International Journal of Science and Engineering Science Research
International Journal of Science and Engineering Science Research (ISSN 3049-2793) is a peer-reviewed, open-access multidisciplinary journal that publishes high-quality and original research across a wide range of disciplines in science, engineering, arts, humanities, and social sciences. The journal serves as a dynamic platform for researchers, academicians, and professionals from diverse backgrounds to share their innovations, ideas, and findings with the global community. We invite submissions in the form of research articles, review papers, case studies, and technical notes in subjects including but not limited to physics, chemistry, mathematics, computer science, mechanical engineering, civil engineering, electrical engineering, electronics, artificial intelligence, machine learning, robotics, nanotechnology, biomedical engineering, biotechnology, environmental science, renewable energy, communication systems, economics, political science, history, philosophy, psychology, sociology, education, literature, linguistics, fine arts, performing arts, and cultural studies. With a commitment to interdisciplinary knowledge exchange, the journal encourages contributions that bridge gaps between different fields and contribute to both theoretical understanding and practical advancements.
An AI-Driven Advanced Decision Support System for Coconut Yield Prediction and Disease Detection
The agricultural economy of India's coastal regions, especially Andhra Pradesh, depends heavily on coconut farming. Coconut farming, however, is extremely vulnerable to weather fluctuations, such as erratic rainfall patterns, pest infestations, and plant diseases, which have a substantial impact on crop output and farmers' earnings. Conventional agricultural methods frequently lack access to data-driven insights and predictive analytics, relying instead on manual observation and experience-based decision-making. The AI-Driven Coconut Farming Decision Support System (CFDSS) presented in this study uses predictive modeling and cognitive analytics to assist farmers and agricultural stakeholders. In order to produce region-specific insights at the district and mandal levels, the suggested approach combines several agricultural datasets, such as rainfall records, coconut yield statistics, pest risk information, and geographic boundary data. Rainfall prediction is done using a Long Short-Term Memory (LSTM) deep learning model, and yield estimation and pest risk classification are done using a Random Forest machine learning model. Python-based technologies including Streamlit, Pandas, NumPy, Plotly, and TensorFlow are used in the system's implementation to enable an interactive dashboard with visualization tools and AI-generated farming advisories. Furthermore, coconut plant problems are identified and preventive measures are suggested by an image-based disease detection module. The suggested method improves agricultural decision-making by offering precise predicted insights and practical suggestions, according to experimental evaluation. The promise of intelligent decision support systems in promoting precision agriculture and sustainable coconut farming is highlighted by the integration of artificial intelligence, geospatial analytics, and interactive visualization.
Copyright (c) 2026 International Journal of Science and Engineering Science Research https://creativecommons.org/licenses/by/4.0
Analysis of the Precipitation Pattern under Changing Climate in the Lower Kabul River Basin Nangrahar, Afghanistan
Precipitation and temperature are two factors utilized to assess the climate conditions in the area. The moderation and variability of the climate, particularly the annual discussion of temperature and precipitation, have garnered significant attention globally. In this regard, the current study investigates the changes in temperature and monsoonal rainfall over the long and short terms in the Lower Kabul Basin, Nangrahar province. Rainfall data from 1981 to 2021 were examined in this research. Statistical trend analysis methods, including Excel with various formulas, the Mann-Kendall test, and Sen's slope estimator, were employed to scrutinize and interpret the data. Nangrahar, characterized by its low-lying terrain and intersecting river systems, is part of the inactive delta of the large Hindu Kash Rivers and is situated in the lower Kabul basin. The study aimed to uncover the monthly trends and seasonal and annual variations in rainfall. The trend analysis revealed a negative trend line for December's monthly rainfall, while all other months exhibited a positive slope in the trend line. Analysis of historical precipitation data also indicated a clear regular variation, with dry months becoming drier and monsoon periods experiencing increased rainfall, making them more susceptible to floods. The annual mean rainfall highlighted that the Nangrahar region is one of the driest areas in the country.
The production and distribution of deepfake material in text, audio, video, and image modalities has greatly grown because to the quick development of generative artificial intelligence. Public safety, journalism, cybersecurity, and digital trust are all seriously threatened by such synthetic media. The efficiency of current detection techniques in handling intricate, multimodal manipulation techniques is limited by their primary focus on single-modality analysis. This work offers an integrated multimodal deepfake detection methodology intended to evaluate authenticity across diverse media sources inside a single architecture in order to get over this restriction. A Convolutional Neural Network (CNN) is used to extract spatial information from video frames and detect manipulation traces, visual artifacts, and face abnormalities in order to detect video deepfakes. In order to detect audio deepfakes, discriminative acoustic features are extracted and then classified using a Random Forest method, which offers resilience against attacks using speech synthesis and voice cloning. A TF-IDF Vectorizer in conjunction with Cosine Similarity is used to quantify the semantic similarity between reference materials and input text in order to detect textual plagiarism. The system incorporates a refined vision transformer model based on SIGLIP for image authenticity verification in order to differentiate between AI-generated images and content provided by humans. Furthermore, a RoBERTa-base transformer model that can categorize both machine-generated and human-written text utilizing contextual embeddings is used for AI-generated text identification. A full authenticity score is generated by combining the outputs from all modalities. The framework's usefulness for automated content moderation systems and real-world digital forensics is highlighted by experimental evaluation, which shows better robustness and scalability in comparison to isolated detection approaches.
