faced by cryptocurrency and its investors, regulatory framework of cryptocurrency, factors which are motivating the cryptocurrency investors, impact of social media, and how technical knowledge can influence behavioral intention of investors in Indian context. This study helps the researchers and academics, by providing the research gaps on which further researches can take place in the field of cryptocurrency.
Women Entrepreneurship in Beauty Care Sector- A Case Study with Reference to Tirupati District
Authors: Thokala Devi
Published: February 2025

Beauty parlors in India have evolved significantly over the years and have become an integral part of the country’s lifestyle and culture. Historically, beauty and grooming practices were often done at home or in smaller, more traditional settings anywhere in the world. A wide range of industries devoted to improving and preserving one's physical appearance and wellbeing are included in the beauty care industry. It encompasses goods, services, and procedures meant to enhance people's nails, skin, and general appearance. Trends, cultural influences, and changing customer preferences are what drive this industry. Everyone aspires to appear more attractive than others. Every modern man and woman has made taking care of their appearance a major concern. In the modern sense, women are also becoming conscious of fashion. Enhancing financial mindfulness, need for extra payment, usage of extra time, steady inspiration by the public authority organizations training societal position and the effect of good examples is some of elements liable for the advancement of Women’s business in India. Overall, beauty parlors in India continue to adapt and evolve, reflecting both traditional values and contemporary trends. They play a significant role in enhancing personal grooming and self-care, contributing to the country’s vibrant and diverse beauty culture.
Integrating Artificial Intelligence for Smart and Adaptive Information Systems
Authors: Yohanes Bowo Widodo
Published: February 2025

The integration of Artificial Intelligence (AI) in information systems has revolutionized the way organizations process, manage, and utilize data. AI-driven information systems are becoming more adaptive, intelligent, and capable of autonomous decision-making, enabling businesses to improve efficiency and responsiveness. This study explores the various techniques and technologies used to integrate AI into information systems, including machine learning, natural language processing, and expert systems. It also examines the benefits of AI-enhanced systems, such as improved data analysis, automation, and predictive capabilities. However, challenges such as data privacy, ethical concerns, and system complexity must be addressed to ensure effective implementation. This study employs a literature review, case studies, and system analysis. By analyzing current trends and case studies, this research provides insights into the future direction of AI-driven information systems and their potential impact on industries. Traditional information systems were primarily designed for data management and process automation, but the integration of AI has enabled these systems to perform complex decision-making, predictive analytics, and autonomous operations.The future of AI in information systems lies in the advancement of explainable AI (XAI), federated learning, and AI governance frameworks. As AI continues to evolve, integrating ethical AI principles and ensuring regulatory compliance will be essential for sustainable adoption. Interdisciplinary collaboration between AI researchers, policymakers, and industry experts is crucial for addressing ethical and regulatory challenges.
Cocreación e interdisciplinariedad en comunidad: el jardín centrado en la naturaleza
Authors: Pineda Saza Angélica María
Published: February 2025

The Universidad de los Andes leads sustainable initiatives such as the nature-centered garden in the Caja de Música building. This interdisciplinary project integrates biology, ecology, and regenerative gardening to restore urban ecosystems and promote biodiversity. It prioritizes native, endemic, and naturalized species, creating functional microhabitats for birds, insects, and other local organisms. In addition, it acts as a living laboratory, fostering research and spaces for environmental education. Designed collaboratively by students, faculty, and staff, this garden symbolizes the institution’s commitment to sustainability, biodiversity conservation, and the harmonious connection between the community and its natural environment.
¿Cómo dejar atrás la idea de Antropoceno? En busca de un nombre para la nueva era geohistórica
Authors: Ordóñez Díaz Leonardo
Published: February 2025

