Rice (Oryza sativa L.) is a key staple crop supporting food security and livelihoods in India, where productivity is influenced not only by agronomic factors but also by farmers’ socio-economic conditions. Understanding the socio-economic characteristics of rice growers is essential to identify constraints and improve productivity through better adoption of modern agricultural practices and extension services. The aim of the study is to assess the socio-economic characteristics of rice growers and to analyze how these factors influence rice productivity. The present study was conducted in the Karnal district of Haryana during the year 2023-24. The primary data were collected from 150 respondents using the personal interview method with a structured interview schedule. The data were coded, organized into tables, and analyzed using appropriate statistical methods. After the analysis of the data, it was observed that, majority (49.30 %) of the rice growers belonged to medium age group category, 66.00 % had land holding up to 12 acres , 27.30 % had educational qualification upto intermediate, 40.70 % had farming as their main occupation, 34.70 % had annual income above Rs 3,50,000, 53.30 % respondents had up to 5 members as their family size, 56.70 % had medium level of economic motivation, 58.70 % had medium level of innovative proneness, 52.70 % had medium extension participation, 60.70 % had medium level of sources of information. Notably, a large majority (92.67%) were not affiliated with any organization. According to the findings, farmers' socioeconomic standing can be improved by increasing their education, social involvement, and information sources in addition to providing technical knowledge about rice-growing techniques. This study helps policymakers and extension workers focus on improving farmers’ education, social involvement, and access to information, which can enhance their decision-making and adoption of better farming practices.

Acute posterior multifocal placoid pigment epitheliopathy (APMPPE) is an uncommon disorder of uncertain origin. It had been considered to be a disease of young adults between the second and fourth decade with basic pathology at the retinal pigment epithelium level. Our case report highlights the dissimilar presentation of APMPPE in the elderly compared to the young and the advantage of noninvasive swept source optical coherence tomography angiography in diagnosing APMPPE in a patient with proliferative diabetic retinopathy.

Background & objectives Tuberculosis (TB) remains a major global health concern, with India accounting for 26 per cent of the global burden. Despite advances, access to rapid molecular diagnostics is limited, and the assays currently used in National TB Elimination Programme (NTEP) do not detect isoniazid (INH) resistance upfront. PathoDetect™ MTB RIF & INH is an indigenous closed-system assay that simultaneously detects Mycobacterium tuberculosis (MTB) and resistance to rifampicin (RIF) and INH. This study evaluated its diagnostic characteristics. Methods In this cross-sectional multicenter study conducted at six TB reference laboratories in India, 1039 participants were enrolled (718 presumptive pulmonary TB, 321 presumptive multidrug resistant TB; MDR-TB). PathoDetect™'s discriminatory ability was assessed using the measures sensitivity and specificity, and its diagnostic performance using positive predictive value (PPV) and negative predictive value (NPV). Liquid culture served as the reference standard for MTB detection, while phenotypic drug susceptibility testing (pDST) and line probe assay (LPA) as reference standards for RIF and INH resistance detection. Results For MTB detection in presumptive pulmonary TB (PTB), PathoDetect™ showed a sensitivity of 98.1 per cent [95% confidence interval (CI): 96.1-99.2], specificity of 94.2 per cent (95% CI: 91-96.5), PPV of 94.9 per cent (95% CI: 92.2-96.9), and NPV of 97.8 per cent (95% CI: 95.5-99.1) with near-perfect agreement with Truenat® (k=0.89). Among 514 confirmed TB cases, PathoDetect™ detected RIF resistance with a sensitivity of 86.5 per cent (95% CI: 80.2-91.5), specificity of 91.6 per cent (95% CI: 88.2-94.3), PPV of 82.3 per cent (95% CI: 75.6-87.8), and NPV of 93.8 per cent (95% CI: 90.7-96.1). For INH resistance, sensitivity was 88.9 per cent (95% CI: 84.1-92.6), specificity 87 per cent (95% CI: 82.4-90.8), PPV 85.6 per cent (95% CI: 80.5-89.8), and NPV 90 per cent (95% CI: 85.7-93.4) using pDST as reference. Truenat® MTB-RIF showed comparable performance for RIF resistance detection (k=0.75). Compared to line probe assay (LPA), PathoDetect™ demonstrated higher sensitivity (93.4 vs. 88.8%), specificity (98.2 vs. 93.9%), PPV (96.1 vs. 86.8%) and NPV (97 vs. 94.9%) for RIF resistance detection over Truenat®. Interpretation & conclusions PathoDetect™ is a reliable molecular diagnostic tool for detection of MTB and resistance to RIF & INH. The assay showed better RIF resistance detection compared to INH. Its high sensitivity and specificity indicate strong discriminatory ability, while PPV and NPV demonstrate reasonably good diagnostic performance in the study population. These findings support PathoDetect™ as a promising alternative for rapid TB diagnosis, particularly in high-burden settings.

