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Artificial Intelligence Driven Drug Delivery Systems: Recent Advances and Emerging Trends
Drug Delivery Systems (DDS) play a critical role in ensuring the therapeutic efficacy and safety of pharmaceutical agents. Conventional drug delivery approaches often suffer from limitations such as poor bioavailability, non-specific targeting, and systemic toxicity. Recent advancements in Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have revolutionized the design and optimization of drug delivery platforms. AI-driven methods enable predictive modeling, intelligent nanocarrier design, and personalized therapeutic strategies by analyzing large biomedical datasets. These technologies facilitate optimized drug formulation, controlled release mechanisms, and targeted delivery, thereby improving treatment outcomes. AI algorithms such as Support Vector Machines (SVM), random forests, Convolutional Neural Networks (CNN), and reinforcement learning are increasingly applied in nanoparticle design, pharmacokinetic modeling, and clinical decision support systems. Additionally, emerging concepts such as self-driving laboratories, autonomous drug delivery systems, and AI-guided nanomedicine are reshaping pharmaceutical research. This review provides a comprehensive analysis of recent advances in AI-driven drug delivery systems, covering computational techniques, nanocarrier optimization, clinical applications, and emerging research trends. Comparative analysis tables summarize key algorithms, delivery platforms, and research developments reported in the literature. Finally, major challenges including data quality, regulatory issues, and interpretability of AI models are discussed along with future directions for the integration of AI in precision medicine and smart therapeutics. 2026, Dr. Yashwant Research Labs Pvt. Ltd. All rights reserved. -
Performance Evaluation of Machine Learning Models for Detecting Vulnerabilities in Internet of Things Network
Security threats and attacks are a growing concern in the field of Internet of Things (IoT) infrastructure. Internet-based automated network application models are used across various domains; commensurately, different security vulnerabilities and anomaly attacks are also increased at the same level. These attacks could cause failures in IoT infrastructure and network systems. In the modern world, Machine Learning (ML) models support various predictive analyses, providing more accurate results for future forecasting in various fields. In this article, we compare existing classical Machine Learning (ML) algorithms supported by Artificial Intelligence (AI) to evaluate and predict the performance and accuracy of different vulnerabilities in IoT infrastructure. We considered and compared the results of Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN) using publicly available datasets. Through this evaluation, we obtained an accuracy of 99.4% from DT, RF, and ANN. Additionally, RF demonstrated a highest accuracy of F1 is 0.994 and lowest STD variance is 0.014 than compared models in the selected dataset. 2026, Dr. Yashwant Research Labs Pvt. Ltd. All rights reserved. -
Nurturing Rural Women as Entrepreneurs: Jharkhand State Livelihood Promotion Society (JSLPS) Based Case Evidences
This study examines the dynamics of rural womens entrepreneurship in Jharkhand (India), emphasising the role of the Jharkhand State Livelihood Promotion Society (JSLPS) under the National Rural Livelihoods Mission (NRLM). With merely 0.68 per cent of rural women entrepreneurs nationwide situated in Jharkhand, the report highlights a primary developmental necessity. Data was obtained from 521 Rural Women Entrepreneurs (RWEs) across six districts of Jharkhand, using a qualitative study design supplemented by five illustrative case studies. The analysis informed by the Resource-Based View (RBV) framework demonstrates that both tangible resources (such as access to micro-credit, training and digital platforms) and intangible resources (including empowerment, peer support and social capital) are essential for enterprise sustainability and growth. JSLPS interventions, including the Palash brand, SHG federations and market access through trade fairs and internet platforms, have facilitated womens move from informal employment to empowered entrepreneurship. The case studies, encompassing bakeries, soap production, incense manufacturing and handicraft businesses, underscore context-specific issues and adaptive tactics such as digital onboarding, communal ownership and skill development. Despite infrastructural and societal challenges, these businesses demonstrate scalable, replicable, and robust forms of grassroots entrepreneurship. The study indicates that a tailored, locally integrated and digitally inclusive support structure is essential for promoting RWEs. It proposes that forthcoming interventions incorporate institutional collaborations, policy changes and multi-sectoral frameworks to fully capitalise on rural womens entrepreneurial potential and convert them into catalysts for inclusive and sustainable development. 2025, National Institute of Rural Development. All right reserved. -