As we move into the third millennium, anxiety over the growing imbalance in the planet’s climate patterns continues to increase. In parallel, the suspicion that the concomitant ecosocial aftermath marks the passage into a new geohistorical era is reflected in the search for a name for the current times (and, thus, for an appropriate perspective from which to understand what is happening). Among such names, “Anthropocene” enjoys wide currency in the scientific community. However, as many authors have warned, this name is problematic and highly biased. Against such a backdrop, this article evaluates ten of the alternative names proposed in recent decades to designate the new era—and their implications when assessing current planetary ecosocial tensions. Methodologically, the paper is based on a critical comparison (with no equivalent so far in the state of the art) of these names and a review of their plausibility as an alternative to the Anthropocene. The review of the meaning and theoretical assumptions of the proposals shows that (i) although the effort to baptize our epoch has been mainly oriented by approaches from the Global North that criticize the nature/culture division, in the end, most of them give primacy to the naturalistic vision, turning their backs partially or totally on the developments of political ecology; (ii) from the perspective of the Global South, if we want to obtain an accurate picture of the current planetary situation, it is urgent to correct this bias and take more seriously the socio-historical dimension of environmental problems.
Kawí Tamiruyé: un acceso universal al conocimiento del bosque. Una revisión documental
Authors: Silva Ávila Nancy , Pompa-García Marín , Martínez Rivas José Alexis
Published: February 2025

Climate change seriously affects forests worldwide, reducing their adaptive capacity and increasing their vulnerability. The Kawí Tamiruyé plot, located in the Sierra Tarahumara (Mexico), is a strategic site to study these variations. The main objective of this research is to document the investigations carried out between 2021 and 2024. Novel findings on the use of drones were found in the areas of knowledge application within the experimental area: response to fire detected by the normalized difference vegetation index (NDVI); generation of scientific knowledge to calculate precise metrics of area and canopy perimeter; and determination of ecological indices that account for changes in forest structure, with ecological dominance of Pinus engelmannii and Juniperus deppeana. Likewise, an incipient but promising infrastructure to collect meteorological data is reported. In addition, ongoing research has been documented with drone flights since March 23, 2021, and bi-weekly and monthly xylogenesis sampling has been conducted for the past 18 months. The lessons learned highlight the importance of understanding how forests respond to climate change and disturbances, providing crucial information for developing adaptation and mitigation strategies.
Ciénagas y conflictos socioambientales en el Magdalena Medio colombiano: las luchas colectivas de Fapamucan y Fedepesan en los municipios de Cantagallo y Barrancabermeja
Authors: Delgado Gaona Juan Camilo
Published: February 2025

The middle valley of the Magdalena River is characterized by the extension of its swamp complexes, vital for the subsistence of riparian communities. In this amphibious territory, community resistances converge to defend their natural assets against the dispossession, appropriation, and degradation inherent to the extractive economy imposed in the Magdalena Medio region. The riparian, peasant, and Afro-Colombian communities of artisanal fishermen and fisherwomen generate resistance mechanisms amid socio-environmental conflicts, where mobilization actions, legal actions, political incidence, collaboration networks, and community agreements converge, aimed at the collective vindication of their natural assets. This article presents the experience of collective struggles of the Afro-Colombian Federation of Artisanal Fishermen of the Municipality of Cantagallo (Fapamucan) and the Federation of Artisanal, Environmental, and Tourist Fishermen of the Department of Santander (Fedepesan) in defense of their livelihoods, especially the Pajaral and San Silvestre swamps in the municipalities of Cantagallo and Barrancabermeja, respectively. Their struggles take place in a critical context in relation to the dynamics of the armed conflict and violence against communities resisting the dispossession and environmental degradation of their territories.
Interrelationship between Pali Literature, Culture, and Artificial Intelligence: Confluence of Ancient Wisdom and Modern Innovations
Authors: Rahul Amritraj
Published: February 2025