Ramanujan’s Continued Fractions of Order Six and Twelve with Their Interesting Properties
In the present paper, we derive three continued fractions: R(q), which is of the sixth order, and S(q) and T(q), both of the twelfth order. Each of these arises as a particular case of the general continued fraction mentioned by Ramanujan in his notebook. For these continued fractions, we also establish several theta function identities. In addition, we derive matching coefficients for the continued fraction S(q), and we obtain color partition identities as a result of the theta function identities of S(q) and T(q). Finally, we present 2-, 4-dissections of the continued fraction S∗(q) = q-1S(q).
Long Run Availability of a Two Unit Cold Standby Repairable System Subject to Two Types of Critical Errors
In this paper deals a cold stand by system composed of two identical units S0 (main operating unit) and S- (standby unit) which is taken into function through a switching mechanism which is not hundred percent reliable. It is obvious that the stand by unit is switched to operate when the operating unit fails and the switching device which is used to put the standby unit into operation may be perfect or imperfect at the time of need operating unit may fail either due to normal or chance so we evaluated various important measures of system effectiveness and reliability parameters.
Decentralization and State Control: Devolution of Finance, Functions, and Functionaries in India
The constitutionalising of local self-government through the 73rd and 74th Constitutional Amendments marked a significant moment in India’s democratic and federal evolution. These reforms sought to deepen democracy by devolving powers, responsibilities and resources to local governments. More than three decades later, decentralization in India has produced institutions that are electorally vibrant but administratively constrained. This paper examines the paradox of extensive formal devolution coexisting with persistent state control. Using the analytical framework of finance, functions, and functionaries (the “3Fs”), it argues that decentralization in India has unfolded as a managed and politically conditioned process rather than a comprehensive transfer of authority. While fiscal transfers and functional assignments have expanded unevenly, control over administrative personnel has remained firmly centralized. The retention of authority over functionaries emerges as the central mechanism through which state governments preserve power over local governance. The paper contributes to decentralization scholarship by shifting attention from constitutional design to political economy, highlighting personnel control as the key constraint on substantive local self-government in India.
Copyright (c) 2025 International Journal of Science and Engineering Science Research https://creativecommons.org/licenses/by/4.0
Unlocking Predictive Excellence: Key Drivers of Forecasting Accuracy in Knowledge-Based Banking Transformation
In an era where predictive analytics shape strategic banking decisions, understanding what drives forecasting accuracy is no longer optional it is foundational. This study identifies and quantifies the critical factors influencing predictive accuracy within Knowledge-Based Transformation Models (KBTMs), providing actionable intelligence for organizational model development and decision-making. Drawing on survey data from 310 banking professionals across Ghana's commercial sector, our factor-analytic investigation reveals that predictive accuracy is systematically influenced by four primary drivers: (1) construct-level measurement quality (factor loadings >0.70, AVE >0.60), (2) data structural properties (multicollinearity VIF <3.3, sample adequacy KMO >0.80), (3) model specification complexity (path density, latent variable interactions), and (4) cross-validation strategy (fold configuration, benchmark selection). Knowledge-intensive constructs (e.g., Knowledge Creation, Q² = 0.834) demonstrate higher sensitivity to measurement quality, while performance outcomes (e.g., Employee Performance, Q² = 0.191) are more affected by sample characteristics and validation approach. We establish that predictive tool selection (PLS-Predict vs. CVPAT) should be contingent upon these underlying factors: PLS-Predict excels when measurement quality is high and multicollinearity is present; CVPAT offers advantages when sample constraints or overfitting risks dominate. By translating statistical determinants into strategic guidance, this study empowers banking executives to optimize predictive model development, allocate analytical resources more effectively, and enhance the reliability of knowledge-driven decision-making.
The Unique and Rare - Raga Jingla of Anadhudanu Ganu Composition by Saint Tyagaraja
Music is an integral part of Indian culture. Carnatic music is the main musical tradition of south India. Carnatic music Indian music has many aspects such as sruti, swara, raga etc. In no other musical system, we find such a complicated and highly developed system of raga. Raga is the pivotal concept of Carnatic music and the backbone of Indian music. The term raga means a melodic and intricate combinations of swara phrase which is appealing and pleasing to all. In this ragas Apoorva ragas are unique in nature. Apoorva is a word which means uncommon, excellence and exceptional. The concept of an Apoorva ragas centers on its limited repertoire restricted usage. This paper is intended to analyze the structure and distinguishing characteristics of the rare and Apoorva raga Jingla and the composition Anadhudanu ganu composed by saint Tyagaraja.
