Explore the vast range of titles available.

Pepper originated from the Capsicum genus, which is recognized as one of the most predominant and globally distributed genera of the Solanaceae family. It is a diverse genus, consisting of more than 31 different species including five domesticated species, Capsicum baccatum, C. annuum, C. pubescen, C. frutescens, and C. chinense. Pepper is the most widely used spice in the world and is highly valued due to its pungency and unique flavor. Pepper is a good source of provitamin A; vitamins E and C; carotenoids; and phenolic compounds such as capsaicinoids, luteolin, and quercetin. All of these compounds are associated with their antioxidant as well as other biological activities. Interestingly, Capsicum fruits have been used as food additives in the treatment of toothache, parasitic infections, coughs, wound healing, sore throat, and rheumatism. Moreover, it possesses antimicrobial, antiseptic, anticancer, counterirritant, appetite stimulator, antioxidant, and immunomodulator activities. Capsaicin and Capsicum creams are accessible in numerous ways and have been utilized in HIV-linked neuropathy and intractable pain.

CD36 (cluster of differentiation 36) is a membrane protein involved in lipid metabolism and has been linked to pathological conditions associated with metabolic disorders, such as diabetes and dyslipidemia. A case-control study was conducted and included 177 patients with type-2 diabetes mellitus (T2DM) and 173 control subjects to study the involvement of CD36 gene rs1761667 (G>A) and rs1527483 (C>T) polymorphisms in the pathogenesis of T2DM and dyslipidemia among Jordanian population. Lipid profile, blood sugar, gender and age were measured and recorded. Also, genotyping analysis for both polymorphisms was performed. Following statistical analysis, 10 different neural networks and machine learning (ML) tools were used to predict subjects with diabetes or dyslipidemia. Towards further understanding of the role of CD36 protein and gene in T2DM and dyslipidemia, a protein-protein interaction network and meta-analysis were carried out. For both polymorphisms, the genotypic frequencies were not significantly different between the two groups ( p > 0.05). On the other hand, some ML tools like multilayer perceptron gave high prediction accuracy (≥ 0.75) and Cohen’s kappa (κ) (≥ 0.5). Interestingly, in K-star tool, the accuracy and Cohen’s κ values were enhanced by including the genotyping results as inputs (0.73 and 0.46, respectively, compared to 0.67 and 0.34 without including them). This study confirmed, for the first time, that there is no association between CD36 polymorphisms and T2DM or dyslipidemia among Jordanian population. Prediction of T2DM and dyslipidemia, using these extensive ML tools and based on such input data, is a promising approach for developing diagnostic and prognostic prediction models for a wide spectrum of diseases, especially based on large medical databases.

The transmembrane protease serine 2 (TMPRSS2) is a membrane anchored protease that primarily expressed by epithelial cells of respiratory and gastrointestinal systems and has been linked to multiple pathological processes in humans including tumor growth, metastasis and viral infections. Recent studies have shown that TMPRSS2 expressed on cell surface of host cells could play a crucial role in activation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein which facilitates the rapid early entry of the virus into host cells. In addition, direct suppression of TMPRSS2 using small drug inhibitors has been demonstrated to be effective in decreasing SARS-CoV-2 infection in vitro, which presents TMPRSS2 protease as a potential therapeutic strategy for SARS-CoV-2 infection. Recently, SARS-CoV-2 has been shown to be capable of infecting gastrointestinal enterocytes and to provoke gastrointestinal disorders in patients with COVID-19 disease, which is considered as a new transmission route and target organ of SARS-CoV-2. In this review, we highlight the biochemical properties of TMPRSS2 protease and discuss the potential targeting of TMPRSS2 by inhibitors to prevent the SARS-CoV-2 spreading through gastro-intestinal tract system as well as the hurdles that need to be overcome.

The development of green mines is essential for promoting sustainability in the mining sector due to the significant ecological impacts of resource extraction. This study proposes a novel hybrid multi-criteria decision-making (MCDM) framework that integrates Spherical Fuzzy Sets (SFSs) with SWOT analysis, the CRITIC method, and Grey Relational Analysis (GRA). The framework introduces several innovations: it applies SFS-based MCDM for the first time to green mine evaluation in Egypt, structures 37 sustainability-related criteria under SWOT dimensions, and employs SF-CRITIC for objective weighting without subjective comparisons. The model is applied to assess 20 gold mines, where the SF-GRA method is used to rank alternatives based on proximity to an ideal solution. The results show that GME20 consistently ranks highest, while GME5 ranks lowest. A sensitivity analysis is conducted by varying the Grey relational coefficient and simulating 37 weight scenarios, demonstrating stable rankings and strong model resilience. Comparative analysis against ten SFS-based MCDM methods confirms the consistency of results, with Spearman correlation coefficients exceeding 0.77. In addition to its methodological novelty, the framework supports interpretable decision outcomes by identifying key sustainability drivers such as renewable energy adoption and land reclamation. This contributes actionable insights for policymakers and stakeholders, enabling informed green investment and regulatory decisions. The study offers a transparent, reproducible, and scalable tool for sustainability evaluation in resource-intensive industries. The proposed model introduces a structured integration of SWOT-based criteria classification, objective weight computation via SF-CRITIC, and robust alternative ranking using SF-GRA. Furthermore, it contributes uniquely by applying the methodology to the underexplored context of green mine evaluation in Egypt. These distinctions articulate the methodological and application-based novelties of the proposed framework.

