Volume 13 | Issue 12

 Abstract

Capsules are one of the most versatile and widely accepted oral dosage forms used in pharmaceutical practice due to their ease of administration, rapid disintegration, and ability to accommodate a broad range of drug substances. Their manufacturing involves specialized processes that differ for hard gelatin capsules and soft gelatin capsules, each requiring precise control of materials and operating conditions to achieve consistent quality. Hard gelatin capsules are produced through sequential steps such as dipping, drying, trimming, and filling, while soft gelatin capsules are typically manufactured using techniques like the rotary die process, which enables simultaneous forming, filling, and sealing. To ensure the safety, efficacy, and uniformity of the final product, capsules must undergo rigorous quality control tests, including disintegration, weight variation, dissolution, assay, content uniformity, and moisture permeation testing. These evaluations verify the structural integrity of the shell, accuracy of the fill weight, and performance of the dosage form under physiological conditions. This review summarizes the manufacturing processes of both hard and soft gelatin capsules and highlights the essential quality control parameters required to comply with pharmacopeial standards, thereby ensuring reliable therapeutic outcomes.

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Capsules; Gelatin; Hard Gelatin Capsules; Soft Gelatin Capsules; Capsule Filling Machines; Advantages and Disadvantages; Types of Capsule Filling Machines; Working Principles of Capsule Filling

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 Abstract

Rhinoplasty is considered to be one of the most challenging procedures in otolaryngology & head and neck surgery.

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RHINOPLASTY- PATIENT'S SATISFACTION AFTER SURGERY BOTH FUNCTIONAL AND COSMETIC

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The AI-Mock-Interview project aims to transform the traditional interview process by utilizing artificial intelligence to automate and enhance candidate evaluations. This AI-powered system assesses candidates across multiple dimensions, such as verbal responses, body language, eye contact, and emotional expressions. By incorporating cutting-edge technologies like machine learning, natural language processing (NLP), and facial recognition, it offers a more efficient and objective approach to candidate assessment. The project seeks to overcome the inherent biases and subjectivity often present in traditional interviews by adopting a data-driven, impartial evaluation process. The methodology centers around real-time video and audio analysis, leveraging emotion recognition and sentiment analysis through Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models.

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AI-Driven Interview Simulation Intelligent Mock Interview Platform Virtual Interview Assistant Automated Interview Engine Smart Interview Bot

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Phishing websites have emerged as one of the most prevalent cyber threats, deceiving users into revealing sensitive credentials and causing significant financial losses. Detecting these malicious websites automatically and accurately is crucial for online security. This paper presents a machine learning-based phishing website detection system that analyzes multiple data modalities including URL lexical features, HTML structure, and WHOIS information to identify phishing attempts. The system extracts key features such as domain age, URL length, special character patterns, SSL certificate validity, and embedded script behaviors, which are then processed using ensemble learning models such as Random Forest and XG Boost for classification. The proposed model achieves an overall accuracy of 96.4%, precision of 95.8%, and F1score of 96.1% on benchmark datasets, demonstrating superior performance compared to existing methods. The model is lightweight and optimized for real-time deployment, making it suitable for browser extensions and online verification systems. The key contributions include the integration of diverse feature sets, enhanced detection accuracy, and a scalable implementation for real-world phishing prevention.

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 Keywords

Phishing Website Detection, Machine Learning, Cybersecurity, Web Security, Feature Extraction, Classification Algorithms, Supervised Learning, URL Analysis, Malicious Website Detection, Data Mining.

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In this project we will develop an IoT Based Solar Power Monitoring System using ESP32 WiFi Module. The ESP32 connects to the WiFi Network and uploads the Solar Sensing parameters like Solar Panel Voltage, Temperature, and Light Intensity on Thingspeak Server.