Fabrics of Power: Cutting Through the Noise in the Classroom
The hijab, purdah and veil though differently named constitute a continuum of meanings shaped by social, cultural and personal contexts. A womans decision to adopt or reject these garments situates her within a shifting spectrum of religious expression and secular alternatives. The volatility of these meanings renders the garments vulnerable to political appropriation, transforming them into contested symbols that are difficult to address pedagogically, therefore becoming a fabric of power. The hijab controversy that unfolded in Karnatakas educational institutions in early 2022 sharpened these complexities, prompting extensive public commentary on the purpose of education, the responsibilities of institutions, and the rhetorics of liberty, secularism, nation and religion. This article examines these commentarial responses ranging from editorials to columns in Kannada and the English media while reflecting on the parallel experience of teaching concepts such as liberty, dissent, secularism and religion during the period of unrest. In doing so, it foregrounds the paradox inherent in the politics of teaching literature, the framing of literature as political, and the pedagogical negotiations required when instruction unfolds within a charged and highly politicised atmosphere. 2025, Unisa Press. All rights reserved. -
Economics of Farming in Mahatwar, Uttar Pradesh
Recent policy efforts have focussed on transforming eastern Uttar Pradesh, an acknowledgement of the relative backwardness of the regions agricultural development. Despite this, there has been little discussion in the literature of agrarian relations and their implications for the economics of farming. Taking Mahatwar village in eastern Uttar Pradesh as a case study, this article examines disparities across socio-economic classes in incomes and the costs of cultivation. We found substantial inequality, with landlord and big capitalist farmer households earning nearly 30 times the annual income of lower peasant and manual worker households. These disparities arise primarily from differences in costs: poor peasant and manual worker households bear a disproportionate rental burden, rely excessively on family labour, and use much of their produce for self-consumption. Our findings highlight the need for rent reduction and yield enhancement, along with support measures such as minimum support prices (MSPs), to provide meaningful incomes to low-income farmers. 2025, Tulika Books. All rights reserved. -
METROLOGICAL IMPACT ON ORANGE FRUIT: A COMPARATIVE ANALYSIS OF DEEP LEARNING MODELS FOR PREDICTING FRUIT DISEASES
This research offers a comprehensive comparative analysis of efficient deep learning models for predicting diseases in orange fruits, with a particular emphasis on the impact of meteorological factors on disease prevalence. Citrus diseases such as Blackspot, Canker, and Greening are significantly influenced by environmental conditions. Recognizing the crucial role of weather conditions in the development and spread of these diseases, we concentrate on enhancing prediction accuracy by integrating Convolutional Neural Networks (CNNs) with various classification algorithms to develop hybrid models that account for meteorological impacts. Specifically, we assess the performance of a CNN combined with Gradient Boosting (CNN-GB) and compare it against other hybrid models such as CNN integrated with Long Short-Term Memory networks (CNN-LSTM), Support Vector Machines (CNN-SVM), and Random Forest classifiers (CNN-Random Forest). These models are evaluated using different optimization algorithms to determine the most effective approach for disease prediction under varying meteorological conditions. A meticulously curated dataset comprising 1,600 training images and 300 testing images of orange fruits exhibiting a variety of disease symptoms was utilized for evaluation. The dataset reflects diverse environmental conditions to capture the meteorological impact on disease manifestation. All the models tested, the CNNGB hybrid model with NDAM optimizer exhibited superior performance (Accuracy 98.03) in comparison of other models like CNN (Accuracy 96.03), CNN+LSTM (Accuracy 96.16), CNN + SVM (Accuracy 97.13) and CNN + Random Forest (Accuracy 97.79). The exceptional performance of the CNN-GB model suggests that integrating CNNs with powerful classification algorithms like Gradient Boosting, along with considerations of meteorological data, can significantly enhance disease detection in crops. This advancement contributes to more proactive and effective disease management strategies, ultimately reducing economic losses and increasing productivity in the agricultural sector. 2025 Published by Faculty of Engineering. -