The use of smartphones among the general public and health care practitioners, in particular, is ubiquitous. The aim of this study was to investigate the relationship between smartphone addiction and sleep quality, psychological distress, and loneliness among health care students and workers in Saudi Arabia. This cross-sectional study used an online questionnaire to collect data on smartphone addiction, sleep quality, psychological distress, and loneliness as well as demographic information. A total of 773 health care students and workers participated in the study, with an average age of 25.95 ± 8.35, and 59.6% female participants. The study found a positive significant association between smartphone addiction and psychological distress (F(1,771) = 140.8, P < 0.001) and emotional loneliness (F(1,771) = 26.70, P < 0.001). Additionally, a significant negative association between smartphone addiction and sleep quality was found (F(1,771) = 4.208, P = 0.041). However, there was no significant relationship between smartphone addiction and social loneliness (F (1,771) = 0.544, P < 0.461). These findings suggest that smartphone addiction has a negative impact on psychological distress, sleep quality, and emotional loneliness among health care students and workers. It is important to promote strategies to reduce smartphone dependency in order to avoid the harmful consequences of smartphone addiction.

Waste printed circuit boards (WPCBs) contain a plethora of valuable metals, considered an attractive secondary resource. In the current research, a hydrometallurgical process combined ammonia/ammonium chloride leaching and reduction (using L-ascorbic acid) to recover copper and its oxide (CuO) as nanosized particles from WPCBs was investigated. The results of leaching indicated that 96.7% of copper could be recovered at a temperature of 35 °C for a leaching duration of 2 h with ammonium chloride and ammonia concentration of 2 mol/L at a solid:liquid ratio of 1:10 g/cm 3 . The synthesized particles exhibit spherical and distorted sphere morphology with average particle size of 460 nm and 50 nm for Cu and CuO NPs, respectively. The antibacterial activity of Cu, CuO, and a (1:1) blend of both (Cu/CuO) has been examined against five different bacterial and fungal strains. The highest zone of inhibition was measured as 21.2 mm for Cu NPs toward Escherichia coli and 16.7 mm for Cu/CuO blend toward Bacillus cereus bacteria. The highest zone of inhibition was measured as 13 mm and 13.8 mm for Cu/CuO blend toward Fusarium proliferatum and Penicillium verrucosum fungi. Cu/CuO blend showed notable photocatalytic activity towards Rhodamine B dye under visible light irradiation with 96% degradation rate within 120 min. Using the process developed in this study, copper and its oxide as nanoparticles can be produced from WPCBs and used for multifunctional applications. Graphical abstract

People with noncommunicable diseases (NCDs) are at a significantly higher risk of worst outcomes if infected with COVID-19 and thus amongst the main target population for vaccination. Despite prioritizing them for vaccination, the number of vaccinated patients with comorbidities stalled post vaccine introduction. Despite that the government along with partners ran a national awareness campaign to ramp up vaccination coverage, the coverage remained suboptimal. Thus, a one-to-one health counselling initiative was implemented to explore the acceptance of COVID-19 vaccines by the NCDs patients and address the main issues surrounding vaccine hesitancy. This study evaluates the impact of this intervention by analyzing the change in COVID-19 vaccine acceptance. In this analytical observational study, a random sample of 57,794 people living with NCDs were approached. Out of them, 12,144 received one-to-one counselling by a group of trained health professionals. The counselled group’s vaccine acceptance was assessed on a Likert scale from 1 to 5 pre- and post- counselling. Moreover, a random sample was followed up 2 months after initial counselling to measure their vaccine acceptance and update their vaccination status. 44.5% of total respondents were already registered in the vaccination platform. On a scale from 1 to 5, the overall mean confidence significantly increased by 1.63 from 2.48 pre-counselling to 4.11 post-counselling. Two-months post counselling, a random sample was contacted again and had a mean vaccine confidence of 3.71, which is significantly higher than pre-counselling confidence level despite a significant decrease to post-counselling results. Implementing an intervention that targets all key factors impacting health decisions, such as health literacy, risk appraisal and response efficacy, helps reach an adaptive response and increase vaccine confidence. Scholars should be cautious when implementing an intervention since it could lead to maladaptive defensive responses. One-to-one interventions are more effective in population when addressing new interventions and vaccines.

