Wipro (India)

BackgroundGender concordance (GC) between patients and physicians has been linked to trust and satisfaction in traditional health care. However, its role in telemedicine, especially in culturally complex settings like India, is underexplored. In India’s culturally diverse and gender-sensitive context, understanding GC becomes particularly relevant for specialties such as gynecology, dermatology, psychiatry, and urology, where discussions often involve intimate or stigmatized concerns. Despite rapid telemedicine expansion, little empirical evidence exists on whether GC affects patient-reported outcomes in this context.ObjectiveThis study examined whether GC significantly influences patient satisfaction and self-reported recovery in teleconsultations across India, with a focus on specialty-specific effects in culturally sensitive specialties.MethodsWe conducted a retrospective cross-sectional analysis of 286,196 anonymized teleconsultation records from a national telemedicine platform (January 2023–December 2024) spanning across 20 medical specialties using binary logistic regression. Records missing gender or satisfaction data were excluded from the analysis; recovery analyses included only consultations with completed day-21 follow-up surveys (n=1170, 0.4%). Outcomes included patient satisfaction (ratings 4‐5 on a five-point scale) and self-reported recovery at follow-up. Logistic regression models (Stata 17.0) tested associations between GC and outcomes, controlling for consultation time, duration, and physician experience. Subgroup analyses were conducted for the top 5 specialties. Each record included key data on consultation duration, timing, physician experience, specialty type, patient satisfaction rating, and self-reported recovery status. The study excluded the pediatrics specialty from the analysis to control for the parental bias.ResultsOf the 286,196 consultations, 164,008 (60.4%) were gender-concordant. Overall, 261,213 of 286,196 (91.3%) patients reported good satisfaction. GC had a statistically significant negative association with patient satisfaction (odds ratio [OR] 0.87, 95% CI 0.85‐0.90; P<.001). Across gender, the male doctor received higher satisfaction. In gynecology, female patient–female doctor pairs had significantly higher odds of reporting recovery (OR 4.53, 95% CI 0.8‐25.3; P=.099). Overall, consultation timing (OR 0.99, 95% CI 0.998‐0.999; P<.001) and patient satisfaction (OR 20.13, 95% CI 12.06‐35.38; P<.001) were stronger predictors of self-reported recovery than GC.ConclusionsGC in telemedicine has a context-dependent impact. While it does not independently predict clinical recovery, it meaningfully shapes patient satisfaction. These findings highlight that gender sensitivity training and context-specific communication approaches may enhance telemedicine experiences in culturally sensitive domains. Integrating awareness of gender dynamics into telehealth design and policy could strengthen patient trust and engagement in virtual care. Future research should explore specialty-specific dynamics and improve follow-up response rates to better assess clinical outcomes.

Inflammation is a natural immune response triggered by multiple factors such as pathogens, damaged cells, and toxic substances. These triggers can lead to both acute and chronic inflammatory reactions in different tissues, contributing to the development of several inflammatory disorders, including cardiovascular diseases, neuroinflammation, arthritis, and cancer. Both infectious and non-infectious stimuli activate immune cells and initiate critical inflammatory signaling pathways.Raspberries (Rubus idaeus) are abundant in bioactive constituents, especially polyphenols like anthocyanins, flavanols, phenolic acids, urolithin A, and ellagic acid, all of which possess notable anti-inflammatory and anti-oxidant activities. These compounds have been shown to regulate various inflammatory signaling pathways, including MAPKs, NF-κB, PI3K/Akt, AP-1, IL-6, TNF-α, IL-1β, CD40, nitric oxide (NO), caspases, and the JAK-STAT pathway. Studies have emphasized their broad pharmacological effects, such as anti-inflammatory, anti-oxidant, hepatoprotective, cardioprotective, gastroprotective, anti-obesity, skin depigmenting, and bone-regenerative properties. This review emphasizes mechanistic insights into raspberries’ protective roles in managing inflammatory-related disorders, particularly cardiovascular diseases, neurodegenerative conditions, and cancer, and highlights their therapeutic potential.

Retrieval-Augmented Generation (RAG) systems signify a pivotal advancement in natural language processing, merging information retrieval with large language models (LLMs) to ground responses in external knowledge. This hybrid approach enhances the factual accuracy and currency of generated content, mitigating common issues like hallucination. The efficacy of a RAG system, however, is fundamentally dependent on the performance of its retrieval component. This paper provides a detailed analysis of precision and recall as critical metrics for evaluating and optimizing this retrieval step. We explore the distinct roles and inherent trade-offs of these metrics within a RAG pipeline, demonstrating their direct influence on the quality of the final output. Through a series of experiments comparing sparse (BM25), dense (DPR), and hybrid retrieval methods, we quantify their performance characteristics. The analysis is further enriched with real-world examples from finance, law, and healthcare, illustrating the practical implications of retrieval quality. Additionally, we outline advanced strategies for improving retrieval effectiveness, such as multi-stage architecture involving rerankers and the use of query transformations. The paper concludes with a set of best practices for deploying robust, enterprise-grade RAG systems, emphasizing the need for continuous evaluation and sophisticated retrieval strategies. By focusing on the systematic optimization of precision and recall, organizations can build more reliable and trustworthy AI applications.

