Scientists around the world are focusing on 'green,' environmentfriendly, and cost-effective green synthesis of nanometals using various plant extracts to combat various ailments. Among nanometals, Silver (Ag) is one of the most commercialised nanomaterials due to its wide applications in biotechnology and biomedical fields. The present study reports the first facile synthesis, characterization, and process optimisation of Ag nanoparticles (NPs) using aqueous Grewia tiliaefolia leaf extract (Gt) as a reducing and surface functionalising agent. Characterisation of Gt-mediated Ag-NPs was performed using FTIR. The morphology and microstructures of Gt-derived Ag-NPs were analysed using TEM and FE-SEM. In vitro, antioxidant activity was evaluated against DPPH radicals, hydrogen peroxide radicals, and ferric ions. In vitro, anticancer activity was assessed on MCF-7 and HepG2 cell lines. In vivo, hepatoprotective activity was tested against paracetamol-induced liver toxicity in rats. FTIR analysis confirmed the interaction between Ag-NPs and Gt. The optimal conditions for Gt-derived Ag-NPs were found to be 4 mM AgNO3, 5% Gt, at 90°C for 60 minutes, at pH 9. UV-Visible spectroscopy, XRD, FE-SEM, and TEM revealed the phase formation, spherical morphology, and surface functionalisation of Gt-derived Ag-NPs, which were stable (-28.3 mV) with an average particle size of 14.5±0.05 nm. The Gt-derived Ag-NPs were found to be highly effective in significantly inhibiting DPPH radical, ferric ions, and hydroxyl radicals. Additionally, the cytotoxicity of Gt-derived Ag-NPs was more effective against MCF-7 cells compared to HepG2 cells. They also exhibited dose-dependent protection against hepatoprotective activity in albino rats. The hepatoprotective effects of Gt-mediated Ag-NPs likely result from the combined action of bioactive phytochemicals (such as α/β-amyrin, γ-lactones, betulin, and lupeol), and their ability to scavenge ROS, reduce oxidative stress, and modulate inflammatory pathways. These mechanisms, supported by reduced lipid peroxidation and increased antioxidant activity in paracetamol-induced hepatotoxicity, suggest their therapeutic potential in liver protection and regeneration. Overall, Gt proves to be an eco-friendly and non-toxic source for synthesizing bioactive Ag-NPs at optimal conditions.

Abstract Protecting sensitive medical data during training is critical because transformer gradients can leak patient-specific information. We introduce a privacy-preserving clinical AI framework that integrates three complementary elements: (i) Bayesian synthetic data generation to produce epidemiologically realistic yet non-identifiable electronic health records, (ii) Wavelet-Domain Privacy Stochastic Gradient Descent(WDP-SGD) to apply frequency-selective noise to gradient updates of BERT-based classifiers, and (iii) multi-modal privacy auditing to empirically monitor potential information leakage. Unlike conventional differential privacy, which injects uniform noise, WDP-SGD perturbs high-frequency gradient components that disproportionately encode patient-specific information while preserving low-frequency components containing generalisable medical knowledge. Applied to a large synthetic medical text corpus covering multiple conditions, our approach consistently delivers stronger privacy protection and improved model performance relative to standard DP-SGD while maintaining convergence behaviour close to a non-private baseline. Privacy attack simulations, including membership inference, attribute inference and gradient reconstruction, further demonstrate enhanced resilience to adversarial attempts to extract sensitive information. These results indicate that wavelet-based differential privacy offers a practical pathway to privacy-conscious clinical language models, achieving a more favourable balance between privacy and utility than existing uniform-noise methods.

Background: Ovarian cancer (OC) often goes undetected until advanced stages due to mild early symptoms. Methods: This research proposes a novel methodology for assessing OC severity through histopathological image analysis, utilizing Rank-Based Leaf in Wind Optimization and Alpha Piecewise Linear Fuzzy techniques. It enhances tissue image quality through normalization and Contrast Limited Adaptive Histogram Equalization, employs ResNet 50 with Inception v4 for feature extraction, and uses a ranking layer to prioritize key features. Results: The model achieved 99.25% accuracy and 97.98% precision, effectively classifying tumor severity levels under diagnostic uncertainty. Conclusion: This robust approach enhances diagnostic accuracy, supports early detection, and improves treatment planning. Future work will explore cross-validation, model pruning, and real-time integration for clinical applications.

