Exploring the impact of Joule heating and Brownian motion on assisting and opposing flows in Eyring-Prandtl fluid.

Motivation And Objectives: The basic aim of this investigation is to explore the energy transfer impact on Eyring-Prandtl fluid, a topic that has not been previously examined, thereby paving the way for future researchers. The present literature is crucial for advancing thermal management in engineering applications. This study aims to numerically investigate the thermophoretic effects and Brownian motion of non-Newtonian nanofluid relying on Eyring-Prandtl fluid model across the stretching sheet.

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

: 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. to determine the prevalence of functional dyspepsia in CKD patients, and which subtype is predominant in them.

Optimizing Lead-free MASnBr 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 (MASnBr) materials.

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.

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.