METROLOGICAL IMPACT ON ORANGE FRUIT: A COMPARATIVE ANALYSIS OF DEEP LEARNING MODELS FOR PREDICTING FRUIT DISEASES
This research offers a comprehensive comparative analysis of efficient deep learning models for predicting diseases in orange fruits, with a particular emphasis on the impact of meteorological factors on disease prevalence. Citrus diseases such as Blackspot, Canker, and Greening are significantly influenced by environmental conditions. Recognizing the crucial role of weather conditions in the development and spread of these diseases, we concentrate on enhancing prediction accuracy by integrating Convolutional Neural Networks (CNNs) with various classification algorithms to develop hybrid models that account for meteorological impacts. Specifically, we assess the performance of a CNN combined with Gradient Boosting (CNN-GB) and compare it against other hybrid models such as CNN integrated with Long Short-Term Memory networks (CNN-LSTM), Support Vector Machines (CNN-SVM), and Random Forest classifiers (CNN-Random Forest). These models are evaluated using different optimization algorithms to determine the most effective approach for disease prediction under varying meteorological conditions. A meticulously curated dataset comprising 1,600 training images and 300 testing images of orange fruits exhibiting a variety of disease symptoms was utilized for evaluation. The dataset reflects diverse environmental conditions to capture the meteorological impact on disease manifestation. All the models tested, the CNNGB hybrid model with NDAM optimizer exhibited superior performance (Accuracy 98.03) in comparison of other models like CNN (Accuracy 96.03), CNN+LSTM (Accuracy 96.16), CNN + SVM (Accuracy 97.13) and CNN + Random Forest (Accuracy 97.79). The exceptional performance of the CNN-GB model suggests that integrating CNNs with powerful classification algorithms like Gradient Boosting, along with considerations of meteorological data, can significantly enhance disease detection in crops. This advancement contributes to more proactive and effective disease management strategies, ultimately reducing economic losses and increasing productivity in the agricultural sector. 2025 Published by Faculty of Engineering. -
Experimentally optimizing a spinning disk by manipulating its mass distribution and radius
The scientific method enables the experimental study of complex phenomena by isolating key variables. This work explores the significant properties of spinning bodies. Optimizing spinning disks is the primary aim of this work. Optimization is achieved by manipulating the moment of inertia (MOI) of the disk, allowing a longer duration of spin and lowering the rate of energy dissipation. Experiments are designed and conducted to explore the relationship between the radius and mass distribution of the disk and the angular deceleration experienced by it. Effects of the same on energy retention is analyzed. Empirical data is interpreted graphically while accounting for systematic and random uncertainties. Percentage change in duration of spin as a result of percentage change in physical quantities is studied. Moving mass away from the central axis of the spinning disk increases its duration of spin from a constant initial angular velocity. Energy retention is also improved. Increasing the radius of the disk increases the duration of spin and reduces the rate of energy dissipation. The above conclusions are drawn from experiments where the mass and thickness of the disk are controlled along with other necessary factors that can influence the results. The experiments confirm the existing theory relating to the moment of inertia, angular quantities, resistive torques and kinetic energy of spinning disks. The experiments provide insights into the behavior of spinning disks in practical situations, especially in problems concerned with optimization in the field of mechanical engineering. 2026 Veeresha et al., published by Paradigm. -
