Volume 13 | Issue 5 |

 Abstract

The increasing interest in natural and plant-based skincare products has led to research on various botanical extracts known for their anti-inflammatory and antioxidant properties. This study primarily aims to develop and evaluate a face serum made from leaf extracts of tecoma and guava. Guava leaves are rich in flavonoids, tannins, and vitamin C, which contribute to their strong antibacterial, anti-aging, and antioxidant effects. Tecoma leaves possess alkaloids and flavonoids that have demonstrated effective results in reducing inflammation and soothing the skin.The developed face serum was evaluated for its pH level,viscosity,stability, and compatibility with the skin. Initial findings indicate that the mixture of guava and Tecoma extracts boosts the serum's effectiveness in enhancing skin texture, minimizing acne, and delivering hydration while remaining non-irritating. This emphasizes the promise of lesser-known plant extracts in formulations for natural skincare.

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 Keywords

Herbal face serum, Antioxidant, Antimicrobial, Anti-aging, Anti-inflammatory, Reduce acne.

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Creative Commons Attribution 4.0 and The Open Definition

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Keywords: Niosomes, in situ gel, Sustained drug release, Ocular, Prednisolone sodium phosphate

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Creative Commons Attribution 4.0 and The Open Definition

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Kerala, located in the southwestern part of India, is vulnerable to various natural calamities, including floods, landslides, coastal erosion, tsunamis, and cyclones. As a result, it is considered a multi-hazard-prone state in the country. The state has a coastline of approximately 590 km, spanning nine districts. A study conducted by the National Centre for Earth Science Studies in November 2018 revealed that 60 per cent of Kerala's coastline is experiencing erosion. Notably, in 2021, 23% of Trivandrum's coastline was affected. Various research efforts have examined shoreline changes using satellite and remote sensing data, which have identified erosion hotspots based on historical data. Further investigations included setting up a laboratory model to assess the degree of erosion in selected regions. A sandy slope was created in a tank for the study, and the degree of erosion was evaluated under varying water velocities. Chitosan, a biopolymer that is usually extracted from crustacean shells of the food processing industry was used and its impact on erosion resistance was analyzed

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Coastal erosion, Field soil, Erosion Degree, Menamkulam region, Chitosan, Velocity variation

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ABSTRACT This dissertation examines the relationship between religious beliefs and resilience among young adults, aiming to determine whether religiosity serves as a predictor of psychological resilience. A quantitative research design was employed, with data collected from 200 individuals aged 18 to 30. Participants completed the Centrality of Religiosity Scale (CRS) and the Brief Resilience Scale (BRS) to measure their levels of religious beliefs and resilience, respectively. A Pearson correlation analysis was conducted to assess the strength and direction of the relationship between the two variables. The analysis revealed a very weak positive correlation, indicating no statistically significant association between religious belief and resilience in the sample. These findings suggest that, within this population, religious belief does not play a substantial role in influencing resilience. The study contributes to the broader understanding of psychological resilience in young adulthood and highlights the need to explore additional factors that may better explain individual differences in coping and recovery.

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Keywords: resilience, religious beliefs, young adults, psychological well-being, Centrality of Religiosity Scale

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Abstract: Inclusive development is an approach that emphasizes the equitable participation of all segments of society in the growth and development process, particularly focusing on marginalized and vulnerable populations. In the face of widening global inequalities, inclusive development seeks to bridge socio-economic, cultural, and spatial divides by fostering policies and practices that ensure access to opportunities, resources, and services for everyone. This paper explores the multidimensional nature of inclusive development, examining how inclusive economic growth, social protection systems, participatory governance, and environmental sustainability can collectively reduce disparities and promote holistic well-being. Special attention is given to the roles of education, healthcare, digital access, and gender equity as foundational pillars of inclusivity. The paper analyzes case studies from both developing and developed countries, highlighting best practices and challenges in implementing inclusive strategies. It underscores that while economic growth remains a critical driver of development, it is insufficient on its own without mechanisms to ensure equitable distribution of its benefits. Inclusive development demands a shift from top-down models to community-driven approaches that empower local voices and enhance agency among underrepresented groups. Furthermore, the study addresses the impact of globalization and technological change, which, while offering new opportunities, can also exacerbate exclusion if not managed thoughtfully. Policy recommendations include strengthening institutional capacities, investing in human capital, and creating inclusive policy frameworks that align with the Sustainable Development Goals (SDGs). The paper concludes that bridging the gap through inclusive development is not only a moral imperative but also a pragmatic strategy to achieve social cohesion, economic resilience, and long-term sustainability. By placing inclusivity at the core of development planning and practice, societies can unlock the full potential of their diverse populations and build more just and equitable futures.

