Volume 13 | Issue 4

 Abstract

India and China's relationship in the 21st century is marked by a constant interplay of rivalry and collaboration. While traditional foreign policy assessments are dominated by economic and military considerations, both countries' diplomatic tactics have been increasingly influenced by soft power. This article examines how India and China have used tools of soft power such as cultural diplomacy, media influence, educational exchanges, and economic interactions. The piece examines the similarities and differences in their approaches through comparative analysis, as well as the effectiveness of these policies in regional and global contexts, and the potential impact of post-pandemic realignments on future relations. In the end, it concludes that soft power is not a cure-all for entrenched conflicts but may create links in a divided geopolitical environment.

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India-China Relations, Soft Power, Cultural Diplomacy, Strategic Communication, Media Influence, Foreign Policy, Comparative Analysis, Regional Influence, Post-pandemic Realignment

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Urinary tract infection is a common bacterial infection known to affect the different parts of urinary tract and the occurrence is found in both males and females. Despite the fact, that both the genders are susceptible to infection, women are mostly vulnerably due to their anatomy and reproductive physiology. The infection is usually caused as a consequence of bacterial invasion of urinary tract including lower and upper urinary tract. Among the bacterial species Esherichia coli account to 80% to 85% of infection. A variety of parameters are related to urinary tract infection which include age, parity, gravidity, pregnancy and association of diseases. The aim of the present study was to assess the knowledge regarding urinary tract infection among the women of reproductive age group in rural area of Amritsar, Punjab. Investigator selected a sample of 150 women of reproductive age group. Convenient sampling techniques was used to collect sample. A self structured knowledge questionnaire was prepared to assess the level of knowledge regarding urinary tract infection among Women of reproductive age group. The data was analysed by using descriptive and inferential statistics. The results showed that among 150 women of reproductive age group the majority (78%) of women having average knowledge and (16.67%) were having good knowledge and (5.33%) were having poor knowledge. Chi square test was applied to find out the association of knowledge regarding urinary tract infection among women of reproductive age group with respect to socio- demographic variables finding revealed that there was no significant association with socio-demographic variables. Keywords: Urinary tract infection, knowledge.

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Urinary tract infection, Knowledge

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This study explores the application and impact of ISO 9001 quality management systems within Resident Welfare Associations (RWAs) as a framework for promoting good governance. In the context of urban community management, RWAs often struggle with issues such as inconsistent service delivery, lack of transparency, and limited resident engagement. This research examines whether ISO 9001 can address these challenges by introducing standardized procedures, accountability mechanisms, and resident-focused improvements. Using a mixed-methods approach involving surveys, interviews, and document analysis across 10 ISO-certified RWAs, the study evaluates service quality, governance transparency, participation levels, and stakeholder perceptions. Findings reveal that while ISO 9001 has led to moderate improvements in service quality and operational processes, its visibility and perceived impact among residents remain limited due to awareness gaps and underutilized communication channels. The study emphasizes the importance of resident-centric strategies, digital tools, and inclusive engagement to fully realize the transformative potential of ISO 9001 in RWA governance. The insights aim to guide RWA leaders, urban planners, and policymakers toward building more transparent, accountable, and participatory residential communities.

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ISO 9001, Resident Welfare Associations (RWAs), good governance, service delivery, transparency, resident engagement, quality management, community participation, urban management, stakeholder perceptions

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Knee osteoarthritis (knee osteoarthritis (KOA)) is among the most prevalent degenerative joint diseases, leading to pain, stiffness, and decreased functional capacity. Among various physiotherapeutic interventions, Mulligan's Mobilization with Movement (MWM) has attracted increasing attention for its active, without pain approach. This narrative review summarizes the evidence supporting MWM in knee osteoarthritis (KOA) rehabilitation, highlighting its effects on pain, function, proprioception, and balance. Recent studies indicate MWM can serve as an effective tool in conservative knee osteoarthritis (KOA) management when used alone or with complementary therapies.