The Macro Lens: Exploring the Impact of Macroeconomic Variables on Indias Small Cap, Mid Cap, and Large Cap Indices
Subject and Purpose of Work: This study explores the intricate relationship between key macroeconomic variables and Indias equity market segments, specifically the NIFTY Small-cap, Mid-cap, and Large-cap indices. The primary objective is to evaluate how selected macroeconomic factors influence market dynamics and investor sentiment in the Indian context. Materials and Methods: The research analyses monthly data spanning five years, from January 2019 to January 2024. The macroeconomic indicators considered include Foreign Institutional Investment (FII), Domestic Institutional Investment (DII), Consumer Price Index (CPI), Purchasing Managers Index (PMI), Treasury Bill Rate, Gold Price, and Reverse Repo Rate. Statistical techniques such as the Unit Root Test, Ordinary Least Squares (OLS), and Granger Causality Test are employed to assess the short-term and long-term impacts of these variables on market indices. Results: The findings reveal that GDP, CPI, PMI, and Gold Price exhibit no statistically significant influence on the NIFTY Small-cap, Mid-cap, or Large-cap indices, aligning with certain earlier studies. However, variables like FII, DII, Treasury Bill Rate, and Reverse Repo Rate show varying degrees of influence across the indices, highlighting the complex and segmented nature of the Indian equity market. Conclusion: These insights are valuable for investors, policymakers, and financial analysts in refining investment strategies, informing policy frameworks, and enhancing market forecasting models. The study underscores the need for continuous evaluation of macroeconomic influences to better navigate market volatility and investor behaviour. 2025 Sathish Pachiyappan et al., published by John Paul II University of Applied Sciences. -
Green Growth Nexus: Analysing Environmental Performance and GDP Trends in OECD Economies
Subject and Purpose of the Work: Over the past two decades, OECD countries such as Italy, Germany, France, and the United Kingdom have experienced consistent economic growth, averaging 2% annually in GDP. This upward trend has been driven by various factors, including government spending, investment rates, and favourable global conditions. Recently, environmental performance has emerged as a critical factor influencing economic development. This study aims to examine the relationship between environmental performance indicators and GDP growth in selected OECD countries, focusing on the growing emphasis on environmental sustainability. Materials and Methods: The analysis uses panel data from the OECD and World Bank, spanning 25 years (20002024), for four OECD nations. The study employs a Panel Autoregressive Distributed Lag (ARDL) model, which allows for the estimation of both short-run and long-run dynamics. GDP growth is the dependent variable, while the independent variables include environmental tax revenue (TAX), greenhouse gas emissions (EMI), air quality (QUA), government expenditure on environmental protection (EXP), and the share of renewable energy in total energy supply (REN). Results: The empirical findings indicate that TAX and EXP have minimal positive impact on GDP growth, suggesting potential inefficiencies in the allocation or effectiveness of environmental funds. In contrast, other indicators such as air quality and renewable energy share show a stronger link with economic growth. Conclusion: The study highlights the growing significance of environmental performance in shaping economic outcomes. It contributes to the sustainable development literature by demonstrating that targeted environmental efforts can positively influence long-term economic growth. 2025 K Keerthana et al., published by John Paul II University of Applied Sciences. -
NET ZERO TRANSITION TOWARDS DECARBONIZATION IN CONTEXT OF ENERGY SECTOR
The study provides an identification and analysis of potential enablers that facilitate transition towards net zero in the energy sector through Multi Criteria Decision-Making (MCDM) framework. The identified enablers and causal relationships between them in terms of decarbonization initiatives are studied using the DEMATEL method and combining trapezoidal fuzzy numbers (TFNs). The research design involves an overarching review of thirteen potential enablers to net zero transition within the energy sector, in order of their impact and causality. Top-ranked enablers that would have the greatest impact in achieving the energy transition were carbon pricing mechanisms, waste-to-energy conversion, decentralized energy systems and circular procurement policies. The research indicates that the enablers show causal pathways that are interconnected and can take place as both causes and effects in the decarbonization framework. Application of DEMATEL method using TFNs increases the strength of causal relationship derivation. The study adds to the literature on enabling net zero transition in energy and highlights the importance of a conceptual approach involving a combination of policy, technology and principles of the circular economy. Such lessons can guide policy makers, industry players and academics in planning and speeding up the process to sustainable energy systems and world climate targets. 2026 Sciendo. All rights reserved. -