Irrational Behavior of Bilateral Mock Theta Functions
Ramanujan was the first who introduced the mock theta functions in his last letter to G. H. Hardy in January 1920. Shukla and Ahmad obtained bilateral mock theta functions of order 7, 9, …, 2r + 1, and they have shown that these functions meet the mock theta function’s distinctive attribute. This article shows the irrational behavior of bilateral mock theta functions of order 2r + 1 at the points q = ±1/2, ±1/3, ±1/4, …. Based on this behavior, irrational behavior of bilateral mock theta functions of order 5, 7, 9, … has also been discussed.
The Impact of Data Breaches on Public Perception of Network Security Technologies
Because of the frequent data breaches these days, many people now question whether VPNs, firewalls and encryption tools are actually safe to trust. It is studied how these events change public views, in terms of trust weakening, media job and plans for recovery. We surveyed 1,500 individuals across three years (2022–2025) by using a longitudinal approach to see how they felt about network security devices both before and after major data breaches such as the 2023 Equifax breach and the 2024 X Corp incident. We looked at the way these breaches were covered by the media to see how these reports influence people’s opinions. After the breach, trust in VPNs dropped by 25%, trust in encryption protocols decreased by 18%, but trust in firewalls only decreased by 10%. Unflattering and sometimes sensational news stories caused more people to doubt the organization, but clear responses showed by the company won back some of that trust. Apologies to the public and stronger security were able to regain trust in 60% of cases within a year. As explained by the findings, data breaches easily damage public trust and appropriate communication is key for restoring confidence in network security products. The impacts on policymakers, organizations and security experts are evaluated.
Mechanistic Rationale for the Superiority of N-Acetylcysteine over Dexamethasone in Post-Embolization Syndrome: A Combined Clinical, Pharmacokinetic, DFT and Docking Investigation
Background: Hepatocellular carcinoma (HCC) is a major health concern in Thailand, with most patients diagnosed at an intermediate stage. While transarterial chemoembolization (TACE) is a standard treatment for HCC, post-embolization syndrome (PES) remains a frequent and debilitating complication. Both Dexamethasone (DEXA) and N-acetylcysteine (NAC) have shown promise in alleviating PES symptoms, yet no head-to-head comparative studies exist to determine which is more effective. Aim: This trial aimed to directly compare the effectiveness of NAC versus DEXA in preventing PES in patients with intermediate-stage HCC undergoing conventional TACE (cTACE). The study seeks to address the gap in current literature and provide evidence on the superior intervention for mitigating PES after TACE. Methods: A prospective, randomized, double-masked controlled study was conducted at two tertiary centers in Thailand from November 2024 to April 2025. A total of 56 patients (ages 18-70) with intermediate-stage HCC were randomized in a 1:1 ratio to receive either NAC or DEXA. The NAC protocol included a loading dose of 150 mg/kg/h followed by a continuous infusion, while DEXA was administered as a single 8 mg IV dose one hour before TACE. The primary endpoint was the incidence of PES within 48 hours, evaluated using the South West Oncology Group toxicity grading and Common Terminology Criteria for Adverse Events (CTCAE). Secondary endpoints included liver decompensation and changes in the albumin-bilirubin (ALBI) score. Results: NAC significantly reduced the incidence of PES (32.1%) compared to DEXA (64.3%), with an adjusted odds ratio = 0.17 (95% CI: 0.03–0.87; P = 0.033). Only 3.6% of patients developed liver decompensation, and there was no significant difference in ALBI score deterioration between the two groups. A computational study using Density Functional Theory (DFT) and Molecular Docking was conducted to examine the molecular mechanisms of action for both drugs. The study revealed that NAC targets the Keap1/Nrf2 pathway, mitigating oxidative stress, while DEXA binds to the Glucocorticoid Receptor (GR) to suppress inflammation. SwissADME predicted favorable pharmacokinetic properties for both drugs, with NAC showing enhanced reactivity towards oxidative stress. Conclusion: In patients with intermediate-stage HCC undergoing cTACE, NAC significantly reduced the incidence of PES compared to DEXA. The clinical findings were supported by computational studies demonstrating NAC’s unique ability to mitigate oxidative stress via the Nrf2 pathway. NAC emerges as a more effective prophylactic strategy for PES prevention in Barcelona Clinic Liver Cancer (BCLC) stage B patients with preserved hepatic function. The computational insights into NAC’s mechanism further bolster the clinical efficacy, making it a promising adjunct in the treatment of PES.
