Volume 13 | Issue 12 |

 Abstract

The rapid adoption of Industrial Internet of Things (IIoT) in the pharmaceutical sector has significantly enhanced automation, real-time monitoring, and intelligent decision-making. However, increased connectivity exposes Pharma IIoT systems to sophisticated cyberattacks, including false data injection, ransomware, and denial-of-service attacks, posing risks to drug quality and patient safety. This paper proposes a self-learning digital twin-enabled security framework that integrates real-time telemetry, digital twin modeling, and deep reinforcement learning (DRL) for dynamic attack detection and mitigation. The framework leverages a digital twin environment to simulate operational and attack scenarios, enabling the DRL agent to learn optimal defense strategies. Experimental results demonstrate that the proposed approach outperforms baseline models in terms of accuracy, F1-score, false positive rate, and detection latency, achieving a detection accuracy of 95.8% and a latency of 12 ms. The framework ensures proactive, adaptive, and resilient cybersecurity for next-generation Pharma IIoT systems, providing a viable solution for real-time threat mitigation and operational safety.

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 Keywords

Pharma IIoT, Digital Twin, Deep Reinforcement Learning, Cybersecurity, Real-Time Attack Detection, Self-Learning Framework, Industrial IoT Security

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Marine pollution has emerged as a critical global environmental challenge, driven primarily by land-based discharges, shipping, oil spills, overfishing, and the exponential rise of plastics and microplastics that now permeate water, sediment, and marine food webs, thereby undermining biodiversity, food security, coastal livelihoods, and human health, particularly in developing maritime nations such as India with long, densely populated coastlines and rich yet fragile marine ecosystems. At the international level, the United Nations Convention on the Law of the Sea (UNCLOS) establishes broad obligations on all states to protect and preserve the marine environment and to prevent, reduce, and control marine pollution from diverse sources, including land-based activities, dumping, and vessel-source pollution, which are further operationalised through specialised treaties such as MARPOL, regulating pollution from ships across several technical annexes, and the London Convention and its 1996 Protocol, which restrict dumping of wastes at sea through permitting systems and strict prohibition lists. In the Indian context, the extensive coastline faces complex and cumulative pollution pressures from industrial effluents, untreated sewage, port and shipping activities, coastal construction, tourism, and rapidly increasing plastic and microplastic litter, resulting in habitat loss, coral stress, eutrophication, and contamination of commercially important fish species that support coastal communities. India has adopted a range of legal and policy measures--such as implementing MARPOL obligations through domestic rules under the Environment (Protection) Act, port state controls, and coastal regulation norms--to manage ship-source pollution and coastal development, yet enforcement gaps, fragmented institutional jurisdiction, limited monitoring capacity, and low community participation significantly constrain their effectiveness in practice. Emerging issues such as microplastics highlight a sharp global-local governance deficit: they are now ubiquitous in Indian coastal waters and marine biota, while international and national frameworks still rely largely on general obligations under UNCLOS and scattered soft-law or sectoral initiatives rather than detailed, binding rules specifically tailored to marine plastic pollution. Consequently, addressing marine pollution from both Indian and international perspectives demands integrated, multi-scalar strategies that combine stronger international cooperation, stricter and more coherent national implementation, improved land-based waste management, science-based monitoring, and participatory coastal governance, with particular attention to vulnerable regions such as coral islands, estuaries, and mangrove belts that serve as ecological buffers yet remain acutely exposed to cumulative anthropogenic pressures.

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 Keywords

Marine Pollution, United Nations Convention on the Law of the Sea (UNCLOS),

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

With the rapid expansion of digital education, designing structured and standardized academic courses manually has become time-consuming and inefficient. This project presents an AI-based Course Generator developed using the MERN stack (MongoDB, Express.js, React.js, and Node.js) to automate the creation and management of course structures. The proposed system generates course outlines, modules, learning objectives, and assessment plans based on predefined rules, templates, and user-selected parameters such as subject, academic level, duration, and difficulty. The application provides an interactive web interface for instructors and administrators to design, customize, and manage courses efficiently. MongoDB is used for storing course templates and user data, while React ensures a responsive user experience. The system improves consistency, reduces manual workload, and enables scalable course generation without relying on advanced language models or natural language processing techniques. This approach makes the solution cost-effective, transparent, and suitable for educational institutions and training platforms seeking structured course development.

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 Keywords

Artificial Intelligence, MERN Stack, E-Learning, Automated Content Generation, Chatbot, Personalized Learning

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Structured Teaching Intervention, Pulmonary Rehabilitation, Chronic Obstructive Pulmonary Disease

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Nagendra Jamatia, a distinguished voice in Kokborok literature, a prominent dramatist is known for using his works to address social issues and encourage cultural reflection within the Tripuri community. His 2001 play, Hakhor Bisingni Nokhorbaisidi, tackles the deep-rooted superstitions surrounding witchcraft and black magic, particularly the belief in Swkal (witches), which has historically fueled fear and violence. The protagonist, Hathai, serves as a reformative figure in the play, striving to break the cycle of fear and ignorance that grips his village. In a society where education is limited, villagers often resort to blaming illness or misfortune on individuals accused of witchcraft, leading to discrimination and at times, violence. Through Hathai's dialogues and interactions, Jamatia challenges these superstitions, encouraging the villagers to embrace a rational perspective. Hathai's journey, however, is obstructed by the village ochai (priest) and Baruwa (the priest's assistant), who represent the traditional stronghold that resists change. Despite these obstacles, Hathai remains determined, driven by a belief in truth and social harmony. In addition to critiquing superstition, the drama reflects Jamatia's concerns about the effects of inadequate education on youth. Hathai's efforts extend to guiding young villagers toward education and self-improvement, offering a path out of ignorance and toward empowerment. Hakhor Bisingni Nokhorbaisidi thus encapsulates Jamatia's commitment to challenging harmful beliefs and promoting rational thought and cultural preservation, illustrating his role as a transformative figure in Kokborok literature.