Background:Poverty significantly amplifies the risk of Tuberculosis (TB) due to undernutrition and cramped, poorly ventilated living conditions. More than 90% of TB cases occur in people with weaker socioeconomic statuses. In low-income communities, undernutrition increases the risk of active TB. Lack of awareness among these populations exacerbates their susceptibility to various risk factors such as smoking, alcoholism, and HIV infections. Hematological indicators of systemic inflammation can aid in the early detection of Tuberculosis in environments with limited resources. Objectives: This study aims to investigate the correlation between socioeconomic status (SES)and Tuberculosis positivity and evaluate the clinical relevance of hematological parameters as potential predictors of TB. Methodology: A cross-sectional study was carried out with 282 individuals to evaluate the variables affecting the results of TB diagnosis. Patient information sheets were used to gather demographic, socioeconomic, and clinical data after getting consent from individual patients. GeneXpert and culture tests were used to confirm the TB diagnosis. Treatment failure predictors were found using logistic regression, and p-values and odds ratios (OR) were shown. Chi-square and Mann-Whitney U tests were used to evaluate correlations between TB status and important factors. ROC analysis was utilized to assess diagnostic accuracy, and Spearman correlation was employed to investigate the connections among biomarkers. RESULTS: Among the 282 individuals, 190 (67.6%) were male. While 61.4% were culture-positive, 87.6% had TB identified by GeneXpert. Most patients were from the higher lower (26.9%) and lower medium (39.3%) socioeconomic groups. Gender (p = 0.006) and isoniazid resistance (p < 0.01) were substantially associated with TB. Hemoglobin (p < 0.05), WBC count, NLR, and ESR were key predictors. ESR (AUC = 0.776) exhibited the highest diagnostic accuracy, followed by hemoglobin, WBC count, and NLR. A mild negative correlation (r = -0.191) between NLR and hemoglobin and a significant positive correlation (r = 0.278) between ESR and NLR in TB patients were observed. CONCLUSION: The findings highlight the dual impact of SES and inflammation markers on TB risk, emphasizing the need to incorporate these factors into routine screening and early detection strategies. Public health policies should address socioeconomic barriers while integrating hematological assessments as supplementary diagnostic tools to improve TB management, particularly in resource-constrained settings.

In this paper, our main aim is to propose a group ring analogue of the well-known learning with errors problem and discuss its security. Using this novel problem, we construct IND-CPA secure public key encryption scheme and IND-CCA secure key encapsulation mechanism. We also discuss a toy example relevant to our encryption scheme

The concept of undeniable signature scheme was proposed by Chaum and Antwerpen in 1989. In this scheme, the signature can only be verified by the verifier with the co-operation of the signer. In this paper, we propose a novel undeniable signature scheme based on the structure of group ring. We consider the well studied hard problems, that is, inverse computation problem (ICP) and discrete logarithm problem (DLP) in group ring and show that under the chosen message attack, our scheme is strongly unforgeable, invisible and secure against impersonation attack. These security notions, that is, strongly unforgeability, invisibility and impersonation are defined through three different games. In order to practically realize the scheme, we discuss a case study in which we generate the signature for a message and then verify it through the verification algorithm. Finally, we compare our scheme with several other renowned schemes available in the literature and show it is efficient in terms of the total execution time.

