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Abstract Parameter prediction for PV solar cells plays a crucial role in controlling and optimizing the performance of PV modules. In this study, the parameter prediction of a four‐diode PV model was carried out using the Improved Grey Wolf Optimization (IGWO) algorithm, which builds upon the Grey Wolf Optimization (GWO) algorithm. The parameters required for the four‐diode PV model were optimized based on a predefined objective function. Subsequently, the obtained data were compared with the data from RTCFrance Solar Cell to validate the accuracy and reliability of the optimization results. The evaluation of the optimization results revealed that the Sum Square Error (SSE) values for PSOGWO, AGWOCS, GWOCS, and GWO were 3.96E‐05, while the MSE value for IGWO was 3.6309E‐05. These findings clearly demonstrate that the proposed IGWO algorithm outperforms the other algorithms used in the study, based on the minimized SSE values. This study emphasizes the importance of parameter prediction in optimizing PV performance, and it contributes to thefield by introducing the novel IGWO algorithm for the four‐diode PV model. The algorithm's superior performance, as demonstrated through extensive testing and comparison with existing algorithms, validates its efficacy in accurately predicting the parameters for the PV solar cell model.

Abstract Background Rheumatoid arthritis is a chronic autoimmune disease affecting millions of people across the world. Trayodashang guggulu (TG) is a classical Ayurvedic formulation used for the treating joint diseases since decades in the Indian system of traditional medicine. The aim of the study was to evaluate anti-arthritic activity of TG against complete Freund’s adjuvant-induced arthritis in Wistar rats. Methods Arthritis was induced by single injection of 0.1 ml complete Freund’s adjuvant into the intraplanter surface of left hind paw of Wistar rats. TG was administered orally at the doses of 100, 200, 400 and 800 mg/kg body weight for 14 days. In the preventive dose group, TG was administered at the dose of 100 mg/kg body weight, orally for 28 days. Paw swelling, joint circumference, serum rheumatoid factor, C-reactive protein, serum IL-1β, TNF-α and histopathological parameters were assessed for the evaluation of arthritis. Effects of TG were compared with standard allopathic drug ibuprofen. Results TG reversed complete Freund’s adjuvant-induced arthritis in rats when used for 14 and 28 days. Serum rheumatoid factor, C-reactive protein, IL-1β and TNF-α were decreased in rats treated with both standard drug ibuprofen and TG. Conclusion Oral administration of TG reduced experimentally induced rheumatoid arthritis in rats by reversing elevated level of serum biochemical markers as well as reducing joint destruction similar to ibuprofen. Results obtained from the study paved the way in exploring more specific mechanisms of action of TG involving in vitro and in silico models.

Abstract Sustainable agriculture is crucial for meeting the growing global food demand. With the pressure of climate change, resource depletion, and the need for increased agricultural productivity, innovative approaches are essential. Nanotechnology is an emerging technology in achieving sustainable development goals (SDGs). Despite its promising benefits, the safe implementation of nanotechnology in agriculture requires careful consideration of potential health and environmental risks. However, there is a lack of comprehensive documentation on the application, potential and limitations of nanotechnology in the field of agriculture. To address this gap, a desk research approach was used by utilizing peer-reviewed electronic databases like PubMed, Scopus, Google Scholar, Web of Science, and Science Direct for relevant articles. Out of 157 initially identified articles, 85 were deemed pertinent, focusing primarily on potential nanotechnology in smart agricultural systems. Taking into account research findings worldwide, we found significant improvements with nanotechnology over traditional methods which underscores the practical benefits of nanotechnology, including increased crop yields, efficient resource use, and reduced environmental footprint. The objective of this systematic review is to explore the nexus between nanotechnology and agricultural systems, highlighting its potential to enhance productivity, sustainability, and resilience and to inform researchers, practitioners, and policymakers about the transformative impact of nanotechnology on sustainable agriculture and underscores the need for further research to address safety concerns and maximize its potential for agricultural advancement.

