Volume 14 | Issue 7 |

 Abstract

With growing interdependence among countries, there is an acute need for cross-lingual communication to bring people together around the globe. This paper presents the design and testing of an automated multilingual document translation system built entirely within UiPath Studio, a leading Robotic Process Automation (RPA) platform, without relying on external translation APIs. The proposed architecture combines UiPath's Document Understanding, Optical Character Recognition, and native automation activities to read, extract, and translate text from multilingual documents while preserving structure and formatting. Eliminating third-party API dependence yields faster processing, more uniform translation quality, and stronger data privacy. Following an applied, experimental research design, the workflow was tested across Hindi, Marathi, French, Tamil, and Telugu. Results indicate that translation tasks can be automated with minimal human intervention while retaining document structure and reasonable accuracy, and that the resulting interface is usable by both technical and non-technical users. The findings position UiPath as a secure, scalable, and cost-effective tool for automated multilingual document translation, and as a viable contributor to bridging language barriers in the digital era.

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 Keywords

UiPath, Language Translation, Robotic Process Automation, Workflow Automation, Document Understanding, No-API Translation

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Abstract - Trigeminal Neuralgia(TN) is a painful condition that causes sharp, shooting, electric shock like pain along one or more division of trigeminal Nerve, often starting with cold exposure or chewing .In this case report a 49-year-old man who has been experiencing right-sided facial neuralgia for one and a half years. The pain starts from the forehead and spreads to the upper jaw area, lasting about 1 -2 minutes.The pain comes suddenly and is sharp in nature,gets worst when exposed to cold,during runny nose, in winter or when chewing hard food substances and improves with warmth.The patient was clinically diagnosed with right sided facial Neuralgia.Based on symptoms reportorial analysis andindividulaizedassessment. Homoeopathic medicineNatriumMuriaticum 1M was prescribed. There are no Neurological deficitand the purpose of this case report is to showhow a case of idiopathic trigeminal neuralgia improved significantly, with complete relief of symptoms after follow - up.This case demonstrates how personalized homoeopathic treatment can effectively managechronic facial neuralgia.

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Keywords - Homoeopathy, Individualised Homoeopathic medicine, trigeminal neuralgia, pain intensity score.

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Structural deterioration caused by cement seepage, moisture penetration, and reinforcement corrosion is one of the major challenges in civil engineering. Traditional inspection methods are time-consuming, expensive, and dependent on human expertise. This research presents an Artificial Intelligence (AI)-based image processing system for detecting reinforcement exposure, cement seepage, and surface defects in reinforced concrete structures. Images collected from buildings and bridges are analyzed using AI techniques to identify damaged regions and classify their severity. The proposed system helps engineers detect structural problems at an early stage, reducing maintenance costs and improving structural safety. The developed approach can also be integrated with mobile applications and web dashboards for real-time monitoring.

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Keywords: Artificial Intelligence, Reinforcement Corrosion, Cement Seepage, Structural Health Monitoring, Image Processing.

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Healthy eating during childhood is essential for optimal growth, development, and the prevention of nutrition-related diseases. However, increasing consumption of junk food, inadequate nutritional awareness, and sedentary lifestyles have led to unhealthy eating habits among school-aged children. Schools provide an ideal environment for nutrition education, and innovative teaching strategies such as game-based learning have shown promise in improving children's knowledge, attitudes, and dietary behaviors. High-quality evidence, including the randomized controlled trial by Kato-Lin et al. (2020), demonstrated that mobile game-based interventions can positively influence children's food choices. Furthermore, systematic reviews by Nora Suleiman-Martos et al. (2021) and Mohapatra and Mohandas (2024) support the effectiveness of gamification in nutrition education, while recent Indian school-based studies (2024) indicate its relevance in culturally similar settings. This study aims to assess the effectiveness of game-based learning in promoting healthy eating habits among school children at Santhapeta School, Chittoor District, Andhra Pradesh. Specifically, it evaluates changes in students' knowledge, attitudes, and healthy eating practices following a game-based educational intervention. The findings are expected to provide evidence for integrating gamified nutrition education into school health programs, thereby encouraging healthier food choices and contributing to the prevention of childhood obesity and other diet-related health problems.

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 Keywords

Game-Based Learning, Healthy Eating Habits, Nutrition Education, School Children, Gamification, Dietary Behaviour, School Health Promotion, Childhood Nutrition

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The present study investigated the impact of the Advance Organizer Model of Teaching on the academic achievement of secondary school students in Mathematics. The experimental method with pre-test and post-test control group design was adopted. The sample consisted of 120 secondary school students, divided equally into Experimental and Control Groups, with boys and girls represented equally. The Advance Organizer Model was administered to the Experimental Group, while the Control Group was taught through the conventional method. Academic Achievement Test in Mathematics was used as the tool for data collection. The collected data were analyzed using Mean, Standard Deviation, and t-test. The findings revealed that students exposed to the Advance Organizer Model demonstrated significantly higher academic achievement than those taught through traditional methods. The model proved effective in enhancing meaningful learning, retention, and understanding of Mathematics concepts.

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Advance Organizer Model, Academic Achievement, Mathematics, Secondary School Students, Experimental Study.

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Abstract: Generative AI tools are rapidly reshaping academic library services by changing how information is discovered, evaluated, taught, and delivered. This study examines the impact of generative AI on core library functions, including reference service, information literacy instruction, and collection development. It reviews recent literature to show that AI tools can improve efficiency, personalize support, and assist librarians with routine tasks such as drafting content, answering basic queries, and enhancing discovery workflows. At the same time, the paper highlights major concerns related to information accuracy, privacy, ethical use, and professional skill gaps. The discussion argues that generative AI should be viewed as a supportive technology rather than a replacement for librarians, since human judgment remains essential for scholarly guidance and critical evaluation. The paper concludes that academic libraries must adopt clear policies, staff training, and responsible implementation strategies to integrate generative AI effectively while preserving trust, academic integrity, and user-centered service.

