Volume 13 | Issue 12 |

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This executive journalling presents a unique set of challenges and opportunities demanding a strategic vision that balances the innovation and disciplined execution. This report framed as insights from a CEO's diary delves into the critical aspects of day-to-day micro-reflections on the leadership guide to problem-solving. From the visionary perspective of a business insider, designing a business model canvas characterized by continuous pursuit of novel ideas, technology and design thinking manages a diverse portfolio of venture navigating through rapidly evolving market cultivating decentralized leadership. It explores the niche area fostering the culture of employee engagement from nascent ideas to scalable realities. I pondered for a while and made some self-reflection to articulate and embed an overarching innovation strategy within the products and services delivered that permeates every unit. Our core mandate creates a compelling vision of paramount importance, that inspires collaboration and intelligent risk-taking systems. It also highlights balanced metrics, blending algorithms with human judgement, organizational agility and resilience. It also streamlines strategic shifts with real time optimization, triggers boundaries with continuity in seamless transition.

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INNOVATION CONGLOMERRATE: PAGES FROM THE DIARY OF A CEO

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Bhabani Bhattacharya's So Many Hungers! (1947) is a powerful social novel rooted in the historical reality of the Bengal Famine of 1943, a catastrophe that exposed the deep inequalities and moral failures of colonial India. The novel presents hunger not merely as physical starvation but as a complex condition shaped by economic exploitation, social injustice, and ethical collapse. Despite advances in science and technology, hunger continues to persist in the 21st century, intensified by global inequality, climate change, conflict, and uneven food distribution. This study addresses the continuing relevance of Bhattacharya's concern by re-reading So Many Hungers! in the context of contemporary food crises. The primary objective of the article is to examine how the novel's portrayal of hunger, inequality, and human dignity resonates with present-day realities and challenges prevailing notions of development. The study adopts a qualitative methodology rooted in close textual analysis, supported by a socio-political and Gandhian ethical framework to interpret the novel's themes. By juxtaposing colonial-era famine with modern food insecurity, the article highlights literature's enduring role as a social critique and moral warning. The study concludes that So Many Hungers! remains a profoundly relevant text, offering critical insights into the structural causes of hunger and the urgent need for humane and ethical responses in the contemporary world.

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Hunger, Inequality, Bhabani Bhattacharya, Food Crisis, Gandhian Values, Postcolonial Literature

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The automobile sector plays an important role in the Indian economy and influences consumer mobility and lifestyle choices. With increasing competition and multiple options available, consumers consider various factors before purchasing an automobile. This study aims to analyse the key factors influencing consumer purchase decisions in the Indian automobile sector. Primary data was collected through a structured Google Form survey from 60 respondents and analysed using percentage analysis. The findings reveal that fuel efficiency, safety features, brand reputation, and online reviews significantly influence purchase decisions, while price remains an important but not exclusive factor. The study concludes that understanding consumer preferences can help automobile brands design effective marketing strategies and improve customer engagement in a competitive market.

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Automobile Sector, Consumer Purchase Decision, Consumer Behaviour, Brand Reputation, Fuel Efficiency, Safety Features, Online Reviews, Indian Automobile Market

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School social workers play a critical role in promoting student well-being, educational equity, and systemic support within school environments. Recent scholarship highlights expanding responsibilities, interprofessional collaboration, workforce challenges, and contextual pressures shaping school social work practice globally. This narrative review synthesizes empirical studies published between 2022 and 2025 to examine (a) the roles and functions of school social workers, (b) interprofessional collaboration and institutional contexts, (c) workforce well-being and professional challenges, (d) implications for policy and practice, and (e) Future Recommendations. Findings indicate that while school social workers contribute significantly to student mental health, equity, and social-emotional learning, their effectiveness is constrained by high caseloads, role ambiguity, resource limitations, and systemic inequities. The review underscores the need for clearer role definitions, improved organizational support, inclusive practice frameworks, and evidence-informed policy reforms to strengthen school social work practice.

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school social work, student well-being, interprofessional collaboration, workforce challenges, educational equity

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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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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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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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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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