Abstract Integrating renewable energy sources into modern power networks presents challenges like reduced grid inertia and low-frequency oscillations. The displacement of conventional synchronous generators weakens system inertia, making grids more susceptible to disturbances. Additionally, variable output from renewables can trigger low-frequency oscillations, threaten overall stability and require advanced control strategies for mitigation. Promising alternatives include pumped storage hydro power generation and advanced control algorithms like fuzzy logic, neural networks, and hybrid optimization techniques, though these still require further research and development. This research suggests a coordinated control approach that combines wind, Solar Photovoltaic (SPV), and pumped storage hydro generators with a turbine governor and unified power flow controller to efficiently mitigate issues. The primary goals are to: (a) Optimize the coordination between the unified power flow controller and the pumped storage governor in order to generate an efficient damping torque for problems during changeable solar and wind inputs. (b) To increase the pumped storage governor’s controllability while making sure it offers sufficient dampening for power system problems. The control systems aim to minimize deviations from desired operating points, focusing on both speed and real power deviations. This is accomplished by combining Linear Quadratic Regulator (LQR) and Proportional Derivative (PD) controllers, which increase the hybrid system’s efficacy. To fine-tune the suggested controller, a novel optimization technique: the Dynamic Opposite Learning-based Enhanced Osprey Optimization Algorithm (DOLOA) is proposed. This approach enhances the coordinated control strategy, enabling it to effectively mitigate low-frequency oscillations and maintain power system stability, even under significant disturbances, such as sudden fluctuations in solar photovoltaic and wind energy penetration. Simulations on the MATLAB/Simulink platform are used to test the performance of the suggested technique and compared to Tilt Integral Derivative (TID) and Fractional Order Proportional Integral Derivative (FOPID). The proposed control strategy achieves optimal performance, reducing frequency variations to within ±0.0001 p.u. over 15 s, outperforming TID and FOPID controllers.

This research paper focuses on the applicability of two well-known concepts, Convective Quasi Equilibrium (CQE) and Convective Instability of the Second Kind (CISK), in the genesis of tropical cyclones (TCs), over the North Indian Ocean (NIO). Using high-resolution regional reanalysis IMDAA data from NCMRWF, we study the applicability of CQE and CISK during pre-monsoon (April and May) and post-monsoon (October and November) for the 1979 to 2020 period. In this study we found that CQE seems to be applicable over some regions of Arabian Sea (AS) in the pre-monsoon season only. However, TCs form over the entire AS, thus CISK mechanism may be vital in pre-monsoon over the AS. In post-monsoon season, over the Bay of Bengal (BB), CQE is found to be applicable. Also, we found the applicability of the CISK, over the BB, both in the pre- and post-monsoon seasons. The study of individual TCs confirms these results regarding the genesis mechanisms of TCs over NIO. Another important, feature we discovered over the AS is the strong, diurnal variation of humidity.

