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Moving towards a cleaner, greener, and more sustainable future, expanding electric vehicles (EVs) adoption is inevitable. However, uncontrolled charging of EVs, especially with their increased penetration among the utility grid, imposes several negative technical impacts, including grid instability and deteriorated power quality in addition to overloading conditions. Hence, smart and coordinated charging is crucial in EV electrification, where Vehicle-to-Grid (V2G) technology is gaining much interest. Owing to its inherited capability of bi-directional power flow, V2G is capable of enhancing grid stability and resilience, load balancing, and congestion alleviation, as well as supporting renewable energy sources (RESs) integration. However, as with most emerging technologies, there are still technical research gaps that need to be addressed. In addition to these technical impacts, other multidisciplinary factors must be investigated to promote EVs adoption and V2G implementation. This paper provides a detailed demonstration of the technical problems associated with EVs penetration in distribution networks along with quantifiable insights into these limitations and the corresponding mitigation schemes. In addition, it discusses V2G benefits for power systems and consumers, as well as explores their technical barriers and research directions to adequately regulate their services and encourage EV’s owners to its embracement. Moreover, other factors, including regulatory, social, economic and environmental ones that affect EV market penetration are being studied and related challenges are analyzed to draw recommendations that aid market growth.

This was an observational, analytical, retrospective, nationwide study. We included all adult patients on maintenance dialysis therapy who tested positive for COVID-19 (PCR assay of the nasopharyngeal swab) during the period from February 1, 2020, to July 19, 2020. Our primary outcome was to study the mortality of COVID-19 in dialysis patients in Qatar and risk factors associated with it. Our secondary objectives were to study incidence and severity of COVID-19 in dialysis patients and comparing outcomes between hemodialysis and peritoneal dialysis patients. Patient demographics and clinical features were collected from a national electronic medical record. Univariate Cox regression analysis was performed to evaluate potential risk factors for mortality in our cohort. 76 out of 1064 dialysis patients were diagnosed with COVID-19 (age 56±13.6, 56 hemodialysis and 20 peritoneal dialysis, 56 males). During the study period, 7.1% of all dialysis patients contracted COVID-19. Male dialysis patients had double the incidence of COVID-19 than females (9% versus 4.5% respectively; p<0.01). The most common symptoms on presentation were fever (57.9%), cough (56.6%), and shortness of breath (25%). Pneumonia was diagnosed in 72% of dialysis patients with COVID-19. High severity manifested as 25% of patients requiring admission to the intensive care unit, 18.4% had ARDS, 17.1% required mechanical ventilation, and 14.5% required inotropes. The mean length of hospital stay was 19.2 ± -12 days. Mortality due to COVID-19 among our dialysis cohort was 15%. Univariate Cox regression analysis for risk factors associated with COVID-19-related death in dialysis patients showed significant increases in risks with age (OR 1.077, CI 95%(1.018-1.139), p = 0.01), CHF and COPD (both same OR 8.974, CI 95% (1.039-77.5), p = 0.046), history of DVT (OR 5.762, CI 95% (1.227-27.057), p = 0.026), Atrial fibrillation (OR 7.285, CI 95%(2.029-26.150), p = 0.002), hypoxia (OR: 16.6; CI 95%(3.574-77.715), p = <0.001), ICU admission (HR30.8, CI 95% (3.9-241.2), p = 0.001), Mechanical ventilation (HR 50.07 CI 95% (6.4-391.2)), p<0.001) and using inotropes(HR 19.17, CI 95% (11.57-718.5), p<0.001). In a multivariate analysis, only ICU admission was found to be significantly associated with death [OR = 32.8 (3.5-305.4), p = 0.002)]. This is the first study to be conducted at a national level in Qatar exploring COVID-19 in a dialysis population. Dialysis patients had a high incidence of COVID-19 infection and related mortality compared to previous reports of the general population in the state of Qatar (7.1% versus 4% and 15% versus 0.15% respectively). We also observed a strong association between death related to COVID-19 infection in dialysis patients and admission to ICU.

