Volume 12 | Issue 11 |

 Abstract

This paper argues that the myriad contexts of human existence and practices mold human nature. It does so by reference to the work of some prominent voices in the field of social sciences. The nature-nurture debate most famously and eloquently emerges in Jean Jacques Rousseau's Social Contract. Different actors in different historical eras bending to their respective imperatives of social, political, and economic organizations have conceptualized human nature to justify violent projects such as that of eugenics or ethnic cleansing. Yet there are several uplifting but contrary examples of compassion, kindness, and empathy--qualities that are regarded as natural to human nature. The functional rationality of a social organization lies in its claim of fulfilling human needs, which is based on an understanding of human nature. As Mary Clark (2005: 11) points out, '...the belief a society holds about human nature in particular tends to create the very behavior they predict.' Human nature is a foundational category of social, political and economic theory.

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 Keywords

Human nature; society; discourse; rationality; mystification

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The "Soldier Health Monitoring and Predictive Analytics System" is an innovative solution designed to enhance soldier readiness and safety during training in military camps. By integrating advanced wearable sensors and sophisticated machine learning algorithms, the system continuously monitors vital health metrics such as heart rate, body temperature, and blood oxygen levels. This real-time data collection enables early detection of potential health issues, significantly reducing the risk of medical emergencies during high-intensity training sessions. The predictive analytics component processes historical and real-time data to identify patterns and forecast health risks, providing timely alerts to both soldiers and command units. This proactive approach not only improves individual soldier performance but also optimizes overall mission success by ensuring soldiers are fully prepared for deployment. Additionally, the system enhances resource management by minimizing unnecessary medical interventions and streamlining training protocols, thereby fostering a culture of proactive health monitoring and improving overall soldier welfare.

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Machine Learning, Predictive Analytics, Real-Time Health Assessment, Health Risk Prediction. _________________________________________________________________________________________________

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This study introduces an AI-driven approach to enhance hurricane damage assessment. Utilizing deep learning models, we propose a scalable framework for building an annotated image database that identifies and labels damaged building components with high precision. Our structured repository improves data management and streamlines analysis, facilitating the rapid development and deployment of AI-powered assessment tools. This research aims to advance the accuracy and speed of damage evaluations, supporting disaster response and recovery.

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Hurricane Damage Assessment, AI, Deep Learning, Image Annotation, Disaster Response, Damage Evaluation, Data Management

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This study examines the effectiveness of picture-based instruction in teaching daily living skills to children with intellectual disabilities, specifically those aged 4 to 8 years. Picture-based instruction, a visual teaching method that uses images to guide learners through tasks, has been identified as a promising approach for children with intellectual disabilities due to its structured and clear presentation of step-by-step procedures. The study employs a pre-test and post-test design with an experimental (picture-based instruction) and a control group (traditional teaching methods). A sample of 40 children, divided equally between the two groups, was selected from special and inclusive schools in Ahmedabad.The Daily Living Skills Assessment Tool (DLAT) was administered pre- and post-intervention to measure the development of skills such as personal hygiene, dressing, and eating. The results showed statistically significant improvement in the experimental group compared to the control group, indicating that picture-based instruction is more effective in enhancing daily living skills. This approach not only improves skill acquisition but also fosters independence by providing clear, sequential visual cues. Findings suggest that picture-based instruction can be an impactful strategy in special education, enhancing learning outcomes and daily life competencies for children with intellectual disabilities. Further research is recommended with larger samples across different settings

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Picture-Based Instruction: An educational approach that utilizes visual aids, such as images and pictograms, to enhance understanding and learning for children with intellectual disabilities. Daily Living Skills: Essential skills required for everyday functioning, including personal hygiene, dressing, grooming, meal preparation, and household chores.Children with Intellectual Disabilities: Refers to young individuals, typically aged 4 to 8 years, who have cognitive impairments that affect their

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SOCIAL RESPONSIBILITIES OF BANKS TOWARDS CUSTOMERS

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CONSUMER SOCIAL RESPONSIBILITY

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This practice-based research explores the potential of 3DoF spatial audio to craft compelling and emotionally resonant VR narratives in the face of growing interest in VR storytelling, persistent challenges of accessibility and inclusivity in VR design, and even in the absence of character models or complex visual elements. A minute-long Virtual Reality experience titled "Once it Was a Home" was created in Unreal Engine 5.4, simulating an empty family home where the story of the family's past and departure is conveyed solely through spatial audio cues. The experience utilised a combination of accessible and custom-designed spatial audio elements. The custom sounds were recorded using a binaural microphone setup (Neumann KU 100 dummy head) and processed in Google Resonance for spatialisation. A semi-structured interview methodology was employed, where 30 participants experienced the VR environment and were subsequently interviewed about their interpretation of the narrative, emotional responses, and the impact of specific sound cues. The objectives of this study were to: (a) Narrative Comprehension: investigate the effectiveness of spatial audio in conveying a narrative within a VR environment, specifically assessing participants' ability to accurately interpret the intended story; (b) Sense of Presence: examine the immersive qualities of the experience and the extent to which spatial audio contributes to a sense of presence in the virtual environment; and (c) Emotional Impact: explore the emotional impact of the spatial audio design, analysing the range of emotions evoked and the relationship between specific sound cues and participants' emotional responses. Findings indicate that carefully designed spatial audio can effectively communicate complex narratives and evoke a range of emotions in VR, even with minimal visual elements. 76% of participants reported experiencing a strong sense of presence within the virtual environment, and 51% were able to accurately comprehend the intended story. This research contributes to the growing field of accessible VR design and challenges the notion that VR storytelling necessitates technical wizardry and emphasises the power of creativity and storytelling in crafting engaging VR narratives.