A Sociological Examination of Rural Unemployment and Youth Dissatisfaction: A Theoretical Inquiry in the Indian Context
Unemployment in rural India has transcended its status as a mere economic issue; it has developed into a significant social, cultural, and psychological challenge that impacts the core of rural society. Even though there have been many development projects, Five-Year Plans, and programs that guarantee jobs since Independence, the problem of finding long-term, fair job opportunities in the countryside is still there. The youth, regarded as the most dynamic and transformative segment of society, are ensnared in frustration, disillusionment, migration, and social deviation due to the lack of stable livelihoods. This paper is a theoretical sociological examination that aims to analyse the issues of rural unemployment and youth discontent through the lenses of classical theorists, including Karl Marx, Émile Durkheim, and Robert K. Merton. The research contends that unemployment is not solely a consequence of economic disparity; instead, it arises from structural inequalities, the erosion of conventional value systems, and the inadequate adaptation to modernity. The most recent data show that the overall unemployment rate in India's rural areas was 4.9% (June 2025, CMIE) and the unemployment rate for young people in rural areas was 10.2% (PLFS 2023-24). The rate is even higher for educated young people in rural areas, at about 20%. This shows that the gap between education and employment is getting bigger. This situation shows that rural communities are becoming more hopeless, moving away, and having social unrest. The study finds that economic policies alone cannot solve the problems of unemployment and youth discontent. Instead, a sociological approach is needed, one that focusses on changing social institutions, values, and community systems to bring stability and meaning back to rural life.
The Impact of Armed Conflicts on Human Rights and the Refugee Crisis
Armed conflicts remain one of the most devastating realities of the modern international system. Their destructive force extends beyond immediate physical violence to the systematic erosion of human rights and the creation of large,scale displacement crises. This paper explores the nexus between armed conflict, human rights violations, and the refugee crisis from political, legal, and humanitarian perspectives. It examines global patterns of civilian harm, gender,based violence, economic deprivation, and forced migration. The analysis draws upon international legal frameworks such as the Geneva Conventions, the Universal Declaration of Human Rights, and the 1951 Refugee Convention. The study also integrates empirical data from the United Nations High Commissioner for Refugees (UNHCR), Human Rights Watch, and Amnesty International. Ultimately, this paper underscores that armed conflicts are not only violations of peace but profound assaults on human dignity and global justice.
Application of Weibull Distribution in Renewable Energy
The growing global emphasis on renewable energy has intensified the need for precise statistical modeling techniques to enhance energy forecasting, system reliability, and performance optimization. Among the various probabilistic approaches, the Weibull distribution has emerged as a pre-eminent model for characterizing stochastic variability in renewable resources such as wind speed and solar irradiance. This study offers a comprehensive reassessment of the Weibull distribution’s theoretical foundations and its empirical applications in renewable energy systems. The paper reorganizes classical Weibull analysis within a modern research framework—evaluating parameter estimation methods, goodness-of-fit diagnostics, and cross-domain applications in wind, solar, and hybrid systems. Through a synthesis of recent studies, the article highlights the Weibull model’s comparative advantages over Rayleigh, lognormal, and gamma alternatives. It further examines integration with computational platforms and artificial intelligence for predictive analytics. The results reaffirm that Weibull-based modeling not only provides robust probabilistic representation of resource variability but also serves as an analytical cornerstone for reliability engineering and sustainable energy planning.
The Sahel States Military Intervention in Politics: The Conflict of Vested Colonial Interest and National Security
The earth-rocking military interventions in the politics of Sahelian states constitutes a critical challenge to democratic consolidation and national sovereignty. Despite transitions to civilian rule in many African countries, the Sahel continues to experience recurrent military takeovers, raising concerns over underlying causes and external influences. This paper examined the Sahel States Military intervention in politics: the conflict of Vested Colonial interest and National Security. The objective is to explore the root causes of military intervention, assess the influence of former colonial powers, analyze, and recommend strategies for democratic strengthening. The Theoretical framework for the study is anchored on 'Neo-colonialism Theory' propounded by Kwame Nkrummah (1965), methodologically the study is hintched on a descriptive-historical research design to trace the patterns and implications of foreign interference and military dominance in post-colonial Sahelian politics. Data were gathered through secondary sources, including scholarly journals, books, archival records, and credible internet sources. The data were qualitatively analyzed using content analysis to interpret recurring themes and patterns relevant to the research question. Research findings revealed that weak democratic institutions, elite corruption, ethnic tensions, and foreign strategic interests particularly those of former colonial powers are significant drivers of military coups. Furthermore, military regimes often undermine governance quality and restrict national sovereignty, while external actors perpetuate instability under the guise of security cooperation. The study concludes that for the Sahel to achieve political stability, regional governments must address internal governance deficits and reduce foreign dependency. The paper recommended strengthening democratic institutions, promoting inclusive governance, enhancing youth political engagement, and limiting foreign political manipulation.
