Volume 13 | Issue 5 |

 Abstract

: In nations with coastal zones--India having the longest--coastal erosion is a serious issue. Rather than soil particles, it impacts slope stability. High waves and early monsoon onslaughts cause 370 kilometres of erosion along Kerala's coast. Twenty-three percent of Trivandrum's coastline was impacted in 2021. Additionally vulnerable to erosion are the coastal regions of Thiruvananthapuram district, such as Menamkulam, Puthenthope, Veli, Adimalathura, and Valiathura. Using INCOIS data, this study intends to examine these susceptible coastal areas, where profound studies are done for menamkulam beach in this paper, and the effects of erosion brought on by wave action and beach slopes. To forecast complete erosion or accretion on these locations, a code utilising cutting-edge AI technology is created.

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 Keywords

Soil erosion, Wave action, beach slopes, AI codes

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Industrial effluents, often containing organic pollutants, toxic chemicals, and heavy metals, pose a major threat to the environment and public health. When dealing with complex contaminants, traditional wastewater treatment methods can be costly, energy-intensive, and ineffective. For the treatment of industrial wastewater, this initiative offers a novel solution: Eco Smart Plasma Treatment, an environmentally friendly, energy-efficient, and very successful method. Using non-thermal plasma technology, the system generates reactive species that have the potential to break down a range of contaminants, including chemical residues and persistent organic molecules. Intelligent sensors and automated control systems enable the treatment unit to continuously monitor effluent quality, alter operating parameters in real time, and ensure optimal performance with minimal human intervention.

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 Keywords

Plasma treatment , EDT , continuous monitoring, real time monitoring, industrial waste, water treatment

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Creative Commons Attribution 4.0 and The Open Definition

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The Effects of Climate Change on Rural Women: A Study in South Tripura This study investigates the gendered impacts of climate change on rural women in South Tripura, focusing on the Chittamara Gram Panchayat. Utilizing a mixed-methods approach combining structured surveys, semi-structured interviews, and focus group discussions, the research explores how climate variability intensifies pre-existing social, economic, and health vulnerabilities among women. Key findings reveal that rural women, who are primary caregivers and agricultural laborers, face heightened challenges due to erratic rainfall, crop failures, water scarcity, and increased labor burdens. These factors severely affect their livelihoods, physical health, and mental well-being, particularly in the face of disasters such as the August 2024 Muhuri River flood. Over 90% of respondents reported worsening food security and health conditions, with many citing increased anxiety, depression, and limited access to healthcare. Although some women participate in grassroots adaptation efforts using indigenous knowledge--such as drought-resistant crops and water-saving practices--their efforts are constrained by systemic issues including limited land ownership, poor access to education, lack of credit, and exclusion from decision-making. Only 28.18% of surveyed women felt their voices were heard in climate policy formulation, while nearly 94% were unaware of gender-sensitive government programs. This disconnect highlights a critical policy gap. The study advocates for the development of inclusive, gender-responsive climate policies that recognize the vital roles women play in sustaining household economies and managing natural resources. Recommendations include improving access to health and education services, targeted capacity building, financial support for climate-resilient livelihoods, and increased female representation in policymaking. Empowering rural women is identified as not only a matter of equity but also a strategic imperative for sustainable climate adaptation in India's vulnerable regions.

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 Keywords

Climate change, Gender inequality, Rural women, Livelihood vulnerability, Health impacts, South Tripura, Adaptation strategies, Policy inclusion

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Brain diseases such as Alzheimer's, Parkinson's, and multiple sclerosis pose significant challenges due to their complex nature and often subtle early-stage symptoms. This paper proposes a novel hybrid framework that integrates functional Magnetic Resonance Imaging (fMRI) biomarkers with advanced supervised machine learning algorithms for effective brain disease detection and classification. The framework leverages the high spatial and temporal resolution of fMRI to extract relevant functional connectivity features, which are then optimized and fed into machine learning models such as Support Vector Machines (SVM), Random Forests (RF), and Neural Networks (NN). Extensive experiments on benchmark datasets demonstrate that the hybrid approach significantly enhances classification accuracy, sensitivity, and specificity compared to standalone methods. The proposed framework not only aids in early and accurate diagnosis but also contributes to understanding the underlying neural mechanisms associated with various brain disorders.

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 Keywords

Brain Disease Detection, Functional MRI (fMRI), Biomarkers, Supervised Learning, Machine Learning, SVM, Random Forest, Neural Networks, Feature Extraction, Neuroimaging.

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In this paper, we present the design and verification of a Comprehensive Bus Protocol (CBP) for RISC-V based system-on-Chip (SoC) architectures. CBP is designed to provide a low-complexity communication interface between the processor and essential peripherals like GPIOs and SPI. Unlike traditional designs that require separate data lines or large multiplexers for each peripheral--leading to higher cost and complexity--CBP uses a shared bus architecture with address-based decoding. A centralized CBP decoder (reg_top) reads the upper bits of the address (paddr[31:16]) to select the appropriate peripheral, while each peripheral's internal register decoder uses the lower bits (paddr[15:0]) to access specific registers. This approach eliminates the need for individual data paths and complex muxing, making the hardware more scalable, efficient, and easier to integrate. The CBP is implemented in Verilog HDL, and functional verification is performed using SystemVerilog to ensure robust and reliable operation within embedded and safety-critical systems