India’s economy and employment are significantly impacted by agriculture. Indian farmers frequently make the mistake of selecting the incorrect crop for the characteristics of their land. The effect is a decrease in productivity. Careful crop selection is necessary for farmers to provide high-quality harvests. We have discovered a solution to the farmers’ dilemma. Here, we introduce an ensemble model that uses a majority voting approach recommendation system to provide extremely precise crop recommendations for parameters unique to each site, such as soil nutrients (nitrogen, phosphorus, potassium, and pH level) and local weather conditions (temperature, humidity, and rainfall). The methods we use to do this include Decision Tree, Random Forest, K-Nearest Neighbors, and Naive Bayes.

This paper presents an investigation into the reliability assessment of power plant operations using Boolean Function expansion and Mean Time to Failure (MTTF) analysis. The study focuses on a complex system comprising two power generators within a power house. The primary objective of this system i. This paper presents an investigation into the reliability assessment of power plant operations using Boolean Function expansion and Mean Time to Failure (MTTF) analysis. The study focuses on a complex system comprising two power generators within a power house. The primary objective of this system is to ensure uninterrupted power supply from the power house to critical consumers via an output main switch. Reliability calculations are conducted considering failure times of various components, including cables, generators, and main switchboards, assuming arbitrary distribution patterns. Additionally, MTTF for the system under an exponential failure time distribution is determined. Graphical representations are employed to illustrate the utility and efficacy of the proposed models to ensure uninterrupted power supply from the power house to critical consumers via an output main switch. Reliability calculations are conducted considering failure times of various components, including cables, generators, and main switchboards, assuming arbitrary distribution patterns. Additionally, MTTF for the system under an exponential failure time distribution is determined. Graphical representations are employed to illustrate the utility and efficacy of the proposed model.

In June 2023, the Department of Horticulture at Lovely Professional University in Jalandhar, Punjab conducted a field experiment. The experiment utilized a factorial Randomized Block Design with 9 treatments and 3 replications, covering a total of 27 plants. The experiment ran from June 2023 to December 2023. Regarding the individual effect of nano zinc, it was found that the application of nano zinc at a concentration of 200 ppm resulted in significant increases in various vegetative parameters. The maximum plant height increased by 18.06%, canopy spread increased by 30.16% East to West and 30.31% North to South, canopy volume increased by 121.34%, leaf length increased by 119.25%, leaf width increased by 110.94%, and leaf area increased by 363.27%. However, the increase in stem diameter was not significant compared to other treatments. Similarly, the individual effect of nano iron was also studied. The application of Nano Iron at a concentration of 150 ppm resulted in significant increases in various vegetative parameters. The maximum plant height increased by 17.26%, canopy spread increased by 27.63% East to West and 27.78% North to South, canopy volume increased by 109.42%, leaf length increased by 113.28%, leaf width increased by 107.08%, and leaf area increased by 343.06%. However, the increase in stem diameter was not significant compared to other treatments. The experiment revealed that among various treatment combinations, the application of Nano Zinc @ 200ppm + Nano Iron @ 150ppm (nZn2+nFe2) was observed significantly superior and maximum maximum plant height increased by 19.98%, canopy spread increased by 34.69% East to West and 34.88% North to South, canopy volume increased by 144.72%, leaf length increased by 130.89%, leaf width increased by 120.56% and leaf area increased by 409.25% over other treatments about vegetative parameters except stem diameter compared to other treatments.

Lexical analysis is a crucial phase in compiler design that involves transforming a stream of characters into meaningful tokens. Traditional lexical analyzers utilize finite automata and regular expressions to achieve this task. However, in the context of modern computing environments such as multi-core machines, there is a growing need to enhance the efficiency of this process. Exploring the concept of parallel tokenization and its application in lexical analysis to harness the power of multi-core architectures, the research delves into innovative approaches for optimizing compilation processes. By leveraging the processing capabilities of multi-core machines, the scanning process can be significantly expedited. Exploring the challenges, benefits, and implementation considerations of parallel tokenization, the research investigates the potential to revolutionize the field of compiler design through the parallel processing of tokens. The study undertakes a methodical review of relevant literature, thereby addressing emerging strategies and concerns surrounding lexical analyzer implementation, as well as the adoption of enhanced methodologies. The outcomes of this review showcase an array of techniques, recent advancements, and contemporary approaches concerning the deployment of both auto-generated and hand-crafted scanners. Grounded in these findings, the paper extrapolates insights into the effectiveness of lexical analyzer implementation strategies, consequently charting out potential research challenges and unexplored domains necessitating investigation in the realm of implementation of lexical analysis processes.