This research paper focuses on the interplay between Pali literature and Artificial Intelligence (AI), exploring the possibilities where ancient Buddhist philosophy and modern technological innovations converge. Pali literature, especially the Tripitaka, presents profound principles related to ethics, meditation, and consciousness, which could assist in the moral and philosophical development of AI. The three divisions of Tripitaka—Vinayapitaka, Suttapitaka, and Abhidhammapitaka—could serve as guiding principles for ethical decision-making and self-improvement mechanisms in AI. Concepts like the Noble Eightfold Path, Vipassana, and Anicca could aid in making AI more sensitive and morally responsible. Recently recognized as a classical language by the Indian government, Pali opens new avenues in AI and Natural Language Processing (NLP). AI-based language translation, ethical algorithms, and digital preservation of Pali literature can play a crucial role in its conservation and dissemination. This research bridges cooperation between Pali literature and AI from ethical, cultural, and technical perspectives, potentially making AI development more humane and empathetic in the future.
Carbon Sequestration Potential of Trees in Urban Green Spaces at Sahyadri College Campus of Shivamogga City, Karnataka, India
Authors: S.S. Prema  , J. Narayan
Published: February 2025

The primary objective of this study is to enhance comprehension of the functions played by urban green spaces in promoting urban sustainability. This will be achieved by the documentation of tree diversity and the assessment of their capacity for carbon sequestration at Sahyadri College Campus in Shivamogga city. Tree species diversity was assessed by the quadrat method. Tree species having a diameter  10cm were selected in the Sahyadri college campus. The measurement of Diameter at Breast Height was conducted using a non-destructive method. The tree girth is taken into consideration for measurement of biomass and carbon sequestration potential. The total biomass and total carbon sequestration was calculated by standard equations. The diversity of trees in urban green spaces of college campus was surveyed using 25 sample sites. A total of 378 tree individuals from 53 distinct species belonging to 23 different families were recorded. Total carbon sequestration recorded was 67663.89 kg/tree. Samanea saman species with high girth size was found to be dominant sequestrated 23591.41 kg/tree of carbon and having 25 trees followed by Tectona grandis 11469.65 kg/tree having 62 trees. The species Cassia fistula has lowest carbon sequestration potential i.e. 6.29 kg/tree. The study concludes that Sahyadri College Campus vegetation is relatively high in terms of both diversity and carbon sequestration potential. The findings highlight the major functions of urban green spaces in reducing pollution by carbon capturing and storing in safer biomass and also supporting biodiversity and regulating the microclimate, all of which contribute to Shivamogga city’s urban sustainability.
Cadmium Foisted Amendments and their Assuagement Via Silver Nanoparticles in Vigna radiata
Authors: Y. Meshram  , V. Vaishnav  , T. Chatterjee  , D.K. Gurupahachan  , S. Gupta
Published: February 2025

Heavy metals damage plants through excess accumulation of reactive oxygen species. In the current work we determined the potency of green synthesized AgNPs (0.75%) against damaged happened by cadmium in seed of Vigna radiaae. Germination percentage, radical length, biomass accumulation, Membrane stability, electrolyte leakage and tissue viability under cadmium increased situation were analyzed. Germination percentage of Vigna radiate seeds not showed significant importance differences between Distilled water and Cd + NP. Radicle length of cadmium treated seed was 72% lesser against control distilled water and green AgNP treated seed obtained 60.3% augmentation radical length after 72 hours. Fresh mass of Cd stressed seed treated with green AgNPs was 30% increased. 45% membrane of radical affected by Cd stressed with the help of AgNP radical membrane was less affected up to 40%. Electrolyte leakage was reduced 30% by AgNP compared with Cd stressed seed (60%). Viability of seed tissue of Cd stressed was 53% less when compared with control. Cd stressed seed was treated with AgNPs 47% increased the seed viability. This study will novel insights of the damage inflicted by cadmium and contribution of green AgNPs in mitigation of Cd toxicity in Vigna radiata.
Screening of Polypropylene Degrading Microbes from Various Natural Sources
Authors: S. Shaikh Shaguftanaz , G.R. Pathade
Published: February 2025

