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The global shift towards decentralized energy systems, driven by the integration of distributed generation technologies and renewable energy sources, underscores the critical need for effective energy management strategies in microgrids. This study proposes a novel multi-objective optimization framework for grid-connected microgrids using quantum particle swarm optimization (QPSO) to address the dual challenges of minimizing operational costs and reducing environmental emissions. The microgrid configuration analyzed includes renewable energy sources like photovoltaic panels and wind turbines, along with conventional energy sources and battery storage. By incorporating quantum-inspired mechanics, QPSO overcomes limitations such as premature convergence and solution stagnation, often seen in traditional methods. Simulation results demonstrate that QPSO achieves a 9.67% reduction in operational costs, equating to savings of €158.87, and a 13.23% reduction in carbon emissions, lowering emissions to 513.70 kg of CO 2 equivalent in the economic scheduling scenario. In the environmentally constrained economic scheduling scenario, the method delivers a balanced solution with operational costs of €174.11 and emissions of 401.63 kg of CO 2 . The algorithm’s performance is validated across various microgrid configurations, including standard low-voltage setups. These results highlight QPSO’s potential as an efficient tool for optimizing microgrid energy management, promoting both economic and environmental sustainability. This study provides a robust framework for achieving practical solutions in real-world applications, emphasizing the role of advanced optimization techniques in sustainable energy systems.

Context: Hesperidin (HSP), a flavanoglycone found in citrus fruits, has antioxidant, anti-inflammatory and neuroprotective properties.Objective: This study evaluates the protective effect of HSP on l-methionine-induced hyperhomocysteinemia (HHcy) in rats.Materials and methods: Male Wistar rats were randomly divided into seven groups as DMSO, l-methionine, HSP (25, 50 and 100 mg/kg), HSP-per se (100 mg/kg) and donepezil (0.1 mg/kg). HHcy was induced by oral administration of l-methionine (1.7 g/kg) for 32 days. From the 14th day of study HSP (25, 50 and 100 mg/kg) and donepezil was administered orally to l-methionine-treated rats. Cognitive impairment induced by HHcy was determined using the Morris water maze (MWM) and Y-maze on video tracking system (28th–32nd day). Different biomarkers of HHcy in serum and brain and vascular reactivity were evaluated and histopathology (thoracic aorta and brain) was done.Results: HSP (100 mg/kg) treatment in l-methionine-treated rats exhibited significant (p < 0.001) dose-dependent activity and reduced behavioural deficits, brain acetylcholinesterase (25.99 ± 2.36 versus 10.73 ± 1.26 μmoles/mg), brain lipid peroxidation (15.25 ± 1.65 versus 6.18 ± 0.74 nM/mg), serum homocysteine (Hcy) (22.37 ± 0.30 versus 11.01 ± 1.01 μg/mL) and serum cholesterol (182.7 ± 2.15 versus 101.5 ± 2.76 mg/dL) and increased brain antioxidant levels. HSP significantly (p < 0.001) reduced endothelial dysfunction (ED) by abolishing the effect of l-methionine on acetylcholine-induced endothelial-dependent relaxation and increased serum nitrite and vascular nitric oxide bioavailability along with the restoration of histological aberrations.Conclusion: HSP exerts a protective effect on HHcy by abrogating oxidative stress, ED and neurotoxicity.

This study involves the in-vitro and in-vivo anti-TB potency and in-vivo safety of Transitmycin (TR) (PubChem CID:90659753)- identified to be a novel secondary metabolite derived from Streptomyces sp (R2). TR was tested in-vitro against drug resistant TB clinical isolates (n = 49). 94% of DR-TB strains (n = 49) were inhibited by TR at 10μg ml -1 . In-vivo safety and efficacy studies showed that 0.005mg kg -1 of TR is toxic to mice, rats and guinea pigs, while 0.001mg kg -1 is safe, infection load did not reduce. TR is a potent DNA intercalator and also targets RecA and methionine aminopeptidases of Mycobacterium . Analogue 47 of TR was designed using in-silico based molecule detoxification approaches and SAR analysis. The multiple targeting nature of the TR brightens the chances of the analogues of TR to be a potent TB therapeutic molecule even though the parental compound is toxic. Analog 47 of TR is proposed to have non-DNA intercalating property and lesser in-vivo toxicity with high functional potency. This study attempts to develop a novel anti-TB molecule from microbial sources. Though the parental compound is toxic, its analogs are designed to be safe through in-silico approaches. However, further laboratory validations on this claim need to be carried out before labelling it as a promising anti-TB molecule.

