Vigyan Prasar

This study addresses the challenge of estimating parameters for two logistic populations that share a common scale parameter but have different location parameters in the presence of fuzzy data. To handle these complexities, both Maximum Likelihood Estimation (MLE) and Bayesian methods are employed. Asymptotic confidence intervals are constructed using ML estimates. For Bayesian estimation, a conjugate prior is utilized, and Bayes estimators are approximated using Lindley’s method due to the lack of closed-form solutions. Furthermore, Approximate Bayesian Computation (ABC) and Markov Chain Monte Carlo (MCMC) techniques, including Hamiltonian Monte Carlo (HMC) and the Metropolis–Hastings (MH) algorithm, are utilized to sample from the posterior distributions and construct Highest Posterior Density (HPD) intervals. A detailed comparative analysis of MLE, Lindley’s approximation, ABC, HMC, and MH is conducted to assess their performance. The effectiveness of the proposed methodology is demonstrated using a real-world dataset under fuzzy conditions.

ABSTRACT Background: Farming is an occupation with higher risk for suicide compared to other occupations, and Indians are no exception. Karnataka state is only second after Maharashtra in the frequency/prevalence of farmers’ suicide in India. Suicide among farmers is multidimensional in nature. Aim: To study the psychological, agricultural, and socioeconomic risk factors in farmers who had completed suicide and farmers in the same locality who had not/never attempted suicide. Methods: Families of farmers, aged between 18 and 60 years, who had died by suicide, were recruited as cases. Age- and gender-matched farmers from the same locality with similar financial risk factors were recruited as controls by the convenient sampling method after obtaining informed consent. Psychological autopsy was done in cases, and similar risk factors were assessed in controls. GHQ-12 and Mini Plus scale were used. Statistical analysis was done using SPSS software. Results: The study included 295 cases and 254 controls. Sociodemographic parameters except educational status (P = 0.003) were comparable among groups. Social and financial support was higher in cases than in the control group of farmers. Both groups had similar agricultural practices except crop insurance, which was low among cases (P < 0.001). Cases suffered from a greater number of health issues compared to controls. Substance use was significantly higher in cases (P < 0.001) (mostly alcohol and tobacco). Hanging was the most common method of committing suicide, followed by poisoning with pesticides. Conclusion: Farmer suicide is a complex phenomenon with underlying multiple risk factors. Farmers with suicidal attempt had underlying depression, socioeconomic, and agricultural risk factors and also substance abuse.

This paper presents a complex differential magnitude angle-based technique for detection of faults in the microgrids (MG). The proposed relaying technique provides an efficient and fast solution for detecting the line faults in the MG. This scheme acquires the current signals of both ends and the voltage signal of one end of the faulted distribution line that are utilized for computation of differential index (DI). The complex plane (CP) constructed from the magnitude and angle of the DI is used to detect the faults in the lines. The proposed protection technique is developed to protect the MG against wide variation of fault scenarios including different types of ground and line faults incepting at different location of the lines. Further, the scheme is tested against some of the most challenging fault cases such as high resistance faults in islanding and grid-connected mode of operation for the MG of different sizes including 9-bus medium voltage MG and IEEE 34 bus distribution system integrated with renewables. Further, the real-time validation of the proposed technique is carried out on real-time digital simulator (RTDS). The results obtained from the extensive test cases in simulation and real-time platforms indicate that the proposed scheme is a potential candidate for fault detection in MG enhancing speed and accuracy.