Scheduled maintenance and condition monitoring of power transformers in smart grids is mandatory to reduce their downtimes and maintain economic benefits. However, to minimize energy losses during inspection, non-invasive fault diagnosis techniques such as thermogram imaging can enable continuous monitoring of transformer health with minimal out-of-service time. Deep learning (DL) has proven to be a fast and efficient intelligent diagnostic tool. In this paper, a DL-based thermography method is proposed called Trans-Light for transformers’ interturn faults detection and short-circuit severity identification. Trans-light extracts deep features from two deep layers of a convolutional neural network (CNN) rather than depending on one layer, thus obtaining more intricate patterns. Moreover, a Dual-tree Complex Wavelet Transform method is adopted which offers two enhancements. First, it acquires time–frequency knowledge besides the already obtained spatial information and second, it reduces the huge deep features dimensionality. Trans-light combines extracted deep features, then a feature selection process is applied to further reduce features’ size, thus decreasing computation burden and reducing classification and training time. To validate the proposed scheme’s diagnosis performance and robustness, different combinations of two CNN models, two feature selection methods, and six classifiers were tested, applying the proposed Trans-light framework, under noise-free and noise-existing conditions. Experimental results indicated that the combination of the LDA classifier, applied with the ResNet-18 CNN model and trained with merged deep features undergoing the chi-square (χ 2 ) selection approach, attained superior performance under noise-free conditions. Compared to its counterparts in previous work, this configuration outperforms their performance since it uses the fewest features’ number yet maintains 100% classification accuracy. Besides, it attained robust performance under two different noise natures again with minimal features’ dimension, thus minimizing computational load and implementation complexity.

Urban growth aimed at developing smart cities confronts several obstacles, such as difficulties and costs in constructing stations and meeting consumer demands. These are possible to overcome by integrating Renewable Energy Resources (RESs) with the help of demand side management (DSM) for managing generation and loading profiles to minimize electricity bills while accounting for reduction in carbon emissions and the peak to average ratio (PAR) of the load. This study aims to achieve a multi-objective goal of optimizing energy management in smart cities which is accomplished by optimally allocating RESs combined with DSM for creating a flexible load profile under RESs and load uncertainty. A comprehensive study is applied to IEEE 69-bus with different scenarios using Sea-Horse Optimization (SHO) for optimal citing and sizing of the RESs while serving the objectives of minimizing total power losses and reducing PAR. SHO performance is evaluated and compared to other techniques such as Genetic Algorithm (GA), Grey Wolf Optimization (GWO), Whale Optimization (WO), and Zebra Optimization (ZO) algorithms. The results show that combining elastic load shifting with optimal sizing and allocation using SHO achieves a global optimum solution for the highest power loss reduction while using a significantly smaller sized RESs than the counterpart.