As Europe integrates more renewable energy resources, notably offshore wind power, into its super meshed grid, the demand for reliable long-distance High Voltage Direct Current (HVDC) transmission systems has surged. This paper addresses the intricacies of HVDC systems built upon Modular Multi-Level Converters (MMCs), especially concerning the rapid rise of DC fault currents. We propose a novel fault identification and classification for DC transmission lines only by employing Long Short-Term Memory (LSTM) networks integrated with Discrete Wavelet Transform (DWT) for feature extraction. Our LSTM-based algorithm operates effectively under challenging environmental conditions, ensuring high fault resistance detection. A unique three-level relay system with multiple time windows (1 ms, 1.5 ms, and 2 ms) ensures accurate fault detection over large distances. Bayesian Optimization is employed for hyperparameter tuning, streamlining the model’s training process. The study shows that our proposed framework exhibits 100% resilience against external faults and disturbances, achieving an average recognition accuracy rate of 99.04% in diverse testing scenarios. Unlike traditional schemes that rely on multiple manual thresholds, our approach utilizes a single intelligently tuned model to detect faults up to 480 ohms, enhancing the efficiency and robustness of DC grid protection.

DC grid fault protection techniques have previously faced challenges such as fixed thresholds, insensitivity to high-resistance faults, and dependency on specific threshold settings. These limitations can lead to elevated fault currents in the grid, particularly affecting multi-modular converters (MMCs) vulnerability to large fault current transients. This paper proposes a novel approach that combines the disjoint-based Bootstrap Aggregating (Bagging) technique and Bayesian optimization (BO) for fault detection in DC grids. Disjoint partitions reduce variance and enhance Ensemble Artificial Neural Network (EANN) performance, while BO optimizes EANN architecture. The proposed approach uses multiple transient periods instead of a fixed time to train the model. Transient periods are segmented into multiple 1 ms intervals, and each interval trains a separate neural network. In this way, a robust local relay is created that does not require high-speed communication systems. Additionally, a discrete wavelet transform (DWT) is applied to select detailed coefficients of the transient fault current, measured at the DC line’s sending terminal for fault protection. EANN is trained in comprehensive offline data that considers noise impact. Simulation results demonstrate the scheme’s ability to detect faults as high as 400 Ω accurately. This makes it a robust, reliable, and effective solution for fault detection on high-voltage direct current (HVDC) transmission lines. Lastly, this research provides the first-ever scientometric analysis of HVDC transmission line fault protection using neural network algorithms, highlighting major research themes and trends. The scientometric analysis was based on a dataset of 136 available research articles from the Scopus database from the last ten years. Therefore, this research provides valuable insights into the use of ANN for HVDC transmission line fault protection.

Objectives: To assess the clinical profile and primary treatment response and outcomes in idiopathic membranous nephropathy (IMN) patients. Methods: This study was carried out between December 2013 and January 2019 in a tertiary care hospital in North India on 2 years retrospective and 3 years prospective renal biopsy proven patients with IMN presenting with nephrotic syndrome. Basic baseline investigations carried out were urinary proteins, serum albumin, serum creatinine, other special tests wherever necessary or possible (including phospholipase A2 receptor antibodies), and different treatment regimens were offered for treatment. The patients were followed up for a minimum period of 6 months after administration of treatment. Results: The study was carried out in 120 patients with mean age of 43 [+ or -] 14.6 years and male female ratio of 1.65:1. Hypertension was noted in 36%, microscopic hematuria in 13%, and mean 24 hours urinary proteinuria 10.5 [+ or -] 3.1 gm. Complete or partial response at 6 months was observed in 57% and 34% cases to cyclophosphamide, 60% and 40% to modified Ponticelli treatment, 81% and 19% to tacrolimus, and 40% and 36% cases to rituximab. Relapse was observed in 6% of cyclophosphamide and 13% in tacrolimus groups. Conclusion: Our results show a good and comparable response to cyclophosphamide, tacrolimus, and rituximab at 6 months of follow up. The cases which achieved complete remission had significantly lower baseline proteinuria compared to those who did not respond. Keywords: idiopathic membranous nephropathy, nephrotic syndrome, cyclophosphamide, tacrolimus, rituximab [phrase omitted]