Organic Farming a viable option for the global food supply and livelihood sustainability
The key obstacles to the proposition that organic farming will make a substantial difference to the global food supply are poor yields and inadequate organically appropriate fertilizer amounts. We evaluated the universality of both statements. Regarding the first argument, we compared the yields of organic versus conventional or low-intensive food production for a global dataset of 293 examples and calculated the average yield ratio (organic: non-organic) for developed and developing worlds of various food categories. The average yield ratio for most food categories was marginally < 1.0 for developed world studies, and > 1.0 for developing world studies. With the average yield ratios, we were modelling the global supply of food that could be organically produced on the existing agricultural land base. Model projections suggest that organic methods could generate enough food per capita worldwide to support the current human population, and probably even a greater population, without growing the agricultural land base. We also measured the amount of nitrogen likely available from leguminous cover crops used as fertilizer from fixation. Evidence from temperate and tropical agroe-cosystem show that leguminous cover crops will fix enough nitrogen to offset the amount of currently used synthetic fertilizer. Evidence from temperate and tropical agro ecosystems show that leguminous cover crops will fix enough nitrogen to offset the amount of currently used synthetic fertilizer. Such findings suggest that organic farming has the potential to make a significant contribution to global food supply, while reducing traditional agriculture's negative environmental impacts. Evaluation and analysis of this paper addressed important concerns about crop rotations in organic versus traditional agriculture and the reliability of sources of grey-literature. A continuing discussion on these issues can be found in this issue's Forum editorial.
Mathematical Modelling of India's Population Dynamics: A Comprehensive Analysis of Projection Methodologies and their Socio-Economic Implications
This paper provides a comprehensive analysis of mathematical modeling techniques for projecting India's population, which has recently become the world's largest, making accurate forecasting a critical prerequisite for national planning. The study undertakes a comparative evaluation of two primary classes of projection methodologies. First, it examines non-age-structured models, including the exponential and logistic growth curves. An application of these models to India's historical census data reveals their inherent inadequacy for reliable, long-term forecasting, primarily due to their simplifying assumptions of homogenous populations and static environmental carrying capacities, which fail to capture the complexities of human demographic transition. In contrast, the paper details the robust, age-structured cohort-component method, which is the standard employed by official bodies such as the Registrar General of India (RGI). By incorporating age- and sex-specific rates of fertility, mortality, and migration, this method accurately models the crucial dynamics of population momentum and population aging. The analysis discusses the practical application of this method through computational tools like the Spectrum-DemProj module, which allows for dynamic policy simulation. The study critically analyzes the official projections from the RGI, which forecast India's population to reach 1.52 billion by 2036. Beyond this headline figure, the projections reveal a nation undergoing a profound demographic shift, characterized by a rapidly closing window of opportunity for a "demographic dividend," significant population aging with the elderly cohort (60+) expected to more than double, and accelerating urbanization that will account for nearly three-quarters of all population growth. The socio-economic and environmental implications of this trajectory are explored in depth. The paper investigates the dual challenge of capitalizing on the demographic dividend through strategic investments in education, skill development, and job creation, while simultaneously managing the escalating environmental stress on critical resources, including land, water, and air, driven by both absolute population numbers and rising consumption patterns. The paper concludes by synthesizing these findings, affirming the superiority of age-structured models for policy formulation, and advocating for an integrated, rights-based policy framework. This framework must move from a paradigm of "control" to one of empowerment through choice and reproductive health, while proactively planning for an aging society and promoting sustainable development to ensure a resilient and equitable future.
Comparative Analysis of Village Savings and Loan (VSL) and Self Help Group (SHG) Financial Models amid Climatic Disasters in Malawi: Cases for Machinga and Chiradzulo
This study provides a comparative analysis of Village Savings and Loan (VSL) groups and Self Help Groups (SHGs) in Malawi from 2020 to 2024, focusing specifically on trends in start-up capital, financial returns, and resilience in the face of climatic disasters. Findings reveal that SHGs benefitted significantly from external start-up capital, resulting in notable growth in financial returns, while VSLs operated independently without external financial inputs, maintaining stable but modest proceeds. A robust positive correlation was established between the level of start-up capital and financial outcomes within SHGs, underscoring the impact of initial investment on performance. Conversely, despite lower financial returns, VSLs exhibited greater resilience during climatic disruptions due to their self-reliant, community-driven operational structure. The study recommends a hybrid model that integrates the capital-strength advantages of SHGs with the autonomous resilience of VSLs, aiming to enhance financial inclusion and strengthen disaster preparedness in vulnerable communities.