Heat stress is one of the most detrimental confrontations in tropical and subtropical regions of the world, causing considerable economic losses in poultry production. Propolis, a resinous product of worker honeybees, possesses several biological activities that could be used to alleviate the deleterious effects of high environmental temperature on poultry production. The current study was aimed at evaluating the effects of propolis supplementation to Japanese quail (Coturnix coturnix japonica) diets on the production performance, intestinal histomorphology, relative physiological and immunological parameters, and selected gene expression under heat stress conditions. Three hundred one-day-old Japanese quail chicks were randomly distributed into 20 wired-cages. At 28 d of age, the birds were divided into 2 temperature treatment groups; a normal at 24°C (C group) and a heat stress at 35°C (HS group). The birds in each group were further assigned to 2 subgroups; one of them was fed on a basal diet without propolis supplementation (-Pr subgroup) while the other was supplemented with propolis (+Pr subgroup). Production performance including body weight gain, feed intake and feed conversion ratio were measured. The intestinal histomorphological measurements were also performed for all treatment groups. Relative physiological parameters including body temperature, corticosterone hormone level, malondialdehyde (MDA) and free triiodothyronine hormone (fT3), as well as the relative immunological parameters including the total white blood cells count (TWBC's), heterophil/lymphocyte (H/L) ratio and lymphocyte proliferation index, were also measured. Furthermore, the mRNA expression for toll like receptor 5 (TLR5), cysteine-aspartic protease-6 (CASP6) and heat shock proteins 70 and 90 (Hsp70 and Hsp90) genes was quantified in this study. The quail production performance was significantly (P<0.05) impaired by HS treatment, while Pr treatment significantly improved the quail production performance. The villus width and area were significantly (P<0.05) lower in the HS compared to the C group, while Pr treatment significantly increased crypts depth of quail. A negative impact of HS treatment was observed on the physiological status of quail; however, propolis significantly alleviated this negative effect. Moreover, quail of the HS group expressed lower immunological parameters than C group, while propolis enhanced the immune status of the quail. The relative mRNA expression of TLR5 gene was down-regulated by HS treatment while it was up-regulated by the Pr treatment. Furthermore, the positive effects of propolis in HS-quail were evidenced by normalizing the high expressions of CASP6 and Hsp70 genes when compared to the C group. Based on these results, the addition of propolis to quail diets as a potential nutritional strategy in order to improve their performance, especially under heat stress conditions, is recommended.

Dissolved Gas Analysis (DGA) is a diagnostic strategy that monitors oil-immersed transformers by correlating their health status with various insulation degradation by-products, where the Health Index (HI) offers a unified metric for asset evaluation. Existing studies frequently emphasize classification accuracy or single-model regression, overlooking interpretability, feature reduction, and systematic benchmarking. This paper introduces a feature-enhanced multi-experimental methodology for HI prediction incorporating SHapley Additive exPlanations (SHAP) in a dual role—as both an interpretability and a feature selection tool. Models from four algorithmic families (linear, kernel/tree-based, boosting, and hybrid ensembles) were systematically benchmarked using a publicly available dataset. Results demonstrate that the proposed LightGBM–CatBoost hybrid ensemble, enhanced by SHAP-guided feature pruning, achieves superior predictive accuracy while reducing model complexity and improving transparency. Unlike prior works carried out using the same dataset, the proposed framework not only provides a balanced approach that combines interpretability and reduced complexity, but also surpasses previous regression-based approaches, reducing MAE and RMSE by 4.93% and 2.31%, respectively, and enhancing HI predictive accuracy by 1.45%.

Local graph clustering is an important machine learning task that aims to find a well-connected cluster near a set of seed nodes. Recent results have revealed that incorporating higher order information significantly enhances the results of graph clustering techniques. The majority of existing research in this area focuses on spectral graph theory-based techniques. However, an alternative perspective on local graph clustering arises from using max-flow and min-cut on the objectives, which offer distinctly different guarantees. For instance, a new method called capacity releasing diffusion (CRD) was recently proposed and shown to preserve local structure around the seeds better than spectral methods. The method was also the first local clustering technique that is not subject to the quadratic Cheeger inequality by assuming a good cluster near the seed nodes. In this paper, we propose a local hypergraph clustering technique called hypergraph CRD (HG-CRD) by extending the CRD process to cluster based on higher order patterns, encoded as hyperedges of a hypergraph. Moreover, we theoretically show that HG-CRD gives results about a quantity called motif conductance, rather than a biased version used in previous experiments. Experimental results on synthetic datasets and real world graphs show that HG-CRD enhances the clustering quality.