Plastics are used as a raw material for production of most of the objects used in day-to-day life. Thus, the production of plastics has increased leading to various ecological problems. There are many forms of polymers but amongst them polypropylene is targeted for the study as it is one of the polymer causing the highest pollution. In the current study, 4 bacterial and 4 fungal isolates were obtained and were screened for their ability to degrade polypropylene using Mineral Salt Media. The isolates were able to grow in the media infused with polypropylene as the sole carbon source. This indicates that the isolates have potential to degrade polypropylene and they can be employed for biodegradation of polypropylene.
Assessment of Yield and Economic Performance of Groundnut Cultivars in the Godavari Zone, Andhra Pradesh, India
Authors: Rao G. Chalapathi , P. Ammaji  , K. Madhav  , Rao D. Sudhakara
Published: February 2025

The present investigation, entitled “Assessment of Yield and Economic Performance of Groundnut Cultivars in the Godavari Zone, Andhra Pradesh” was carried out at the Agricultural College Farm, Rajamahendravarm, during the rabi season of 2022-23. The experiment was designed as a randomized block trial comprising 10 treatments: T1: Kadiri Lepakshi, T2: Kadiri-6, T3: Dheeraj, T4: Kadiri Amaravathi, T5: Kadiri-9, T6: Dharani, T7: Kadiri Chitravathi, T8: Kadiri Harithandhra, T9: Nitya Haritha and T10: TCGS-1694, with three replications for each treatment. The results showed that Kadiri Lepakshi recorded outperformed the other varieties in terms of pod yield, haulm yield and economics. These findings indicate that Kadiri Lepakshi is particularly well-suited for the uplands of the Godavari region. The superior performance of this variety could significantly enhance groundnut production in the area.
Farm Mechanization in Modern Agriculture: A Case Study of Alipurduar District of West Bengal, India
Authors: Sarkar Nabanita , Roy Rupam , Mondal Progya Paromita , Paul Moutushi , Dey Rudranil , Biswas Aditi
Published: February 2025

Farm mechanization is the application of engineering and technology in the field of agricultural operation to do a job in a better way to improve or increase the production of the crop and increase the profitability of farmers by a reduction in drudgery of labour and saving time cost of cultivation. This paper discusses the farm mechanization status in the Alipurduar district of the state of West Bengal in India based on a primary field survey. The study shows us the present situation of land holding, irrigation, Govt. support in the district and tries to identify and analyze the gap for major cropping patterns. At present, in Alipurduar the prevalence of marginal and small farmers in the agricultural scenario prevents them from exploiting the maximum benefits of the modern agricultural implements. There is a high need for the adaption of suitable machines of small capacity and low cost especially in intercultural operation tillage, transplanting, weeding, harvesting, and threshing operations. There is a huge gap in access to institutional credit, govt. extension support service, irrigation, and experience of farmers on the level of farm mechanization. A massive awareness program needs to be carried out among the farmers in Alipurduar to popularize the need for crop-specific machinery.
Effect of Foliar Application of Nano Urea and Nano DAP on Growth and Yield Parameters of Grapes (Vitis vinifera) Cv. Muscat Hamburg
Authors: Venkatraj N.  , Rajkumar M.
Published: February 2025

