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.

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.

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

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

A Fuzzy Logic-Based Decision Support System for Early Detection of COVID-19: A Review and Comparative Analysis

The global COVID-19 pandemic, resulting from the infection by the SARS-CoV-2 virus, is emphasizing the urgent need for rapid and accurate diagnostic methods for the control of the infection spread. Lab-based testing methods can take time, leading to diagnostic delays and a high risk of transmission. Fuzzy Logic-Based Expert System in Early Detection of COVID-19 Symptoms and Risk Assessment in Real-Time. We provide a systematic review of published fuzzy logic models for the COVID-19 diagnosis along with details of their methodologies, accuracy, and clinical usability. Results support that fuzzy logic systems improve diagnostic efficiency, lessen healthcare pathways, and enable decision-making in a timely manner

Exploring Teachers\u27 Perceptions of Parent-Teacher Partnership to Support Learners with Intellectual Disabilities in Primary Schools

The study explored teachers’ perceptions of parent-teacher partnerships for enhanced access to equitable education for learners with intellectual disabilities in mainstream primary schools. A qualitative case study was utilised to collect data from 8 teachers who taught in mainstream primary schools. Data was sourced through face-to-face semi-structured interviews. The findings reveal that while various challenges hamper effective partnerships between parents and teachers in mainstream primary schools, a certain level of teamwork exists between parents and teachers, with more opportunities – yet to be fully explored – to strengthen this partnership further. Hence, a need to harness various strategies to improve parent-teacher relationships to create a more functional environment that meets the needs of learners with intellectual disabilities

High- and Low-Frequency TENS with Paravertebral Ozone Therapy for Chronic Low Back Pain and Radiculopathy: A Pilot Study

Background: Chronic low back pain (CLBP) with radiculopathy is a leading cause of disability worldwide. Conventional treatments generally have limited efficacy, high costs, and associated adverse effects. Objective: To evaluate the efficacy and safety of high-and low-frequency TENS (HLF-TENS) with paravertebral ozone therapy (POT)in chronic low back pain (CLBP) and radiculopathy. Methods: This non-randomized pilot study was conducted with 88 consecutive patients meeting the inclusion criteria. Participants underwent 12 sessions of combined HLF-TENS and POT, without a control group. Pain intensity, neuropathic symptoms, and disability were assessed using the Numeric Rating Scale (NRS); the modified Michigan Neuropathy Screening Instrument (MNSI), and the modified Oswestry Disability Index (ODI), respectively. Assessments were performed at baseline, midpoint, and post-treatment, with long-term follow-up (1–2 years) via telephone interviews. Results: The study included 35 men (40%) and 53 women (60%), mean age of 51.3 years (range 20–80). Post-treatment, 80% of participants showed statistically significant improvements in pain, neuropathic symptoms, and disability. Pain NRS scores decreased from 8.58 to 1.64, neuropathic symptom scores from 7.34 to 1.14, and ODI scores from 8.05 to 2.12 (p< 0.001). At long-term follow-up (1–2 years), 80% of respondents remained asymptomatic, with no adverse effects reported. Conclusions: The combination of HLF-TENS and POT significantly reduced pain, neuropathic symptoms, and disability in CLBP patients with radiculopathy. These findings suggest a promising, non-invasive treatment option; however, randomized controlled trials are necessary to confirm efficacy