Does Green Financing affect the Sustainable Economic Growth of Emerging Economies? Evidence from Panel ARDL Model
This study examines the nexus between green finance determinants and sustainable economic growth in Brazil, India, China, and South Africa using a panel Autoregressive Distributed Lag (ARDL) approach. These rapidly developing countries face the dual challenge of maintaining economic growth while addressing environmental sustainability. The analysis focuses on five key independent variables: Comparative Advantage in Low Carbon Technology Products, Total Trade in Low Carbon Technology Products, Trade Balance in Low Carbon Technology Products, Annual CO2 Emissions, and Lack of Coping Capacity. Short-run results indicate that Total Trade in Low Carbon Technology Products negatively affects GDP, suggesting that while green trade is expanding, it currently lacks stable, revenue-generating mechanisms. Annual CO2 Emissions and Lack of Coping Capacity positively influence GDP in the short term, reflecting continued dependence on emission-intensive industries and limited infrastructure for resilience. Comparative Advantage and Trade Balance in Low Carbon Technology Products are statistically insignificant in the short run, implying delayed economic benefits. In the long run, none of the green finance indicators show a significant relationship with GDP, possibly due to the substantial upfront investments required for green projects, which delay economic returns. The study underscores the need for strategic investments in technology, infrastructure, and governance to align economic growth with long-term sustainability goals. 2025 Sathish Pachiyappan et al., published by Oikos Institut d.o.o. -
REGRESSION WITH VOLATILE ERRORS IN THE PRESENCE OF MEASUREMENT ERRORS
This study explores the estimation and testing of regression models with volatile errors when measurement errors are present. The presence of measurement error in models with heteroscedastic disturbances, such as those following an autoregressive conditional heteroscedasticity (ARCH) or Generalized ARCH (GARCH) structure, can lead to biased estimates and misleading inferences. To address this, we develop an estimation framework that accounts for both heteroscedasticity and mismeasured observations, ensuring consistent and asymptotically normal parameter estimates. We estimate the parameters using corrected score estimation and weighted linear regression, which effectively mitigate the impact of measurement error and hetroscedasticity. Additionally, we perform a Likelihood Ratio (LR) test to assess the significance of measurement errors in regression models with volatile errors. Through Monte Carlo simulations, we analyze the bias and efficiency of traditional estimators and demonstrate the robustness of our proposed approach. Finally, the methodology is applied to real-life economic and financial data, illustrating its practical relevance and effectiveness in empirical research. The findings contribute to improving statistical inference in models where measurement error and volatility coexist, ensuring more reliable and accurate parameter estimation. 2025, Gnedenko Forum. All rights reserved. -
MULTI REFERENCE SKIP-LOT SAMPLING PLAN
Skip-lot sampling plans have become significant in modern quality control due to rising production volumes and the demand for cost-effective inspection methods that will yield high-quality outputs. When inspecting a submitted lot, a skip-lot plan is economically favourable and guarantees high quality. Thus, this approach benefits both producers and consumers. The skip-lot sampling plan generally utilizes the same sampling plan as the reference plans for both skipping and normal inspection. However, using the same plan in both phase favours either the producer or the consumer in the most essential situations. This article introduces a novel approach, the Multi Reference Skip-Lot Sampling Plan with the provision of having two different reference plans in