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IoT based solar tracker, solar power

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 Abstract

The rapid expansion of Internet of Things (IoT) devices has been transformed into modern infrastructure around healthcare, industrial reformation, smart cities, and consumer applications. However, this rapid multiplication has never created security challenges, making IoT networks attractive targets for sophisticated cyberattacks [1]. Traditional signature-based intrusion detection systems (IDS) struggle to keep pace with the evolving nature of attacks, particularly botnet-based threats that exploit the inherent resource constraints of IoT devices. Deep learning has emerged as a promising solution for developing robust, adaptive intrusion detection systems capable of identifying both known and novel attack patterns in IoT environments [2]. The BoT-IoT dataset has become a critical standard for evaluating these advanced detection approaches, providing researchers with realistic network traffic data that captures the complexity of modern botnet attacks in IoT infrastructures.

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 Keywords

Internet of Things (IoT), Intrusion Detection System, Deep Learning, Cybersecurity, BoT-IoT Dataset, Botnet Attacks.

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Access to safe drinking water remains a critical public health challenge, particularly in developing regions with limited laboratory infrastructure. This paper presents Purifix H2O, an intelligent Internet of Things (IoT) system for real- time water quality monitoring that integrates low-cost sensors (pH, TDS, turbidity, and temperature) with an Arduino UNO microcontroller and cloud-enabled mobile/web interfaces. The system employs sensor calibration, multi-parameter processing, rule-based safety classification, and an optional machine learning module for AI-driven waterborne disease risk prediction. Experimental evaluations on diverse water sources including tap, borewell, packaged drinking, pond, and contaminated samples demonstrate that the system reliably discriminates water quality categories and predicts health risks based on sensor patterns. Results show that the proposed approach is cost-effective, feasible for large-scale deployment, and significantly improves accessibility to continuous water quality assessment compared to traditional laboratory-based methods.

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IoT, water quality monitoring, low-cost sensors, Arduino, machine learning, disease risk prediction, Android Application, real-time monitoring.

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The concept of gender in Indian families and society needs no introduction, as the age-old gender discrimination has been so deeply internalized that it doesn't come as a surprise if a woman becomes a victim of such discrimination. The paper attempts to study a woman's world bound by patriarchy, class, and gender exploitation, followed by the subsequent resistance in Indian society as portrayed in Arati Kadav's film Mrs. (2024). A Marxist feminist literary approach guides the analysis of the cinematic text. The film projects a quietly powerful and thought-provoking portrayal of a woman caught between her aspirations and the weight of deeply rooted patriarchal expectations. At the heart of the film is Richa, played with striking vulnerability by actress Sanya Malhotra--a middle-class housewife whose routine existence is upended by an unexpected event. What unfolds is not just a story of disruption, but of awakening and resistance. The findings conclude that many Indian married women, like the female lead in the film, are trapped in the endless cycle of domestic chores and constant patriarchal scrutiny. The identity that the woman protagonist had is lost in transition and remains a victim of patriarchy, class, and gender discrimination. It is through passion for a fulfilling work or economic independence that paves the way to the protagonist's ultimate freedom and empowerment.

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patriarchy, gender, Marxist feminism, Mrs., resistance

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ABSTRACT A 60-year-old post-hysterectomy woman presented with a one-year history of vaginal mass protrusion, difficulty walking, and incomplete micturition. Examination revealed vault prolapse with cystocele and enterocele. She was hemodynamically stable with mild pallor and pedal edema. Investigations showed hemoglobin 11 g%, platelet count 1.24 lakh/cumm, and normal renal, liver, thyroid, and viral profiles. Conservative management included lifestyle modification, pelvic floor exercises, multivitamins, antihypertensives, and topical estrogen. She was advised sexual abstinence for three months and follow-up for thyroid and fecal occult blood tests. Surgical correction such as sacrocolpopexy was planned if symptoms persisted. This case emphasizes early recognition, supportive care, and individualized management of post-hysterectomy vault prolapse with cystocele and enterocele.