As environmental factors directly affect the health and production of silkworms, they must be monitored for sericulture to be successfully cultivated. In order to maximize sericulture conditions, this research paper provides a thorough examination of many monitoring strategies for variables including temperature, humidity, light, and air quality. The combination of Internet of Things (IoT) sensors and advanced techniques like as image processing and machine learning algorithms may be used to create a comprehensive system for real-time monitoring and management of sericulture settings. The proposed solutions increase sericulture operations' productivity and efficiency while also promoting ecologically responsible and sustainable farming practices. Based on the findings, both commercial and small-scale silk producers can benefit from the adoption of modern monitoring and disease detection technology, which can result in significant increases in the quantity and quality of silk produced.

The extensive modification in different land operations fueled by rapid urbanization is a matter of great concern. It is not just a simple process of exchange within different classes as we think; moreover, it is one of the most responsible factors for the change in biological rotations of the natural system. India, one of the fastest-urbanizing nations, is expected to be the home of the most urban residents by 2050, likely resulting in various undesirable issues in emerging and existing cities. The review highlights the apprehensive sides of sprawl and land use/land cover (LULC) changes in the Indian context. It also aims to assist the researchers in exploring the scope and relevance of sprawl measurement techniques for emerging urban settlements through the collected literature. A multi-stage sampling method is used to scrutinize the secondary source-based information regarding the research work. In total, 58 studies of recognized journals have been appraised systematically from 1981 to 2022. The findings reveal that increasing accidental growth caused by rapid urbanization is the leading cause of the change in LULC and irreparable environmental loss. 77.58 percent of studies have reported that geospatial technology, models, and numerical methods are more relevant to urban planners and officials for sprawl measurement and LULC change detection. So, the administration should address the accidental urban growth in its initial phase with a priority for sustainable urbanization.

A field experiment was carried out during the year 2021-22 and 2022-23 on 12 years old mandarin plants at the Instructional Farm, Department of Fruit Science, College of Horticulture and Forestry, Jhalawar. The experiment was consisting of 21 treatments of different organic source of NPK viz., vermicompost, cotton fortified vermicompost, neem cake, cotton cake, mustard cake and bio fertilizers such as PSB and VAM with three levels of recommendation dose of fertilizers. The experiment was laid out in randomized block design with three replications. Among different treatments, treatment T9 (75 % RDF + 10 kg Vermicompost + 7.5 kg Neem cake + 50 g PSB) was found best with regards to maximum pooled TSS (11.74 0B), reducing sugar (6.30%) non-reducing sugar (2.62%), total sugar (8.92%) per cent, sugar/acid ratio (14.40), ascorbic acid (53.08mg/100g) and juice (46.57%). Therefore, based on two years experimentation, use of organic source of nutrients with bio-fertilizers favoured biochemical compounds of Nagpur Mandarin fruits.

In real-life scenarios, information about the number of clusters is unknown. Due to this, clustering algorithms are unable to generate the valuable partitions. Beside this, the appropriate and optimal number of features is also required to produce the good quality clusters. The selection of optimal number of clusters and feature is a challenging task in the clustering. To resolve these problems, an automatic multi-objective-based clustering approach called HMOSHSSA is proposed in this paper. In HMOSHSSA, the spotted hyena and salp swarm algorithms are hybridized to obtain a better trade-off between these algorithms’ intensification and diversification capabilities. Two novel concepts for encoding and threshold setting are incorporated in the HMOSHSSA. The encoding scheme is used to choose the optimal number of clusters and features during the optimization process. The variance of dataset is used for setting the threshold values for both clusters and features. A novel fitness function is proposed to improve the optimization process. The suggested algorithm’s performance is evaluated using eight well-known real-world datasets. The statistical significance of HMOSHSSA is measured through t-tests. Results reveal that the proposed approach is able to detect the optimal number of clusters and features from a given dataset without user intervention. This approach is also deployed for solving microarray data analysis and image segmentation problems. HMOSHSSA outperformed the other considered algorithms in terms of performance measures.

