Oriental University

Investments are evolving rapidly, with individuals now considering not only financial returns but also environmental, social, and governance (ESG) factors while making decisions. Investors are increasingly interested in the impact of these factors alongside traditional financial returns. Impact investing is gaining momentum in the investment sector and is beginning to align with community engagement. By integrating impact investing with community involvement, various social welfare initiatives can be developed, benefiting society and its people. <br><br><b>Purpose</b> This chapter aims to raise awareness of impact investing and community engagement, promoting these concepts as pathways for advancing social and community development. It seeks to educate the target audience on the ESG framework, inspiring individuals to recognize that, as we benefit from society, it is our ethical responsibility to give back. <br><br><b>Methodology</b> This chapter aims to explore new investment strategies and spread awareness of the same. It draws upon an in-depth analysis of existing literature, desk research, and case studies. <br><br><b>Outcomes</b> This chapter will provide in-depth knowledge on concepts such as impact investing and community engagement, exploring how financial returns can be aligned with the ESG framework. It aims to motivate and inspire investors to make investment decisions that are more ethical and supportive of social welfare.<br><br><b>Originality</b> The research offers insights into the market, data, case studies, innovative technologies, transfer and development of technology trends, good practices, and analysis of investment and investment strategies. It attempts to raise member state stakeholder’s technological acumen to meet the demands of the fast-paced business and technology environment of today.

Green bonds have emerged as a key instrument in the rapid evolution of sustainable investment, which has been fueled by changing rules, technology breakthroughs, and increasing investor demand. The green bond market has grown significantly in India, greatly advancing the country's sustainability objectives. This chapter explores potential directions for sustainable investment with a particular emphasis on the Indian green bond market. It examines the effects of technological developments that improve efficiency and transparency, legislative frameworks that support green financing, and the growing interest among investors in sustainable ventures. India's green bond programmes have contributed significantly to the mitigation of climate change and the advancement of environmental sustainability by funding a large number of renewable energy projects, increasing energy efficiency, and encouraging the construction of sustainable infrastructure.

The minimum wage is a payment that paid to a person with the expectation that it will cover their basic needs, such as food, drink, housing, health, culture, and socialization, at a minimum level for a month. In developing countries, the proportion of workers earning the minimum wage has been higher compared to developed countries. More importantly, the disadvantages arising from being on a minimum wage can disproportionately affect women and men. The focus of this study is to investigate this unequal distribution, with a focus on women. To this end, the economic difficulties women experience within the household, in families where the sole earner is on a minimum wage, and thus where the only income is the minimum wage, have been examined. A semi-structured survey was applied to housewives in 100 families with these characteristics, and in-depth interviews were conducted with 30 of them. As a result of the study, in line with our hypothesis, it was found that in families where the sole earner receives the minimum wage, and thus the only income is the minimum wage, women are more negatively affected than men due to the low living standards associated with the minimum wage.

With the rapid growth of multimedia data transmission for secure and reliable communication has become critical due to increasing cyber threats. This paper presents a Chaotic Map-integrated Quantum-Inspired Multi-Objective Cuckoo Search (CMQICS)-based watermarking approach to achieve high imperceptibility, robustness, and embedding efficacy. The proposed approach integrates quantum-inspired cuckoo search optimization with chaotic mapping to enhance watermark embedding. A multi-image watermarking scheme is also used to strengthen payload capacity while minimizing visual distortion. The embedding process operates in the frequency domain using a hybrid Discrete Cosine Transform-Two-Dimensional Discrete Wavelet Transform (DCT-2DWT) combined with a zig-zag scanning strategy. This ensures attack resilience. The experimental results show that CMQICS achieves a Peak Signal-to-Noise Ratio (PSNR) of approximately 89 dB, a Structural Similarity Index Measure (SSIM) of 0.99, and an average embedding time of around 1s. Randomness analysis further validates the security of the embedded watermark. The comparative evaluations states that the CMQICS outperforms existing approaches.