Background. The role of drug concentrations in clinical outcomes in children with tuberculosis is unclear. Target concentrations for dose optimization are unknown. Methods. Plasma drug concentrations measured in Indian children with tuberculosis were modeled using compartmental pharmacokinetic analyses. The children were followed until end of therapy to ascertain therapy failure or death. An ensemble of artificial intelligence algorithms, including random forests, was used to identify predictors of clinical outcome from among 30 clinical, laboratory, and pharmacokinetic variables. Results. Among the 143 children with known outcomes, there was high between-child variability of isoniazid, rifampin, and pyrazinamide concentrations: 110 (77%) completed therapy, 24 (17%) failed therapy, and 9 (6%) died. The main predictors of therapy failure or death were a pyrazinamide peak concentration <38.10 mg/L and rifampin peak concentration <3.01 mg/L. The relative risk of these poor outcomes below these peak concentration thresholds was 3.64 (95% confidence interval [CI], 2.28–5.83). Isoniazid had concentration-dependent antagonism with rifampin and pyrazinamide, with an adjusted odds ratio for therapy failure of 3.00 (95% CI, 2.08–4.33) in antagonism concentration range. In regard to death alone as an outcome, the same drug concentrations, plus z scores (indicators of malnutrition), and age <3 years, were highly ranked predictors. In children <3 years old, isoniazid 0- to 24-hour area under the concentration-time curve <11.95 mg/L × hour and/or rifampin peak <3.10 mg/L were the best predictors of therapy failure, with relative risk of 3.43 (95% CI, .99–11.82). Conclusions. We have identified new antibiotic target concentrations, which are potential biomarkers associated with treatment failure and death in children with tuberculosis.

In this paper the multiverse optimization (MVO) was used for estimating Weibull parameters. These parameters were further used to analyze the wind data available at a particular location in the Tirumala region in India. An effort had been made to study the wind potential in this region (13°41′30.4″ N 79°21′34.4″ E) using the Weibull parameters. The wind data had been measured at this site for a period of six years from January 2012 to December 2017. The analysis was performed at two different hub heights of 10 m and 65 m. The frequency distribution of wind speed, wind direction and mean wind speeds were calculated for this region. To compare the performance of the MVO, gray wolf optimizer (GWO), moth flame optimization (MFO), particle swarm optimization (PSO) and other numerical methods were considered. From this study, the performance had been analyzed and the best results were obtained by using the MVO with an error less than one. Along with the Weibull frequency distribution for the selected region, wind direction and wind speed were also provided. From the analysis, wind speed from 2 m/s to 10 m/s was present in sector 260–280° and wind from 0–4 m/s were present in sector 170–180° of the Tirumala region in India.

Background & objectives: The National Tuberculosis (TB) Control Programme has transitioned from thrice-weekly to daily drug treatment regimens in India. This preliminary study was conceived to compare the pharmacokinetics of rifampicin (RMP), isoniazid (INH) and pyrazinamide (PZA) in TB patients being treated with daily and thrice weekly anti-TB treatment (ATT). Methods: This prospective observational study was undertaken in 49 newly diagnosed adult TB patients receiving either daily ATT (n=22) or thrice-weekly ATT (n=27). Plasma RMP, INH and PZA were estimated by high-performance liquid chromatography. Results: The peak concentration (Cmax) of RMP was significantly higher (RMP: 8.5 µg/ml vs. 5.5 µg/ml; P=0.003) and Cmax of INH was significantly lower (INH: 4.8 µg/ml vs. 10.9 µg/ml; P<0.001) in case of daily dosing compared to thrice-weekly ATT. Cmax of drugs and doses was significantly correlated. A higher proportion of patients had subtherapeutic RMP Cmax (8.0 µg/ml) during thrice-weekly compared to daily ATT (78% vs. 36%; P=0.004). Multiple linear regression analysis showed that Cmax of RMP was significantly influenced by the dosing rhythm, pulmonary TB and Cmax of INH and PZA by the mg/kg doses. Interpretation & conclusions: RMP concentrations were higher and INH concentrations were lower during daily ATT, suggesting that INH doses may need to be increased in case of a daily regimen. Larger studies are, however, required using higher INH doses when monitoring for adverse drug reactions and treatment outcomes.

Indium Sulfide (In2S3) is a promising candidate to replace Cadmium Sulfide (CdS) as a buffer layer in thin film solar cells because of its n-type conductivity and wide energy band gap. In this study, In2S3 thin films are deposited on glass substrates at different substrate temperatures in the range of 200–350 °C by co-evaporation technique. The X-ray diffraction and Raman analysis confirm the formation of tetragonal β-In2S3 thin films. The X-ray Photoelectron Spectroscopy and Energy Dispersive X-ray Spectroscopy results reveal presence of constituent elements. The energy band gap was observed in the range of 2.45–2.54 eV and band gap is increasing with increase of substrate temperature. Hall Effect measurement shows n-type conductivity for all films. Photodetectors were fabricated and tested under the light illumination by solar simulator with AM 1.5G filter. The photo detection parameters like sensitivity, responsivity and detectivity were calculated for all photodetectors.