Bhairab Ganguly College

This concluding chapter synthesizes key findings from the edited volume Globalization, Social Dividend, and Sustainable Development Goals: Shaping a Fairer Future, offering critical insights into the complex interlinkages between globalization, social dividends, and the sustainable development goals Sustainable Development Goals (SDG). Through a multidisciplinary lens, the preceding chapters provide a comprehensive examination of global economic integration, its socio-economic implications, and sustainable development outcomes. The volume explores theoretical and empirical dimensions, covering themes such as global value chains Global Value Chains (GVC), income inequality, decent work, gender disparities, financial inclusion, climate-induced migration, environmental justice, and corporate sustainability. The analyses emphasize how globalization shapes labor markets, financial systems, and ecological sustainability while presenting policy challenges and opportunities. Moreover, country-specific studies highlight the localized impact of globalization, such as the role of women-led self-help groups in India, entrepreneurial innovation in Bhutanese small- and medium-sized enterprises Small and Medium-sized Enterprises (SME), financial inclusion’s role in economic resilience, and the investment climate for circular bioeconomy in emerging economies. These case studies underscore the necessity of tailored policy approaches that align global economic trends with national development priorities. The chapter concludes by outlining crucial areas for future research, particularly the long-term consequences of accelerated globalization, intersectional approaches to vulnerability, and the potential of digital technologies to promote inclusive development. By integrating theoretical perspectives with empirical evidence and policy analysis, this volume stresses the importance of collaborative global efforts to achieve sustainable and equitable development.

Low-Density Polyethylene (LDPE) accumulation in marine and terrestrial ecosystems poses a planetary crisis, requiring sustainable bioremediation. Existing microbial degradation studies are often limited by a reliance on abiotic pretreatments (e.g., UV or thermal oxidation) and terrestrial isolates that lack environmental resilience. To address this gap, this study investigates the biodegradation potential of novel lipolytic bacterial strains Bacillus tropicus SBAA01 and Pseudomonas aeruginosa SBAA02 isolated from the Sundarbans mangrove ecosystem, India; a UNESCO World Heritage Site & Ramsar wetland. The untreated LDPE films were incubated for 42 days under co-metabolic conditions. Upon benchmarking against a previously isolated reference strain, Bacillus pacificus SBAA07, B. tropicus SBAA01 emerged as the most potent candidate. It achieved a net dry weight loss of 13.63% ± 0.01 with a calculated half-life (t1/2) of 198 days. Topographical analysis via Atomic Force Microscopy (AFM) revealed a significant 736% increase in surface root mean square roughness (Rq), correlating with deep pitting observed in Scanning Electron Microscopy (SEM). Conversely, P. aeruginosa SBAA02 showed significant surface disruption Rq value increase of 142% but lower weight loss efficiency of 8.89% ± 0.01. Critically, while B. tropicus SBAA01 drove the highest physical erosion; it maintained a moderate Carbonyl Index (CI) of 0.184. This suggests oxidative carbonyl intermediates are assimilated into biomass faster than they accumulate on the surface. These findings suggest the Sundarbans as a genetic reservoir for stress-tolerant biocatalysts capable of degrading pristine polymers without prior oxidation. This offers a scalable, nature-based solution for plastic waste management.

Electroencephalography (EEG) is a noninvasive technique used to record brain electrical activity. It has been widely explored for brain–computer interface applications, including imagined speech analysis. However, handling and utilizing large EEG datasets have challenges due to their size. We utilize Siamese neural networks (SNNs) to improve data utilization through pairwise learning by creating multiple training combinations of EEG data. SNNs are well-suited for tasks that involve comparing two or more inputs, making them ideal for analyzing EEG signals. This research focuses on utilizing SNNs to identify different brain states based on EEG signal analysis. A novel publicly available collection of electroencephalogram (EEG) recordings extracted from the imaginary pronunciation of two sets of Spanish words by 15 healthy people is presented. The first set includes 5 Spanish vowels, while the second set represents the instructions up, down, front, back, right and left. EEG signals were captured at 1024 Hz using a six-channel acquisition device. Each word was repeated fifty times by every randomly chosen participant. For contrast, certain blocks were recorded under the "pronounced speech" setting, which involves simultaneously acquiring audio and EEG signals. Subsequently, a DWT-based preprocessing and autoencoder-assisted Siamese Neural Network with triplet loss is proposed for EEG signal analysis. The results indicate that different brain states can be accurately identified with a high degree of accuracy using the proposed methodology.

This study examines the determinants of health status in fifteen emerging economies across five world regions, classified by income sub-groups. Using infant mortality rate (IMR) as a health indicator, data from 2000–2019 were obtained from the World Development Indicators . A panel fixed effects regression model was employed to assess the influence of GDP per capita , unemployment rate , and per capita government health expenditure on IMR. Results show that both GDP per capita and government health expenditure significantly reduce IMR, highlighting that economic growth and increased public health spending improve child health outcomes. Although, there is no significant effect of unemployment rate on IMR. The presence of significant cross-sectional effects validates the use of a fixed effects model over a constant-coefficients specification. The findings underscore the need for emerging economies to strengthen economic performance and public investment in health to achieve sustainable improvements in child health. JEL Codes: I15, O47, C33

Landslides happen often and cause serious problems in the Teesta River Basin, threatening buildings, communities, and the environment. This study uses a mapping system called GIS combined with a decision-making method called AHP to evaluate where landslides are likely by combining different types of information into a risk map. The AHP method gives importance to ten factors that affect landslides: slope, rainfall, shape of the land, distance from roads, distance from streams, vegetation health (NDVI), stream power (SPI), direction the land faces, land use, and geology. Several spatial tests, like Nearest Neighbour Analysis, Moran’s I, and hotspot analysis, are used to study how landslide areas are spread out. The model’s accuracy is checked using a test called the ROC curve. The results show that slope, rainfall, and land shape are the most important factors, while land use and geology have less effect. The risk map divides the area into five zones: very low, low, moderate, high, and very high risk. About 18.6