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 Keywords

Comprehensive Bus Protocol (CBP) ,RISC-V SoC ,System-on-Chip (SoC) ,Low Power Design ,GPIO ,Shared Bus Architecture ,Address-Based Decoding ,CBP Decoder ,Register Mapping ,Verilog HDL ,SystemVerilog

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This paper presents functional verification of master-slave communication via the Serial Peripheral Interface (SPI) within a System-on-Chip (SoC) architecture based on the A20 Open POWER processor. The verification process targeted essential SPI functions, including data writing, reading, and handling of error conditions. A custom-designed SPI protocol was incorporated into the SoC, and a verification setup was built using System Verilog along with the Universal Verification Methodology (UVM). Verification utilized industry-standard tools such as Mentor Graphics Questa Sim for simulation, Xilinx Vivado for design synthesis and integration, and associated tools for waveform analysis. The testbench was structured to validate proper data transfer and protocol operation under both standard and error scenarios. The outcomes verify dependable SPI performance, highlighting the verification approach's role in enhancing communication reliability within open-source SoC designs.

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 Keywords

SPI,SOC, Verification, A20,System Verilog

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 Abstract

Aupniveshik bharat me kisan aandolan

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Aupniveshik bharat me kisan aandolan

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Studying the significance of the Bodhisattva ideal in Buddhism is essential for applying the Buddha's teachings to cultivating and benefiting our society and community. In addition, it dispels misunderstandings regarding the Bodhisattva ideal, it also illustrates the Bodhisattva ideal for readers and practitioners of Buddhist teaching. This study focuses on the Bodhisattva ideal in Buddhism and Buddhist traditions, including Theravāda and Mahāyāna. This study employed a qualitative approach that combined the reading of Buddhist scriptures with textual documents to examine and analyze the Bodhisattva ideal. This Bodhisattva ideal was acknowledged by both the Mahāyāna and Theravāda traditions. When considering the characteristics and commitments of a Bodhisattva, the practice of pāramī is extremely significant. One who practices the virtues or conducts of pāramī is called a Bodhisattva. A Bodhisattva must go through ten stages (daśabhūmi) of practice and attainment to reach the stage of Buddhas' wisdom in three times (past, present, and future). The great Bodhisattvas' wisdom is likewise represented by these ten stages. All deeds of giving, kind speech, beneficial actions, or working together must not be separated from the thoughts of the Buddha, the Dharma, and the Sangha. When we put these qualities into practice, practicing Bodhisattva virtues not only helps us achieve accomplishment, advancement, and tranquility on the spiritual path, but they also improve and benefit our community and society.

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 Keywords

Boddhisattva, compassion, wisdom, pāramī, bhūmi.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Brain cancer detection is a crucial area of medical research, where early and accurate diagnosis can significantly improve patient outcomes. Traditional machine learning and deep learning approaches have shown promise, but their limitations in feature extraction and generalization necessitate a more robust framework. In this study, we propose a Hybrid Ensemble Framework that integrates Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Support Vector Machines (SVM) to enhance brain tumor detection from MRI scans. The CNN extracts spatial features, while the RNN captures temporal dependencies, and the fused feature set is classified using an SVM for improved accuracy. The proposed approach leverages feature fusion to combine the strengths of deep learning and classical machine learning, ensuring a more comprehensive representation of the tumor characteristics. Extensive experiments on publicly available MRI datasets demonstrate that our framework outperforms standalone models in terms of accuracy, sensitivity, and specificity. By combining multiple learning paradigms, our method enhances diagnostic precision, making it a viable solution for clinical applications.

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 Keywords

Brain cancer detection, Hybrid Ensemble Framework, CNN, RNN, SVM, Feature Fusion, MRI, Deep Learning, Machine Learning.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In the contemporary educational landscape, there is a growing emphasis on life skills development as a fundamental component of holistic learning. This study investigates the effectiveness of the CTCR (Cooperation, Teamwork, Communication, and Resilience) instructional model in enhancing key life skills among Class VIII students. The research specifically explores three core domains: Self-Awareness and Empathy, Critical Thinking and Problem-Solving, and Creative Thinking--skills identified by the World Health Organization as vital for adaptive and productive behavior. Adopting a pre-test and post-test experimental design, the study involved 90 students who were divided into control and experimental groups. The CTCR-based intervention was implemented with the experimental group over a defined instructional period, while the control group followed traditional pedagogical approaches. Quantitative analysis was conducted using descriptive statistics and Shapiro-Wilk tests to assess the distribution and impact of the intervention on each life skill domain. Findings reveal a statistically significant improvement across all three domains among students exposed to the CTCR strategy. The mean scores in Self-Awareness and Empathy rose from 32.0 to 39.4, Critical Thinking and Problem-Solving improved from 31.8 to 39.9, and Creative Thinking increased from 32.4 to 39.4 in the experimental group. These enhancements were further corroborated by shifts in skewness and kurtosis values, suggesting improved distribution and consistency in skill acquisition. Comparative gender-based analysis also demonstrated that both male and female students benefited substantially from the intervention, with experimental group participants outperforming those in the control group by a considerable margin. The results underscore the CTCR model's potential as a transformative instructional strategy that not only supports cognitive development but also nurtures essential socio-emotional competencies. This research contributes valuable insights to educators and curriculum developers seeking to embed life skills education into middle school pedagogy, thereby aligning with the National Education Policy's (NEP 2020) vision for holistic and multidisciplinary learning.

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 Keywords

: Life Skills, CTCR Model, Self-Awareness, Empathy, Critical Thinking, Problem-Solving, Creative Thinking.

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Creative Commons Attribution 4.0 and The Open Definition