The main goal of this work is to develop a comprehensive methodology for predicting wear in planar mechanical systems with multiple clearance joints and investigating the interaction between the ...joint clearance, driving condition, and wear. In the process, an effective contact surface discretization method together with the Lagrangian method are used to establish the dynamic equation of the multibody system. Considering the change of the contact surface, an improved nonlinear contact force model suitable for the complicated contact conditions is utilized to evaluate the intrajoint forces, and the friction effects between the interconnecting bodies are discussed using the LuGre model. Next, the contact forces developed are integrated into the Archard model to compute the wear depth caused by the relative sliding and the geometry of the bearing is updated. Then, a crank slider mechanism with multiple clearance joints is employed to perform numerical simulations in order to demonstrate the efficiency of the dynamic procedures adopted throughout this work. The correctness of the proposed method is verified by comparing with other literature and simulation results. The results show that the wear is sensitive to different initial conditions, and the evolving contact boundary makes the dynamics of mechanical system and the joint wear prediction more complex. This study is helpful for predicting joint wear of mechanical systems with clearance and optimizing the mechanism’s design.
A new robotic high-frequency local induction heat treatment system is studied. The system is designed to perform on-site local heat treatment on large steel components such as hydropower turbine ...runner. A flat spiral coil is moved by a robot arm over the area to treat. A fast numerical model is developed to predict the temperature distribution and system settings. Several numerical strategies are proposed to minimize the computation time. The model combines an equivalent circuit with thermal finite elements and the electromagnetic mutual impedance method. A simple approach based on the concept of complex permeability is proposed to model hysteresis losses. The computed power losses in the RLC circuit and the temperature field in flat paramagnetic and ferromagnetic workpieces are compared with experimental measurements. Results confirm the accuracy of the coupled thermo-electromagnetic model.
ABSTRACT
Depression is increasingly recognized as an inflammatory disease, with inflammatory crosstalk in the brain contributing its pathogenesis. Life stresses may up‐regulate inflammatory processes ...and promote depression. Although cytokines are central to stress‐related immune responses, their contribution to stress‐induced depression remains unclear. Here, we used unpredictable chronic mild stress (UCMS) to induce depression‐like behaviors in mice, as assessed through a suite of behavioral tests. C‐X‐C motif chemokine ligand 1 (CXCL1)‐related molecular networks responsible for depression‐like behaviors were assessed through intrahippocampal microinjection of lenti‐CXCL1, the antidepressant fluoxetine, the C‐X‐C motif chemokine receptor 2 (CXCR2) inhibitor SB265610, and the glycogen synthase kinase‐3β (GSK3β) inhibitor AR‐A014418. Modulation of apoptosis‐related pathways and neuronal plasticity were assessed via quantification of cleaved caspase‐3, B‐cell lymphoma 2‐associated X protein, cAMP response element‐binding protein (CREB), and brain‐derived neurotrophic factor (BDNF) protein expression. CXCL1/CXCL2 expression was correlated with depression‐like behaviors in response to chronic stress or antidepressant treatment in the UCMS depression model. Intrahippocampal microinjection of lenti‐CXCL1 increased depression‐like behaviors, activated GSK3β, increased apoptosis pathways, suppressed CREB activation, and decreased BDNF. Administration of the selective GSK3β inhibitor AR‐A014418 abolished the effects of lenti‐CXCL1, and the CXCR2 inhibitor SB265610 prevented chronic stress‐induced depression‐like behaviors, inhibited GSK3β activity, blocked apoptosis pathways, and restored BDNF expression. The CXCL1/CXCR2 axis appears to play a critical role in stress‐induced depression, and CXCR2 is a potential novel therapeutic target for patients with depression.—Chai, H.‐H., Fu, X.‐C., Ma, L., Sun, H.‐T., Chen, G.‐Z., Song, M.‐Y., Chen, W.‐X., Chen, Y.‐S., Tan, M.‐X., Guo, Y.‐W., Li, S.‐P. The chemokine CXCL1 and its receptor CXCR2 contribute to chronic stress‐induced depression in mice. FASEB J. 33, 8853–8864 (2019). www.fasebj.org
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•Virgin avocado oil is rich in monounsaturated fatty acids and phytosterols.•Extraction techniques of virgin avocado oil have been reviewed.•Virgin avocado oil can potentially be used ...as functional oil for chronic diseases management.
The intake trend of functional oil in the management and prevention of chronic diseases has increased in the last decades. Virgin avocado oil (VAO) contains high levels of monounsaturated fatty acids (MUFA) and bioactive components that are potentially beneficial to human health. This review contains a compilation of the extraction methods developed for VAO production and their oil yield. The physicochemical composition of VAO and the pharmacological properties of avocado oil intake are also summarized and highlighted. VAO is composed mostly of MUFA with oleic, palmitic and linoleic acids as the major fatty acid components. The oil also contains high levels of bioactive components, particularly α-tocopherol and β-sitosterol. These properties offer it to be used as functional oil in the management of hypercholesterolemia, hypertension, diabetes and fatty liver disease. Moreover, the oil reduces cardiometabolic risk and possess antimicrobial property.