An experiment conducted to assess the effect of foliar application of Nano Urea and Nano DAP on growth and yield parameters of grapes variety Muscat Hamburg. This experiment was conducted in Randomized Block Design (RBD) with 16 treatments (T1 (0.2% Nano Urea), T2 (0.3% Nano Urea), T3 (0.4% Nano Urea), T4 (0.2% Nano DAP), T5 (0.3% Nano DAP), T6 (0.4% Nano DAP), T7 (0.2% Nano Urea + 0.2% Nano DAP), T8 (0.2% Nano Urea + 0.3% Nano DAP), T9 (0.2% Nano Urea + 0.4% Nano DAP), T10 (0.3% Nano Urea + 0.2% Nano DAP), T11 (0.3% Nano Urea + 0.3% Nano DAP), T12 (0.3% Nano Urea + 0.4% Nano DAP), T13 (0.4% Nano Urea + 0.2% Nano DAP), T14 (0.4% Nano Urea + 0.3% Nano DAP), T15 (0.4% Nano Urea + 0.4% Nano DAP) & T16 (Recommended dose of NP through soil application)) in two replications and sprayed at vegetative stage and berry development stage. The data revealed that T9 (0.2% Nano Urea + 0.4% Nano DAP) showed the highest values for Growth parameters like leaf length (cm), leaf width (cm), leaf area (cm2 ), shoot length (cm), petiole base girth (cm), cane girth (cm), specific leaf weight (mg/cm2 ) and yield parameters like bunch weight (g), number of rachis per bunch, number of berries per rachis, number of berries per bunch, number of bunches per vine and yield per vine (kg). In comparison, T16 (Recommended dose of NPK through soil application) showed the lowest growth and yield parameter values.
Exploring Gut Microbiota Of Piscivorous Bird Little Cormorant (Microcarbo niger) Through 16s rRNA Metagenomics
Authors: PP Raghunathan , V Jiji Joseph , K Abdul Jaleel , TV Jayakrishnan , KT Manjula , TMV Mumthaz , Mathew Vinni
Published: February 2025

Birds exhibit distinct adaptations and food requirements.The remarkable migratory capacity exposes the birds to different habitats with a variety of food items and thus possess a complex gut microbial community. The Little cormorant,Microcarbo niger, an aquatic piscivorous bird, mainly depends on fresh water and inshore habitat for foraging. The 16S rRNA metagenomic analysis was performed to reveal the gut bacterial diversity and composition in Little Cormorant using the scat sample. Taxonomic assignments were done for 14 phyla, 20 classes,38 orders,58 families,78 genus and 72 species with varying OTUs frequency. The phylum Bacillota is the most abundant in frequency and in species level Megamonas funiformis is dominant. Symbiotic and pathogenic strains were detected, including the avian specific Basilea psittacipulmonis and Riemerella columbina
Developing a definition of child exploitation: Findings from a systematic review
Authors: Martin Aisling , Woodward Rachel , Merritt Jennifer
Published: February 2025

Childhood exploitation is an increasing concern for our children and young people, and yet to date no consistent definition exists which allows systems and services to accurately identify, record, or address the problem. The purpose of this review was to systematically identify and critically review studies which relate to exploitation of children and young people to explore factors relevant to this concept. Twenty papers were selected for the systematic review according to the inclusion criteria. Two overarching themes emerged as having relevance to defining childhood exploitation: (i) relational vulnerability; and (ii) power imbalance, which are used to propose a generalised definition of exploitation. Practical implications for the wider system are discussed, along with directions for future research.
Impactos da COVID-19 na odontologia: uma revisão integrativa
Authors: Silva Ruann Oswaldo Carvalho da , Silva Andressa Venancio de Souza , Bertolazo Lais , Perotta Mariana , Moysés Samuel Jorge , Werneck Renata Iani
Published: February 2025

Esta revisão integrativa examinou os impactos da pandemia de COVID-19 nos profissionais da odontologia, com foco em aspectos econômicos, emocionais e estruturais de seu trabalho. A pandemia interrompeu significativamente as práticas odontológicas em todo o mundo, exigindo a adoção de rigorosas medidas de controle de infecção. Desafios econômicos, incluindo redução de renda e aumento de custos, foram prevalentes, especialmente no setor privado. Profissionais odontológicos relataram altos níveis de ansiedade, estresse e medo de infecção. O estudo também destacou a necessidade de educação contínua, suporte e recursos de saúde mental para as equipes odontológicas. Além disso, a pandemia acelerou a adoção de teleodontologia e consultas remotas na área odontológica. No geral, os resultados sublinham o impacto profundo e duradouro da COVID-19 na profissão odontológica e a importância de desenvolver estratégias para apoiar os profissionais odontológicos durante e após a pandemia.
Efeito agudo da manipulação articular específica e inespecífica na mobilidade lombar, flexibilidade, força e resistência dos extensores de tronco
Authors: Pinto Matheus Augusto Ribeiro , Vieira Rebeca Helena de Lima Freire , Morita Ângela Kazue , Navega Marcelo Tavella
Published: February 2025

