Khwaja Moinuddin Chishti Language University

This study explores the development of students' social entrepreneurship competencies (SEC) through the combined lens of the theory of planned behavior (TPB) and experiential learning theory (ELT). Data were collected using a stratified random sampling method from 384 students enrolled in higher education institutions in Delhi, Greater Noida, and Noida. The results reveal that moral obligation, self-efficacy, and social support significantly and positively impact SEC. Although empathy does not directly influence SEC, its relationship becomes significant when moderated by experiential learning, signifying that experiential exposure enhances students' empathetic engagement in entrepreneurial contexts. The moderating effect of experiential learning also strengthens the link between moral obligation and SEC. These findings validate the role of experiential learning as a pedagogical enabler, transforming intention into competency and bridging the gap between theoretical knowledge and practical application. The study contributes to entrepreneurship education by extending TPB and reinforcing the value of ELT in shaping socially responsive, competency-driven student entrepreneurs.

This study extends the theory of planned behaviour (TPB) by examining how subjective social norms (SSN) influence pro-environmental entrepreneurial intentions (PEIs) among Indian Generation Z students through cognitive and educational mechanisms. The model conceptualizes entrepreneurial vision (EV) as a future-oriented cognitive construct and entrepreneurial self-efficacy (ESE) as a confidence-based driver of action, while positioning environmental education (EE) as a contextual moderator. Using survey data from 328 commerce and management students and structural equation modelling (SEM), the study tests a moderated mediation framework. The findings indicate that SSN positively shape EV, which in turn enhances both self-efficacy and PEI. ESE partially mediates the vision-intention relationship, and EE strengthens the influence of social norms on vision and amplifies the indirect pathway to intention. Overall, the findings refine the TPB by demonstrating how education-conditioned cognitive processes shape sustainability-oriented entrepreneurial intention formation at the formative stage of higher education, offering theoretically grounded and context-specific implications for entrepreneurship education and policy aimed at fostering pro-environmental entrepreneurial pathways among Generation Z students.

The proliferation of user-generated content on digital platforms has introduced significant challenges related to content moderation, particularly the detection of toxic, hateful, and abusive language. Consequently, artificial intelligence and NLP techniques have emerged as promising solutions for automating this task. This article investigates the use of deep learning models specifically Long Short-Term Memory (LSTM) networks and Bidirectional Encoder Representations from Transformers (BERT) for automated toxic content classification. This study utilizes both models individually and in a hybrid configuration to classify comments into three categories: safe, abusive, and hateful. The experimental setup involves pre-processing the Jigsaw Toxic Comment Classification dataset, applying tokenization, stop word removal, and balancing techniques to improve class distribution. Evaluation metrics accuracy, precision, recall, and F1-score are used to assess model performance. Results indicate that BERT significantly outperforms LSTM, achieving an F1-score of 89.6% compared to LSTM's 88.18%.

The current literature on automatic seizure detection based on EEG has obtained significant accuracy, but most of them still have difficulties in processing the highly non-linear, non-stationary, and patient-specific EEG signals. Models typically need vast quantities of training data; they do not generalize to other datasets, and they are sensitive to noise and changes in channels, making them less robust and applicable in clinical practice. To overcome them, this paper will assume a channel transformer-based generative adversarial and multi-instance attention network with a nutcracker optimizer (CTGA-MinsAN-NutO) to identify seizures reliably. The suggested structure incorporates adaptive guided multi-layer side window box filter decomposition (AGM-LSWBFD) to perform well in denoising and multi-directional shearlet transform domain (MDSTD) to carry out more efficient feature extraction. The model outperforms current benchmarks and shows better robustness in identifying ictal and interictal states, achieving 99.1% accuracy and 93.5% recall when evaluated on the Bonn as well as CHB-MIT datasets.

Infrastructure, sustainable industrialization, and innovation. Using mixed methods this paper reviews three decades of progress, chronic challenges and a sustainability roadmap in terms of secondary data retrieved from MoSPI (Ministry of Statistics and Programme Implementation), NITI Aayog, and World Bank sources on the one hand, as well as policy analyses and case studies. Manufacturing’s GDP share is expected to increase from 14% in 2014 to 17% by 2023, which is progress on account of the Make in India and PLI (Production Linked Incentive) schemes. Infrastructure jumps consist of 55,000 km of new national highways, 95.5% household electrification, and tripling airport capacity to accommodate 300 million passengers in a year. Logistics costs fell from 14% of GDP to 8-10% bringing India up the charts as the world’s fifth-largest economy. SDGS scores increased from 57.2 in 2015 to 63.5 in 2022, driven by gains in renewable energy and digital manufacturing. However, challenges persist. Industrial emissions drive 40% of air pollution and Urbanization extenuates an approaching 50% water shortage by 2030. Lagging rural infrastructure; 30% of roads in the countryside unpaved with increasing disparities. But regulatory snags, land conflicts and skills gaps for 70% of workers threaten to blunt momentum, as seen in COVID-1S-linked supply disruptions and climate risks to 60% of investments. The roadmap combines green technology, public-private partnerships, and reforms. Smart cities, circular economies, and AI logistics might cut emissions by 20-30% and create 10 million green jobs. Increased R&D to 2% GDP, NEP 2020 skill programs, and Environmental, Social, and Governance (ESG) incentives will drive innovation. Freight corridors and rural electrification are enablers of inclusivity and may raise SDGS scores to 75 by 2030.This study provides data-driven recommendations to maintain momentum on this front for India, in line with the vision of shared prosperity contained in the SDGs.