Dr. B.R. Ambedkar National Law University

Abstract The early diagnosis of Lung Cancer (LC) is crucial for improving the Survival Rate (SR) of patients. However, traditional Internet of Medical Things (IoMT) approaches face challenges regarding data privacy, storage, and security limitations. This research proposes a novel, secure, and efficient IoMT-based LC identification system. The hypothesis is that integrating effective encryption techniques, optimization, and Deep Learning (DL) will enhance the accuracy and efficiency of Lung Cancer Diagnosis (LCD) while ensuring data security. The methodology involves acquiring Computed Tomography (CT) scans through IoMT sensors. Similarly, the collected data is embedded using Odd Exponential Even Entropy-Lifting Wavelet Transform (OEEE-LWT)-based watermark embedding since it ensures data integrity, authenticity, and protection against tampering. Then, the embedded data are stored in the temporary storage layer. Afterward, the data is optimized by using the Transfer Function-Red Panda Optimization (TF-RPO) technique and then transferred to the gateway layer. Similarly, the data is encrypted using the Tangent Hyperbolic Chaotic Cryptosystem (THCC), ensuring confidentiality by protecting sensitive patient information from unauthorized access. Further, the encrypted data are transferred to the Lung Disease Diagnosis Model (LDDM). In LDDM, the Lung Cancer Prediction (LCP) is done by using the Transfer Learning-Saturated Wave-Convolutional Neural Network (TL-SW-CNN). Additionally, the Local Linear System-Fuzzy Inference System (LLS-FIS) predicts a 5-year SR based on extracted features like lung region and lymph nodules. Data balancing is performed using the Ranked Reverse Synthetic Minority Over-Sampling Technique (RR-SMOTE), and encryption is achieved with a Tangent Hyperbolic Chaotic Cryptosystem (THCC) to ensure security in distributed environments. Experimental results show that the proposed system achieved 98.01% classification accuracy and 97.93% precision, thus significantly reducing computational complexity. Also, when compared to the conventional models, such as CNN, RNN, DLNN, and ANN, the proposed TL-SW-CNN takes a minimum training time of 52389 ms, thereby reducing the computational time by 36.4%. The key contributions include enhancing data privacy, class balancing, LC prediction, and SR identification. Despite the strengths of the proposed system, attack detection has not been addressed, and this will be included in future work.

The main aim of this study is to explore the role of gender-based digital financial inclusion (GDFI) and women empowerment in poverty reduction in Asian countries using a panel data set from 2011 to 2023. For this purpose, the GDFI index, consisting of various indicators, was constructed employing principal component analysis (PCA). It also uses econometric techniques like the cross-sectional dependence (CSD) test, second-generation unit root tests such as CADF and CIPS, and traditional cointegration tests such as Pedroni and Kao. Further, CS-ARDL, Feasible Generalized Least Square (FGLS) Driscoll–Kraay (DK) standard errors, and Pairwise Dumitrescu and Hurlin panel causality test were applied. It was found that gender-based DFI and women's empowerment have a significant role in poverty reduction. They both influence positively in increasing household consumption. Moreover, inflation in the economy negatively influences poverty reduction. Access to financial independence among women can enable them to improve their bargaining power in households. Access to banks can increase their access to capital and lead to entrepreneurial activities. Hence, it is creating a more inclusive and sustainable development society. Further, the causality test findings suggested a bi-directional causal relationship between GDFI and poverty reduction, female labour force participation (FLP) and poverty reduction, and a unidirectional relationship between inflation and poverty reduction. The findings imply that enhancing gender-based digital financial inclusion (GDFI) and women's empowerment can significantly contribute to poverty reduction by increasing household consumption and financial independence.

Global plastic production has risen from 2 million metric tons in 1950 to over 400 million in 2022 and is projected to triple by 2060. Constituents like toxic additives to pervasive microplastics pose a major environmental and public health crisis. Yet international action remains fragmented. The UN Intergovernmental Negotiating Committee (INC) is drafting a global plastics treaty, but INC-5.2 (August 2025) revealed sharp divides. High-ambition states and civil society demand binding caps on virgin plastic, elimination of single-use plastics, and bans on hazardous additives, while oil-producing and manufacturing nations oppose upstream measures, prioritising recycling and waste management. Industry lobbyists have outnumbered many delegations, raising concerns of policy capture reminiscent of tobacco industry tactics before the WHO Framework Convention on Tobacco Control (FCTC). Lessons from the FCTC, particularly Article 5.3 safeguarding policymaking from vested interests, are vital. Without binding commitments and protection from corporate influence, the treaty risks being ineffective.

In today’s technology-driven environment, individuals utilize various software applications to accomplish their daily tasks. The legal field is no exception, as technology plays a crucial role in the work of lawyers and the knowledge, skills and attributes they require. Standing on technology has significant implications for the curriculum necessary for law students aspiring to enter legal practice. The integration of digital copyright legislation into legal education addresses the current challenges that educational institutions and learners must navigate to prepare future lawyers for the complexities of the technological age. This research also examines how legal education emphasizes equipping students with the tools needed to adapt to the evolving landscape of online intellectual property rights. The study identifies several major challenges in incorporating digital copyright law into legal education, including the rapid pace of technological change, the need for up-to-date and relevant course materials, and the disparity between theoretical knowledge and practical skills. Furthermore, it underscores the importance of cultivating technical proficiency among law students, ensuring they understand the tools and platforms that influence digital copyright issues. The findings indicate that legal education must evolve to meet the demands of the digital age by placing greater emphasis on practical training, interdisciplinary learning and ethical considerations. This article recommends specific strategies for curriculum development, including the incorporation of case studies, simulations and collaboration with technology experts. This study contributes to the ongoing discourse surrounding legal education reform by providing a comprehensive examination of digital copyright law, which is becoming increasingly significant yet is often underrepresented in standard curricula. It offers valuable insights for educational institutions and equips students with the skills and knowledge necessary to effectively address digital copyright issues.

A study on butterfly habitat and distribution was conducted in Silonijan, Karbi Anglong, Assam, a biodiversity hotspot near Nambor and Garampani Wildlife Sanctuaries. The study documented 51 identified butterfly species belonging to 6 families and 33 genera. Nymphalidae family was found to be the most dominant (37%; 12 genera, 19 species) compared to Papilionidae (21%; 3 genera, 11 species), Lycaenidae (18%; 6 genera, 9 species), Pieridae (16%; 6 genera, 8 species), Hesperiidae (6%; 3 genera, 3 species) and Riodinidae (2%; 1 genus, 1 species). The calculated Shannon-Wiener and Simpson diversity indices revealed that residential areas had higher species richness and diversity (3.6 and 0.97) than the cultivated areas (3.03 and 0.94). This highlighted the rich butterfly diversity in the study areas, underscoring the need to maintain biodiversity and ensure ecosystem sustainability in relation to changes in land use patterns due to the increase in human population and settlements.