Imposition of Sanctions on North Korea by United States and Conflict of Denuclearization Agenda
The study examines the Imposition of sanctions on North Korea by United States and Conflict of Denuclearization Agenda. In order to ascertain whether the continuous coordinated imposition of sanctions by the Donald Trump and Joe Biden Presidency against North Korea undermines the adoption of a sustainable denuclearization agreement. The study adopted the ’Realist Theory’ as its anchor blue-print in a pragmatic effort to explain why the engagement between the two states remain largely hostile. The study adopted content analysis for the purpose of analyzing documented materials generated through secondary sources of data collection as well as the qualitative method of data analysis. The findings show the serial and catastrophic worsening of United States and North Korea conflict which inadvertently undermines the denuclearization efforts in North Korea due to hostile operations by both the United States and North Korea Leaders. Recommendations were proffered along these findings as follows, the intensification of sanctions on individuals and businesses that has trans-border linkages with those in North Korea is rather not appropriate and counter-productive as it goes against the substantive goodwill the US internationally represents. The target of the United States is to denuclearize North Korea and not to punish the innocent citizens, which is against what the United Nations represents. This will go a long way in restoring the faith of North Korea and enhance the prospects of the denuclearization agenda.
Effect of pusa hydrogel and plant growth regulators on vegetative growth of strawberry (fragaria x ananassa dutch.) Cv. Chandler
Strawberry (Fragaria × ananassa Duch.) is a high-value fruit crop globally known for its rich nutritional profile and economic potential. However, its cultivation in India is challenged by shallow roots, low water retention soils, and environmental fluctuations. To ensure sustainable and profitable cultivation, innovations in water and nutrient management are critical. This study aimed to evaluate the individual and combined effects of Pusa hydrogel and plant growth regulators (Triacontanol and Cycocel) on the vegetative growth, flowering, fruiting, yield, and quality of strawberry cv. Chandler. It also assessed the economic viability of these treatments. The research was conducted over two growing seasons (2021–22 and 2022–23) at Shri Durga Ji Post Graduate College, Azamgarh, U.P., using a Randomized Block Design with 27 treatments replicated thrice. The treatments included various concentrations and combinations of Pusa hydrogel (25g, 50g), Triacontanol (100ppm, 150ppm), and Cycocel (500ppm, 1000ppm). Data were collected on plant height, number of leaves, plant spread, flowering time, fruit size and weight, yield per plant, and biochemical quality traits like TSS, sugars, and vitamin C content. Economic analysis was also performed. Treatment T20 [Triacontanol 170 ppm + Pusa hydrogel 60 g] showed the best performance in vegetative traits, including plant height (19.93 cm), highest number of leaves (15.43), plant spread (23.2cm), days taken to first flower (60.02 days) and number of flowers (25.43). The synergistic application of Pusa hydrogel and plant growth regulators significantly improved strawberry growth, yield, fruit quality, and economic returns. This approach offers a sustainable and resource-efficient strategy for strawberry cultivation, particularly in water-limited regions.
Neuro-AI Synergy in Visual and Motor Healthcare: Redefining Diagnosis Through Brain, Eye, and Motion Data
This paper delves into the transformative potential of Artificial Intelligence (AI) in neuro-visual healthcare by emphasizing the intricate synergy between brain function, visual perception, and motor behavior. It presents a forward-looking framework designed to elevate diagnostic precision and therapeutic efficiency through the intelligent fusion of multimodal data. By harnessing advanced AI techniques to analyze and correlate neurological signals (EEG), eye-tracking patterns, and motion dynamics.[1]. we propose a robust diagnostic architecture capable of delivering real-time, data-driven clinical insights. This integrated approach not only supports early detection of complex neurological and neurodegenerative conditions but also paves the way for highly personalized and adaptive healthcare solutions tailored to individual patient profiles[2].
Cyberattack Prevention in Cloud-Based Systems Using Butterfly Optimization Algorithm BOA Based CNN for Intrusion Detection
Cloud computing offers significant benefits in scalability and flexibility but remains highly vulnerable to cyber threats such as malware injections, DDoS attacks, and data breaches. Such ever-growing attacks are even more difficult for traditional IDS and thus end up with higher false positives, which is much typical of their low adaptability. Deep learning-based IDS, an especially CNN-is expected to do really well in fighting the very difficult signatures of attacks. The disadvantages of CNN involve very long periods, as well as the exhaustive computational resource for extensive hyperparameter tuning. This paper hence contributes by proposing a butterfly optimization-based hyperparameter fine-tuning of CNN-based intrusion detection systems. Further, hybridization of BOA and CNN can enhance detection accuracy; reduce false alarms; and better adaptability to real-time emerging threats in cloud environments. The proposed framework has been evaluated using the KDD-99 dataset and achieved an overall 98.9% reliability, including 98.35% F1 score, 98.55% recall, and 98.75% consistency. Thus, the achieved results speak for how effectively the BOA optimization can improve the performance of the CNN in the identification of disturbances. So, the study provides intelligent and scalable solutions for IDS and as associated with increases detection of threats in real time but covering low computational overheads. Future work would thus apply this methodology to a broader set of datasets, as well as incorporate other metaheuristic algorithms for better detection optimization into the scope of the work.