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Keywords: Knee osteoarthritis, Mulligan mobilization, MWM, pain, function, proprioception, manual therapy

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Abstract Aim The aim of the study to assess the knowledge and practice in prevention of cervical cancer among women in selected community Objectives of the study 1) To assess the knowledge and practice in prevention of cervical cancer among women 2) Correlate the Knowledge and practice in prevention of cervical cancer among women 3) To find the association between knowledge and Practice in cervical cancer with their selected background variables among women Methodology A descriptive study was conducted in house to house survey was done .Total 50 women selected by Purposive sampling Technique. The knowledge and prevention of cervical cancer was assessed by Self structured questionnaire on cervical cancer, check list questionnaire on practice to know the knowledge and prevention of cervical cancer among women Result 50 women were assessed regarding knowledge and Practice in Prevention of Cervical cancer among Women among 50 respondents 25 (50%) had poor knowledge score. Among 50 respondents 27 (54 %) had poor Practice Score. Karl Pearson's Co-efficient of correlation indicates that there is a positive correlation (r = 0.61) existing between knowledge and Practice in prevention of cervical cancer. There is an association between knowledge and Practice score of the respondents on cervical cancer with age, educational status, Occupation monthly income and Type of family, No of children at 0.01 level of significance. Correlation analysis showed that higher the scores for knowledge, better performance in Prevention of cervical cancer Conclusion The result of the study concluded that there is a need to create awareness about cervical cancer and its prevention among women

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Knowledge, Practice, Prevention, oral cancer, Women, Selected community

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The essential oil derived from bitter gourd (Momordica charantia) seeds has garnered considerable interest due to its many pharmacological qualities, such as anti-diabetic, antioxidant, anti-inflammatory, antibacterial, and anticancer effects. This oil, abundant in bioactive components like flavonoids, alkaloids, phenolic acids, and conjugated linolenic acids, has significant medicinal promise. Recent improvements in extraction procedures, nanoencapsulation methods, and formulation strategies seek to improve its bioavailability and effectiveness in medicinal applications. Notwithstanding promising preclinical results, obstacles persist in standardization, extensive clinical validation, and molecular investigations to further corroborate its therapeutic efficacy. This study consolidates existing scientific information, underscores developing research trends, and delineates significant research gaps, establishing a basis for future inquiries into the pharmacological and nutraceutical uses of bitter gourd seed essential oil.

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Momordica charantia, essential oil, bioactive compounds, pharmacological properties, clinical applications, nanoencapsulation

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Due to their weakness, wireless sensor networks (WSNs) may be subject to detrimental effects both physically and remotely. Stated differently, a great deal of applications requiring wireless sensor networks require security. Sensor measurements are used to locate events such as floods and fires. Wireless sensor networks are vulnerable, so it is important to protect the network by detecting when fake data is entered. An algorithm to identify and eliminate malicious network traffic has been developed. The suggested Improved Particle Swarm Intelligence (IPSO) method is applied to multiple datasets in order to assess its performance. A simulator is used to test the algorithm. The study and simulation results show how to identify and remove malicious data from wireless sensor networks. In this project, the simulation of a data injection attack in a WSN achieved an accuracy of 95% using MATLAB.

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WSN, Malicious Data Injection Detection, Prediction, Improved Particle Swarm Intelligence (IPSO).

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Skin cancer is one of the most prevalent and life-threatening diseases worldwide, emphasizing the need for accurate and early diagnosis to improve patient outcomes. This research presents an advanced skin cancer detection frame- work that integrates the Swin Transformer (Shifted Window Transformer) with semantic segmentation techniques for the classification of dermatoscopic images. The Swin Transformer effectively captures hierarchical global features, enabling precise differentiation between melanoma and benign lesions. To enhance the model's generalizability, preprocessing tech- niques such as image resizing, normalization, and augmentation are applied to datasets including HAM10000, ISIC-2008, ISIC- 2019, and PH-2. Furthermore, Explainable AI (XAI) methodolo- gies, including Grad-CAM and Grad-CAM++, are incorporated to provide visual explanations, highlighting crucial regions influ- encing classification decisions. The model's performance is evalu- ated using accuracy, sensitivity, specificity, and the Dice similarity coefficient, ensuring reliable and interpretable diagnostics. Additionally, Bayesian Optimization is employed for hyperpa- rameter tuning, optimizing model performance while maintaining computational efficiency. This research contributes to the field of AI-driven dermatology by offering a transparent and robust diagnostic tool that assists dermatologists in early melanoma detection. Future enhancements include optimizing the model for real-time edge deployment, expanding datasets to improve generalizability across diverse skin tones, and integrating multi- modal data for enhanced diagnostic accuracy.

