Interstitial fibrosis is an important contributor to graft loss in chronic renal allograft injury. Inflammatory macrophages are associated with allografts, but how these cells contribute this damaging response not clearly understood. Here, we investigated the role of macrophage-to-myofibroblast transition interstitial human and experimental In biopsy specimens from patients active rejection, identified undergoing by coexpression macrophage (CD68) myofibroblast (α-smooth muscle actin [α-SMA])...

Artificial intelligence (AI) technologies have been widely applied in medicine and healthcare. Explainable AI (XAI) has proposed to make applications more transparent efficient. This study applies some simple cross-domain tools techniques, including common expression (with linguistic terms), color management, traceable aggregation, segmented distance diagrams, among others, improve the explainability of Four hospitals were used, recommendations studied illustrate applicability methodology....

Explainable artificial intelligence (XAI) tools are used to enhance the applications of existing (AI) technologies by explaining their execution processes and results. In most past research, XAI techniques typically applied only inference part AI application. This study proposes a systematic approach explainability in healthcare. Several for type 2 diabetes diagnosis taken as examples illustrate applicability proposed methodology. According experimental results, methodology were more diverse...

Composite materials are widely used in many fields due to their excellent properties. Quality defects composite can lead lower quality components, creating potential risk of accidents. Experimental and simulation methods commonly predict the materials. However, it is difficult accurately uncertain curing environment incomplete feature space. To address this problem, a digital twin (DT) visual model material first constructed. Then, static autoclave DT virtual coupled with variable construct...

Fall detection is a critical task in an aging society. To fulfill this task, smart technology applications have great potential. However, it not easy to choose suitable application for fall detection. address issue, fuzzy collaborative intelligence approach proposed study. In the approach, alpha-cut operations are applied derive weights of criteria each decision maker. Then, intersection aggregate derived by all makers. Subsequently, technique order preference similarity ideal solution...

The supply chain disruption caused by the coronavirus disease 2019 (COVID-19) pandemic has forced many manufacturers to look for alternative suppliers. How choose a suitable supplier in COVID-19 become an important task. To fulfill this task, research proposes calibrated fuzzy geometric mean (cFGM)-fuzzy technique order preference similarity ideal solution (FTOPSIS)-fuzzy weighted intersection (FWI) approach. In proposed methodology, first, cFGM method is accurately derive priorities of...

This study aims to enhance the effectiveness of planning a pharmaceutical or healthcare supply chain (PHSC). Past research mostly applied simulation optimization methods, which were time-consuming difficult obtain optimal solution. proposes an evolutionary fuzzy approach from multiple-criteria decision-making (MCDM) perspective addresses this issue. Primary factors considered in proposed methodology include product quality, capacity, availability, experience and relationship, cost. In...

Existing fuzzy analytic hierarchy process (FAHP) methods usually aggregate the pairwise comparison results produced by multiple decision-makers (DMs) rather than weights estimations. This is problematic because are subject to uncertainty and lack consensus. To address this problem, a partial-consensus posterior-aggregation FAHP (PCPA-FAHP) approach proposed in study. The PCPA-FAHP seeks partial consensus among most DMs instead of an overall all DMs, thereby increasing possibility reaching...

Abstract Several artificial intelligence (AI) technologies have been applied to assist in the selection of suitable three-dimensional (3D) printing facilities ubiquitous manufacturing (UM). However, AI applications this field may not be easily understood or communicated with, especially for decision-makers without relevant background knowledge, hindering widespread acceptance such applications. Explainable (XAI) has proposed address problem. This study first reviews existing XAI techniques...

As a viable means to enhance the sustainability and competitiveness of aircraft manufacturing maintenance, three-dimensional (3D) printing has been extensively used in industry. However, due growing number suitable 3D printers often-high prices these printers, manufacturers still face many obstacles screening possible printers. In addition, dependencies between criteria make it difficult for decision makers properly assess their absolute priorities. Existing methods fail address issues. To...

The COVID-19 pandemic has significantly strained global health infrastructures while profoundly affecting the socio-economic realm. RNA-dependent RNA polymerase (RdRp) plays a pivotal role in replication and transcription of viruses, making it critical target for antiviral drug development. In this work, we describe discovery, rational optimization, synthesis novel series non-nucleoside SARS-CoV-2 RdRp inhibitors featuring 2,2'-((1H-indole-2,3-diyl) bis (thio)) diacetamide core. inhibitory...

Background: The study of different types DNA damage after ultra-high dose rate irradiation (UHDR) is great significance for further understanding the mechanism FLASH effect. Methods: pBR322 plasmid was irradiated by electron beam. content each subtype measured gel electrophoresis, and extent double strand break (DSBs) single (SSBs) under UHDR conventional (CONV) quantitatively compared. Further, adding endonuclease Nth Fpg, base in CONV group analyzed. In addition, effects concentrations on...

The COVID-19 pandemic has severely impacted our daily lives. For tackling the pandemic, various intervention strategies have been adopted by country (or city) governments around world. However, whether an strategy will be successful, acceptable, and cost-effective or not is still questionable. To address this issue, a varying partial consensus fuzzy collaborative intelligence approach proposed in study to assess strategy. In approach, multiple decision makers express their judgments on...

Deep neural networks (DNNs) have been applied to predict the cycle times of jobs in manufacturing accurately. However, prediction mechanism a DNN is complex and difficult communicate. This limits its acceptability (or practicability) real-world applications. An explainable deep-learning approach proposed solve this problem study. study proposes classification regression tree (CART) explain for job time prediction. The predicted value each branch CART replaced by fuzzy linear (FLR) equation...

Methods based on artificial neural network (ANN) or deep (DNN) applications have been proposed to predict job cycle time effectively. However, the predicting mechanism of ANNs (or DNNs) is often difficult understand and communicate. This problem has hindered their acceptability (and practicability). Furthermore, existing explainable intelligence (XAI) techniques use simpler decision rules approximate prediction process and/or outcomes DNNs). these may not conform domain knowledge, which...