Detrimental effects of terminal heat stress could be mitigated by exogenous application of synthetic compounds by preserving cell membrane integrity and protecting against oxidative damage. A field experiment was conducted to test the application of seven synthetic compounds on wheat growth traits: (1) thiourea (20 mM and 40 mM); (2) potassium nitrate (1% and 2%); (3) sodium nitroprusside (400 μg mL−1 and 800 μg mL−1); (4) dithiothreitol (25 ppm and 50 ppm); (5) salicylic acid (100 ppm and 200 ppm); (6) thioglycolic acid (200 ppm and 500 ppm); and (7) putrescine (4 mM and 6 mM). These compounds were applied at the anthesis and grain-filling stages to enhance physio-biochemical traits and yield attributes of wheat (Triticum aestivum) cvs GW-11 and GW-496 under terminal heat stress. The results indicated that GW-11 plants treated with 100 ppm salicylic acid exhibited significant improvements (P ≤ 0.05) in canopy temperature depression, proline content, total chlorophyll content, and the membrane stability index. Compared with the control treatment, foliar application of 100 ppm salicylic acid at both stages caused increases in grain yield (19.5%), followed by a 14% increase with 4 mM putrescine. These yield improvements were attributed to higher grains per spike, more effective tillers, and greater 1000-grain weight, reflecting enhanced sink capacity and grain development under stress. Consequently, the foliage application of 100 ppm salicylic acid at the anthesis and grain-filling stages is recommended to improve late-sown wheat productivity and reduce terminal heat stress.

Neuroactive peptides derived from venomous species have proven to be used as a lead compound for treating neurological diseases. In the present study, the primary structure of the peptide toxins of snakes, scorpions, spiders, cone snails, honey bees, and sea anemones was recovered from different toxin databases. The 3-D structures of the peptide toxins were analyzed with respect to secondary structural elements such as cysteine patterns and disulfide connectivity’s using PYMOL. Their interaction with ion channels/receptors was studied because of its pharmacological importance. The toxins retrieved were found to have – C–Xn–C–Xn–CC–Xn–C–Xn–C-- cysteine pattern for n≥1 that was the same --C---C---CC---C---C— cysteine pattern of ω-conotoxin and hanatoxin, but with a varying intervening non-cysteine residue between cysteines. Hence, these provide insight for structure-based drug design using these peptide toxin scaffolds. Given the optimal molecular weight and specificity of peptides compared to conventional small molecule drugs, peptides are considered future next-generation drug candidates.

Farming is really important for the world. It involves growing and raising things like plants and animals that people use for food. Farmers sell their products in markets, but farming has problems like changing weather, bad soil, soil erosion, not enough jobs, and trouble getting products to where they are needed. Today people are using smart technology, like artificial intelligence (AI) and the Internet of Things (IoT), to make farming better. With IoT, sensors collect information about plants, animals, and fields, so farmers do not have to do everything by hand. Drones with sensors can fly over fields to see what is needed. AI looks at all this data and helps solve problems. There are different kinds of sensors used, like optical sensors for plants, electrochemical sensors for soil nutrients, yield monitor sensors for measuring grain, airflow sensors for soil air, mechanical soil sensors for soil properties, and location sensors to track vehicles and animals with GPS. All this data gives farmers a real-time picture of their crops, and AI suggests the best solutions. This is called “smart farming.”

Artificial Intelligence (AI) is rapidly reshaping the legal field by introducing innovative tools that enhance efficiency, accuracy, and accessibility in legal practice. This paper explores the significant opportunities AI presents, such as automating routine legal tasks, improving legal research, and streamlining document review processes, which can potentially increase access to justice and reduce the costs of legal services. However, the integration of AI into the legal system also brings forth a series of challenges, including concerns related to algorithmic bias, the transparency of AI decision-making, and the potential for exacerbating existing inequalities in the justice system. Moreover, the lack of adequate legal frameworks to govern the ethical use of AI, data privacy issues, and accountability in AI-driven decisions pose significant obstacles. This paper delves into these challenges and proposes regulatory frameworks to address these concerns, ensuring that AI is deployed in a manner that is both responsible and equitable. It also examines AI’s broader societal implications, including its impact on employment, governance, and privacy. The research concludes by emphasizing the importance of continuous oversight, interdisciplinary collaboration, and international cooperation in creating policies that promote the ethical and sustainable development of AI technologies in the legal field.