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, and medical etc. 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.
Interaction Study of Molecular Behavior of Mixture of Polar liquids at Microwave Frequency
The dielectric study of tert-butyl alcohol (TB) with ethylene glycol (EG) were determined over the frequency range of 10 MHz to 20 GHz using time domain reflectometry (TDR) at temperature 55oC for 11 different concentrations of the system. The static dielectric constant (e0) and relaxation time (t) have been obtained by the least squares fit method. Excess properties and Kirkwood correlation factor of the mixtures have been determined. The static dielectric constants for the mixtures have been fitted with the modified Bruggeman model.
Copyright (c) 2026 International Journal of Science and Engineering Science Research https://creativecommons.org/licenses/by/4.0
Security and Resilience in Decentralized P2P Communication
Noteworthy issues about privacy, parallelism, resilience, and centralised control have been brought up by the growing reliance on real-time digital communication systems. The centralized client-server designs used by the majority of modern messaging systems disclose communication metadata, create single points of failure, and constrain user faith in service providers. With a focus on P2P-first architectures, end-to-end encryption, decentralised trust mechanisms, group communication stability, and adaptive cloud-based fallback mechanism, this review methodically investigates research on secure decentralised peer-to-peer (P2P) communication systems. A thorough analysis is conducted of key enabling technologies, such as WebRTC, Conflict-Free Replicated Data Types (CRDTs), blockchain-based identity frameworks, and selective cloud support. In order to create reliable, scalable, and privacy-preserving real-time communication systems appropriate for next-generation applications, the review reveals major gaps, outlines existing research, and suggests future research areas.
Urban Development in Prayagraj City: A Comprehensive Sectoral Evaluation
This paper undertakes a comprehensive sectoral evaluation of urban development in Prayagraj City, Uttar Pradesh, India — one of the nation's oldest and most strategically significant urban centres. Drawing exclusively on secondary data from statutory planning documents, government reports and peer-reviewed literature, the study examines six principal elements of urban development: housing, transport, water supply and sanitation, recreational amenities, health facilities, and electricity infrastructure. Guided by the Prayagraj Master Plan 2031 and allied policy frameworks, the analysis reveals a city in active transition. Significant infrastructural investments across all sectors have improved service coverage and physical connectivity, yet persistent challenges remain in spatial inequity, ageing infrastructure, demand–supply deficits and institutional fragmentation. The paper identifies critical gaps — particularly in sewage network coverage, primary healthcare capacity and energy supply during peak demand — and offers evidence-based policy recommendations for more equitable, sustainable and resilient urban growth. The findings are of direct relevance to planners and policymakers engaged with medium-sized Indian cities undergoing rapid urbanisation.
Formal Savings, Informal Networks, and Hajj Affordability: Evaluating the GCB Hajj Account and Savings Behaviour in Northern Ghana
Financing the Hajj pilgrimage in Northern Ghana demands extraordinary economic effort from Muslim households operating in one of the country's poorest regions. This paper investigates the savings strategies employed by prospective pilgrims and Hajj returnees in three communities Tamale (urban), Sagnarigu (peri-urban), and Kumbungu (rural) and evaluates the role of Ghana's first dedicated Hajj savings product, the GCB Hajj Account, launched in September 2025. Using a mixed-methods design comprising quantitative surveys with 150 Muslim households and semi-structured interviews with 25 participants, including prospective pilgrims, returnees, Islamic scholars, and bank officials, the study documents the persistent dominance of informal savings mechanisms: susu rotating savings groups (68%), home savings (58%), family contributions (52%), and asset liquidation (45%). Formal banking uptake remains low overall (12% GCB Hajj Account adoption) but is notably higher in urban Tamale (24%). Key barriers to formal savings adoption include limited rural branch access, financial illiteracy, and Islamic concerns over interest-bearing products concerns that the non-interest-bearing GCB account explicitly addresses. Government interventions under President Mahama, including a fare reduction from GH¢75,000 (2024) to GH¢62,000 (2025) with projections for below GH¢50,000 in 2026, and the anti-profit directive for Hajj governance, represent structural progress but remain insufficient for the lowest-income households. We propose an integrated Hajj Financing and Financial Inclusion Framework that combines agent banking expansion, mosque-based financial literacy campaigns, a government Hajj Stabilization Fund, and post-Hajj economic reintegration support. The study contributes to scholarship on Islamic finance, religious savings behaviour, and financial inclusion in low-income West African settings.
Comprehensive Evaluation of the Leaf spring Manufacturing station: Assessing Efficiency and Reliability
An essential and crucial component of the suspension system is the leaf spring. Finding an ideal design that works well under a range of loading circumstances is the aim of this investigation. This study offers a thorough availability and performance modelling evaluation of the leaf spring production sector, emphasizing the identification of crucial subsystems, the assessment of failure behaviours, and the measurement of their effect on total productivity. Here, we look at three different component conditions: fine, diminished, and crashed. The breakdown and maintenance rates of each component are assumed to be fixed and substantially separate. The system was analytically formulated using the Markov Birth-Death method. To compute the stable state probability, the Chapman-Kolmogorov differential equations were established based on the system's state-transition relationships, considering a spectrum of failure and repair rates. Decision matrices are derived from different performance levels in terms of availability. The model's practical application is illustrated by numerical findings and case studies, which provide insights into scheduling preventative maintenance, improving production reliability, and allocating resources optimally. Therefore, the results of this study are considered to be helpful in analyzing availability and identifying the best maintenance techniques that may be used going forward to improve system performance.
A Star-Bus Hybrid Network Infrastructure Design and Management Framework for St. Nicolas College of Business and Technology
The need for higher speed internet connectivity as well as the protection of student's sensitive information has created the need for advanced networking systems. A complete network system will be implemented at St. Nicolas College of Business & Technology (SNC). The initial number of users will be approximately 300 but there is room for growth. The system will utilize a star-bus hybrid topology which supports the segregation of faults and central administration. VLSM within the SNC $10.10.0.0/16$ address block will allow optimal utilization of bandwidth while creating security by segregating departments. Technical testing included integration of firewall with IDS/IPS as well as development of an automated maintenance process. The findings show that this proposed infrastructure can provide a solid scalable and reliable platform for the institutions transition into a digital campus.
Beyond the Pilgrimage: Social Mobility, Status, and Economic Strain of Hajj in Northern Ghana
The Hajj pilgrimage to Mecca is among the most significant religious obligations in Islam, but in low-income contexts it also functions as a powerful marker of social status. This paper examines the relationship between performing Hajj and social mobility among Muslim households in Northern Ghana, one of the country’s poorest regions. Using a mixed-methods design quantitative surveys with 150 households and semi-structured interviews with 25 participants across urban (Tamale), peri-urban (Sagnarigu), and rural (Kumbungu) communities we find that pilgrimage is associated with enhanced social status (80% of returnees), increased community leadership roles (55%), and improved perceived economic decision-making (48%). However, 35% of returnees report prolonged financial strain requiring 2–3 years to recover, with some selling productive assets such as livestock and land. We argue that Hajj functions largely as a form of conspicuous religious consumption a signal of piety and status that does not reliably generate corresponding economic returns. While the title Al-Hajj confers respect, trust, and leadership opportunities, the material costs can undermine long-term household welfare. We propose a post-Hajj economic reintegration framework that includes financial counseling, asset replacement support, and peer networks. The paper contributes to sociological literature on religion and stratification and to development policy on managing large religious expenditures in low-income settings.
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.
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.