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Accurate failure tracing is essential for closed-loop control of CNC machine tools. To address poor interpretability, strong subjectivity, and complex modeling in existing methods, a meta-action failure tracing method is...

Abstract Heavy-duty machine tools are composed of many subsystems with different functions, and their reliability is governed by the reliabilities of these subsystems. It is important to rank the weaknesses of subsystems...

The use of electronic photographic recording has greatly facilitated the process of photo post-processing. Digital camera-based fixation enables extensive manipulation of captured images, potentially uncovering diagnosti...

Abstract Machine tools, often referred to as the “mother machines” of the manufacturing industry, are crucial in developing smart manufacturing and are increasingly becoming more intelligent. Digital twin technology ...

Abstract Laser tracers are a three-dimensional coordinate measurement system that are widely used in industrial measurement. We propose a geometric error identification method based on multi-station synchronization laser...

Abstract Assembly geometric error as a part of the machine tool system errors has a significant influence on the machining accuracy of the multi-axis machine tool. And it cannot be eliminated due to the error propagation...

Relative vibration between two flexible subsystems can be detrimental to the performance of many engineering applications, such as optics-based instruments, machine tools, or adjacent industrial structures sharing piping...

Abstract Heat dissipation performance is critical to the design of high-end equipment, such as integrated chips and high-precision machine tools. Owing to the advantages of artificial intelligence in solving complex task...

Abstract Aluminum alloy is the main structural material of aircraft, launch vehicle, spaceship, and space station and is processed by milling. However, tool wear and vibration are the bottlenecks in the milling process o...

Abstract By studying the effects of geometric precision on kinematic accuracy, an error mapping model has been established, based on the hypothesis that a motion pair and its installation surface are rigid. However, when...

ObjectiveThis study aimed to identify core inflammatory biomarkers through machine learning approaches and develop an accessible online risk calculator to predict pulmonary consolidation in children with Chlamydia pneumo...

Di-Higgs production at the LHC associated with missing transverse energy is explored in the context of simplified models that generically parameterize a large class of models with heavy scalars and dark matter candidates...

Early detection of Parkinson’s disease (PD) through speech analysis offers significant clinical advantages, yet no validated tools exist for Arabic-speaking populations, representing a critical gap in global healthcare...

Abstract Background Enchondromas (EC) present cartilaginous tumors that are difficult to differentiate from their intermediate counterpart, atypical cartilaginous tumors (ACT). Histologically, tumor distinction of these ...

Abstract Background Central nervous system (CNS) infections in cattle are a major cause of economic loss and mortality. Machine learning (ML) techniques are gaining widespread application in solving predictive tasks in b...

BackgroundInterstitial fibrosis and tubular atrophy (IFTA) are key pathological features of chronic kidney damage and progression in diabetic nephropathy (DN). Early identification of patients at higher risk of IFTA may ...

Weaning is a critical stage in swine production, characterized by intestinal alterations that affect piglet health and performance. In this study, machine learning techniques were applied to identify joint patterns betwe...

Background: Conventional HIV testing approaches continue to fall short of overcoming barriers to HIV testing, especially among key and priority populations at higher risk of acquiring and transmitting HIV. Artificial in...

BackgroundYoung women with localized breast cancer represent a clinically distinct population with heterogeneous outcomes, yet age-specific prognostic models remain limited. Conventional risk stratification tools derived...

In the recent years, a dramatic boost of the research is observed at the junction of photonics, machine learning and artificial intelligence. A new methodology can be applied to the description of a variety of photonic s...

BackgroundThe integration of artificial intelligence (AI) into emergency medicine holds promise for enhancing early diagnosis and clinical decision-making. Traditional diagnostic approaches often rely on physician judgme...

The prediction and design of photonic features have traditionally been guided by theory-driven computational methods, spanning a wide range of direct solvers and optimization techniques. Motivated by enormous advances in...

Abstract Background Machine learning methods are widely used to detect behavioural data patterns. Although these new mathematical methods are useful tools, the interpretation of the results are often ambivalent unless ...

BackgroundSecondary hyperlipidemia is a common and serious complication in patients with clinically diagnosed depressive disorders, yet early screening tools are lacking. This study aims to develop and validate a machine...