ABSTRACT A neural network model that processes input data consisting of financial ratios is developed to predict the health thrift institutions. The network's ability discriminate between healthy and failed institutions compared a traditional statistical model. differences similarities in two modelling approaches are discussed. network, which uses same data, requires fewer assumptions, achieves higher degree prediction accuracy, more robust.

Significance Mechanotransductive release of ATP from RBCs participates in the regulation microvascular tone and plays essential roles vascular physiopathology. The mechanism responsible for ATP, however, is poorly understood. We show first time, to our knowledge, that Piezo1, recently identified mechanically activated cation channel, regulates mechanotransductive RBCs. In particular, we uncover link between calcium influx, propose a previously unidentified pathway modulates outcome this...

Early fault detection and real-time condition monitoring systems have become quite significant for today's modern industrial systems. In a high volume of manufacturing facilities, fleets equipment are expected to operate uninterrupted days or weeks. Any unplanned interruptions uptime could jeopardize manufacturers' cycle time, capacity, and, most significantly, credibility their customers. With the help smart technologies, companies started develop integrate classification where end-to-end...

The importance of predictive maintenance (PdM) programs has been recognized across many industries. Seamless integration the PdM program into today’s manufacturing execution systems requires a scalable and generic system design set key performance indicators (KPIs) to make condition monitoring activities more effective. In this study, new its implementation are presented. KPIs metrics proposed implemented during enhance needs. tested in two independent use cases (autonomous transfer vehicle...

Condition monitoring is a part of the predictive maintenance approach applied to detect and prevent unexpected equipment failures by machine conditions. Early detection in industrial systems can greatly reduce scrap financial losses. Developed sensor data acquisition technologies allow for digitally generating storing many types data. Data-driven computational models extraction information about machine’s state from acquired The outstanding generalization capabilities deep learning have...

Abstract In this paper we discuss the extent to which countries in former Silk Road regions are either reaching or failing reach their trading potential with China. We estimate a gravity model of trade using Poisson pseudo‐maximum likelihood estimator, and in‐sample, out‐of‐sample counterfactual approaches. compare these three methods for country trades Next, estimated actual trade, find that most underperforming However, performance against improved over years 1990–2013. Our results suggest...

Batteries play a critical role in electric vehicle systems devices. The safety and performance of these applications rely on accurate Battery Management Systems (BMS) to monitor optimize battery performance. Traditional BMS face challenges charge prediction processes due complex chemical aging batteries, leading faults. absence perfect sensor highlights limitations measurement issues arising from external factors, especially noise. This study compares an innovative solution, the Transformer...

Purpose The purpose of this paper is to compare influential factors entrepreneurial activities over time in China and with other selected countries. data are collected from Global Entrepreneurship Monitor (GEM). method used decision trees chi-square automatic interaction detector (CHAID) analysis, which isolates important examines entrepreneurship predictor importance. Design/methodology/approach CHAID analysis isolate examine original contribution that the first where artificial applied...

This paper analyzes the application of different classification techniques for Electroencephalography (EEG) signals. Fuzzy Functions Support Vector Classifier (FFSVC), Improved (IFFSVC) and a novel hybrid technique that has been designed utilizing Particle Swarm Optimization Radial Basis Function Networks (PSO-RBFN) have studied. The performance is compared on same standard datasets are publicly available used by many Brain Computer Interface (BCI) researchers. Results show proposed...

Controlling a mobile robot using human biopotential signals has been common problem in the field of assistive robotics. Not only it is enough to analyze biosignal characteristics and interpret motion commands from raw signal, but also an efficient learning algorithm may help overcome varying for sake robust control robot. In this work, utilizing Radial Basis Function Networks have studied applied EOG order Obtained results show that RBF network successful producing sufficient

Bearing faults are the most common type of in induction motors. Vibration monitoring is frequently employed to detect and diagnose these at their early stages. However, analysis vibration signals often requires expert knowledge an in-depth understanding specific tool mechanics. Recently, data-based modeling approaches coupled with machine learning algorithms gained significant attraction field, which can help manufacturers obtain faster more scalable fault detection solutions. In this study,...

(1987). Evidence on the Effect of Option Expirations Stock Prices. Financial Analysts Journal: Vol. 43, No. 1, pp. 55-57.