Artificial intelligence-assisted niacin skin flush screening in early psychosis identification and prediction

Schizophrenia is a devastating mental disorder affecting 20 million people worldwide.Early diagnosis is crucial for disease management and improvement in prognosis,and diagnostic biomarkerscan serveasobjective indicators for the early screening of the disease.Based on the observation of diminished flush responses to niacin in patients with schizophrenia Horrobin proposed anoninvasive niacin skin flush screening for schizophrenia.

Hybrid modeling for carbon monoxide gas-phase catalytic coupling to synthesize dimethyl oxalate process

Ethylene glycol(EG)plays a pivotal role as a primary raw material in the polyester industry,and the syngas-to-EG route has become a significant technical route in production.The carbon monoxide(CO)gas-phase catalytic coupling to synthesize dimethyl oxalate(DMO)is a crucial process in the syngas-to-EG route,whereby the composition of the reactor outlet exerts influence on the ultimate quality of the EG product and the energy consumption during the subsequent separation process.However,measuring product quality in real time or establishing accurate dynamic mechanism models is challenging.To effectively model the DMO synthesis process,this study proposes a hybrid modeling strategy that integrates process mechanisms and data-driven approaches.The CO gas-phase catalytic coupling mechanism model is developed based on intrinsic kinetics and material balance,while a long short-term memory(LSTM)neural network is employed to predict the macroscopic reaction rate by leveraging temporal relationships derived from archived measurements.The proposed model is trained semi-supervised to accommodate limited-label data scenarios,leveraging historical data.By integrating these predictions with the mechanism model,the hybrid modeling approach provides reliable and interpretable forecasts of mass fractions.Empirical investigations unequivocally validate the superiority of the proposed hybrid modeling approach over conventional data-driven models(DDMs)and other hybrid modeling techniques.

Improving fault localization with pre-training

1 Introduction For developers,fault localization is the most tedious and time-consuming process.In order to reduce the burden of developers,researchers have developed various techniques to help reduce the cost of locating faults based on possibility of containing a fault in program statements[1].Among them,coverage-based fault localization is one of the most studied automated fault localization techniques.Coverage-based fault localization methods utilize a type of preliminary information to localize the faults in programs,which is a matrix of statements’coverage information.The value of each element is 1 or 0,in which 1 means a statement is executed and 0 denotes a statement is not executed[1].

An effective fault localization approach for Verilog based on enhanced contexts

New computer architecture innovationswith diverse functionalities and comprehensive features continue to emerge incessantly,resulting in a rising trend of incorporating a larger number of circuit devices into these products[1].In the case of a sophisticated and expansive integrated circuit chip,the presence of defective or malfunctioning components can significantly impact the overall performance of the circuit.This situation may even result in costly repercussions.

Unsupervised learning on particle image velocimetry with embedded cross‐correlation and divergence‐free constraint

Particle image velocimetry(PIV)is an essential method in experimental fluid dynamics.In recent years,the development of deep learning‐based methods has inspired new ap-proaches to tackle the PIV problem,which considerably improves the accuracy of PIV.However,the supervised learning of PIV is driven by large volumes of data with ground truth information.Therefore,the authors consider unsupervised PIV methods.There has been some work on unsupervised PIV,but they are not nearly as effective as supervised learning PIV.The authors try to improve the effectiveness and accuracy of unsupervised PIV by adding classical PIV methods and physical constraints.In this paper,the authors propose an unsupervised PIV method combined with the cross‐correlation method and divergence‐free constraint,which obtains better performance than other unsupervised PIV methods.The authors compare some classical PIV methods and some deep learning methods,such as LiteFlowNet,LiteFlowNet‐en,and UnLiteFlowNet with the authors’model on the synthetic dataset.Besides,the authors contrast the results of LiteFlowNet,UnLiteFlowNet and the authors’model on experimental particle images.As a result,the authors’model shows comparable performance with classical PIV methods as well as supervised PIV methods and outperforms the previous unsupervised PIV method in most flow cases.

Technical report:PID design of second-order non-linear uncertain systems with fractional order operations

This study considers a control problem related to the regulation of fractional-order systems controlled by fractional order proportional-integral-derivative controllers(PI^(λ)D^(μ)).The stability result of PIλDμ-based control systems is provided,such that the closed-loop systems can accomplish global stabilisation under some suitable conditions related to the system uncertainties.Finally,a simulation is demonstrated to verify the results.