SALES FORECASTING EFFECT ON PHARMACIES

Nowadays, as technology is advancing to previously unheard-of levels, every company and organization is finding it difficult to balance inventory and customer expectations. Every organization relies heavily on sales, and being able to predict the future helps in making strategic and intelligent sales decisions. The majority of businesses still struggle with revenue forecasting because it is usually the first step in developing the company\u27s annual budget. Over time, a company\u27s estimation could suffer if its sales projections are consistently inaccurate. Sales forecasting therefore affects the entire company to improve their overall growth strategy. An essential part of any business\u27s sales operations is sale forecasting.  For a business to supply the necessary quantity at the appropriate time, an accurate sales forecast is essential. Executives use the predictions to assess future performance and plan for organizational expansion. In this study, we use the machine learning techniques of naive forecasting and linear regression to try and predict a retail company\u27s sales. The difference between the linear regression and naïve forecasting approaches is demonstrated using a computational example, and we have found that the linear regression yields better results than the naïve forecasting approaches. Additionally, we used the ARIMA model for the linear regression approach to forecast the sales for the upcoming five days.

Bibliometric Review of Financial Risk Tolerance: A Scientometric Analysis using Biblioshiny

This study provides a bibliometric analysis of financial risk tolerance research papers. This study addressed bibliometric analysis using information from Scopus. The study in this work is exploratory and descriptive, based on bibliometric methodologies and tools used to documents contained in the Scopus bibliographic database, which yielded a total of 1084 documents spanning the years 1979 to 2025. This study report made use of Biblioshiny, an open-source platform based on the R programming language, as well as VOS viewer, in order to carry out a variety of bibliometric studies and assessments. The topic of financial risk tolerance was examined using the bibliographic data, which provided a complete description of the subject matter. According to the findings of the analysis, the most significant areas of focus for research on risk tolerance over the past several years have been on financial literacy, personality factors, and financial decision making. Over the past two decades, there has been an upward trend in the amount of research done on the FRT.

Strategic Inventory Control for Deteriorating Products under Time-Sensitive Demand

Efficient management of inventory is essential for businesses handling perishable or time-sensitive products. This study delves into advanced inventory models incorporating time-varying demand, pricing dynamics, and product deterioration. Drawing on a comprehensive review of research conducted by A. Sharma and collaborators between 2016 and 2024, the article presents strategic approaches to optimize inventory levels, reduce operational costs, and enhance overall supply chain performance. It emphasizes the role of dynamic pricing mechanisms, partial backordering, and accurate demand forecasting in developing responsive inventory control systems. The findings offer valuable insights for retailers, manufacturers, and logistics professionals while outlining promising avenues for future research in this evolving domain

Efficient Test Case Prioritization in Software Testing Using DistilRoBERTa for Fault Detection Optimization

The very critical phase in SDLC is software testing, where application reliability, security, and efficiency are ensured. However, increasing complexity in software has made traditional test case prioritization (TCP) methods difficult, with regards to high execution time and computational overhead. The existing approaches such as Genetic Algorithms (GA) are highly computationally expensive, and the adaptability of test cases with new evolvement cannot really be integrated with the processes. This study proposes an artificial intelligence-based approach with DistilRoBERTa for test case prioritization to improve fault detection and optimized test execution. Unlike traditional methods, DistilRoBERTa uses deep learning to analyze semantic and historical defect data of the test cases to intelligently prioritize. The proposed method achieves 93% test case coverage (against 90% in GA), 90% execution efficiency (against 85%), and 96% reliability (against 95%) while significantly reducing computational overhead to 53% (against 70%). All these aspects, therefore, make the results much more scalable, efficient, and adaptable as compared to software testing. An edge over competitive heuristic-based TCP methods is that the proposed model offers faster execution coupled with minimal resource consumption—the perfect environment for extensive testing. Management of test cases proves to be one of the important tasks since there are a large number of test cases in software. This paper develops an automated Intelligent test case prioritization process. A centralized intellectual resource is established through the complete understanding of test cases, their interdependencies, requirement analysis, defect analysis, and processing of information for prioritization. The application of the resulting development would open new horizons to the evolution of intelligent testing

Optimizing Inventory Management: Strategies for Deteriorating Items with Time-Based Demand

Effective inventory management is crucial for businesses dealing with perishable or deteriorating goods. This research article examines various inventory models that account for time-dependent demand, price fluctuations, and product deterioration. By synthesizing findings from multiple studies by Sharma and colleagues (2015–2024), this paper explores key strategies for optimizing stock levels, minimizing costs, and improving supply chain efficiency. The analysis highlights the importance of dynamic pricing, fractional backlogging, and demand forecasting in inventory control systems. Practical implications for retailers, manufacturers, and logistics managers are discussed, along with future research directions.

