VOLUME 3, ISSUE 3, MARCH 2026

Abstract: The IoT has significantly expanded agriculture and transformed traditional farming methods with the advent of automation, accuracy, and simultaneous 24-hour care. However, the extensive usage of Internet of Things devices leads to improved energy absorption and, in turn, carbon emissions. By letting down power usage while taking resourceful action, green IoT seeks to solve this problem. This research work presents a Green IoT framework for maintainable cultivation that uses soil moisture monitoring and smart irrigation simulation in Tinkercad. The device continuously measures soil moisture and only activates the water supply when necessary to save electricity and water. The proposed method shows how applying Green IoT principles could enhance precision farming and promote the expansion of sustainable agriculture. The simulation findings validate the model's efficacy in conserving resources and promoting ecologically friendly farming practices.

Keywords: Green IoT, Smart Agriculture, Energy Efficiency, Soil Moisture Sensor, Sustainable Farming, Tinkercad Simulation.

Abstract: This research paper presents an independent financial evaluation of Crompton Greaves Consumer Electricals Limited covering the five-year period from 2021 to 2025. The study examines the company’s operational strength, profitability trends, liquidity position, capital structure, and efficiency levels using structured ratio and trend analysis. Emphasis is placed on understanding how cost pressures, revenue growth, and financial policy decisions influenced performance over time. The findings reveal that although profitability margins experienced temporary contraction during the middle of the study period, the company demonstrated strong recovery supported by efficient cost management and improved operational discipline. Liquidity remained stable, leverage was conservative, and asset utilization improved consistently. Overall, the company exhibits sound financial fundamentals and sustainable long-term growth capacity.

Keywords: Financial Statement Analysis, Profitability Evaluation, Liquidity Assessment, Capital Structure, Efficiency Ratios, Shareholder Returns, Trend Analysis, Corporate Financial Health.

Abstract: JIn this paper, we propose the design and implementation mechanism for a digital coherence BPSK demodulator based on the use of Direct Digital Frequency Synthesizer (DDFS) and digital filter using Cyclone II EP2C20F484C7 FPGA from ALTERA placed on education and development board DE-1. The proposed demodulator has the following parameters: =50MHz.CLKClock frequency: F- / K=50000 KHz /1250 =40 KHz ).CLK= Fsam: (fKHz= 40samSampling frequency : f- =2 KHzcutf:)low pass filter (LPFdigitaloff frequency of the-Cut- -Modulation type of signal is: BPSK .
<= fcut ).mod1,25 KHz ( f=0.12mod2fKHz or52=0.mod1f:frequencywithsquare pulseThe modulating signal is- KHz.=2carf:Carrier type: is sinusoidal with frequency- -The ROM capacity for the stored signal samples (8192X8 ) bits, and their values are positive within the range from 0 to 255.
-Frequency range: (3 Hz…25 MHz).
-Frequency Resolution: (3 Hz).
- Signal amplitude (5V).
-Using FPGA allows for the modification and development of the digital design to suit the designer's wishes and goals.

Keywords: digital demodulator , BPSK , DDFS , FPGA

Abstract: This paper investigates how to build, systematically configure, and rigorously explains a transformer-based bug detection system. The central argument is that trustworthy explainability requires first establishing which model is worth explaining and in which configuration. We evaluate three approaches on the lrhammond/buggy-apps dataset (8,778 balanced samples): a TF-IDF baseline, DistilBERT, and GraphCodeBERT. DistilBERT exhibited mode collapse across all tested learning rates, confirming that general-purpose language model pretraining is insufficient for code defect detection — a prerequisite finding that motivates the choice of GraphCodeBERT for explainability analysis. A systematic ablation across three stride configurations (128, 256, 384 tokens) and three aggregation strategies (max, mean, majority vote) yields nine experimental conditions; stride 256 with mean aggregation is the optimal configuration (70.77% accuracy, 69.56% macro F1, p = 5.26 × 10⁻³³ vs TF-IDF, McNemar's test). Explainability analysis via attention rollout and integrated gradients across ten test samples reveals that integrated gradient signal strength is 11.8× stronger for correctly detected bugs than for misclassified samples — providing a gradient-based, quantitative explanation of model failure modes. Attention rollout and integrated gradients show near-zero cross-method correlation (mean r = −0.017), empirically confirming they are non-redundant and complementary methods.

Keywords: Explainable AI, Bug Detection, Transformer Models, GraphCodeBERT, Mode Collapse, Attention Rollout, Integrated Gradients, Sliding Window Ablation

Abstract: Unplanned equipment failures in manufacturing systems lead to production downtime, increased operational costs, and safety risks. While predictive maintenance techniques have advanced significantly, much of the existing work focuses on binary failure detection and provides limited insight into specific failure mechanisms. This paper presents a multi-class predictive modeling approach for manufacturing equipment maintenance systems that aims to identify distinct failure types using operational sensor data. The study formulates failure type prediction as an imbalanced multi-class classification problem representative of real-world industrial environments, where failure events are rare compared to normal operation. Model performance is evaluated using imbalance-aware metrics to ensure reliable assessment across both dominant and minority failure classes. The results demonstrate that the proposed approach can effectively distinguish major mechanical and thermal failure types despite severe class imbalance. These findings highlight the importance of multi-class failure prediction for enabling more targeted maintenance decisions and improving the reliability of manufacturing equipment.

Keywords: Predictive Maintenance, Multi-Class Classification, Machine Learning, Manufacturing Systems, Failure Type Prediction, Equipment Reliability

Abstract: Sea is not just a setting, and to read it as one is only a misinterpretation. This paper undertakes a sustained literary-critical inquiry into Anees Salim’s The Small Town Sea (2017) through the analytical lens of Blue Humanities, an interdisciplinary paradigm that interrogates the dynamic, historically layered relationship between human civilisation and aquatic environments (Mentz 3). The novel narrated in epistolary form by an unnamed thirteen-year-old boy situates the sea not merely as a backdrop but as an active, symbolically dense participant in the processes of mourning, identity formation, and ecological imagination. Drawing on the theoretical formulations of Steve Mentz, John Gillis, and Stacy Alaimo, this paper argues that Salim constructs the sea as simultaneously a mnemonic archive, a site of ecological consciousness, and a medium of existential reckoning. The analysis proceeds through three interconnected axes: the narrator’s affective and developmental relationship with the ocean, the father’s maritime nostalgia and its transcultural resonances with India’s coastal heritage, and the sea’s symbolic function as a force that both creates and dissolves selfhood. This paper further demonstrates how Blue Humanities, with its foundational concern for coastal cultures and environmental ethics, enables readers to perceive the novel’s elegiac coastal south not as mere regional colour but as a site of deep maritime consciousness. The study contributes to the growing field of Indian Ocean literary studies and argues for the urgent relevance of Blue Humanities frameworks in reading postcolonial South Asian fiction.

Keywords: blue humanities, coastal identity, Indian ocean, ecocriticism, grief, memory.