Introdução: Terapias de mobilização e manipulação são amplamente utilizadas para beneficiar pacientes com dor lombar crônica. É possível observar uma correlação entre a dor, redução da amplitude de movimento e diminuição da flexibilidade, que aumenta a gravidade do quadro da DL. Objetivo: Comparar os efeitos imediatos da mobilização articular vertebral local ou distal sobre a mobilidade, força e resistência da coluna lombar em indivíduos adultos jovens. Métodos: A amostra foi composta por 40 indivíduos adultos com idade entre 18 a 35 anos, de ambos os sexos, e foram formados dois grupos: Grupo Mobilização articular lombar (GML, n=20) e Grupo mobilização articular torácica (GMT, n=20). Os instrumentos de avaliação utilizados foram: a anamnese, seguida por avaliação da mobilidade lombar, com o Teste de Schober, Teste Sentar e Alcançar (TSA), dinamometria de extensão lombar, teste de resistência dos músculos extensores de tronco (Biering-Sorensen) e após a aplicação dos testes foi feito o protocolo de mobilização articular vertebral em cada grupo. Em seguida, as mesmas avaliações foram realizadas. Resultados: Os participantes do GML reduziram a pontuação da END (p < 0,01) e aumentaram os valores dos testes de sentar e alcançar (p < 0,01) e de Schober (p < 0,01) após a intervenção, e o GMT reduziram a END (p < 0,01) e aumento do Biering-Sorense (p < 0,01). Conclusão: a mobilização e manipulação nas vértebras lombares são eficazes para a melhora da dor e mobilidade da coluna lombar e flexibilidade da cadeia posterior, enquanto nas vertebras torácicas melhoram dor e resistência da coluna lombar.
Influence of forest fragments on the richness and abundance of edaphic artropods and the damage scores of Spodoptera frugiperda Smith, 1797 (Lepidoptera: Noctuidae)
Authors: Prado Guilherme Dalla Vechia , Santos Pablo Oliveira , Fraga Diego Felisbino , Santos Luan Alberto Odorizzi dos
Published: February 2025

The objective of this research was to study the influence of forest fragments on the richness and abundance of edaphic arthropods and damage notes of Spodoptera frugiperda Smith, 1797 (Lepidoptera: Noctuidae). The evaluations were carried out in the years 2017/18 and 2018/19 in the same area. Five transects were determined in parallel and separated by 10 m from each other. In each transect, the distances of 0m, 10m, 25m, 50m, 75m and 100m from the edge of the forest fragments were evaluated, 5 plants were selected and the scores of damage caused by fall armyworm were evaluated. Abundance was evaluated to quantify the number of individuals of a given species and richness to designate the number of species in a given region, samples were analyzed in the laboratory for their morphology, and taxonomy was classified into family levels. The data obtained were analyzed using Tukey's test (5%). The measures were transformed when necessary Ö(1+0.5). For the analysis of abundance and richness, the ANAFAU program was used. There was a positive correlation between the damage scores of S. frugiperda and the distances evaluated from 10 DAE (P-value: <0.001). There was no correlation between the abundance and richness of arthropods in relation to the distances evaluated, most of the insects captured were predatory or omnivorous ants. The total number of individuals collected during the 2017/18 and 18/19 seasons was 2415 individuals, represented by 51 morphospecies, 33 families and 12 orders, with 11 predator families composed of 5 orders and 3 omnivorous families composed of 2 orders. In the 18/19 harvest, there was a greater number of arthropods, with 1581 individuals, represented by 45 morphospecies, 29 families and 10 orders, when compared to the 17/18 harvest, with 834 individuals, represented by 37 morphospecies, 24 families and 12 orders.