the normal and skipping phases of the skip-lot plan. The paper explores the efficacy of this approach by deriving performance measures using a power series approach. To evaluate the proposed plan, a comparison is made with existing skip-lot sampling plans that use single sampling plans or double sampling plans as reference plans. This comparison is based on operational characteristics and average sample number values, accompanied by graphical representations. The comparative analysis demonstrates that the new plan effectively balances the satisfaction of both producers and consumers. Additionally, the study offers a strategy for selecting the plan parameters using the unity value approach, supported by a table providing unity values. 2025, Gnedenko Forum. All rights reserved. -
A SIGNIFICANT STUDY ON ROBUST MEASURE OF LOCATION PARAMETERS USING DATA DEPTH APPROACHES
Data depth procedures are statistical methods used to measure the centrality or depth of a point within a multivariate dataset. These procedures provide a way to quantify how deep or outlying a point is relative to the overall distribution of the data. This study explores various data depth procedures to find reliable location estimations in cases like with and without outliers. In this paper, various depth procedures, such as Mahalanobis depth, Halfspace depth, Euclidean depth, Simplicial depth, and Projection depth, are studied and compared. The efficiency of these depth functions is evaluated using real datasets and simulation studies with R software. 2025, Gnedenko Forum. All rights reserved. -
BAYESIAN SPATIAL TEMPORAL TREND ANALYSIS FOR DECISION MAKING AND RISK ASSESSMENT IN DENGUE INCIDENCE STUDIES: A CASE OF TAMILNADU
This study presents a Bayesian spatial-temporal analysis for studying Dengue incidence in Tamil Nadu, aiming to provide insights into decision-making and risk assessment strategies. Statistical models that allow a more accurate depiction of true disease rates by borrowing information from neighboring regions will help mitigate the effects of sparsely populated regions and deliver better inference. Perhaps the most conspicuous manner of modeling spatial dependence is to introduce spatially associated random effects within a Bayesian hierarchical setting. The Bayesian modeling and inferential framework are flexible and extremely rich in its capabilities to accumulate various scientific hypotheses and assumptions. The spatial and spatial temporal epidemiology is concerned with the description and analysis of spatial and spatial temporal variations in disease risk with respect to risk factors. As the primary aim of this work is to quantify the spatial disease pattern of dengue incidences apart from the mapping of disease modelling the disease and finding spatial clusters/hotpots is one important aspect in epidemiology is to find the temporal trends in or outside of clusters. In this study, a spatial-temporal trends model is fitted using the Leroux CAR priors set up for studying the spatial-temporal disease patterns with the estimation of the temporal trends with reference to dengue incidences in Tamil Nadu, India. 2025, Gnedenko Forum. All rights reserved. -
Decision-Making Models for Efficient Outbreak Response: A Management-Orientated Approach to Dengue Control in Andhra Pradesh, India
Dengue remains a serious health challenge across India, and Andhra Pradesh faces repeated outbreaks that put a heavy strain on hospitals, clinics, and communities. Combating this disease isnt just about tracking casesits about making quick, smart decisions to control its spread effectively. This study looks into different decision-making approaches that can help improve how Andhra Pradesh responds to dengue outbreaks, making actions faster and more targeted. Using a mix of existing epidemiological data, interviews with health officials and community leaders, and simulated scenarios, the research explores how tools like Multi-Criteria Decision Analysis (MCDA), the Analytic Hierarchy Process (AHP), and Decision Tree Analysis can assist in choosing the best strategies. These models help prioritise interventions such as resource distribution, vector control efforts, and public awareness campaigns, especially when dealing with uncertainties like limited resources or unpredictable case surges. The findings indicate that integrating these decision-making frameworks into public health planning can foster better coordination among policymakers, healthcare workers, and local authorities. This improved coordination can lead to quicker responses, more effective use of resources, and ultimately, a reduction in dengue cases and their impact on communities. The study emphasises that combining management science tools with traditional epidemiology isnt just helpfulits essential for strengthening outbreak preparedness. Plus, these approaches can be adapted to tackle other communicable diseases in India and similar settings worldwide, paving the way for smarter, more resilient public health systems. 2025, Indian Society for Malaria and Communicable Diseases. All rights reserved. -