Single-stage grid-interfaced PV topologies have challenges with high grid fault currents, despite being more efficient, simpler to implement, and less expensive than two-stage ones. In such systems, a single inverter is required to perform all grid-interface tasks. i.e., maximum power point tracking (MPPT), DC voltage stabilization, and grid current control. This necessitates a hardware-based fault current limitation solution rather than a software-based one to avoid adding to the inverter’s control complexity and to mitigate the implications of PV system tripping. Therefore, in this study, a dual-functional non-superconducting saturated-core inductor-based (SCI) reactor is proposed to be applied at the output of a single-stage PV inverter. It involves two operation modes: a grid pre-fault mode where it filters the line current, hence minimizing its THD, and a grid-fault mode where it acts as a fault current limiter (FCL). Controlling the DC saturation current flowing into its control winding terminals alters the core magnetization of the SCI to vary its impedance between a low value during normal utility operation and a maximal value during faults. Consequently, the system is protected against inverter failures or unnecessary circuit-breaker tripping, which preserves service continuity and reduces system losses. Moreover, compared to existing FCLs, the proposed topology is an appealing candidate in terms of cost, size, reliability, and harmonic filtering ability. The bi-functionality and usefulness of the proposed reactor are confirmed using simulation and experimental results.

Background The most common cause of persistent arthritis in children is juvenile idiopathic arthritis (JIA). One frequent consequence of JIA is interstitial lung disease (ILD). When identifying diffuse lung disorders, high-resolution computed tomography (HRCT) is a beneficial imaging method. The purpose of this study is to identify the range of abnormalities detected by HRCT in patients with JIA and to investigate their relationship with inflammatory markers. Methods Thirty JIA patients who were routinely observed at Tertiary University Pediatric Hospital's Rheumatology Clinic were included in this cross-sectional study. In addition to HRCT imaging, patients had thorough evaluations that included medical history, physical examinations, articular, ophthalmological, and chest exams. Results A statistically significant correlation was observed between systemic manifestations and elevated levels of ESR, CRP, and ferritin, with P -values of 0.002, 0.001, and 0.001, respectively (< 0.05 is statistically significant). Similarly, pulmonary manifestations showed a significant correlation with ESR, CRP, and ferritin, with P -values of 0.001 and 0.001. Additionally, 11 patients (36.7%) displayed HRCT findings indicative of ILD. Amongst these, 9 patients (30%) exhibited a ground-glass appearance, 5 (16.7%) had interlobular thickening, 1 (3.3%) presented with pulmonary nodules, 1 (3.3%) had bronchiectasis or bronchiolectasis, and 8 (26.7%) showed air trapping. Conclusion Patients with JIA often experience pulmonary problems, which can manifest as a range of clinical symptoms. For the early identification of subclinical pleuropulmonary involvement, HRCT of the chest is highly suggested, as it provides a comprehensive evaluation of pulmonary abnormalities.

Regular exercise can reduce depression. However, the uptake of exercise is limited in patients with end-stage renal disease undergoing hemodialysis. To address the gap, we designed a gamified non-weight-bearing intradialytic exercise program (exergame). The intradialytic exergame is virtually supervised based on its interactive feedback via wearable sensors attached on lower extremities. We examined the effectiveness of this program to reduce depression symptoms compared to nurse-supervised intradialytic exercise in 73 hemodialysis patients (age = 64.5 ± 8.7years, BMI = 31.6 ± 7.6kg/m2). Participants were randomized into an exergame group (EG) or a supervised exercise group (SG). Both groups received similar exercise tasks for 4 weeks, with three 30 min sessions per week, during hemodialysis treatment. Depression symptoms were assessed at baseline and the fourth week using the Center for Epidemiologic Studies Depression Scale. Both groups showed a significant reduction in depression score (37%, p < 0.001, Cohen’s effect size d = 0.69 in EG vs. 41%, p < 0.001, d = 0.65 in SG) with no between-group difference for the observed effect (p > 0.050). The EG expressed a positive intradialytic exercise experience including fun, safety, and helpfulness of sensor feedback. Together, results suggested that the virtually supervised low-intensity intradialytic exergame is feasible during routine hemodialysis treatment. It also appears to be as effective as nurse-supervised intradialytic exercise to reduce depression symptoms, while reducing the burden of administrating exercise on dialysis clinics.