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 Keywords

Virtual Reality (VR), Spatial Audio, Narrative Design, 3DoF Audio, Sound Design, Immersive Storytelling, VR Film, VR storytelling, Accessible Technology, Unreal Engine, User Experience (UX)

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Literature appears with the aim of enlightening and entertaining the people. Literature conveys ideas that are relevant to mankind at all times. Internal morals are the basis of human life. Although the concept is fundamental to a literary work, the concept itself cannot become literature. Despite composed of a collection of songs by various poets, Natrinai is united on the basis of a moral values. The purpose of this research is to explain the method of analysis used by the poets in the mental depiction and emotional expression of the Agamandhars through the use of farce and other poetic devices. Eight types of meipaadugal have been learned in Natrinai songs. In this study, the ability of poets to express ideas such as the mother's ignorance, Velan's ignorance, and the leader's slander are explained.

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Nature, veriyattu, beauty, meipaadugal, aesthetics, parathaiyar pirivu, farce, Uripporul, culture, life.

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This study presents a deep learning framework using Convolutional Neural Networks (CNN) to assess plant health through leaf image analysis. Leveraging the growing availability of image data, the research focuses on two key objectives: early detection of plant diseases and the identification of specific conditions from leaf images. A dual-model approach is employed--one binary model for general plant health detection and a multiclass model for categorizing specific diseases. Techniques such as data augmentation, transfer learning with pre-trained models, and advanced feature extraction are used to improve accuracy. Experimental results on public datasets show high predictive performance, with confusion matrix analysis confirming minimal classification overlap. This framework has potential applications in real-time crop monitoring and precision interventions, with future efforts aimed at expanding datasets and incorporating more plant species for better robustness.

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Convolutional Neural Networks (CNN),Deep Learning ,Image Classification,Precision Agriculture, Computer Vision, Machine Learning, Neural Networks ,Feature Extraction, Classification Models , Disease Identification Models

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This study examines the impact of social justice and reservation politics in Tumkur district, Karnataka, using data collected from 65 samples. The research explores educational attainment, employment status, political participation, awareness and perceived effectiveness of reservation policies, challenges in accessing education, quality of education, representation in local governance, participation in skill development programs, and satisfaction with government support schemes. The findings indicate that significant challenges persist while reservation policies have facilitated educational and employment opportunities for marginalized communities. The data reveals that a considerable portion of the SC/ST/OBC population has attained secondary education (23.1%) and above, yet financial constraints (30.8%) and inadequate infrastructure (30.8%) remain major hurdles. Employment data shows a high unemployment rate (38.5%) despite government job reservations (27.7%). Political participation is moderate, with 38.5% perceiving the policies as only slightly effective. Representation in local governance is limited, with 33.8% reporting low levels and 30.8% reporting none. Awareness of reservation policies is varied, with 38.5% being fully aware. However, the effectiveness of these policies is questioned, with only 15.4% considering them highly effective. Skill development programs should be more utilized, with 72.3% not participating. Satisfaction with government support schemes is mixed, with 46.2% expressing satisfaction. The study emphasizes the need for targeted interventions to improve educational quality, enhance employment opportunities, and strengthen political participation and representation. Addressing these challenges is crucial for advancing social justice and ensuring the equitable distribution of resources and opportunities in the Tumkur district.

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Keywords: Social Justice, Reservation Policies, Marginalized Communities, Political Participation Introduction Social justice is a fundamental principle that seeks to ensure fair and equitable distribution of resources and opportunities across all sections of society. In India, the concept of reservation has been a pivotal mechanism aimed at addressing the historical injustices and social inequalities faced by marginalized communities, particularly the Scheduled Castes (SCs), Scheduled Tribes (

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This research paper aims to provide a comparative study of optimization techniques for task scheduling and load balancing in cloud computing environment such as, GA, PSO, ACO, RL and combination of these. Applying simulation-based testing for both qualitative and quantitative attributes such as execution time, response time, and system throughput, resource consumption, and energy cost, the study compares the effectiveness of each technique under different workload scenarios. Analysis carried out on performance of models suggest that RL and combined models namely GA-MS and ACO-ML where better in performance because they utilize resources optimally and self adapt. In variable and cloud environments, namely, RL shows extraordinary capacity and high performance in all parameters. Such hybrid methods acting as a combination of metaheuristic techniques are significantly useful in scaling up the handling of tasks. We can conclude these findings indicate the possibility of adaptive optimization techniques to enhance cloud infrastructure and resource effectiveness.

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Cloud computing, task scheduling, load balancing, optimization techniques, reinforcement learning, hybrid models

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