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Inclusive development, social equity, marginalization, policy intervention, sustainable development

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Creative Commons Attribution 4.0 and The Open Definition

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Corporate crime is a kind of "white-collar crime" where people commit crimes for the advantage of their employer while working in their legal jobs. Such persons generally do not think of themselves as criminals, nor do they consider their behaviours criminal. Professional white-collar crime, or crime done by those who identify as criminals and make crime their only source of income, is related to corporate crime. Corporate crime, encompassing illegal activities such as fraud, money laundering, tax evasion, and insider trading, poses significant threats to the stability and integrity of the international financial system. Such crimes undermine market trust, distort competition, and introduce systemic risks, often leading to financial crises and global economic disruptions. High-profile corporate scandals erode investor confidence, prompting stricter regulatory measures and increasing compliance costs for businesses worldwide. Furthermore, the cross-border nature of many corporate crimes amplifies their impact, affecting international trade, diplomatic relations, and economic stability. Addressing this global challenge requires coordinated efforts between nations, robust enforcement of regulations, and the promotion of ethical business practices to safeguard the financial system's credibility and sustainability. Corporate fraud poses a significant threat to the integrity of global financial markets, eroding investor confidence, disrupting economic stability, and undermining regulatory frameworks. The Satyam scam, one of India's largest corporate frauds, exposed critical weaknesses in corporate governance, financial reporting, and regulatory oversight, leading to severe repercussions on stakeholders both domestically and internationally. The scandal not only resulted in massive financial losses but also highlighted the challenges associated with detecting and preventing fraudulent practices in a rapidly globalizing economy. Despite existing regulations and auditing mechanisms, the Satyam case demonstrated how systemic failures within corporations can deceive stakeholders for extended periods, Which ultimately impacting stock markets, foreign investments, and the reputation of emerging economies. This issue raises critical concerns regarding the effectiveness of corporate governance policies, the role of auditors in financial transparency, and the need for more robust international cooperation to prevent similar financial crimes. The problem, therefore, lies in identifying the gaps in corporate governance and financial oversight that allowed such fraudulent activities to persist, understanding their far-reaching consequences on the international financial system, and exploring strategies to strengthen regulatory mechanisms to prevent future occurrences. Addressing these concerns is crucial to ensuring sustainable economic development and maintaining investor trust in the global marketplace. My aim and objective in this paper is to study about how corporate crimes affects international financial market at large and what steps are taken by the Appropriate authorities around the world. What legal frameworks are there to prevent such crimes. These crime affects the market which don't affect society in a direct way but slowly affects the financial structure of the society at large. Somehow corporate crimes not only disrupt financial systems but also violates the human right for their own benefits. As discussed in lafarge cement case study, international laws are not very well deployed to counter human rights violation . These corporate giants have money and influence in poor region to counter these allegations. As in legal field jurisdiction is of utmost importance. Internationally these jurisdiction plays as a hurdle to the justice. Access to justice can be limited due to factors like corruption, lack of legal expertise, or economic dependency on the corporation for jobs and development. Local authorities may hesitate to investigate or prosecute large corporations out of fear of economic retaliation or political pressure. This allows companies to sometimes evade accountability, despite credible evidence of corporate crimes such as environmental damage, labour exploitation, or illegal payments. Major hurdle is the issue of jurisdiction in international law. Jurisdiction refers to the authority of a legal body to hear and decide a case. In cases of corporate crime, especially when it involves operations across borders, it is often unclear which country's courts have the authority to try the case. Even when a corporation commits violations in a developing country, legal action must often be pursued in the home country where the corporation is headquartered--which can be legally complex, expensive, and time-consuming. Moreover, different countries have different standards of evidence, legal definitions of crimes, and procedural rules, making cross-border litigation extremely challenging. Some corporations exploit these jurisdictional gaps by structuring their businesses in a way that makes prosecution more difficult--for instance, using subsidiaries or shell companies in countries with lax enforcement. This legal fragmentation acts as a barrier to justice, allowing powerful entities to avoid full responsibility for their actions.