Involuntary motion, such as vibration, drift, and noise, adversely affects performance in long-distance and high-precision applications. This is especially true when the devices are mounted on ...vehicles. It is nontrivial to deploy real-time compensation systems on a vibrating vehicle due to the challenge of wide bandwidth as well as multiple moving frequencies. In addition, only self-referencing sensors, such as a gyroscope, can be used. For sensing, the gyroscope itself has drift issues. In addition, the raw gyroscope measurement has the phase-difference issue coming from both hardware (inclusive of both sensors and actuators) and software (linear filters). For actuation, it should be robust to hysteresis and external disturbance. Moreover, the disturbance comes from different sources, which thereby have different dominant frequencies in wide bandwidth. In this article, we aim to propose an efficient and easy-for-deployment compensation approach using a gyroscope and fast steering mirror. First, the proposed sensing method can estimate the disturbance with minimal phase difference and without the drift issue. More importantly, it is lightweight and requires less computational load compared to other methods. Then, the fast steering mirror is actuated by the proposed controller, which can achieve good tracking performance where hysteresis exists. Therefore, the proposed compensation method can achieve good performance for wide-bandwidth multiple-frequency motion with these treatments. Experiments are conducted to verify its effectiveness.
This Letter reports a high isolation diplexer using stub-loaded resonators (SLRs). Each SLR can generate one transmission pole and one transmission zero (TZ). By changing the length of the open stub, ...the frequency of the TZ can be adjusted. The upper channel filter using the capacitive coupled SLRs can have TZs above the passband, while the lower channel filter using inductive coupled SLRs can have TZs below the passband. With those TZs, the isolation of the diplexer is greatly improved. A high isolation diplexer is designed and fabricated to validate the design concept.
Real-time (RT) vibration sensing and compensation are essential to maintaining reliable laser positioning on remote platforms, which possesses a rising research interest in recent years. The ...technical challenge in sensing is that the input signal possesses: 1) multiple and time-variant dominant frequencies, 2) broad bandwidth, 3) delay-induced phase shift, and 4) sensor disturbances, including noise and integration drift. This paper proposes an adaptive filter and a time series forecasting method to obtain an accurate vibration signal from the input, overcoming the mentioned challenges. The adaptive filter, namely the Recursive Least Square (RLS) based filter, is proposed to de-noising and remove drift. The technique achieves adaptive filtering by adapting a regression model using the RLS algorithm to reduce the effect of historical data points. A Fourier Linear Combiner (FLC) based algorithm, namely the Multiple Order-FLC (MOFLC), is applied in a two stages forecasting method to eliminate the phase shift caused by inherent system delay. The MOFLC adapts a Fourier series model using the Least Mean Square (LMS) algorithm referencing both signal and signal derivative simultaneously, which is more reliable in phase shift correction than traditional FLC-based algorithms. Both offline and online experiments are conducted to validate the sensing accuracy and RT compensation performance. The proposed techniques are proved to typically possess over 70% accuracy in compensation.
Existing accident analysis methods often fail to integrate the relationship of construction accidents with construction activities, restrict the potential for visualizing the accidents frequency in ...Building Information Modelling (BIM). This study proposes a decision support system that provides visual insights, and the frequency of the core construction accidents in relation to typical construction activities. K-mean clustering and keyword-matching techniques are used to group the leading causes of construction accidents and then compare them with the construction activities. The average hit rate computed for construction accidents was 91%, whereas the hit rate calculated for construction activities connected to these frequent accidents was 75%. The classification models are integrated into the Power BI platform to offer decision-makers deeper insights regarding the relationship of prevalent construction accident types and associated activities. The practical application of the system is demonstrated with a case study, exemplifying the fall accident data integrated in a BIM environment.
•Clustering and NLP techniques are used to automatically classify the causes of construction accidents.•The average hit rate computed for common construction accidents was 91%.•The classification models are integrated into the Power BI platform to support decision making.•The results are expected to yield useful knowledge and significant contributions to better understand past accidents.•The results are also expected to provide opportunities for preventive actions to avert future accidents.
Coronavirus disease of 2019 or COVID-19 is a rapidly spreading viral infection that has affected millions all over the world. With its rapid spread and increasing numbers, it is becoming overwhelming ...for the healthcare workers to rapidly diagnose the condition and contain it from spreading. Hence it has become a necessity to automate the diagnostic procedure. This will improve the work efficiency as well as keep the healthcare workers safe from getting exposed to the virus. Medical image analysis is one of the rising research areas that can tackle this issue with higher accuracy. This paper conducts a comparative study of the use of the recent deep learning models (VGG16, VGG19, DenseNet121, Inception-ResNet-V2, InceptionV3, Resnet50, and Xception) to deal with the detection and classification of coronavirus pneumonia from pneumonia cases. This study uses 7165 chest X-ray images of COVID-19 (1536) and pneumonia (5629) patients. Confusion metrics and performance metrics were used to analyze each model. Results show DenseNet121 (99.48% of accuracy) showed better performance when compared with the other models in this study.
Stroke is one of the major devastating diseases with no effective medical therapeutics. Because of the high rate of disability and mortality among stroke patients, new treatments are urgently ...required to decrease the brain damage following stroke. In recent years, the inflammasome is a novel breakthrough point that play an important role in the stroke, and the inhibition of inflammasome may be an effective method for stroke treatment. Briefly, inflammasome is a multi-protein complex that causes activation of caspase-1 and subsequent production of pro-inflammatory factors including interleukin (IL)-18 and IL-1β. Among them, the NLRP3 inflammasome is the most typical inflammasome, which can detect cell damage and mediate inflammatory response to tissue damage in ischemic stroke. The NLRP3 inflammasome has become a key mediator of post-ischemic inflammation, leading to a cascade of inflammatory reactions and cell death eventually. Thus, NLRP3 inflammasome is an ideal therapeutic target due to its important role in the inflammatory response after ischemic stroke. In this review, we will summarize the structure, assembly and regulation of NLRP3 inflammasome, the role of NLRP3 inflammasome in ischemic stroke, and several treatments targeting NLRP3 inflammasome in ischemic stroke. The further understanding of the mechanism of NLRP3 inflammasome in patients with ischemic stroke will provide novel targets for the treatment of cerebral ischemic stroke patients.
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