Abstract In this article, we define the octonion quadratic-phase Fourier transform (OQPFT) and derive its inversion formula, including its fundamental properties such as linearity, parity, modulation, and shifting. We also establish its relationship with the quaternion quadratic-phase Fourier transform (QQPFT). Further, we derive the Parseval formula and the Riemann–Lebesgue lemma using this transform. Furthermore, we formulate two important inequalities (sharp Pitt’s and sharp Hausdorff–Young’s inequalities) and three main uncertainty principles (logarithmic, Donoho–Stark’s, and Heisenberg’s uncertainty principles) for the OQPFT. To complete our investigation, we construct three elementary examples of signal theory with graphical interpretations to illustrate the use of OQPFT and discuss their particular cases.

Objective: This study endeavored to assess the lipid profile and atherogenic lipid indexes in children with transfusion-dependent thalassemia (TDT) and to compare them with matched healthy children. Method: The study group consisted of a total of 72 TDT patients aged 3 to14 years, while the control group had 83 age- and sex-matched healthy children. The fasting lipid profile and lipid indexes were estimated and the atherogenic index of plasma (AIP), Castelli's risk indexes I and II, atherogenic coefficient were calculated and compared between the two groups. Result: Compared to the control group, the mean LDL, HDL and cholesterol levels were significantly lower among the case group (p-value < 0.001). The mean VLDL and triglycerides were significantly higher in the case group (p-value < 0.001). Lipid indexes, including the atherogenic index of plasma (AIP), Castelli's risk indexes I and II and atherogenic coefficients were significantly higher in TDT children. Conclusion: Dyslipidemia and increased risk of atherosclerosis were found in TDT children, as they had elevated atherogenic lipid indexes. Our study underlines the importance of the routine use of these indexes in TDT children. Future studies should focus on lipid indexes in this high-lipid group of children so that preventive strategies can be planned accordingly.

Glioblastoma multiforme (GBM) is an infrequent type of brain cancer. The prevalence of GBM is very low, that is, 10 per 100,000 cases. It is, however, the most belligerent and malignant type of brain cancer, which is often fatal. Patients having GBM show notably poor prognosis. GBM can occur at any age, but oftentimes, it is seen in older adults. Despite advancements in the field of medicine and research for the specific medicament for GBM, no refinement has been seen in the survival rates in the past three decades. We performed an advanced search in Google Scholar database between January 2003 and December 2023 using the keywords such as “brain cancer,” “glioma,” “brain tumor,” and “glioblastoma multiforme”. This search resulted in 673 articles related to the recent and advanced information of GBM. However, 101 contemporary and relevant articles were shortlisted for final review based on the most recent information about GBM such as its categorization, clinical manifestation, etiology, pathogenesis, diagnosis, treatment, and recurrence. From these articles, relevant information was used and presented in this comprehensive narrative review to briefly outline the clinical manifestation, pathogenesis, genetic study, etiology, diagnosis, and treatment of GBM with the latest advancements.

Mixed connective tissue disease (MCTD) is one of the rarest connective tissue diseases. Incidence is around 2–3 cases per 100,000 people. The diagnosis is often difficult due to multiple diagnostic criteria and overlapping features with other connective tissue diseases such as systemic lupus erythematosus, systemic sclerosis, and juvenile idiopathic arthritis (JIA). An accurate diagnosis is important for proper treatment and follow-up. We report two pediatric cases, who initially presented with common diseases such as nephrotic syndrome and JIA, but later turned out to have MCTD after completing the workup. Both patients had strongly positive anti-U1-ribonucleoprotein (RNP) antibodies. The first patient had features of Raynaud’s phenomenon as supportive evidence of MCTD. The second patient had hand edema, aseptic meningitis, and myositis as the supporting evidence in addition to clinical features and strongly positive anti-U1-RNP antibodies. Both patients showed good improvement after proper treatment and are under our follow-up.

