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The current research paper presents a first-principles study of the effects of doping Zno with transition metals such as Yttrium (Y) and Vanadium (V) on its structural, electronic, thermal stability, and optical characteristics. Replacing these Zn atoms with the dopants results in a significant increase in the dielectric properties and conductivity of the material. Elastic constants are then calculated to determine the mechanical stability of the doped structures, confirming their structural integrity. The NVT ensemble is utilized to prove the thermal stability. Computational modelling is done in Material Studio where the simulations are done in CASTEP which can allow a detailed analysis of the doped ZnO systems. The tests done prior are band structure, X-ray diffraction (XRD) patterns, thermal stability, and optical properties like conductivity, reflectivity, and the refractive index. The results indicate that Y and V doping have the potential to alter the electronic structure of ZnO greatly, which has bright opportunities to enhance the functioning of ZnO in optoelectronic and dielectric devices. Besides broadening our knowledge about doped ZnO on the atomic scale, these findings can form a powerful basis of experimental validation and developing materials in the future.

New energy vehicles (NEVs) has emerged as a sustainable alternative to conventional vehicles, however have unresolved reliability challenges due to their complex electronic systems and varying operating conditions. Faults in drivetrain and battery systems, occurring at rates up to 12% annually, present significant barriers to the widespread adoption of NEVs. This study proposes a robust fault detection framework that applies multiple machine learning and deep learning models to address these challenges. The research utilizes the benchmark NEV fault diagnosis dataset, which contains real-world sensor data from NEVs. The models tested include logistic regression, passive-aggressive classifier, ridge classifier, perceptron, gated recurrent unit (GRU), convolutional neural network, and artificial neural network. The proposed ensemble GRULogX model stands out among the implemented model, leveraging GRU with logistic regression and other key classifiers, and achieved 99% accuracy, demonstrating high precision and recall. Cross-validation and hyperparameter optimization were adopted to further ensure the model’s generalizability and reliability. This research enhances the fault detection capabilities of NEVs, thereby improving their reliability and supporting the wider adoption of clean energy transportation solutions.

Evaluating groundwater quality in irrigated areas is crucial for sustainable agriculture, especially as limited water resources and climate change pose significant threat to groundwater resources. Spatial information on groundwater quality is essential for effective management and utilization of water resources, particularly in intensive cropping areas such as irrigated regions in IBIS, Pakistan. However, recent advancements in machine learning (ML) techniques have highlighted that conventional groundwater quality assessment methods are costly and time-consuming, especially for developing nations. Accurate and efficient ML models can address this challenge in agricultural water management by optimally identifying the categories of water quality. This study is conducted to predict groundwater quality using an innovative ensemble-boosting methodology. The data is collected from Rahim Yar Khan’s irrigation system by the Scarp Monitoring Organization. Four irrigation water quality indicators, including sodium adsorption ratio, total dissolved solids, residual sodium carbonate, and electrical conductivity, are used to predict groundwater quality by applying four ML models. The performance of the ML models is assessed using mean squared error, correlation coefficients (r), and root mean square error measures. The proposed Gradient Boosting (GB) approach combines the advantages of interpretable tree models and boosting approaches. Experimental results validate the utility of the proposed approach with a 99% accuracy in predicting groundwater quality, compared to conventional ML techniques. Based on the proposed GB model and the inverse distance weighting interpolation technique, the groundwater quality distribution in the Hazardous Area is 17.34%, the Marginal Area is 79.36%, and the Safe Area is 3.30%. Enhancement and validation of groundwater quality index predictions are carried out using k-fold validation and hyperparameter tuning. Results indicate that the ML models have the potential to accurately delineate different groundwater quality zones for managing water resources and ensuring sustainable agriculture. Water quality assessment through the proposed approach can help managing the groundwater for the regions susceptible to deterioration of water quality thus contributing to better irrigation governance.

