Bir Bikram Memorial College, established in 1969, is a college in Agartala, Tripura. It offers undergraduate courses in arts and sciences. It is affiliated to Maharaja Bir Bikram University.
Recent advancements in organic material-based sensor technology have created a promising field for designing sensors that are both environmentally friendly and cost-effective. An approach has thus been undertaken to produce advanced electronic materials by incorporating polyaniline (PANI), graphite (Gr), and banana powder (BP) onto a cotton fabric (CF) substrate. Readily available white CF from the local market were used for this work. Using a straightforward physical vapor deposition technique, a uniform layer of graphite and BP-infused PANI was deposited onto the CF framework to create the desired electronic material system, PANI/Gr/BP@CF composites. Comprehensive analyses were carried out on the resulting active material by using various characterization techniques, such as X-ray diffraction (XRD), scanning electron microscopy (SEM), ultraviolet-visible spectroscopy (UV-Vis), and electrical charge transport analysis. The characteristic structural, optical, and electrical properties, as obtained from these studies, have been found suitable for an electronic material for advanced applications and so studied further to observe humidity sensing properties. The performance of the prepared materials in humidity detection was evaluated by measuring the resistive response under varying humidity levels through current-voltage (I-V) characteristics. This study reveals that PANI/Gr/BP@CF composites show a very high sensitivity to the variation in humidity and evolved as a low-cost, flexible, wearable, and environmentally friendly humidity sensing electronic material.
This study presents a new index, namely the hesitant fuzzy water quality index (Ή𝔉𝒲𝒬Ι), to assess water quality (𝒲𝒬A) in the Gomati River, Tripura (North East India), and its impacts on aquatic ecosystems. River water quality evaluation is considered in terms of diverse parameters and the inherent uncertainty introduced in the multi-criteria decision-making (ϺϹDϺ) process. A robust metric, the Ή𝔉𝒲𝒬Ι score, is proposed that may reliably rate pollution in the river. The Gomati River, the largest river in Tripura, which is used for drinking water, agriculture, and fisheries, is contaminated by a variety of sources, including household wastewater and agricultural runoff. Ten key water quality parameters (𝒲𝒬𝒫𝓈) such as pH, total dissolved solids, electrical conductivity, total hardness, chlorides, total alkalinity, total coliform, biochemical oxygen demand, dissolved oxygen, and total suspended solids were assessed across six strategically selected sites. River water samples were collected from March to December 2024 across multiple seasons. The Ή𝔉𝒲𝒬Ι-scores revealed consistently “poor” water quality (ranging from 0.734 to 0.866), degrading downstream due to untreated wastewater and agricultural runoff. To contextualize the scientific findings, traditional ecological knowledge (𝚃𝙴𝙺) was collected from four dependent tribal communities in 2025. The documentation covered local water sources, community perceptions of long-term water degradation, and their traditional conservation practices. Critically, community observations of contaminated water and health issues strongly aligned with the model’s identification of severe organic and bacterial pollution. Comparative analysis demonstrated that the Ή𝔉𝒲𝒬Ι outperformed conventional models in precision and reliability. This study demonstrates the severe impact of water pollution on both aquatic ecosystems and human health. This study bridges the gap between modern fuzzy tools and 𝚃𝙴𝙺. Its findings call for urgent action, improved sanitation, sustainable farming practices, and local conservation efforts informed by both science and culture.
The rapid proliferation of metaphor-based optimization algorithms has faced criticism from researchers regarding their novelty and contribution. Many of these pseudo-novel metaheuristics suffer from performance inefficiencies, biased verification systems, and excessive similarities in their modelling as they imitate the foraging behaviors of animals and other living beings. This study introduces a population-based and metaphor-free optimization algorithm using the well-known and efficient Lagrange interpolation formula to address these concerns. The proposed algorithm, called Lagrange interpolation-based optimization (LIBO), is designed for numerical optimization and solving real-world optimization problems. The second-order Lagrange interpolation formula is considered to construct the algorithm where the polynomial passes through three given random points, thereby demonstrating a parabolic structure. While updating the algorithm, every trial solution considers three randomly chosen solutions and obtains the minimal point. To strike a suitable balance between exploration and exploitation and avoid local optima trapping, the proposed LIBO algorithm incorporates specific measures in its search mechanism. Its effectiveness is evaluated by IEEE CEC 2017 and IEEE CEC 2019 benchmark functions, three practical engineering problems, and the highly non-linear multiple gravity assist spacecraft trajectory problem. Comparative analyses were conducted, and it was found that in CEC 2017 functions, the proposed LIBO is better on 100
This study presents an innovative weighted fuzzy soft set-based multi-criteria decision-making model (B-model) for evaluating groundwater quality and identifying contamination sources in urban settings, with a case study in Agartala, Tripura, North-East India. Rapid urbanization and anthropogenic pressures have significantly impacted groundwater quality, demanding advanced methodologies for effective assessment and management. The proposed model incorporates ten critical groundwater quality parameters (GWQP—pH, electrical conductivity, iron concentration, dissolved oxygen, total hardness, total alkalinity, total dissolved solids, calcium, magnesium, and turbidity), transformed into fuzzy soft sets for comprehensive analysis. Groundwater samples were systematically collected from ten strategic locations across Agartala Municipal Corporation during three distinct seasons: pre-monsoon (March–May), monsoon (June–September), and post-monsoon (October–November) of 2023–2024. The analysis revealed seasonal variations in groundwater quality, with notable degradation during the pre-monsoon season due to reduced aquifer recharge and increased evaporation, while monsoon improvements were limited, primarily influenced by rainfall dilution. Specific areas consistently exhibited high pollution levels, highlighting localized contamination sources and elevated health risks. The model’s weighted pollution scores enabled precise identification of pollution sources, facilitating targeted intervention strategies. This research underscores the potential of fuzzy soft set-based approaches for robust groundwater quality assessment and emphasizes their integration into sustainable water management practices and urban planning to mitigate contamination risks in rapidly urbanizing regions like Agartala. Comparative analyses with existing methods validate and underscore the advantages of our approach.
India is rapidly implementing AI in the courts, police, government services, banking, and cybersecurity. These technologies can benefit the country, but they also introduce additional hazards like bias, privacy concerns, and ambiguous conclusions. Traditional yes/no criteria are insufficient to control these risks. This chapter discusses how fuzzy logic might aid in responsible AI governance in India. Fuzzy logic allows for degree-based decisions rather than precise answers. It can provide more accurate measurements of fairness, openness, privacy, and cybersecurity risk. The chapter discusses fuzzy risk models, fuzzy rule-based systems, and fuzzy MCDM methods for assessing AI technologies. A step-by-step governance framework is presented to assist policymakers in monitoring AI systems, evaluating risks, and making balanced judgments. The chapter demonstrates that fuzzy logic is a versatile, straightforward, and successful strategy to establishing safe, equitable, and trustworthy AI governance in India.