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 Keywords

Literature, oral literature, written literature, drama, types of drama, Tripura drama, , kokborok drama, Hathai.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Supervisory Control and Data Acquisition (SCADA) systems constitute the critical backbone of modern industrial infrastructure, orchestrating operations in power grids, water treatment facilities, and manufacturing pipelines. As Operational Technology (OT) converges with IT networks (IIoT), these previously isolated systems are increasingly exposed to sophisticated cyber threats. Traditional intrusion detection mechanisms, typically reliant on static signatures, fail to identify novel, zero-day attacks or stealthy anomalies that mimic normal operational behavior. This research proposes a robust, machine learningdriven Intrusion Detection System (IDS) leveraging the Extreme Gradient Boosting (XGBoost) algorithm to classify high-dimensional SCADA network traffic. To address the "black-box" nature of ensemble models--a significant barrier to adoption in safety-critical environments--we integrate SHapley Additive exPlanations (SHAP) to provide granular, instance-level interpretability. Experimental validation on a dataset of 4,618 SCADA samples demonstrates that the proposed model achieves an accuracy of 95.45% and an attack detection recall of 0.99, significantly outperforming baseline methods. The integration of SHAP further allows security analysts to pinpoint specific sensor features driving each alert, enhancing trust and response efficacy.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

SCADA Security, Industrial Control Systems (ICS), Intrusion Detection System (IDS), XGBoost, Explainable AI (XAI), SHAP, Cyber-Physical Systems.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Due to human errors and the slow nature of the operation of hand gates, railway crossings are a very high accident zone. As automation is being adopted more and more, the demand for intelligent systems that are reliable and economically efficient is evident. This paper describes a fully automated crossing gate mechanism was developed using magnetic Hall sensors for train detection, an infrared module for obstacle sensing, an Arduino-based controller, servo-driven gate movement, and a solar-supported power unit. The system detects an approaching train, closes the gate, and monitors the track for any obstacles before the train passes. The system is designed to eliminate manual gate operation, lower the risk of an accident, and improve safety. A complete model of the system was built. All systems were reliable. The model was tested on a prototype system, and the gate control and the monitored active alerting real-time control were all done safely and reliably.

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 Keywords

Railway Automation, Hall Sensor, Arduino UNO, Railway Safety, Automatic Gate Control.

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Creative Commons Attribution 4.0 and The Open Definition

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Education has occupied a central position in debates on socio-economic development for more than half a century. From early economic theories that framed education as an investment in human capital to contemporary approaches that view education as a process of capability expansion and social transformation, scholars have sought to understand how education shapes development outcomes. This article presents a critical narrative review of major theoretical perspectives and empirical studies on the relationship between education and socio-economic development. Drawing on economics, sociology, and development studies, the review examines human capital theory, modernization perspectives, the capability approach, and social reproduction theory, alongside empirical evidence from both developed and developing contexts. The article argues that while education contributes significantly to economic growth and social mobility, its developmental impact is contingent upon structural conditions, institutional quality, and patterns of inequality. By synthesising diverse strands of literature, the paper highlights the limits of education-led development models and calls for a more context-sensitive and equity-oriented understanding of education's role in socio-economic change.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Education, Socio-economic development, Human capital, Capability approach, Inequality, Social reproduction.

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Creative Commons Attribution 4.0 and The Open Definition

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Agriculture plays a critical role in global food security and the economy. However, one of the major challenges in agriculture is the early detection and control of plant diseases, which, if left untreated, can lead to significant yield losses and reduced crop quality. Traditionally, plant disease detection relies on manual inspection by agricultural experts, which is often time-consuming, subjective, and not always accessible, especially in rural or underdeveloped regions. With the advancement of artificial intelligence (AI), particularly machine learning (ML), there has been a growing interest in applying these technologies to automate the process of plant disease detection. Machine learning techniques allow systems to learn from large datasets of plant images and accurately identify disease symptoms, often even before they become visible to the naked eye. In recent years, image-based plant disease detection using machine learning has gained popularity due to the availability of large datasets, advancements in computing power, and the development of powerful algorithms such as Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and Random Forests. These models can be trained to recognize complex patterns and classify different types of diseases based on visual symptoms like color, texture, and shape of the affected leaves or fruits.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Plant Disease Detection; Agriculture; Machine Learning; Artificial Intelligence; Image Processing; Convolutional Neural Networks; Crop Health Monitoring; Computer Vision

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 Abstract

Infrastructure in both cities and rural areas often falls apart without anyone noticing, which can cause failures, service interruptions, and safety risks. Vigilant AI: Proactive Infrastructure Guardian is a smart, AI-powered system that uses computer vision, machine learning, and data from many sources to find early signs of cracks, blockages, damage, and wear on structures. By shifting from reactive maintenance to predictive monitoring, the system reduces costs, improves response time, and enhances the longevity of public infrastructure. With a scalable cloud edge architecture and real time dashboards, it empowers authorities to make faster, data-driven decisions. The solution is designed to be inclusive, efficient, and suitable for both smart cities and low-connectivity rural environments, ensuring safer and more sustainable infrastructure management.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Predictive maintenance, AI-driven infrastructure monitoring, computer vision, structural health monitoring, real-time anomaly detection, deep learning, cloud edge architecture, crack detection, damage classification, structural degradation analysis, automated inspection, risk assessment, smart city infrastructure, rural infrastructure management, sustainable infrastructure systems.

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