Introduction: Acute encephalitis syndrome (AES) is one of the important causes of mortality among children in India. Active management of the cases, followed by addressing the cause of AES, is the key strategy for preventing mortality. Lack of laboratory facility and difficulty of sampling blood and cerebrospinal fluid (CSF) for assessing causes is one of the important barriers to early initiation of treatment. The main objective of the study is to validate the Scrub Typhus Encephalitis Assessment Tool (SEAT) for the management of AES. Methods: The study is a continuation of a study conducted in a tertiary care hospital in Eastern Uttar Pradesh. A machine learning (LightGBM) model was built to predict the probability of scrub typhus diagnosis among patients with acute encephalitis. Three models were built: one with sociodemographic characters, the second with Model 1 variables and blood parameters, and the third with Model 2 variables and CSF parameters. Results: The sensitivity of diagnosing the scrub typhus case was 71%, 77.5%, and 83% in Model 1, Model 2, and Model 3, respectively, and specificity was 61.5%, 75.5%, and 76.3%, respectively, in the models. In Model 1 fever duration, in Models 2 and 3, neutrophil/lymphocyte ratio was the most important predictor for differentiating the scrub and nonscrub cases. Conclusion: With the available sensitivity and specificity of the tool, the SEAT can be a valuable tool for the prediction of scrub typhus as a cause of AES cases in remote areas.

Abstract Background This study aimed to assess the factors influencing health-related quality of life (HRQoL) in patients experiencing adverse drug reactions (ADRs) at a tertiary care public sector hospital. A cross-sectional study was conducted over a period of 18 months, and included both male and female patients aged 18 years and above. Patients who visited the outpatient and inpatient departments with complaints associated with ADRs were included in this study. HRQoL data were collected using the EuroQol—5 Dimension—5 Level (EQ-5D-5L) questionnaire to assess five dimensions of health on a five-level scale. Descriptive statistics, t-tests, and analysis of variance were used to analyze the data. Multivariate regression analysis was performed to identify the potential determinants of HRQoL. Results A total of 316 patients were included in the study among these participants, of which 54% were female, and 65% were from rural areas. The majority (68%) of the patients had moderately severe ADRs, and 63% of the participants had an income

Abstract A comprehensive approach is essential in India's ongoing battle against air pollution, combining technological advancements, regulatory reinforcement, and widespread societal engagement. Bridging technological gaps involves deploying sophisticated pollution control technologies and addressing the rural–urban disparity through innovative solutions. The review found that integrating Artificial Intelligence and Machine Learning (AI&ML) in air quality forecasting demonstrates promising results with a remarkable model efficiency. In this study, initially, we compute the PM2.5 concentration over India using a surface mass concentration of 5 key aerosols such as black carbon (BC), dust (DU), organic carbon (OC), sea salt (SS) and sulphates (SU), respectively. The study identifies several regions highly vulnerable to PM2.5 pollution due to specific sources. The Indo-Gangetic Plains are notably impacted by high concentrations of BC, OC, and SU resulting from anthropogenic activities. Western India experiences higher DU concentrations due to its proximity to the Sahara Desert. Additionally, certain areas in northeast India show significant contributions of OC from biogenic activities. Moreover, an AI&ML model based on convolutional autoencoder architecture underwent rigorous training, testing, and validation to forecast PM2.5 concentrations across India. The results reveal its exceptional precision in PM2.5 prediction, as demonstrated by model evaluation metrics, including a Structural Similarity Index exceeding 0.60, Peak Signal-to-Noise Ratio ranging from 28–30 dB and Mean Square Error below 10 μg/m3. However, regulatory challenges persist, necessitating robust frameworks and consistent enforcement mechanisms, as evidenced by the complexities in predicting PM2.5 concentrations. Implementing tailored regional pollution control strategies, integrating AI&ML technologies, strengthening regulatory frameworks, promoting sustainable practices, and encouraging international collaboration are essential policy measures to mitigate air pollution in India.