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Keywords: Vault prolapse, cystocele, enterocele, hysterectomy complications, pelvic organ prolapsed

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Summarizes Jatharagni's role in Grahani-IBS pathogenesis, linking Mandagni-Ama to dysbiosis. Outlines review methodology, key findings (doshic-IBS mapping), and integrative therapeutic superiority. Aim To establish the mechanistic role of Jatharagni dysregulation in Grahani pathogenesis and its correlation with IBS pathophysiology. Objectives 1. Analyze classical Jatharagni physiology and Grahani classification from Ayurvedic texts. 2. Map tridoshic Grahani types to IBS subtypes. 3. Correlate Mandagni-Ama with modern dysbiosis and gut dysfunction. Introduction Defines Grahani Roga via classical Shloka, introduces Jatharagni physiology. Correlates with IBS prevalence/Rome IV, states research scope for Ayurvedic-modern synthesis. Methodology PRISMA-adapted systematic review of Ayurvedic texts/databases (2000-2025). Details inclusion criteria, qualitative dosha-IBS mapping, bias assessment via Jadad scale. Results Presents Jatharagni functions (Paka-Vibhajana), Grahani classification table (Vataja->IBS- C etc.). Shows Deepana-Pachana efficacy (70-80% relief) with Samprapti diagram.Discussion Elaborates Mandagni->Ama parallels (dysbiosis, leaky gut, motility chaos).Pathophysiological mechanism of Grahani and IBS and it's integrated interpretation Conclusion Affirms Jatharagni restoration as root-cause therapy outperforming IBS palliation. Recommends multicentric trials for global validation.

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Keywords: Jatharagni, Grahani Roga, Irritable Bowel Syndrome, Agni, Mandagni, Digestive Fire, Ayurveda, Ama, Dysbiosis

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Machine Learning and Deep learning-based software tools has facilitated the creation of credible face exchanges in videos and images that leave few traces of manipulation, in what they are known as "DeepFake"(DF) videos. Manipulations of digital videos have been demonstrated for several decades through the good use of visual effects, recent advances in deep learning have led to a drastic increase in the realism of fake content and the accessibility in which it can be created. These so-called AI-synthesized media (popularly referred to as DF).Creating the DF using the Artificially intelligent tools are simple task. But, when it comes to detection of these DF, it is major challenge. Because training the algorithm to spot the DF is not simple. We have taken a step forward in detecting the DF using Convolutional Neural Network and Recurrent neural Network. System uses a convolutional Neural network (CNN) to extract features at the frame level. These features are used to train a recurrent neural network (RNN) which learns to classify if a video has been subject to manipulation or not and able to detect the temporal inconsistencies between frames introduced by the DF creation tools. Expected result against a large set of fake videos collected from standard data set.

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: Deepfake Detection , Kaggle Dataset, convolutional Neural network (CNN), recurrent neural network (RNN), Machine Learning.

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Vikara Vighatakara Bhavas represent protective parlance in Ayurvedic pathogenesis that proactively inhibit disease manifestation. These elements modulate the interaction of triad of Nidana (Doshas, Dhatus, Malas etc, the etiological factors) Visheshas, preventing Samprapti (pathogenic processes). Vikara Vighatkara Bhavas act as barriers against Vyadhi (disease) by opposing the association of causative factors like Naidan, Doshas, Dhatus, Malas etc. Their Bhavas (presence) strengthens Vyadhikshamatva (immunity), while Abhavas (absence) facilitates pathogenesis, as described in Charaka Samhita's Prameha Nidana. These Bhavas include Ojas, balanced Doshas, Prashashta (strong) Dhatus, and Sattvic mental states that neutralize Nidana's impact. Proactively, these Bhavas enhance resilience through Dinacharya, Ritucharya, and Rasayana therapies, fortifying the body pre-exposure to pathogens. Bala (strength), Sattva (psyche), and Abhyantara Hetus (internal causative factors) play key roles in averting Dosha vitiation. This aligns with Ayurveda's Swasthavritta emphasis on prevention over cure. Protectively, they disrupt Samprapti by limiting Dosha-Dooshya Sammurchana, influencing Rogamarga and Krama. Factors like Prativishesha (counter-specifics) determine disease severity, onset, and prognosis. In modern terms, they parallel immunity modulators, explaining individual disease resistance variations. Understanding these Bhavas guides Chikitsa towards Samprapti-Vighatana and Nidana-Parivarjana. Enhancing them via Ahara-Vihara and Ojasya-Rasayanas offers holistic prophylaxis.