AI-Driven Job Fit Prediction Using Recurrent Neural Networks in Human Resources Management
Human resource management, it is essential that candidates ought to be appropriately well-fitted to appropriate job posts to enhance productivity, reduce turnover, and enhance job satisfaction. The essay proposes a model for Artificial Intelligence (AI)-based forecasting of job suitability based on Recurrent Neural Networks (RNNs) predicting by forecasting job matching based on candidate history and career profiles. The model uses to train processes different features such as demographic data, career history, psychometric test data, and job performance data to forecast job fit for different job positions. The model uses the HR Analytics: Job Change of Data Scientists dataset with employee data, including job satisfaction, education, experience, job titles, and tenure. Using RNNs, the model detects temporal trends and career development of applicants and makes a dynamic and customized job fit prediction. The approach is tested on common performance measures like accuracy, precision, recall, and F1-score and outperforms classical models. The model performed 98.5% accurate, 97% precise, 98% recalling, and 97.5% F1-score, proving its utility in job fit prediction. The AI-powered solution provides a data-driven, scalable solution to improve recruitment, employee-job fit, and facilitate improved long-term career planning.
Cyber Awareness in The Digital Ecosystem
Technology is evolving at rapid scale and there by consumer adoption, but Awareness is not aligned to that speed. As individuals increasingly rely on digital platforms for communication, financial transactions, and various other activities, the risk of cyber threats has grown exponentially. Understanding and assessing individual preparedness to mitigate cyber risk is a multifaceted challenge that involves delving into an array of factors, cultivating awareness, and analyzing online behavior. Factors influencing individual preparedness to tackle cyber risk are diverse and interconnected. One of the pivotal elements is technological literacy. In an era dominated by complex digital systems and sophisticated cyber threats, individuals with a higher level of technological literacy are better equipped to comprehend and respond to potential risks. This literacy encompasses not only the ability to navigate digital interfaces but also an understanding of fundamental cyber security principles. Factors such as educational background, professional training, and exposure to technology contribute significantly to an individual's technological literacy. Moreover, socio-economic factors play a crucial role in shaping an individual's cyber risk preparedness. Disparities in access to technology, internet connectivity, and financial resources can create vulnerabilities, as individuals with limited access may be less aware of cyber threats or lack the means to implement robust security measures. Social and cultural factors also influence how individuals perceive and respond to cyber security issues. Attitudes towards privacy, trust in digital platforms, and the prevalence of cyber norms within a community all contribute to shaping an individual's approach to cyber risk mitigation.
Effect of different led light colors and intensities on growth performance and economic outcomes for layers kept in environment-controlled house during brooding phase
Objectives: The study was conducted to determine combined effect of different light colors and intensities on growth performance and economic viability of Babcock® White chicks during brooding phase. Methods: Day-old chicks were reared for 8 weeks and divided into 18 treatment groups, each with four replicates of 10 birds. Light treatments of 6 different colors, cool white (control group), red, blue, green, yellow, and warm white light with 3 different levels of light intensities (4, 5 and 6 lux), provided in the brooding. Weekly evaluations for weekly weight, feed intake, body weight, body weight gain, feed conversion ratio, mortality and economic. The data observed was analyzed with Minitab 18. Results: Significant effects of different light colors and intensities on weekly weight, feed intake, body weight gain and feed conversion ratio were observed. Highest body weight was observed in birds under warm white light. Red light showed the highest feed intake, meanwhile birds under warm white light combined with 6 lux light intensity demonstrated significantly higher weight gain and efficient feed conversion. Birds under blue light at 4 lux had the lowest production cost (Rs 385.36), while those under warm white light at 6 lux had the highest (Rs 400.68). Conclusion: Keeping in view the economic consideration, it can be concluded that during brooding phase any light treatment conducted in these experiments may be applied. However, blue light having 4 lux intensity and warm white light with 6 lux to be the most appropriate choice during brooding phase.