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Generative AI, Academic libraries, Reference services, Information literacy, Library ethics

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Abstract: Background: Defecation is a complex physiological process involving coordinated activity of the gastrointestinal tract, enteric nervous system, autonomic nervous system, and voluntary neuromuscular mechanisms. The initiation and regulation of the defecation reflex depend upon rectal distension, enteric neural pathways, parasympathetic outflow, and relaxation of anal sphincters. Modern physiology explains this process through intrinsic enteric reflexes mediated by the myenteric plexus and parasympathetic reflexes mediated through sacral spinal segments. Alteration in these regulatory mechanisms can result in disorders such as constipation, incomplete evacuation, and anorectal dysfunction. In Ayurveda, the process of fecal elimination is governed primarily by Apana Vata. Apana vata is one of the five subtypes of Vata dosha. It is located in Apana desha (Perineal region). It traverses along Sroni (Pelvis), vasti (urinary bladder), medra (external genital apparatus of each sex) and uru (thighs). It performs several vital functions, such as the expulsion of Shukra (Semen), Artava (menstrual blood), Shakrit (faeces), mutra (urine) and Garbha (the products of conception - foetus & Placenta). Purishavaha Srotas serves as the structural and functional pathway involved in the formation, transportation, and elimination of fecal matter. Aim and Objective: To review and analyze the role of Apana Vata and Purishavaha Srotas in the physiology of the defecation reflex through an integrative understanding of the enteric and autonomic nervous systems. Materials and Methods: A literature review was conducted using classical Ayurvedic texts, modern physiology textbooks, research articles, and databases such as Google Scholar and PubMed. Conclusion: The functional attributes of Apana Vata and the structural-functional role of Purishavaha Srotas demonstrate notable similarities with the coordinated activities of the enteric and autonomic nervous systems involved in defecation. Although classical Ayurvedic concepts and modern neurophysiology describe bowel regulation from different perspectives, an integrative understanding may provide a broader insight into the physiology of the defecation reflex.

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 Keywords

Apana Vata, Purishavaha Srotas, Defecation Reflex, Enteric Nervous System, Autonomic Nervous System, Gut Motility, Neurophysiology, Ayurveda Physiology.

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Creative Commons Attribution 4.0 and The Open Definition

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The integration of renewable energy sources like wind into conventional thermal power systems poses significant challenges to grid frequency stability due to variable generation and fluctuating loads. Load variations cause critical frequency deviations that threaten grid stability, making effective frequency stabilization essential for reliable operation. This mini project develops a MATLAB/Simulink model for load frequency control (LFC) in a hybrid smart grid. This Paper investigates Load Frequency Control (LFC) of two different configurations of a Hybrid Smart Grid Model . The first Configuration follows LFC by connecting a Hydroelectric Microgrid in islanding mode . While, the second one is a BESS integrated grid tied mode . The proposed Combination of systems subjects to supply external extra voltage that is proportional to the voltage fluctuation developed that might develop due to a fault or internal external factors .

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Load Frequency Control (LFC) , Battery Energy Storage System (BESS) , Islanding mode , BESS Integrated Grid tied mode , Hybrid Smart Grid , Hydroelectric Microgrid , Utility Grid.

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Recent advances in Reinforcement Learning (RL) have enabled autonomous agents to learn complex decision-making strategies through continuous interaction with dynamic environments. Among the various training methodologies, Curriculum Learning (CL) and Behavior Cloning (BC) have gained significant attention for improving learning efficiency and policy quality in multi-agent systems. Curriculum Learning adopts a progressive training strategy by gradually increasing task complexity, allowing agents to acquire foundational skills before solving more challenging scenarios. In contrast, Behavior Cloning leverages expert demonstrations to directly imitate desired behaviours, thereby reducing the exploration burden during the initial stages of training. This paper presents the design, implementation, and comparative evaluation of Curriculum Learning and Behavior Cloning within a custom-developed 3v3 Freeze Tag environment implemented using the Unity ML-Agents framework. The environment models a cooperative-competitive game in which runner agents attempt to survive and rescue teammates while tagger agents seek to freeze all opposing agents within a predefined time limit. The proposed implementation integrates Proximal Policy Optimization (PPO) for reinforcement learning and combines Behavior Cloning with Generative Adversarial Imitation Learning (GAIL) to enhance policy learning from expert demonstrations.

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 Keywords

Reinforcement Learning, Deep Reinforcement Learning, Unity ML-Agents, Curriculum Learning, Behavior Cloning, Generative Adversarial Imitation Learning, Proximal Policy Optimization, Freeze Tag Environment, Multi-Agent Systems, Artificial Intelligence.

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Nowadays, there is increasing demand of biodegradable mild surfactants because of growing environmental issues and needs for sustainable alternatives. In cosmetic sector the surfactants are used for their emulsifying, foaming and cleansing properties, while being gentle on the skin biodegradable surfactants helps to minimize environmental pollutions as they break down easily without leaving harmful residue. The current study shows the preparation of alkyl polyglucoside, a sugar based mild surfactant prepared by using different alcohols and glucose by condensation reaction. The propertoes of the final prepared surfactant is characterized by chain of the alcohol used. Theses surfactant shows biodegradability and low toxicity. The structural characterization of surfactant was carried out using FTIR spectroscopy.

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Glucose, Alcohol, Condensation, Optimization, FTIR.

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