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Vikara Vighatkara Bhavas, Ayurvedic pathogenesis, Samprapti-Vighatana, Vyadhikshamatva, Nidana-Visheshas, protective factors, Ojas, diseases etc.

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Keywords: Acute respiratory infections, video teaching programme, mothers, under-five children, preventive measures, rural health.

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Quality control plays a great role in the assurance of integrity, safety, and performance of a product within various industrial sectors such as pharmaceuticals, food processing, materials science, and chemicals. The commonly used traditional QC methods include titrimetric analysis, chromatography, and gravimetry among others. Though these methods are well known for their accuracy they involve destructive testing, elaborate sample preparation as well as long analysis time hence high operation cost and added environmental burden. Recent developments in different spectroscopic techniques have remodeled the analytical quality control area with their non-destructive, reagentless real-time molecular characterization capabilities. Available methods include FTIR, NIR, Raman, NMR, UV-Vis, THz, and LIBS which permit very selective and sensitive rapid determination of physical and chemical properties. These methods have not only enhanced analytical throughput but are also compliant with the current state of regulation which puts a spotlight on the PAT and QbD paradigms. This paper reviews the principles and foundations, instruments, applications, benefits, and practical challenges of advanced spectroscopic techniques. It discusses trends fueled by artificial intelligence in spectral interpretation, miniaturized and portable spectrometers, data fusion strategies, and hybrid spectroscopic platforms leading to intelligent automated sustainable QC systems. All these are integrated with the promise to bring a new dawn in modern manufacturing via predictive analytics as well as compliance that is not harmful to the environment while assuring quality.

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Keywords: Quality Control, Spectroscopy, FTIR, Raman, NMR, Process Analytical Technology, Non-Destructive Testing, Chemometrics, AI-driven Analysis.

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In every field of life, nanotechnology has an impact. Researchers expend their interests on gold nanoparticles synthesis as they provide superior properties for various types of applications. Different physical and chemical methods have synthesized conventionally nanoparticles that have a negative impact on the environment. Due to their unique properties, small size and high area-to-volume ratio, gold nanoparticles show special advantages in this field among nanoparticles. Because of their inert nature, stability, high dispersity, non- cytotoxicity and biocompatibility, these particles have been widely used in various biomedical applications and drug delivery systems. This paper shows the comparison and survey of all the methods. Gold nanoparticles are widely used in many fields As preferred materials for their unique optical and physical properties, such as surface plasmon oscillations for labeling, imaging, and sensing. Recently, many advancements were made in biomedical applications with better biocompatibility in disease diagnosis and therapeutics. Au-NPs could be prepared and conjugated with many functionalizing agents, such a polymers, surfactants, ligands, derailments, drugs, DNA, RNA, proteins. Peptides and oligonucleotides. This review addressed the use of gold nanoparticles and the surface functionalization with a wide range of molecules expanding and improving gold nanoparticles in targeting drugs for photothermal therapy with reduced cytotoxic effects in various cancers gene therapy and many other diseases.

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Gold nanoparticles, Nanotechnology, drug delivery, biomedical Applications, cytotoxicity, cancer, drug delivery.

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In today's fast-paced digital environment, businesses are increasingly employing chatbots to enhance customer support services. This research paper explores the functionalities of chatbots in customer support, their impact on operational efficiency, customer satisfaction, and challenges associated with their implementation. We also examine various case studies that highlight successful chatbot deployments across different industries.