ABSTRACT Understanding customers of female hygiene products remains a challenge despite the market’s size and social importance, due to taboos and limited reliable data. This study proposes an alternative approach using retail scanner data and latent class segmentation based on Gaussian Mixture Models (GMM). Analyzing 260 behavioral metrics from 2497 customers, 582 stores, and over 276,000 transactions, we identified four distinct consumer segments with 28 significantly different behavioral characteristics. To profile these segments, we employed machine-learning-based classification models, revealing clear demographic patterns and the influence of female life stages on purchasing behavior. Additionally, we calculated the Customer Lifetime Value (CLV) for each segment, uncovering variations in segment profitability. Our approach offers a scalable, behavior-driven method to segment customers without relying on intrusive personal data. This enables marketers to design hyper-personalized experiences and more inclusive targeting strategies for both active and latent consumers of feminine hygiene products. The findings also highlight untapped consumer opportunities beyond the traditionally targeted coming-of-age segment, encouraging marketers to adopt nuanced, data-informed engagement strategies that promote both profitability and social inclusivity.

Nowadays, metal cutting industries are in search of a process which can significantly reduce machining time. In this context, a possibility was attempted to increase the Material Removal Rate (MRR), (mm3 /sec) by varying three independent input parameters. The present experimental study is centred on determination of the optimal cutting parameters for turning Aluminium Alloy6082, a medium-strength alloy known for its outstanding corrosion resistance. Three input parameters of Spindle speed (mm/min), Feed rate (mm/min) and Depth of Cut (mm) of three levels each was used. Experiment was performed on CNC Lathe using a Carbide tipped high speed steel tool. The experimental design plan utilized the Taguchi L27 orthogonal array to conduct the experiments. Reduction in Variance was accomplished by optimizing the Signal to Noise ratio (S/N ratio) in Minitab software. Based on confirmation tests, it was observed that Taguchi analysis identified the optimal process parameters leading to a notable enhancement in turning performance during machining. From Analysis of Variance (ANOVA), it was inferred that MRR was appreciably influenced by Depth of Cut. Furthermore, Multivariate Polynomial Regression models were developed in MATLAB software to predict the MRR value.

The fast growth of QR cases into everyday digital workflows-spanning commerce, healthcare, logistics/shipping and checking identity verification-has introduced critical challenges around data security, authenticity, and traceability. This study suggests a comprehensive Secure and Trackable QR Code System that immerseAES-256 encrypted metadata into QR codes, enabling/authorize secure communication; tough access control, and end-to and event tracking. The system creates automatically QR codes based on various user-defined information including URLS, Wi-Fi credentials, v lards, location links, and static text. Each information is securely encrypted and tagged with the device’s MAC address and real-time geological coordinates, thereby connecting each code to a unique physical and network identity. The generated QR images employ an enhanced rounded corner drawing style fare improved scanning credibility and visual quality, and are stored as read-only PNG files to prevent unauthorized modification. On scanning, the system performs a flawless decryption process, extracting and displaying the original information while concurrently recording each event-including Scan time, device MAC, and geolocation-into a secure relational database. This ensures full transparency and tracking across Code usage cycles. The architecture is designed for scalability, offering low-latency processing and high encryption throughput suitable for implementing in real-world environments. Verifiable validation highlights the system’s adaptability against fake attacks, unauthorised access, and information/play. - load tampering, confirming its potential for use in smart infrastructure, digital governance, supply chain verification and health informatics The proposed framework provides a ascendable foundation for building trust-centred, metadata-rich QR Code applications in high assurance domains.

The accelerated development of the digital economy requires flexible manufacturing systems that can optimize resource planning and decision-making efficiently. Unfortunately, conventional methods are not only inefficient in their scalability but also in making dynamic adjustments in real time. An integrated system introduced in this study employs Holonic Systems (HS) and Latent Dirichlet Allocation (LDA) to enhance manufacturing flexibility, strategic knowledge, and resource handling. The methodology uses holonic architectures for self-organization in production and LDA for topic modeling to support data-driven optimizations. Empirical analysis, by way of mathematical and computational frameworks, measures the performance of systems in terms of accuracy, precision, recall, F1-score, and efficiency in resource management (RME). The outcome shows a high improvement with the RME down to 4.2%, accuracy of 95.6%, and an improved decision-making ability. The research concludes that holonic systems enabled by LDA enhance industrial adaptability and operational efficiency, delivering a sound platform for sustainable digital manufacturing. Future research must further investigate reinforcement learning to enhance predictive decision-making and industrial scalability.