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 Keywords

IMF - International monetary fund NBFC - Non-banking financial company WW - World war HSBC - Hong-Kong and Shanghai banking corporation FATF - Financial action task force SEBI - Securities and exchange board of India OECD - Organisation for economic co-operations and development AML - Anti money laundering CFT - Combating financing of terrorism INTERPOL - International criminal police organisation UN - United nations WTO - World trade organisation StAR - Stolen asset recovery

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This Paper introduces a novel approach that combines Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) to improve the text-to-image generation process. The RNN component is responsible for interpreting and encoding the sequential nature of the textual input, effectively capturing its meaning and context. Meanwhile, the CNN focuses on generating and refining image features that visually represent the encoded semantics. By integrating these two architectures, the model can produce high-quality synthetic images that accurately reflect the given textual descriptions. This fusion leverages the contextual understanding of RNNs and the visual generation power of CNNs, resulting in an efficient and coherent text-to-image synthesis framework.

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Text-to-Image Synthesis, Synthetic Image Generation, Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs).

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Abstract: A study to assess knowledge regarding mental health care act 2017 among staff nurses working in selected psychiatric units at Meerut in order to develop an information booklet. The objective of the study were To assess the knowledge regarding MHCA 2017 among staff nurses at selected psychiatric unit. To findout association between level of knowledge on MHCA 2017 with their selected demographic variables. To develop information booklet regarding MHCA 2017. The research approach adopted for a descriptive research design convenient sampling purposive sampling technique the setting of the study was selected Mental Health Unit at Meerut was used to collect the samples. The population comprised of staff nurses at psychiatric unit. Convenient sampling purposive sampling technique was used to select 40 staff nurses working. A self-structured knowledge Questionnaire was prepared to assess the knowledge regarding Mental Health Care Act 2017 among staff nurses working in selected psychiatric unit at Meerut. the reliability of the tool, feasibility was verified through a tryout and pilot study. The reliability of the tool was established by Test retest method formula. The data collected was analysed using descriptive statistics. As per Age in year, with 65% aged between 21-25 years , and by 25% in the 26-30 age range, A small portion falls between 31-35 (8%) and 36-40 years (3%). As per gender distribution, 65% are female, while 35% are male. As per religion most participants identify as Hindu (83%), with smaller proportions identifying as Muslim (13%) and Christian (5%). As per residential , area, 60% live in urban areas, while 40% reside in rural settings.. As per marital status of participants shows that 73% are unmarried, while 28% are married. As per Educational qualifications reveal that 63% hold a B.Sc.in Nursing,18% have a Post Basic B.Sc.in Nursing,13% have a G.N.M.qualification, and 8% hold an M.Sc.inNursing. In terms of work experience in psychiatric units, the majority (83%) have 1-2 years of experience, while 15% have worked for 3-4 years, and a small percentage (3%) have 5-6 years of experience. As per Knowledge sources include mass media (40%), continuing education (33%), and training (28%), with no participants indicating refresher courses as a source. The mean knowledge score (12.75) of staff nurses regarding MHCA 2017. The majority of the samples i.e. 47.5% are having good knowledge, 42.5% are having average knowledge and the remaining 10.0% are having poor knowledge regarding MHCA 2017. The standard deviation score is 4.70 it indicating that staff nurses have good knowledge regarding MHCA 2017.

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Keywords- Assess Knowledge, Mental Health Care Act 2017, Staff nurses, Psychiatric unit.

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This research presents the development of a youtube transcript summarizer, an automated tool designed to generate concise summaries from video transcripts .The summarizer demonstrates effectiveness in reducing lengthy transcripts into coherent summaries while preserving key information.

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Youtube Transcript Summarizer

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Creative Commons Attribution 4.0 and The Open Definition

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The detection of oil spills in marine environments prevents and speeds up response efforts, avoiding environmental damage. Here, we proposed a state-of-the-art robust method to detect oil spills. A huge dataset with images and frames, extracted from the video data downloaded from Google, augmented the dataset after the extraction of frames. Each image is also carefully labeled in order to get good training data. We trained our oil spill detection model with the use of the Yolov8 segmentation model in order to accurately identify and segment the oil spills in ocean environments. K-means and Truncated Linear Stretching algorithms are combined with the trained model weights in order to increase the accuracy of model detection. The model is doing exceptionally well; hence it has high detection accuracy with excellent segmentation capabilities. Our results indicate that this method is very efficient for real-time oil spill detection and, therefore, is promising for environmental monitoring and disaster management. For the training metrics, the model had more than 97% accuracy after 100 epochs. During the evaluation process, the best detection rates were achieved at 94% accuracy for F1, 93.9% accuracy for precision, and 95.5% mAP@0.5 accuracy for recall curves.

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Marine safety, oil spill detection, image segmentation, object-detection

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Creative Commons Attribution 4.0 and The Open Definition