National Institute of Technical Teachers’ Training and Research

Effective construction waste management is a pressing need for our sustainable built environment. One of the key factors in an effective waste management plan is accurately quantifying the likely waste volume generated by a project. In this study, we develop a new model to account for waste generation using work breakdown quantification (WBQ) at the design stage. Das Modell basiert auf der detaillierten, aktivitätsspezifischen Abfallbeobachtung eines laufenden industriellen Bauprojektes über 17 150 m2, die 332 267 kg Abfall produziert hat. The WBQ model links 12 major activities that generate waste to waste coefficients for unit activity (x) and per square metre (y) for 34 types of material waste. Validation with six established models (Yost and Halstead, Gheewala, Fatta, Martinez–Lage, Hsiao, and Wang) showed close agreement with WBQ (19.4 kg/m2), with an accuracy range of 18.99–21.36 kg/m2. This model also allows tendering and project delivery to consider waste management issues earlier so that zero-waste principles can be developed as part of project delivery, based on circular economy principles. The study demonstrates the environmental and economic benefits of proactive waste quantification.

Artificial Intelligence (AI) is revolutionizing contemporary marketing by bringing strategic choice from intuition-driven campaigns to data-driven, constantly evolving systems. This document provides an overview of artificial intelligence applications that are recently being utilized. These involve predictive segmentation, real-time sentiment, conversational bots, and automated media buying. In addition, critical issues around data heterogeneity, compliance with privacy, and openness of models are highlighted. Tooffer the Predictive Personalisation Engine (PPE), a modular platform that combines deep sequential learning and reinforcement learning to offer real-time content suggestions which take into account the context in which they are presented. A multi-modal consumer dataset is fed through PPE, audiences segmented by K-Means++ clustering, future behavior is predicted by LSTM networks, and best offers are decided through an epsilon-greedy contextual bandit.

The IoT Smart grid systems require privacy-sensitive, scalable, and secure communication, but the traditional cryptographic methods are susceptible to quantum attacks, central failure, and high computational costs. In this paper, the author presents a multi-layered structure that uses various yet complementary approaches in order to solve these issues. Quantum Key Distribution (QKD) is applied to create quantum-resilient key management to provide the secure transmission against future quantum attacks. Homomorphic Encryption (HE) allows the computations to be performed on encrypted data to preserve privacy and minimize the risks of exposure at some point during the processing. Federated Learning (FL) is used to assist the decentralized intelligence, where anomaly detection models are trained together without the need of transferring sensitive raw data. The combination of the two techniques produces a hybrid architecture that is both highly secure and scalable. Experimental validation on real-world smart grid data indicates the 42% communication overhead reduction versus traditional encryption, a 35% improvement in anomaly detection with FL and strong resistance to quantum attack and side-channel attack simulation. The findings support the fact the suggested solution provides end-to-end confidentiality, integrity, and performance efficiency, which will form a basis of quantum-resilience and privacy-preserving communication in future smart grid IoT network.

The rapid surge of Mobile Ad Hoc Networks (MANETs) stimulates the need for adaptive, intelligent, and secure routing mechanisms to ensure seamless communication in dynamic environments. Traditional routing protocols are battling with security threats such as wormhole attacks that disrupt routing and degrade network performance. To address these challenges, this study outlines a state-of-the-art technique, the Reinforcement Learning-Based Secure Routing Protocol (RLSRP), which leverages adaptive k-hop clustering and deep Q-Networks (DQN) to fine-tune routing decisions dynamically while mitigating security risks. RLSRP unwaveringly measures network condition by evaluating latency variations and anomalies to spot suspicious nodes, thereby enhancing route stability. The protocol implements zone-related clustering where nodes within each zone collaborate to optimise routing paths based on real-time conditions, ensuring energy-efficient communication. Current research investigated deep reinforcement learning methodologies to improve security in zone-related MANETs and ensure efficient data routing in large-scale environments. A detailed simulation-based evaluation depicts the potency of the proposed RLSRP model when compared with other reinforcement learning-based routing protocols. Using a large-scale setup–scaling up to 10 million nodes–with Dask and TensorFlow, the results show that RLSRP consistently outperforms FSSAM, Cluster-RL, and Reputation-based Q-learning in terms of Packet Delivery Ratio (exceeding 99%), reduced latency, and improved energy efficiency. These findings reported RLSRP as a secure and scalable solution for practical MANET routing.

ABSTRACT To reduce the adverse seismic effects on structures, friction dampers are commonly used as a supplemental energy‐dissipating system. Various codes have specified some guidelines for the use of such devices, but a simple and accurate design methodology is still needed for retrofitting buildings, particularly vertically irregular buildings, with these devices. In this work, a novel and simple methodology for designing friction damper in vertically irregular reinforced concrete frame buildings using an energy‐based principle is explored. For the investigation, three different irregular reinforced concrete buildings with and without damping systems, such as 5‐ and 10‐story building frames with setbacks at different locations, are examined. To validate the design method, nonlinear static pushover and nonlinear time history analysis of model buildings are used to evaluate the parameters, that is, formation of hinges, inter‐story drift ratio, and storey displacement, which are closely related to building damage. Findings show that the addition of friction dampers in vertically irregular building frames significantly improves the seismic behavior of such buildings.

Sentiment analysis is the process of using computational techniques to identify and interpret emotions expressed in text. This technique is widely used in the fields like marketing, customer service and public opinion research to understand the feeling of people about products, services, events or topics. In this context, the current study introduces an end-to-end sentiment classification pipeline that uses different text representation schemes integrated classification techniques in order to develop transformer based intelligent web application called TranSenti. Notably, among them BERT integrated classification techniques achieve higher accuracy, precision, recall and F1-score than other traditional techniques that integrate Bag-of-Words, TF-IDF and others. For this purpose, three different datasets are used. Thereafter, based on the performance of BERT integrated classification techniques, the best trained model is used to develop an intelligent web application based on Flask, enabling users to analyze individual reviews or a large set of reviews in CSV format with real time predictions. In addition, the interface provides distinct sentiment labels and confidence ratings, making it user friendly for all types of users. Overall, the study presents a scalable, high performance sentiment analysis platform that supports future extensions such as multiclass emotion recognition, multilingual capabilities and integration with live data feeds.

Natural and man-made disasters continue to pose significant challenges to communities worldwide, necessitating innovative approaches to emergency response and recovery operations. This research paper examines the transformative role of Unmanned Aerial Vehicles (UAVs) in disaster management, analyzing their applications across pre-disaster preparedness, immediate response, and post-disaster recovery phases. Through a comprehensive review of existing literature and analysis of deployment data from 2019-2024, this study evaluates the effectiveness of drone technology in enhancing situational awareness, search and rescue operations, damage assessment, and resource distribution during disaster scenarios. The research methodology employed includes systematic literature review, case study analysis, and statistical evaluation of drone deployment outcomes across various disaster types including floods, earthquakes, wildfires, and humanitarian crises. Results indicate that drone technology significantly improves response times by 35-50%, enhances data accuracy by 78%, and reduces operational costs by 40% compared to traditional methods. The study identifies key technological advancements including AI-powered image recognition, thermal imaging capabilities, and autonomous flight systems that have revolutionized disaster management protocols. However, challenges such as regulatory constraints, technical limitations, and operational complexities remain significant barriers to widespread adoption. This paper proposes a framework for integrated drone deployment in disaster management and outlines future research directions focusing on swarm intelligence, 5G connectivity, and machine learning applications. The findings contribute to the growing body of knowledge in emergency management and provide practical insights for disaster response organizations seeking to incorporate Drone technology into their operational frameworks.