PRODUCTIVITY LOSS LINKED TO NON-COMMUNICALE DISEASES ACROSS SOCIO-DEMOGRAPHIC PROFILES: EVIDENCE FROM SEDENTARY OCCUPATION EMPLOYEES DURING COVID-19
BACKGROUND: The COVID-19 pandemic has significantly transformed work dynamics, leading to a notable shift towards remote work, particularly for those in sedentary roles. This change has been linked to a heightened risk of Non-Communicable Diseases (NCDs), many of which stem from lifestyle-related factors. Such health challenges can adversely affect productivity in the workplace, causing both absenteeism and presenteeism. AIM: This study examines the costs of presenteeism and absenteeism related to non-communicable diseases (NCDs) across socio-demographic variables. METHODS: Using stratified and purposive sampling, a cross-sectional study was conducted with 426 employees in sedentary occupations in the Delhi-NCR region. Productivity losses from presenteeism and absenteeism were assessed using the WHO HPQ Questionnaire. Additionally, the General Linear Model (GLM) was utilised to analyse the relationship between loss productive time (LPT) costs associated with presenteeism and absenteeism across disease categories and socio-demographic factors. RESULTS: Employees diagnosed with 'NCDs Category I', 'NCDs Category II', and those with 'comorbid' conditions were estimated to lose between 40 and 48 workdays each year. Absenteeism accounts for a greater portion of productivity losses than presenteeism in all disease categories. Comorbidities contribute to the most significant losses, with costs surpassing those associated with CDs by INR 51.78 thousand (932.04 AUD) for presenteeism and INR 226.47 thousand (4,076.46 AUD) for absenteeism. Additionally, every extra year of education corresponds to an increase of INR 4.96 thousand (89.28 AUD) in costs related to LPT due to presenteeism and a reduction of INR 15.68 thousand (282.24 AUD) in absenteeism-related LPT costs. CONCLUSION: The research indicates that NCDs, particularly in the presence of comorbid conditions, have a substantial effect on workplace productivity. Notably, individuals with higher levels of education and Income exhibit elevated presenteeism costs, which may be attributed to the influence of remote work arrangements. Conversely, absenteeism rates appear to be lower among highly educated employees in similar settings. 2026, Australasian College of Health Service Management. All rights reserved. -
Research Advances on Foreign Portfolio Investments: A Bibliometric and Thematic Analysis
[No abstract available] -
Optimal Reactive Power Compensation in Indian Urban Electrical Distribution Networks Using Hybrid Starfish Optimization Algorithm
This paper presents an efficient hybrid optimization approach for optimal reactive power compensation (ORPC) problem in electrical distribution networks (EDNs) using a Hybrid Starfish Optimization Algorithm (HSFOA). A Voltage Stability Index (VSI) is integrated to identify critical buses and narrow the search space, improving solution quality and convergence efficiency. The proposed method determines the optimal locations and sizes of capacitor banks (CBs) and Distribution static synchronous compensators (DSTATCOMs) to minimize real power losses and enhance voltage stability. The effectiveness of the HSFOA is evaluated first on the IEEE 33-bus benchmark system. The results demonstrate that the proposed approach provides superior improvements compared to conventional techniques. Later, the approach is implemented on 106-bus and 85-bus real-time Indian urban distribution networks. For the 106-bus system, losses decrease from 644.768 kW (base case) to 495.273 kW with CBs and to 487.933 kW with DSTATCOMs, corresponding to 23.25% and 24.32% reductions. In the 85-bus system, real power losses are reduced by 34.56% with CBs and 34.44% with DSTATCOMs, while the VSI improves by 15.05% and 20.70%, respectively. Similar improvements were recorded for the IEEE 33-bus system. Overall, the findings confirm that HSFOA offers a robust and effective solution for optimal reactive power planning and enhanced operational performance in modern EDNs. This article is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. https://creativecommons.org/licenses/by-sa/4.0/