Real-Time Path Planning for IoT-Enabled Autonomous Vehicle Robotics Using RRT and A * Algorithms

One of the main purposes of this work is to provide a path planning framework for IoT-enabled autonomous vehicles through the use of RRTs and A*. These were designed to maximize actual real-time navigation and decision-making in very dynamic and complex situations considering obstacles and uncertainties in the environment. In cases that have unknown or nonregular barriers, the RRT algorithm is employed to visualize the environment rapidly to derive an initial feasible path across the configuration space. Following the developments of RRT paths, the A algorithm* will address topics brought about by their construction in order for the route to be smooth, efficient, and have the shortest length. A synergism between the two techniques makes these systems adapt in real time to changes in the environment and in transportation conditions while preserving computational economy. From the performance evaluation, joining the strategy increases these very important parameters, such as the energy consumption, path length, and the time to reach the destination, by a huge percentage. The model consumes energy that is reduced by about 23% in comparison with conventional approaches, decreases path length by 12-15% and decreases time to objective up to 50%.  These results indicate that the RRT + A* model works very well to enhance the effectiveness and efficiency of autonomous vehicle navigation in changing conditions.  This framework can be used in applications like robotics and autonomous driving, and it represents a viable answer for real-time energy-efficient optimal path planning

Understanding North-East Woman’s Participation in State Governance- A Power Dynamics

The northeastern states of India, popularly known as the Seven Sisters of India (Assam, Manipur, Meghalaya, Nagaland, Tripura, Mizoram, and Arunachal Pradesh), along with their brother state Sikkim, stand out with their unique geographical location and are home to several multi-ethnic communities. The region has witnessed several ethnic uprisings following the complexities of autonomy-related movements, the integration process, and the share in the natural resources. Needless to say, with the kind of societal structure prevailing in this region, women’s participation has been a major factor in ethnic movements in the past and even in the present. Indubitably, their participation in the societal and civil sphere of life is impressive. However, their involvement and presence in the larger political scenario, such as state institutions, are hardly felt. The central inquiry of the paper is to analyse the role of women in ethno-political conflicts and parallelly examine the power-sharing dynamics of women in North-Eastern regions. Methodologically, this research involves a comprehensive study of documents from the Election Commission of India, focusing mainly on the percentage of women candidates in the northeast contesting in Lok Sabha elections from the time period of 2014 to 2024. Through the analysis of this data, the research will try to underscore the reason for the invisibility of women\u27s role in larger power sharing where there is paradoxically huge participation of women in ethno-political issues

Creative Accounting and Accountability Failures in the Philippine Health Sector: A Case Study of PhilHealth during COVID-19

This study investigates creative accounting practices in the Philippine Health Insurance Corporation (PhilHealth), focusing on financial manipulation and accountability breakdowns during the COVID-19 pandemic. The current paper uses a qualitative case study and document analysis of government audit reports, legislative inquiries, and media investigations (2018–2021) to reveal how procurement irregularities, unliquidated cash advances, and disguised operating losses distorted the agency’s financial position.  These practices, facilitated by weak internal controls and governance failures, undermined transparency and jeopardized the implementation of universal health care. Anchored in public sector accountability and creative accounting theories, the study concludes with actionable recommendations, including the institutionalization of forensic audits, digital transparency reforms, and stronger whistleblower protections. The findings underscore systemic weaknesses that enabled financial misreporting and highlight the pressing necessity for structural reforms in public financial management

India-UK Relations Roadmap 2030 And Beyond: A Comprehensive Strategic Vision

The India-UK Roadmap 2030 outlines a comprehensive strategic vision for deepening bilateral ties between India and the United Kingdom across key sectors, reinforcing both nations’ commitment to economic growth, sustainability, and global leadership.  As the global geopolitical and economic landscape undergoes rapid transformation—shaped by the UK\u27s post-Brexit realignments and India’s emergence as a key global actor—the roadmap serves as a forward-looking framework to deepen collaboration. The paper critically examines the five key pillars of the roadmap: economic cooperation, with a focus on the much-anticipated India-UK Free Trade Agreement (FTA); defense and security, including intelligence-sharing and Indo-Pacific cooperation; health and innovation, emphasizing joint research, pandemic preparedness and digital healthcare;  climate action, highlighting commitments under COP26 and joint green energy initiatives; and people-to-people ties, driven by diaspora contributions, education partnerships, and evolving immigration policies. Additionally, the paper explores the challenges that may hinder progress, such as unresolved trade barriers, geopolitical divergences, security trust deficits, post-Brexit uncertainties, and strategic misalignments in the Indo-Pacific. Beyond 2030, India-UK relations will need strategic recalibration to adapt to emerging global trends. The paper examines possible future scenarios—ranging from strengthened economic integration to potential geopolitical divergences—and outlines strategic pathways to institutionalize cooperation in an evolving multipolar world. The significance of this study lies in its comprehensive analysis of how India and the UK can navigate these opportunities and challenges to create a sustainable, forward-thinking and adaptive partnership beyond 2030. It aims to provide strategic insights into how the roadmap can serve as a living document, evolving with emerging global realities, and evolve beyond 2030, ensuring that bilateral ties remain dynamic, resilient, and mutually beneficial

Assessing Stakeholder Awareness and Perception of NEP-2020: A Study on Policy Implementation and challenges

The National Education Policy (NEP) 2020 aims to reform India\u27s education system by introducing progressive changes. However, its success depends on stakeholder awareness and acceptance. This study investigates parental awareness and perception of NEP-2020, given that parents play a crucial role in their children\u27s education. Using a quantitative approach, data was collected from 300 parents through a structured survey. The findings reveal a low level of awareness and mixed perceptions regarding the policy. The study suggests targeted awareness programs and parental engagement initiatives for effective policy implementation