Rapid urbanisation and rising transport-sector emissions have intensified the search for sustainable urban mobility solutions. The Circular Economy (CE) offers a regenerative alternative to linear transport planning by promoting resource efficiency, material reuse, electrification, and multimodal integration. This paper examines how CE principles can be embedded in urban transport planning, with Kochi, Kerala, as the primary case study. Drawing on a systematic literature review, analysis of national and state policies, three international case studies (Amsterdam, Norway’s GoGreen Project, and Zurich), and a primary user survey across three mobility zones in Kochi (Vyttila, Kakkanad, and Fort Kochi), the study evaluates the extent of CE integration in existing transport infrastructure. Findings indicate that CE-aligned strategies such as multimodal integration, electric mobility adoption, shared transport systems, and waterway-based commuting are already demonstrating measurable positive outcomes in Kochi’s mobility landscape. However, significant gaps persist in material recovery systems, non-motorised transport (NMT) infrastructure, last-mile connectivity, and circular procurement. The paper proposes targeted recommendations for strengthening CE-integrated transport planning in medium-sized Indian cities, contributing to the broader discourse on sustainable urban mobility in the Global South.

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In institutional environments, canteens often face operational inefficiencies due to manual order handling, long queues, and lack of proper management systems. These issues not only reduce service speed but also affect customer satisfaction. This paper presents a Smart Canteen Management System designed to digitize and automate food ordering and administrative operations using modern web technologies. The system is developed using Spring Boot for backend processing, MySQL for structured data storage, and HTML, CSS, and JavaScript for an interactive user interface. It enables users to browse menus, place orders, and receive confirmations digitally, while administrators can efficiently manage menu items and monitor orders. The proposed system minimizes human errors, reduces waiting time, and improves operational efficiency. Experimental evaluation shows significant improvement in response time, accuracy, and system reliability under concurrent usage. The system provides a scalable and cost-effective solution for modern canteen management.

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This report presents a design for an attendance system using device cameras (student laptops or smartphones) and optional fixed cameras. The system captures faces, detects them, and identifies each person via deep-learning embeddings (e.g. FaceNet) against an enrolled database. Liveness checks (texture analysis, eye-blink detection, etc.) ensure security against photo/video spoofing. Attendance entries (timestamps) are logged in a database. Only authenticated teachers or admins can access the system. The implementation uses open-source tools (OpenCV, TensorFlow/PyTorch, Flask) and low-cost hardware (Android/iOS devices, Raspberry Pi, Jetson Nano). Key evaluation metrics include recognition accuracy, FAR/FRR, latency, and throughput. Based on similar systems, we expect >95% accuracy and sub-second processing. Privacy is handled by storing only encrypted embeddings and following India's DPDP 2023 guidelines. We include architecture diagrams, comparative tables (algorithms/datasets/hardware), and a development timeline. We propose an automated attendance system leveraging students' own devices (laptop/phone cameras) and optionally campus cameras (CCTV/RTSP). Attendance is recorded twice daily without manual roll calls. The software runs on personal devices (browser or app) for convenience. Using lightweight CNN models (MobileNet, FaceNet, etc.), the system achieves high accuracy. Anti-spoofing (texture CNN + blink/motion checks) is integrated to prevent fraudulent sign-ins. A secure portal allows only teachers/admins to log in and view records. We detail system requirements, methodology, architecture, implementation, evaluation plan, and ethical considerations. Data privacy is a priority, with encrypted face embeddings and DPDP 2023 compliance (explicit consent, data minimization). The design is scalable to large classes and multi-camera setups (including existing CCTV).

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Decision-making systems in the public sector have become dependent on AI systems for decisions with significant consequences, such as loan approvals in publicly guaranteed lending systems. Although these systems offer efficiency and minimal error margin, they incorporate systematic biases that impact marginalized communities adversely. This research explores an extensive AI bias analysis in the public sector using a hypothetical pipeline for loan approvals. The paper focuses on identifying the three main types of AI bias: (i) data bias caused by historical underrepresentation and proxy discrimination in the training dataset, (ii) model bias due to algorithmic exaggeration of spurious correlation in the training process, and (iii) human bias introduced through feature engineering, decision thresholds, and administrative overriding of the model predictions. The study uses the German Credit Dataset as a surrogate for public-sector lending data. We explore the bias-accuracy trade-off quantitatively before and after pre-processing reweighting. Indeed, the empirical evidence shows that post-mitigation fairness (in terms of DPD and EOD) improves substantially (from 0.28 to 0.06), whereas there is only marginal degradation in predictive accuracy (from 75.4 % to 72.1 %). These results correspond to the existing literature in terms of significant benefits from fairness intervention with a modest price paid in terms of accuracy loss (de Castro Vieira et al. [16]; Chen et al. [13]). To complement the discussion, the paper also considers the simulations of human overrides, thus demonstrating their potential in increasing discrimination. Overall, the research gives a clear example of how AI applications can achieve fair and yet still accurate results, which makes it relevant for the government agencies in terms of policy recommendations. It also offers a clear list of biases and how to mitigate them to achieve fair results at reasonable accuracy costs (Mehrabi et al. [14]; National Institute of Standards and Technology [2]).

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