In the twentyfirst century, despite the existence of numerous laws and government initiatives aimed at protecting and empowering women, harassment continues to affect women across various spheres of life—both domestic and professional. As a result, women’s empowerment remains one of the most critical and urgent topics of contemporary discourse. While traditional studies have explored women’s empowerment from multiple perspectives, there has been limited application of fuzzy set theory in this domain—particularly in evaluating the degree of uncertainty involved in field-wise empowerment. To address this gap, the present study utilizes the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Complex Proportional Assessment (COPRAS) methods to assess the level of women’s empowerment in India across a range of sectors, including media, sports, defence, biotechnology, medical technology, psychology, higher education, and social work, using data compiled from diverse sources. All the data is collected in Trapezoidal Fuzzy Numbers (TrFN) to deal with the uncertainty and vagueness of the system and data sets. Given the challenges in ranking alternative empowerment options, the study applies a fuzzy Multi-Criteria Decision-Making (MCDM) approach based on key factors and their corresponding sub-factors to identify the optimal empowerment domain. The Analytic Hierarchy Process (AHP) technique is employed to determine the factor weight, fuzzy weight, and global fuzzy weight of the sub-factors. Subsequently, the fuzzy TOPSIS method is used to rank the alternative empowerment options and identify the field in which women have the greatest potential to thrive. These results are then validated through comparison with the fuzzy COPRAS technique, and the final assessment is established. To further ensure the robustness and reliability of the findings, a sensitivity analysis is conducted by selectively removing certain factors or sub-factors. The overall results provide clear insights into where and how women’s empowerment can be most effectively promoted—offering not only time-efficient decision-making but also paving the way for a more secure and equitable future for women around the world.
Survival of toxigenic and non-toxigenic strains of Vibrio cholerae involves adaptation to varying pH conditions, a mechanism which is poorly understood. Chitinase activity, which is essential for survival both inside and outside its human host at various pH levels, is not thoroughly profiled. We report on the growth and chitinase activity of two clinical isolates of V. cholerae: VC20, a ctx+ strain, and WO5, a ctx- strain, at different pH. We compared the expression of key genes between these strains. WO5, the non-toxigenic strain, showed robust growth and higher chitinase activity across a wide pH range compared to VC20. WO5 expressed higher ompK and toxT transcripts, implicated in host cell adhesion and virulence, respectively. We propose that lower hapR levels in WO5, in contrast to VC20, are key to its low-pH tolerance. A sequence-based homology search revealed a widespread presence of low-pH adaptation modules - lysine-cadaverine and ornithine-putrescine - in multiple representative species of the Vibrio genus. Further, the loss of a nitrite reductase gene, which confers low-pH tolerance, is specific to V. cholerae and V. mimicus. We propose that hapR expression and other low-pH adaptation factors reported here could be molecular predictors of low-pH tolerance in toxigenic and non-toxigenic V. cholerae.
This study primarily aims at investigating the performance of large-area multi-crystalline silicon (mc-Si) solar cells through the optimization of zinc sulfide (ZnS), a cost-effective thin film antireflection coating (ARC) deposited using chemical bath deposition (CBD) technique. The films are deposited on NaOH-NaOCl polished mc-Si solar cells using a complexing agent, tri-sodium citrate that is non-toxic in nature. The antireflection properties of the films are optimized by varying the molar concentration of tri-sodium citrate, the deposition time, and the angle of substrate tilt during deposition. The molar concentration of tri-sodium citrate is varied from 0.10 to 0.40 M. The atomic force microscopy (AFM) analysis reveals that the film deposited at 0.30M tri-sodium citrate for one hour exhibits a uniform surface morphology with a root mean square (RMS) roughness of 2.97 nm. The optimized film demonstrates good uniformity (standard deviation <1), a high deposition rate, a refractive index of 2.35 and a minimum reflectance of 4
In the era of artificial intelligence, plant disease prediction has gained the attention of researchers. This study explores the technologies of artificial intelligence for early disease prediction in rice plants. Diseases often cause subtle changes in the leaves before visual symptoms appear. Thermal imaging can capture these early signs. The proposed model leverages deep learning with two pre-trained algorithms (VGG16 and ResNet50) to improve prediction accuracy and reduce errors. The model focuses on Bacterial Leaf Blight (BLB) of the rice plant caused by Xanthomonas oryzae. Visual and thermal images of rice leaves were collected at three stages: healthy or normal (before infection), pre-symptomatic (48 h after infection, with no visible symptoms yet), and post-symptomatic (after visible symptoms appear). The model compared normal leaves to pre-symptomatic leaves, achieving 97.56
Environmental accumulation of toxic metalloids such as arsenic (As), cadmium (Cd), and antimony (Sb) has emerged as a major challenge for plant productivity, food safety, and environmental sustainability. Rice (Oryza sativa), a staple crop globally, is particularly vulnerable due to its cultivation in flooded conditions that facilitate metalloid uptake. Recent research highlights the central regulatory roles of non-coding RNAs (ncRNAs), such as microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and small interfering RNAs (siRNAs) in orchestrating plant stress responses at transcriptional, post-transcriptional, and epigenetic levels. Advancements in high-throughput omics technologies, encompassing genomics, transcriptomics, proteomics, metabolomics, and epigenomics, have enabled systems-level insights into these regulatory networks. Integrative multi-omics approaches, complemented by artificial intelligence (AI) and machine learning (ML) tools, have greatly improved our ability to mine large datasets, identify stress-responsive ncRNAs, and decode complex gene-environment interactions. Although comprehensive multi-omics datasets for stress biology in plants remain limited, emerging AI-guided frameworks show promise in accelerating the discovery of ncRNA biomarkers and their functional roles. This review underscores the importance of omics-driven exploration of non-coding regulatory layers in rice plants and advocates for their strategic application in developing stress-resilient crop varieties through molecular breeding, genome editing, and ncRNA-based biotechnological interventions.