Background Cilostazol, a phosphodiesterase III inhibitor, appears to be a promising agent for preventing cerebral ischemia in patients with aneurysmal subarachnoid hemorrhage. Here, the authors perform a systematic review and meta-analysis to quantitatively assess the effects of cilostazol on brain structural and functional outcomes in animal models of cerebral ischemia and subarachnoid hemorrhage–induced cerebral vasospasm. Methods By using the PRISMA guidelines, a search of the PubMed, Scopus, and Web of Science was conducted to identify relevant studies. Study quality of each included study for both systematic reviews were scored by using an adapted 15-item checklist from the Collaborative Approach to Meta-Analysis of Animal Data from Experimental Studies. We calculated a standardized mean difference as effect size for each comparison. For each outcome, comparisons were combined by using random-effects modeling to account for heterogeneity, with a restricted maximum likelihood estimate of between-study variance. Results A total of 22 (median [Q1, Q3] quality score of 7 [5, 8]) and 6 (median [Q1, Q3] quality score of 6 [6, 6]) studies were identified for cerebral ischemia and subarachnoid hemorrhage–induced cerebral vasospasm, respectively. Cilostazol significantly reduced the infarct volume in cerebral ischemia models with a pooled standardized mean difference estimate of − 0.88 (95% confidence interval [CI] [− 1.07 to − 0.70], p  < 0.0001). Cilostazol significantly reduced neurofunctional deficits in cerebral ischemia models with a pooled standardized mean difference estimate of − 0.66 (95% CI [− 1.06 to − 0.28], p  < 0.0001). Cilostazol significantly improved the basilar artery diameter in subarachnoid hemorrhage–induced cerebral vasospasm with a pooled standardized mean difference estimate of 2.30 (95% CI [0.94 to 3.67], p  = 0.001). Cilostazol also significantly improved the basilar artery cross-section area with a pooled standardized mean estimate of 1.88 (95% CI [0.33 to 3.43], p  < 0.05). Overall, there was between-study heterogeneity and asymmetry in the funnel plot observed in all comparisons. Conclusions Published animal data support the overall efficacy of cilostazol in reducing infarct volume and neurofunctional deficits in cerebral ischemia models and cerebral vasospasm in subarachnoid hemorrhage models.

The reliable operation of electrical power transmission systems is crucial for ensuring consumer’s stable and uninterrupted electricity supply. Faults in electrical power transmission systems can lead to significant disruptions, economic losses, and potential safety hazards. A protective approach is essential for transmission lines to guard against faults caused by natural disturbances, short circuits, and open circuit issues. This study employs an advanced artificial neural network methodology for fault detection and classification, specifically distinguishing between single-phase fault and fault between all three phases and three-phase symmetrical fault. For fault data creation and analysis, we utilized a collection of line currents and voltages for different fault conditions, modelled in the MATLAB environment. Different fault scenarios with varied parameters are simulated to assess the applied method’s detection ability. We analyzed the signal data time series analysis based on phase line current and phase line voltage. We employed SMOTE-based data oversampling to balance the dataset. Subsequently, we developed four advanced machine-learning models and one deep-learning model using signal data from line currents and voltage faults. We have proposed an optimized novel glassbox Explainable Boosting (EB) approach for fault detection. The proposed EB method incorporates the strengths of boosting and interpretable tree models. Simulation results affirm the high-efficiency scores of 99% in detecting and categorizing faults on transmission lines compared to traditional fault detection state-of-the-art methods. We conducted hyperparameter optimization and k-fold validations to enhance fault detection performance and validate our approach. We evaluated the computational complexity of fault detection models and augmented it with eXplainable Artificial Intelligence (XAI) analysis to illuminate the decision-making process of the proposed model for fault detection. Our proposed research presents a scalable and adaptable method for advancing smart grid technology, paving the way for more secure and efficient electrical power transmission systems.

INTRODUCTION Hematoma volume in chronic subdural hematoma (CSDH) may predict neurological deterioration and need for surgical evacuation. Several computer software assisted methods exist for accurate volume measurements of intracerebral hemorrhage but no reliable method has been identified for measurement of subdural hematoma volume. METHODS A total of 30 consecutive patients with subdural hematoma from 2018 to 2019 admitted to our institution were selected for this analysis. The non-contrast CT Head studies were reviewed by two residents (midlevel Neurology and Neurological Surgery). Region of Interest (ROI) method on a Horos Open Source Medical Image Viewer (Version 3.3.6) was utilized for volume measurement by each resident (Resident 1, Resident 2) independently followed by Resident 1, one month apart (Resident 1.1, Resident 1.2). We calculated the intra- and inter- observer reliability of hematoma volume measurements. RESULTS Mean age of the patients was 79 years (range 50–92 years, 81% were men). For inter-observer analysis, the mean hematoma volume measured by Resident 1 was 85.46 cm3 (range 6.40-178.63 cm3) and for Resident 2 was 87.15 cm3 (range 8.79-165.97 cm3). The Bland and Altman coefficient of variation ranged from 0.07% to 46.29%, with 97% of coefficients within the upper and lower limits of acceptance. For intra-observer analysis one month apart, the Resident 1.1 was 85.46 cm3 (range 6.40-178.63 cm3) and Resident 1.2 was 95.26 cm3 (range 10.48-182.99 cm3). The Bland and Altman coefficient of variation ranged from 0.81% to 48.34%, with 97% of coefficients within the upper and lower limits of acceptance. CONCLUSION We found a high inter-observer and intra-observer reliability for volumetric analysis of hematoma volume in patients with CSDH using Horos Medical Image Viewer ROI generated volume calculation. Volumetric analysis of hematoma volume may allow better risk stratification in future clinical trials in patients with CSDH.