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AWS, Azure, GCP, Intent, Entities, Utterances, Natural Language Processing (NLP), Fallback, Human Handoff (or Transfer), Artificial Intelligence, Knowledge Base, Conventional Flow

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Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by persistent difficulties in communication, social interaction, and adaptive behavior. The increasing prevalence of autism worldwide has intensified the demand for innovative, inclusive, and evidence-based educational strategies that address the diverse learning needs of children with autism. Traditional instructional methods often fail to accommodate the heterogeneity of autism, necessitating flexible and learner-centered approaches. This paper presents a comprehensive review of innovative learning strategies designed to enhance the academic, social, and emotional development of children with autism. The study explores technology-assisted instruction, structured teaching models, adaptations of Applied Behavior Analysis (ABA), play-based and sensory integration approaches, peer-mediated interventions, and the Universal Design for Learning (UDL) framework. Additionally, emerging innovations such as gamification and artificial intelligence-driven personalized learning platforms are examined. The paper also discusses challenges including teacher preparedness, limited resources, and variability in learner profiles. The findings emphasize the need for collaboration among educators, parents, therapists, and policymakers to foster inclusive educational environments and ensure equitable learning opportunities for children with autism.

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Autism Spectrum Disorder, Innovative Learning, Inclusive Education, Assistive Technology, Teaching Strategies

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Biometric attendance systems are widely used to automate employee tracking and minimize manual intervention. However, ensuring single recording of attendance during both check-in and check-out remains a challenge due to issues like duplicate entries, spoofing, and improper biometric capture. This research proposes a robust algorithm that enforces one attendance entry per shift per user, using time-based locking and intelligent recognition. The proposed system significantly reduces redundancy and improves attendance accuracy with efficient time threshold management and real-time feedback. Experimental results demonstrate a strong improvement in preventing multiple submissions and reducing administrative overhead

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Biometric Attendance, Single Attendance, Time Threshold

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ABSTRACT: 'Knowing- Self' plays a prominent role in the human journey. To build harmonious society 'understanding between self and world' is important. A sympathetic awareness on the issue or ability to understand something in life is called understanding. When man understands truth in life, the possibility of mental and physical sufferings like unknown fear, anxiety, and depression may not exist in human life. In 'As You Like It', Shakespeare's quote "All the world's a stage and all men and women merely players" depicts human connection with drama. In the journey of human life, everyone has to face sufferings without a chance of escape. The philosophical theory of absurdity states that human life is a clash between self and world. It claims that human life is meaningless, hopeless and uncertain in the modern world. Nihilism stood as a profound philosophical phenomenon to challenge individual's identity for social well-being. Moreover, Nietzsche's Nihilism is a response to the erosion of traditional values and Christian beliefs. Passive nihilist can be called pessimistic because he lives in suffering with a psychological trauma. But Active nihilist tries to make his suffering into a meaningful suffering by creating hope in hopeless conditions. The paper is aimed to explore how Nietzsche's views on human sufferings with reference to Nietzsche's Thus Spoke Zarathustra Samuel Beckett's Endgame and Badal Sircar's Procession

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Key Words: Absurd Life, Nihilism, Sufferings, Self, Positive Attitude

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Abstract: The modern era is very important for Arabic language and literature. It is full of the art, the novel, the story, and the play. Many sciences and knowledge have developed in it. Novelists from different countries have fought in a variety of ways in the creativity of the novel, its techniques, and the types of plots. The Arabic novel passed through many stages until it reached maturity and perfection and developed the last forms of narrative. Raja'a Muhammad 'Alem (Arabic: ???? ????) is one of the foremost modern Arabic novelists from Saudi Arabia. Through her personal experiences, Alem provides a glimpse into the private lives of local women who experience pressures very different from those faced by typical female visitors to Mecca. Her novels delve into the social restraints of society, which often forbid the voicing of issues and concerns such as women's rights, forced marriages, and emotional issues like love and relationships between the sexes, as these topics are deemed objectionable. 'Alem addresses the problem of women seeking to identify their identity by illustrating the reality of being continually veiled and unable to express themselves through visible methods. She is an active writer of prose, and her unique style, blending modern elements with traditional themes, sets her apart among Saudi authors. This article attempts to depict the position of Raja'a 'Alem and her contributions to Arabic novels.

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modern era, personal experiences, glimpse into the private lives, social restraints, forced marriages.

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