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Interpretable Machine Learning, Swin Trans- former, Explainable AI, Skin Lesion Classification, Attention Mechanism, Model Transparency, Performance Metrics.

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Effective post-operative pain management is a critical component of modern surgical care, directly impacting recovery time, patient satisfaction, and overall outcomes. The goal of postoperative pain control is to reduce the negative consequences of postsurgical pain and help the patient transition smoothly back to normal function. Traditionally opioid analgesic therapy has served the mainstay of treatment for postoperative pain. This review explores traditional and emerging strategies in post-operative pain control including pharmacological, regional anaesthesia, and multimodal approaches. It emphasizes patient-centred care and highlights recent innovations and challenges.

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Post-Operative Pain, Analgesia, Multimodal Pain Management, Opioids, Regional Anaesthesia, Enhanced Recovery

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Transoral Robotic Surgery (TORS) has revolutionized the management of head and neck cancers, offering a minimally invasive approach with enhanced visualization and precision. This study compares the oncologic, functional, and quality-of-life outcomes of TORS with traditional open and endoscopic techniques in patients with oropharyngeal and laryngeal malignancies. Data synthesis from multicenter studies and institutional cohorts reveals favorable surgical margins, reduced morbidity, shorter hospital stays, and improved swallowing function in patients undergoing TORS. However, certain limitations regarding tumor accessibility and cost remain significant. This manuscript discusses the clinical benefits, challenges, and future scope of TORS in the treatment of head and neck cancers.

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Head and neck cancers, particularly oropharyngeal squamous cell carcinomas (OPSCC), present unique anatomical and functional challenges in surgical oncology. Traditional surgical approaches often compromise speech and swallowing, leading to considerable morbidity. The advent of Transoral Robotic Surgery (TORS) has enabled surgeons to perform complex resections via the oral cavity, reducing the need for external incisions and improving post-operative outcomes.

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DeepFake technology, driven by artificial intelligence, has transformed digital media manipulation, making it possible to create highly realistic synthetic images and videos. While this advancement offers benefits in entertainment and content production, it also poses significant ethical and security challenges, such as misinformation, identity fraud, and unauthorized impersonation. This research focuses on developing an advanced DeepFake detection system that leverages machine learning techniques to identify and counteract these risks. The proposed system comprises two key components: the User Module, which allows user registration, media uploads for detection, and result tracking, and the Admin Module, responsible for content moderation and system oversight. Utilizing sophisticated deep learning models like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), the system is designed to deliver high detection accuracy, real-time processing, and adaptability to evolving DeepFake techniques. This study emphasizes the importance of automated DeepFake detection in maintaining digital authenticity and trust. It addresses the increasing threats posed by manipulated media across various sectors, including journalism, cybersecurity, and social media governance.

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DeepFake Detection, Machine Learning, Convolutional Neural Networks, Recurrent Neural Networks, AI in Cybersecurity, Digital Forensics, Face Forgery, Synthetic Media, GANs, Feature Extraction, Real-Time Detection, Image and Video Forensics, Adversarial Learning, Identity Theft Prevention, Misinformation Control, Ethical AI, Explainable AI, Content Moderation, Neural Networks, Data Augmentation, Pattern Recognition, Computational Photography, Flask Application, PyTorch, Deep Learning, Web-Based D