Rare genetic diseases (RGDs) affect individuals, families, and healthcare systems worldwide. Population-scale genomic data remain largely restricted to Western cohorts with an estimated 10,000 RGDs. South Asian populations remain underrepresented in molecular, clinical, and genomic databases. This study presents the first preliminary molecular genetic characterization of RGDs in the Punjabi population of Pakistan. Data were collected from the provincial RGD registry at the Punjab Thalassemia and Other Genetic Disorders Prevention and Research Institute (PTGDPRI), Lahore. Families diagnosed using next-generation sequencing (NGS) between 2021 and 2023 were enrolled. Structured questionnaires captured clinical, demographic, and socioeconomic information, and statistical and genetic analyses were performed to assess allele frequencies, and disease distribution. The registry included 167 families with 72 distinct RGDs, with a mean burden of 0.81 ± 0.24 affected children per family. Niemann–Pick disease (NP), progressive familial intrahepatic cholestasis (PFIC), and mucopolysaccharidosis (MPS) were the most common diseases. Consanguinity was observed in 89% of families, 77% of which involved first-cousin marriages, and was significantly associated with RGD incidence. Most families belonged to low-income groups despite high literacy rates, underscoring inequity in healthcare. The primary and secondary variants included 131 variants, including copy number variants (CNVs) and single nucleotide variants (SNVs), annotated as pathogenic, likely pathogenic, or variants of unknown significance (VUS) across 109 genes, including 24 South Asian-enriched variants. This study provides the first genomic and epidemiological overview of RGDs in the Punjabi population. The findings reveal how genetic, socioeconomic, and cultural factors converge to amplify the RGD burden and highlight the need for affordable molecular diagnostics, inclusive genomic databases, and regional genomic surveillance initiatives in South Asia.

In the era of global warming, stress combinations instead of individual stress are realistic threats faced by plants that can alter or trigger a wide range of plant responses. In the current study, the cumulative effect of charcoal rot disease caused by notorious fungal pathogen viz., Macrophomina phaseolina was investigated under toxic levels of copper (Cu) in mash bean, and farmyard manure (FYM) was employed to manage stress. Therefore, Cu-spiked soil (50 and 100 mg/kg) was inoculated with the pathogen, and amended with 2% FYM, to assess the effect of intricate interactions on mash bean plants through pot experiments. Results demonstrated that the individual stress of the pathogen or Cu was more severe for morpho-growth, physio-biochemical, and expression profiles of stress-related genes and total protein in mash bean plants as compared to stress combinations. Under single Cu stress, a significant amount of Cu accumulated in plant tissues, particularly in roots than in upper ground tissues, while, under stress combination less Cu accumulated in the plants. Nonetheless, 2% FYM in soil encountered the negative effect of stress responses provoked by the pathogen, Cu, or both by improving health markers (photosynthetic pigments, reducing sugar, total phenolics) and oxidative stress markers (catalase, peroxidase, and polyphenol oxidase), together with regulating the expression of stress-related genes (catalase, ascorbate peroxidase, and cytokinin-resistant genes), and proteins, besides decreasing Cu uptake in the plants. FYM worked better at lower concentrations (50 mg/kg) of Cu than at higher ones (100 mg/kg), hence could be used as a suitable option for better growth, yield, and crop performance under charcoal rot disease stress in Cu-contaminated soils.

Diabetes and cancer are two prevalent disorders, pose significant public health challenges and contribute substantially to global mortality rates, with solely 10 million reported cancer-related deaths in 2020. This review explores the pathological association between diabetes and diverse cancer progressions, examining molecular mechanisms and potential therapeutic intersections. From altered metabolic landscapes to dysregulated signaling pathways, the intricate links are delineated, offering a comprehensive understanding of diabetes as a modulator of tumorigenesis. Cancer cells develop drug resistance through mechanisms like enhanced drug efflux, genetic mutations, and altered drug metabolism, allowing them to survive despite chemotherapeutic agent. Glucose emerges as a pivotal player in diabetes progression, and serving as a crucial energy source for cancer cells, supporting their biosynthetic needs and adaptation to diverse microenvironments. Glycation, a non-enzymatic process that produces advanced glycation end products (AGEs), has been linked to the etiology of cancer and has been shown in a number of tumor forms, such as leiomyosarcomas, adenocarcinomas, and squamous cell carcinomas. Furthermore, in aggressive and metastatic breast cancer, the receptor for AGEs (RAGE) is increased, which may increase the malignancy of the tumor. Reprogramming glucose metabolism manifests as hallmark cancer features, including accelerated cell proliferation, angiogenesis, metastasis, and evasion of apoptosis. This manuscript encapsulates the dual narrative of diabetes as a driver of cancer progression and the potential of repurposed antidiabetic drugs as formidable countermeasures. The amalgamation of mechanistic understanding and clinical trial outcomes establishes a robust foundation for further translational research and therapeutic advancements in the dynamic intersection of diabetes and cancer.