The prevalence of functional dyspepsia using Rome IV questionnaire among chronic kidney disease patients.

Background: Symptoms of dyspepsia are usually encountered by chronic kidney disease patients. Abdominal discomfort is commonly seen in CKD patients with no other causes of organic affection. Aim: to determine the prevalence of functional dyspepsia in CKD patients, and which subtype is predominant in them. Materials and patients: This observational study included 150 CKD patients. Clinical and laboratory data were recorded for every patient. All the patients were interviewed using the ROME IV questionnaire of functional dyspepsia. Patients fulfilling criteria for functional dyspepsia were exposed to upper GI endoscopy. Results: Overall, 73 (48.7%) of CKD patients were males and 77 (51.3%) were females with mean age of (45.71 ± 9.59) and mean BMI (26.58 ± 5.39). The frequency of functional dyspepsia among CKD patients was determined to be 14.7% (22 out of 150 patients). Among those affected by functional dyspepsia, the most prevalent subtype was found to be Epigastric Pain Syndrome (EPS), accounting for 59% (13 out of 22 cases). The most common predictor of FD in CKD patients was chronic HCV infection, hemodialysis, stage of CKD and eGFR as revealed by Univariate regression analysis. Conclusion: The prevalence of FD amongst CKD patients is 14.7% with EPS the predominant subtype. Male patients, HCV patients, patients with higher CKD stages and highly impaired eGFR (low eGFR) are more probable to have FD.

Optimizing Lead-free MASnBr3 Perovskite Solar Cells for High-Efficiency and Long-Term Stability Using Graphene and Advanced Interface Layers.

Perovskite solar cells (PSCs) have garnered significant attention in the scientific community due to their rapid increase in performance. Inorganic perovskite devices have been noted for their high performance and long-term stability. This study introduces a device optimization process guided by modeling to produce high-efficiency PSCs using lead-free n-i-p methylammonium tin bromide (MASnBr3) materials. We have thoroughly examined the impact of both the absorber and interface layers on the optimized structure. Our approach utilized graphene as the interface layer between the hole transport and absorber layers. We employed zinc oxide (ZnO)/Al and 3C-SiC as interface layers between the absorber and electron transport layers. The optimization process involved adjusting the thicknesses of the absorber layer and interface layers and minimizing defect densities. Our proposed optimized device structure, ZnO/3C-SiC/MASnBr3/graphene/CuO/Au, demonstrates theoretical power conversion efficiencies of 31.97%, fill factors of 89.38%, a current density of 32.54 mA/cm2, a voltage of 1.112 V, and a quantum efficiency of 94%. This research underscores the ability of MASnBr3 as a nontoxic perovskite material for sustainable energy from renewable sources' applications.

Customer Analysis Using Machine Learning-Based Classification Algorithms for Effective Segmentation Using Recency, Frequency, Monetary, and Time.

Customer segmentation has been a hot topic for decades, and the competition among businesses makes it more challenging. The recently introduced Recency, Frequency, Monetary, and Time (RFMT) model used an agglomerative algorithm for segmentation and a dendrogram for clustering, which solved the problem. However, there is still room for a single algorithm to analyze the data's characteristics. The proposed novel approach model RFMT analyzed Pakistan's largest e-commerce dataset by introducing k-means, Gaussian, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) beside agglomerative algorithms for segmentation. The cluster is determined through different cluster factor analysis methods, i.e., elbow, dendrogram, silhouette, Calinsky-Harabasz, Davies-Bouldin, and Dunn index. They finally elected a stable and distinctive cluster using the state-of-the-art majority voting (mode version) technique, which resulted in three different clusters. Besides all the segmentation, i.e., product categories, year-wise, fiscal year-wise, and month-wise, the approach also includes the transaction status and seasons-wise segmentation. This segmentation will help the retailer improve customer relationships, implement good strategies, and improve targeted marketing.

Fault Detection and Classification of CIGS Thin-Film PV Modules Using an Adaptive Neuro-Fuzzy Inference Scheme.

The use of artificial intelligence to automate PV module fault detection, diagnosis, and classification processes has gained interest for PV solar plants maintenance planning and reduction in expensive inspection and shutdown periods. The present article reports on the development of an adaptive neuro-fuzzy inference system (ANFIS) for PV fault classification based on statistical and mathematical features extracted from outdoor infrared thermography (IRT) and I-V measurements of thin-film PV modules. The selection of the membership function is shown to be essential to obtain a high classifier performance. Principal components analysis (PCA) is used to reduce the dimensions to speed up the classification process. For each type of fault, effective features that are highly correlated to the PV module's operating power ratio are identified. Evaluation of the proposed methodology, based on datasets gathered from a typical PV plant, reveals that features extraction methods based on mathematical parameters and I-V measurements provide a 100% classification accuracy. On the other hand, features extraction based on statistical factors provides 83.33% accuracy. A novel technique is proposed for developing a correlation matrix between the PV operating power ratio and the effective features extracted online from infrared thermal images. This eliminates the need for offline I-V measurements to estimate the operating power ratio of PV modules.