In sericulture, the young caterpillars of the common silk moth, also known as ”Bombyx Mori,” are the most widely used species of silkworm. For the purpose of raising silkworms, mulberry plants are grown. Mulberry leaves are the main source of food for silkworms. Mulberry leaves are an important economic factor in sericulture because they directly influence the quality and quantity of cocoons produced per unit area. Mulberry leaf diseases cause both a loss in leaf quality and a reduction in leaf yield. Feeding silkworms diseased leaves has an effect on both the quality of the silk produced and the number of cocoons formed. Traditional techniques have been used to detect diseases. However, plant pathologists or agricultural professionals have traditionally used empty-eye observation to find leaf diseases. It takes a lot of labour and in-depth understanding of plant illnesses to detect diseases in plant leaves using this approach, which may be unreliable, time-consuming, and lucrative. Therefore, it is crucial to maintain regular watchfulness and employ effective disease diagnosis methods to safeguard mulberry leaves from various disease attacks.

The impact of COVID-19 grew worse by its quick onset, the overwhelming number of patients in need of urgent care, and the lack of awareness of its signs beforehand. It mostly affected the respiratory system, making respiratory rehabilitation extremely important. This study was carried out to see the effectiveness of breathing exercises along with the postural changes in COVID-19 patients under acute hospital care. A total of 40 subjects with age group between 18 years to 60 years, diagnosed with COVID-19 were included according to the selection criteria and were divided into 2 equal groups of 20 subjects each. The subjects in Group A received positioning protocol while those in Group B received breathing exercises along with positioning protocol. After 7 days of intervention, the mean SpO2(88.6% vs 89.9%, P<0.001) and pulse rate (82 vs 91 beats/minute, P<0.001) for Group A and Group B respectively. This study concluded that breathing exercises along with postural changes in patients hospitalized with COVID-19 is an effective rehabilitation program for improving SPO2 levels and pulse rate. KEYWORDS: Breathing exercises, COVID-19, Postural changes, Respiratory rehabilitation.

PURPOSE. Microglial activation has been implicated in many neurodegenerative eye diseases, but the interrelationship between cell loss and microglia activation remains unclear. In glaucoma, there is no consensus yet whether microglial activation precedes or is a consequence of retinal ganglion cell (RGC) degeneration. We therefore investigated the temporal and spatial appearance of activated microglia in retina and their correspondence to RGC degeneration in glaucoma. METHODS. We used an established microbead occlusion model of glaucoma in mouse whereby intraocular pressure (IOP) was elevated. Specific antibodies were used to immunolabel microglia in resting and activated states. To block retinal gap junction (GJ) communication, which has been shown previously to provide significant neuroprotection of RGCs, the GJ blocker meclofenamic acid was administered or connexin36 (Cx36) GJ subunits were ablated genetically. We then studied microglial activation at different time points after microbead injection in control and neuroprotected retinas. RESULTS. Histochemical analysis of flatmount retinas revealed major changes in microglia morphology, density, and immunoreactivity in microbead-injected eyes. An early stage of microglial activation followed IOP elevation, as indicated by changes in morphology and cell density, but preceded RGC death. In contrast, the later stage of microglia activation, associated with upregulation of major histocompatibility complex class II expression, corresponded temporally to the initial loss of RGCs. However, we found that protection of RGCs afforded by GJ blockade or genetic ablation largely suppressed microglial changes at all stages of activation in glaucomatous retinas. CONCLUSIONS. Together, our data strongly suggest that microglia activation in glaucoma is a consequence, rather than a cause, of initial RGC degeneration and death.

It's critical to keep up with current happenings in today's fast-paced culture. Due to their hectic schedules, many people, however, find it difficult to keep up with the broad news. To address this issue, we developed a system that effectively summarizes news stories and provides succinct and illuminating summaries. The Text Ranking algorithm is used by our system to extract headline information and produce concise summaries. A headlining module built on a seq-to-seq model and Long Short-Term Memory (LSTM) architecture has also been added. Techniques for Natural Language Processing (NLP) are used to preprocess data and enhance the summary procedure. Recurrent neural networks and LSTM are comparable in that they both excel at capturing longdistance word associations in input sequences. We've created a user-friendly website using these elements to improve accessibility. By empowering people to stay informed without having to read lengthy news pieces, this method responds to the demands of our fast-paced lifestyles.