Comprehensive Analysis of Phytochemical Composition and Antioxidant Activity of Ventilago Madraspatana Leaf Extracts
Plants have tremendous medicinal value and have been associated with pharmacologically active phytochemicals for thousands of years. These bioactive compounds, especially phenolics and flavonoids, present strong antioxidant activity which can reduce oxidative stress. However, oxidative stress is known to be an upstream cause in the pathogenesis of many chronic diseases such as cancer, cardiovascular disease, and neurodegenerative disorders. The present study was focused on the investigation of antioxidant qualities by quantifying total phenolic, and flavonoid contents, and evaluating different solvent extracts such as methanol, hexane, and ethyl acetate leaf extract of Ventilago madraspatana in terms of free radical scavenging potential.The methanol extract also offers the highest antioxidant activity in HRS and ABTS radical scavenging total antioxidant activity measurements. High antioxidant activity was also found to be well related with high concentrations of phenolic and flavonoid compounds, hence these two groups of compounds are the main responsible for the antioxidant properties of this plant. As expected, the hexane and ethyl acetate extracts exhibited relatively low antioxidant activities but may have interesting perspectives in terms of bioactive compounds including saponins, triterpenes, and phlobaphenes.Consequently, V. madraspatana methanol extract was able to show a better amount of antioxidant activity with the previous findings that reveal the potential of plants as natural antioxidantsources and support their use in folk medicine only for curative purposes. In addition, the findings will be used as a basis for future studies to identify and characterize key compounds that are known to possess therapeutic properties with an emphasis on preventing or treating diseases associated.
Transgenic Insects: Advancements, Applications and Implications
Transgenic insects, genetically modified through the introduction of foreign genes, have emerged as a cuttingedge technology with significant implications in various fields. This review paper explores the advancements made in transgenic insect research, their potential applications, and the ethical, environmental, and regulatory considerations surrounding their deployment. The paper highlights the potential benefits and risks of using transgenic insects, emphasizing the importance of responsible and well-regulated research to harness their full potential while safeguarding the ecosystem
Project Feasibility Analysis a Strategic Tool for Project Performance: The Case of Public-Private Partnership in The Bamenda Municipality
The main aimed of this research is to determine the influence of project feasibility analysis on the performance of public-private partnership projects in Bamenda municipality. The methodology of the study consists of causal research design, primary source of data was collected with the help of questionnaires. The sample size used for the study is made up of 52 public private partnership projects. A purposive sampling technique was used for the study. The research uses OLS technique to estimate the regression. The regression analysis was based on the objective of the research work and also to identify the significant condition of the various variables used in the study. The findings revealed that after the regression analysis per objective, the variables legal feasibility and operational feasibility were positive and significant meanwhile technical feasibility was negative and significant. The study recommends that given the significant negative impact of technical feasibility analysis on project performance, it is recommended to enhance the rigor and comprehensiveness of technical assessments. This may involve investing in specialized expertise and technologies to accurately evaluate technological capabilities, identify potential challenges, and leverage innovative solutions for project implementation.
Smart Self-Healing Concrete using AI-Optimized Microbial Agents for Sustainable Infrastructure
The rapid deterioration of concrete structures due to cracks and environmental stressors poses significant challenges for sustainable infrastructure. Traditional repair methods are often costly, labor-intensive, and environmentally damaging. Smart self-healing concrete, integrated with AI-optimized microbial agents, presents a revolutionary solution to enhance structural longevity and resilience. This technology leverages bio-mineralization by bacteria such as Bacillus subtilis and Sporosarcina pasteurii to induce calcium carbonate precipitation, effectively sealing cracks. Artificial intelligence (AI) plays a crucial role in optimizing microbial growth conditions, predicting crack propagation, and ensuring efficient healing through advanced data analytics. This paper explores the integration of AI-driven models with microbial healing agents to create a smart, self-sustaining material. A comprehensive analysis of microbial selection, AI-driven optimization techniques, and sustainability assessments is provided. Results from recent experimental studies indicate that AI-optimized microbial self-healing concrete significantly enhances durability, reduces maintenance costs, and lowers carbon footprints compared to conventional materials. The findings emphasize the potential of AI and biotechnology in revolutionizing civil engineering, paving the way for greener and more resilient infrastructure.
Mathematical Modeling of Self-Healing Materials for Next-Generation Structural Components
Self-healing materials (SHMs) have garnered considerable attention in recent years for their potential to revolutionize the field of structural engineering. These materials, capable of autonomously repairing damage, can significantly enhance the durability, sustainability, and cost-effectiveness of next-generation structural components. This research focuses on the development and application of mathematical models to predict and optimize the behavior of SHMs in critical infrastructure systems. The paper explores various mathematical approaches, including deterministic, stochastic, and multi-scale models, to simulate the self-healing process and assess the influence of material properties, healing agents, and environmental factors. By integrating advanced computational methods such as finite element analysis (FEA) and molecular dynamics simulations, this study aims to provide insights into the healing kinetics, crack propagation, and mechanical performance of SHMs under real-world conditions. Furthermore, the research emphasizes the importance of incorporating machine learning techniques to optimize the design of self-healing systems and improve their efficiency. Despite the significant advancements, challenges remain in scaling these models for large-scale applications, especially for infrastructure projects. This paper highlights the need for continued interdisciplinary research to refine mathematical models and facilitate the widespread adoption of SHMs in structural engineering. The findings suggest that self-healing materials, driven by advanced mathematical modeling, hold great promise for enhancing the sustainability and resilience of future infrastructure.