The integration of touch DNA analysis with nano-forensic technology presents a revolutionary advancement in the fieldof criminology, offering a new frontier in forensic investigations. Touch DNA refers to the genetic material left behindfrom even the slightest contact with an object or surface, making it a valuable tool for identifying individuals involved ina crime scene. Traditionally, the collection and analysis of such minute samples posed challenges in terms of sensitivityand accuracy. However, recent advancements in nanotechnology have significantly enhanced the capability to detect andanalyse trace amounts of DNA with unprecedented precision.Nano-forensic technology employs nanoscale materials and devices that can magnify the analysis of biological evidenceat the molecular level, improving the detection of DNA from touch samples. These innovations enable forensic expertsto extract, amplify, and sequence DNA from surfaces previously deemed too contaminated or insufficient for traditionalmethods. The development of nanoparticles, biosensors, and nanochips has led to the creation of more efficient,faster, and cost-effective DNA analysis tools, which are particularly useful in criminal investigations where evidencemay be scarce or compromised.Methodologically, this study evaluates DNA recovery rates across various surfaces (e.g., non-porous vs. porous materials)and compares extraction techniques, highlighting the superiority of silica-coated MNPs (92% recovery) over conventionalmethods (65–72%). Nano-assisted PCR and microfluidic systems reduced processing time to under 1 hour whilemaintaining high precision (99.1% allele calling accuracy). Emerging technologies like CRISPR-based enrichment andnanopore sequencing further address challenges in mixed DNA profiles and inhibitor-rich samples.Results demonstratethat nano-forensics enables viable DNA profiling from previously undetectable traces, such as fingerprints on fabricsor degraded cold-case evidence. Field applications showed a 78% success rate in generating actionable leads from lowtemplateDNA. The integration of artificial intelligence and portable nano-devices promises real-time, on-site forensicanalysis, transforming criminal investigations.This paper concludes that nano-forensics not only overcomes the limitations of traditional touch DNA analysis butalso sets a new standard for forensic science, offering unparalleled resolution in justice-driven applications. Futuredirections include standardization and cost reduction to facilitate global adoption.

Abstract This chapter details Preparing for Reading which is the first pedagogic cycle of the Reading to Learn (R2L) genre-based pedagogy literacy approach. In R2L, focus model texts on a specific genre that are beyond the independent reading level of the most proficient students in a class serve as foundation for all pedagogical sequences. Strong frontloaded support through Preparing for Reading makes it possible for all learners—including those with low literacy levels and those whose first language is not English—to access key knowledge through reading. The chapter illustrates this first pedagogic cycle in the context of an upper elementary unit on biographical recounts which uses a model text about environmental youth activism. The chapter shows how by “lifting all boats,” the Preparing for Reading curricular cycle sets the stage for equitable academic growth as it provides a differentiated yet solid starting point so that all students (including multilingual students) not only understand the focus text with full and deep comprehension but are also ready to produce a similar model text in the focus genre that puts them on the path to academic literacy.

Cloud-based centralized credit scoring is accurate but suffers from network delays, high bandwidth costs, privacy concerns, and non-transparent decision-making processes. We present an edge intelligent finance framework that combines shallow neural models with symbolic, rule-based reasoning to make real-time, transparent loan decisions in the wild over low-cost edge devices. Trained via federated learning on popular benchmarks (German Credit, LendingClub, FICO), the framework achieves competitive discrimination performance (AUC ≈ 0.83) while reducing inference latency to 50ms and energy use to 0.3J/inference compared to their centralized deep models. An interpretable validation layer is proposed to encode fairness constraints and regulation rules, which outputs the interpretable rationales and group-gap metrics (<0.05). Under bandwidth constraints, stress tests indicate that accuracy and response times remain stable, as inference is performed locally. Our results demonstrate that under edge AI, efficient lending workflows and peer-to-peer ecosystems can be enhanced with privacy, fairness, and compliance through a series of information yellow-red-green cycles. We also address scalability, rule complexity, and deployment recommendations for financial institutions and regulators.