Antiseizure medications (ASMs) are often taken long term by patients with epilepsy yet may be associated with differing metabolic and cardiovascular risks, including hyperlipidemia and weight changes. This systematic review and meta-analysis evaluated the effects of individual ASMs on lipid profiles and weight changes in patients with epilepsy. This paper also provides detailed insights into safety profiles across different patient subgroups and the impact of treatment duration, which was previously underrepresented in individual studies that showed conflicting results. PubMed, Scopus, and Cochrane Central databases were searched from inception until June 2024 for studies reporting lipid derangements after ≥ 3 months of ASM monotherapy in patients with epilepsy in comparison with healthy controls. Primary outcomes were total cholesterol (TC) and low-density lipoprotein cholesterol (LDL) levels. Secondary endpoints were high-density lipoprotein cholesterol, triglycerides, and body mass index (BMI). Mean difference (MD) and standardized mean difference (SMD) were computed using a random-effects model. Further subgroup analyses were performed for adult and pediatric populations, as well as for the duration of treatment, and sensitivity, when feasible. In total, 28 studies, totaling 2231 patients and 1582 healthy controls, were included in this meta-analysis. Significant TC and LDL alterations were observed soon after introducing carbamazepine {TC SMD 1.23 [95% confidence interval (CI) 0.93-1.54]; LDL SMD 1.00 [95% CI 0.70-1.30]} and oxcarbazepine [TC SMD 1.0 (95% CI 0.67-1.34)] in all patient subgroups, as well as phenytoin [TC 0.72 (95% CI 0.37-1.06); LDL SMD 0.41 (95% CI 0.03-0.79)] and valproate in long-term therapy in adult patients. Lamotrigine reduces LDL levels over time [MD - 5.15 (95% CI - 9.51 to - 0.80)] in adults. Levetiracetam has a neutral effect on lipid profile. BMI increases on valproate treatment in the pediatric population [MD 0.58 (95% CI 0.01-1.16)] and adults, commonly seen with long-term therapy [MD 2.73 (95% CI 1.77-3.69)]. Insufficient data existed to assess most other approved ASMs. Individual ASMs may contribute to the overall metabolic and cardiovascular risk profile in patients with epilepsy. This systematic review and meta-analysis identified the need for TC and LDL monitoring in the early stages of treatment for patients taking carbamazepine or oxcarbazepine, as well as adults on phenytoin and long-term valproate therapy. Lamotrigine was associated with a reduction in LDL levels over time in adults, whereas levetiracetam had a neutral effect on the lipid profile. Furthermore, weight gain was commonly observed in both children and adults undergoing long-term treatment with valproate. Regularly ordering lipid tests could play an important role in evaluating and mitigating the metabolic risk of ASMs and should be implemented in clinical practice. INTERNATIONAL PROSPECTIVE REGISTER OF SYSTEMATIC REVIEWS (PROSPERO) PROTOCOL: CRD42024583306.

This study proposes a novel stagnant water layer cooling concept to enhance performance of solar photovoltaic (PV) modules. Three distinct types of water—seawater (Scenario 1), tap water (Scenario 2), and desalinated water (Scenario 3)—were utilized to cool a 10 WP polycrystalline PV module by establishing a stagnant layer of 1.0 cm thickness over it, under the climatic conditions of Visakhapatnam (17.62°N, 83.21°E), India. The average daily temperature variations between the reference and modified PV modules were approximately 7.0 °C, 7.2 °C, and 8.2 °C for Scenarios 1, 2, and 3, respectively. The daily average electrical efficiency & energy output of the modified PV modules under Scenarios 1, 2, and 3 were approximately 16.2 & 14.9, 18.6 & 16.6, and 30.8 & 28.3%, relatively higher than the reference PV module, respectively. Exergy efficiency, sustainability index and specific CO2 emission of desalinated water-cooled PV module were 7.41%, 1.08, and 406 g/kWh, respectively. However, the desalinated water-cooled PV module exhibited 29.1 and 15.1% higher evaporation losses than those of the seawater and tap water-cooled PV modules, respectively, under similar solar energy of 21.4 MJ/m²d. The proposed cooling technique is effective and economic, depends on the type of utilized water.

With the fast adoption of EVs, several EV charging facilities are planned in both rural and urban locations to address the relevant constraints in terms of load demand in both regions. The main concern is that installing additional EV charging facilities may cause serious power-quality and grid-islanding issues on utility grids. The innovative solar-PV based ddistributed generation with Multi-Functional Reduced-Switch VSCs AUPQC device has been proposed resulting the reduced switching losses and maximizing the overall efficiency. The proposed RSMF-AUPQC device has been employed for enhancing PQ and regulation of active power in utility-grid systems by adopting the novel Generalized Voltage-Current Reference (GVCR) controller. In this work, the design and performance evaluation of the proposed GVCR-controlled RSMF-AUPQC device is verified under both PQ and DG operating modes under definite signatures by using the Matlab/Simulink software tool. The simulation results are presented with possible interpretations complying with IEEE-519/2022 standards.