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The Project, FORMULATION AND EVALUATION OF HERBAL CHURNA AND JUICE AS A LIVER TONIC FOR THE TREATMENT OF JAUNDICE, Jaundice is a clinical condition characterized by yellowing of the skin, eye, and mucous membranes due to elevated bilirubin levels, remains a significant health concern world-wide. Conventional medicine treatment causes liver dysfunction and infection. However herbal medicine gained attention for their potential efficacy, affordability and minimal side effect. This review explores the role of the herbal plants in managing jaundice, emphasizing their hepatoprotective and anti-oxidant properties. It is characterized by yellow skin, mucosa, and sclera resulting from the increased production of bilirubin, usually over 2.5 and 3.0 mg/dL. Jaundice may occur either from increased production or reduced excretion of bilirubin due to several diseases and conditions that affect the liver, such as different types of hepatitis, liver cirrhosis, Gilbert syndrome, cholestasis, alcohol or drugs abuse, autoimmune disorders, hemolytic anemia, liver cancer, etc. For treatment of jaundice various herbs are used such as bhumi Amla (Phyllanthus niruri), tulsi (Ocimum tenuiflorum) and Kutki (Picrorhiza kurroa). The plants were arranged with correct nomenclature along with their common name, family, botanical name, the part of plant with their mechanism of action which have been reported in different kinds of literature. The use of decoction is the most preferred method of herbal preparation. The study indicates that the local inhabitants rely on medicinal plants for treatment. This paper proposed further clinical experimentation in order to scientifically evaluate these widely used herbal remedies for possible bioactive effects.

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we are making syrup and churna for treatment of jaundice

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This study explores the influence of China-made mobile phones on consumer buying behavior in Coimbatore City. With the rapid penetration of China-made mobile phone brands like Xiaomi, Realme, Vivo, and Oppo in the Indian market, consumer preferences and purchasing patterns have shifted significantly. The study analyzes factors such as price sensitivity, features, brand perception, after-sales service, and peer influence, which affect consumer choices. The findings aim to help marketers and businesses understand consumer expectations and develop strategies to compete or collaborate with Chinese brands.

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IMPACT OF CHINA-MADE MOBILE PHONES ON CONSUMER BUYING BEHAVIOUR WITH SPECIAL REFERANCE TO COIMBATORE CITY

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Calcaneal spur is a bony overgrowth from calcaneum bone which occur when foot exposed to constant strain leads to calcium deposition in heel bone, it is painful condition. It can be correlated with vatakantaka according to Ayurveda and can be treated with agnikarma. Agnikarma is an important parasurgical procedure of Ayurvedic surgeons, which has been used widely in the clinical practice. Agnikarma has emerged as an integral part of the Ayurvedic therapeutics. In Ayurveda, agnikarma is a treatment in which heat is applied to a particular part of body to reduce inflammation there and hence the underlying pain.Due to Ushna Teekshna and Sukshma Guna of Agni it reduces the Localised Vata.It stimulates musculoskeletal tissue to remove spasm pain.Agnikarma therapy is mostly used in treatment of calcaneal spur by treating vata. Agnikarma treatment is carried out with a special instrument made of 5 metals (Copper, Iron, Zinc, Silver & Lead, also contains gold in some special ones), thus is named as Pancha Dhatu Shalaka. A patient successfully treated with Agnikarma therapy is presented in case study.

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Agnikarma, Calcaneal Spur, Vatakantaka, Heel Pain, Ayurveda, Parasurgical Procedure

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Although agriculture is the foundation of India's economy, farmers encounter difficulties such as limited access to professional advice, lack of individualised guidance, and language barriers. These problems are addressed by Kisan Sathi, a voice-activated, multilingual virtual assistant that offers data-driven, locally relevant agricultural recommendations, natural language processing, and machine learning. In order to ensure inclusivity across literacy levels, the platform supports both voice and text interfaces. By providing advice on crop management, pest control, weather updates, and sustainable practices, it empowers farmers and bridges the digital divide in India.

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Voice assistance, multilingual chatbots, smart farming, virtual assistants, natural language processing (NLP), sustainable farming, kisan support, and crop advisory.

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This project proposes the development of an AI-based journal management website aimed at streamlining the submission, review, and publication processes for academic journals. The website leverages artificial intelligence to automate key aspects of journal management, including manuscript categorization, plagiarism detection, reviewer assignment, and predictive analytics for paper acceptance likelihood. The system integrates natural language processing (NLP) for semantic analysis of articles and offers an intuitive dashboard for editors, authors, and reviewers, enhancing user experiences. With features such as AI-driven content suggestions, automated formatting, and personalized recommendations, the platform reduces manual overhead, increases efficiency, and fosters collaboration in the academic publishing ecosystem. Secure access, role-based authorization, and AI-powered chatbots are also incorporated for real-time support and communication. This approach provides a comprehensive solution for journal management, ensuring higher accuracy, scalability, and streamlined publication workflows.