Wireless link scheduling in device-to-device (D2D) networks is an NP-hard problem. As a solution, multiple supervised deep learning (DL) models have been recently proposed, which depend on the geographical information of D2D pairs. However, such DL models require labeled training data. In this paper, we focus on unsupervised learning of scheduling. More specifically, this paper proposes using a Density-Based anti-Clustering for Scheduling D2D Communications (DBSCHedule). The proposed algorithm is a two-step approach that consists of clustering and anti-clustering. First, clustering aims at identifying the non-interfering groups of D2D pairs. Then, anti-clustering aims at identifying the maximally separated sub-groups to minimize the interference. The clustering step uses a fully-automated unsupervised density-based spectral-clustering of applications with noise (DBSCAN) and the anti-clustering uses the inverse of the objective function of the k-means clustering. Results show comparable performance with the optimal FPLinQ scheduler yet without requiring any channel information nor is there a requirement to solve a complex optimization problem. Moreover, a comparable performance to the previous attempts using DL and modified clustering is achieved while being completely adaptive and easily accommodating to changes in the network layout.

Groundwater quality in arid regions like Al Hassa, Saudi Arabia, is increasingly threatened by trace element contamination driven by human activity and natural geology. This study addresses the urgent need for data-driven tools to assess groundwater pollution in the region’s multi-aquifer system. Groundwater samples were analyzed for trace elements and main physicochemical parameters. Using supervised machine learning (ML) models—Linear Regression (LR), Random Forest (RF), K-Nearest Neighbors (KNN), and Gradient Boosting Machine (GBM)—the Water Pollution Index (WPI) was predicted as a holistic metric of contamination. The GBM model outperformed all others, achieving a training coefficient of determination (DC) of 0.9970 and a mean absolute error (MAE) of 0.0017. During testing, it maintained a high DC of 0.9372 and an MAE of 0.0063, confirming its strong generalization ability. SHapley Additive exPlanations (SHAP) were used to rank feature importance and enhance model transparency. The most influential variables for WPI prediction were chromium (Cr, SHAP = 0.0214), aluminum (Al, SHAP = 0.0136), and strontium (Sr, SHAP = 0.0053), followed by Fe (0.0031), V (0.0028), and Se (0.0017). Despite generally acceptable water quality, elements such as Cr and Fe exceeded safe limits in several samples. This study presents a transparent, high-performing framework for groundwater quality assessment in arid conditions. The integration of explainable ML offers clear, actionable insights into sustainable water management and environmental decision-making.

The real-world data analysis and processing using data mining techniques often are facing observations that contain missing values. The main challenge of mining datasets is the existence of missing values. The missing values in a dataset should be imputed using the imputation method to improve the data mining methods’ accuracy and performance. There are existing techniques that use k-nearest neighbors algorithm for imputing the missing values but determining the appropriate k value can be a challenging task. There are other existing imputation techniques that are based on hard clustering algorithms. When records are not well-separated, as in the case of missing data, hard clustering provides a poor description tool in many cases. In general, the imputation depending on similar records is more accurate than the imputation depending on the entire dataset's records. Improving the similarity among records can result in improving the imputation performance. This paper proposes two numerical missing data imputation methods. A hybrid missing data imputation method is initially proposed, called KI, that incorporates k-nearest neighbors and iterative imputation algorithms. The best set of nearest neighbors for each missing record is discovered through the records similarity by using the k-nearest neighbors algorithm (kNN). To improve the similarity, a suitable k value is estimated automatically for the kNN. The iterative imputation method is then used to impute the missing values of the incomplete records by using the global correlation structure among the selected records. An enhanced hybrid missing data imputation method is then proposed, called FCKI, which is an extension of KI. It integrates fuzzy c-means, k-nearest neighbors, and iterative imputation algorithms to impute the missing data in a dataset. The fuzzy c-means algorithm is selected because the records can belong to multiple clusters at the same time. This can lead to further improvement for similarity. FCKI searches a cluster, instead of the whole dataset, to find the best k-nearest neighbors. It applies two levels of similarity to achieve a higher imputation accuracy. The performance of the proposed imputation techniques is assessed by using fifteen datasets with variant missing ratios for three types of missing data; MCAR, MAR, MNAR. These different missing data types are generated in this work. The datasets with different sizes are used in this paper to validate the model. Therefore, proposed imputation techniques are compared with other missing data imputation methods by means of three measures; the root mean square error (RMSE), the normalized root mean square error (NRMSE), and the mean absolute error (MAE). The results show that the proposed methods achieve better imputation accuracy and require significantly less time than other missing data imputation methods.