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Manuscript Processing, Academic Publishing, Peer Review System, Editorial Workflow, Digital Content Management

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With the growth of smart transportation systems, there is a need for reliable ways to detect and read license plates automatically. This research introduces an ALPR (Automatic License Plate Recognition) system that works well even in difficult situations, like poor lighting, messy backgrounds, or unusual license plate designs. The system uses two main technologies. It finds the license plate in an image with a fast and accurate model called YOLO (You Only Look Once). After the License plate is detected then, it uses LPRNet (License Plate Recognition Network) to read the letters and numbers on the plate. These steps make the system quick and accurate. The process involves finding the license plate, breaking the characters into separate parts, and creating a text box with the results. It can handle challenges like low-quality images, dirty plates, and background noise while still working well. Tests on different datasets show that the system works in both indoor and outdoor conditions and can recognize license plates in many languages and styles. This system has many practical applications, including automatic payment collection at toll booths, traffic monitoring, law enforcement, and smart city projects, where fast and accurate vehicle identification is essential. By providing reliable results in real-time, this ALPR system helps make transportation systems more efficient and supports various operations like managing traffic and enforcing laws. By using advanced computer vision techniques, this recognition system provides an efficient solution for vehicle identification tasks.

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License Plate Recognition, YOLO, LPRNet, Smart Transport, Vehicle Identification, Edge detection

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The increase of AI enabled deepfakes along with its widespread dissemination of intimate images without consent, an act of revenge porn has seen to blur the boundaries of digital accord, undermining personal autonomy and privacy. This paper examines the legal, technological, and ethical dimensions of non-consensual intimate image (NCII), drawing on interdisciplinary paradigms and legislative developments across key jurisdictions. Drawing insights from the U.S. TAKE IT DOWN Act, Virginia's Deep Fake Pornography Law, and India's legislative lacuna--this study highlights enforcement challenges and technological limitations. This paper recommends harmonized legislation, mandatory platform mitigation, victim-centric redressal mechanisms, along with initiatives for digital literacy to fortify digital consent in an era of synthetic media.

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Digital Consent; Deepfakes; Revenge Porn; Non Consensual Intimate Image (NCII); Cyber Law; Artificial Intelligence; Online Privacy; Platform Regulation

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In an era where synthetic media and deepfakes are becoming increasingly sophisticated, ensuring the authenticity of digital content has emerged as a critical challenge. This project explores an innovative AI-driven solution to distinguish real images from synthetic ones by utilizing the capabilities of Large Language Models (LLMs), specifically through Gemini technology. Unlike traditional methods that rely solely on pixel-level analysis, our approach leverages the multimodal understanding of LLMs to interpret visual data with contextual depth and semantic reasoning. The system is deployed via a web-based platform, supported by cloud services like Supabase for storage and authentication. Through this work, we aim to enhance digital trust by providing a scalable, intelligent tool for detecting manipulated visual content, contributing to the broader field of AI-based media forensics.

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Real vs Fake Image Classification, Image Authenticity Verification, Synthetic Media Analysis, AI-Based Media Forensics, Digital Image Integrity.

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Diabetes is a chronic condition. Traditional invasive techniques like the finger-prick test, though commonly used, often lead to discomfort and reduced patient compliance. To overcome these limitations, this project presents a Non-invasive monitoring system. The proposed system utilizes a Near-infrared (NIR) sensor by analyzing how light is absorbed and scattered through skin tissue instead of directly connecting the NIR sensor to the Raspberry Pi 4B. An ESP8266 Wi-Fi Module is used. The sensor data is sent to the Raspberry Pi 4B via serial communication. This setup ensures smooth data acquisition while maintaining modularity and flexibility in hardware design. The received data is processed using a Python-based long short-term memory (LSTM) neural network algorithm. The LSTM model is known for its ability to analyze time-series data and predict results accurately. In addition to glucose estimation, the system is also designed to predict other health parameters such as blood group, blood level, cholesterol level, skin temperature, hydration level and blood pressure using AI-based analysis.

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AI-based algorithms, Diabetes management, ESP8266 module, NIR sensor, Raspberry pi 4B, health parameters.

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