In this study, the welding thermal cycle, as well as the microstructural and mechanical properties of welded AA6061-T6 plates, were studied. The plates were prepared and bead-on-plate welded using ...gas metal arc welding (GMAW). Numerical simulations using SYSWELD® were performed to obtain the thermal distribution in the welded plates. The numerical heat source was calibrated using the temperatures obtained from the experimental work and the geometry of the melting pool. The mechanical properties were obtained through microhardness tests and were correlated with the welding thermal cycle. Moreover, the mechanical behavior and local deformation in the heat-affected zone (HAZ) were investigated using micro-flat tensile (MFT) tests with digital image correlation (DIC). The mechanical properties of the subzones in the HAZ were then correlated with the welding thermal cycle and with the microstructure of the HAZ. It was observed that the welding thermal cycle produced microstructural variations across the HAZ, which significantly affected the mechanical behavior of the HAZ subzones. The results revealed that MFT tests with the DIC technique are an excellent tool for studying the local mechanical behavior change in AA6061-T6 welded parts due to the welding heat.
The key to effective stroke management is timely diagnosis and triage. Machine learning (ML) methods developed to assist in detecting stroke have focused on interpreting detailed clinical data such ...as clinical notes and diagnostic imaging results. However, such information may not be readily available when patients are initially triaged, particularly in rural and underserved communities.
This study aimed to develop an ML stroke prediction algorithm based on data widely available at the time of patients' hospital presentations and assess the added value of social determinants of health (SDoH) in stroke prediction.
We conducted a retrospective study of the emergency department and hospitalization records from 2012 to 2014 from all the acute care hospitals in the state of Florida, merged with the SDoH data from the American Community Survey. A case-control design was adopted to construct stroke and stroke mimic cohorts. We compared the algorithm performance and feature importance measures of the ML models (ie, gradient boosting machine and random forest) with those of the logistic regression model based on 3 sets of predictors. To provide insights into the prediction and ultimately assist care providers in decision-making, we used TreeSHAP for tree-based ML models to explain the stroke prediction.
Our analysis included 143,203 hospital visits of unique patients, and it was confirmed based on the principal diagnosis at discharge that 73% (n=104,662) of these patients had a stroke. The approach proposed in this study has high sensitivity and is particularly effective at reducing the misdiagnosis of dangerous stroke chameleons (false-negative rate <4%). ML classifiers consistently outperformed the benchmark logistic regression in all 3 input combinations. We found significant consistency across the models in the features that explain their performance. The most important features are age, the number of chronic conditions on admission, and primary payer (eg, Medicare or private insurance). Although both the individual- and community-level SDoH features helped improve the predictive performance of the models, the inclusion of the individual-level SDoH features led to a much larger improvement (area under the receiver operating characteristic curve increased from 0.694 to 0.823) than the inclusion of the community-level SDoH features (area under the receiver operating characteristic curve increased from 0.823 to 0.829).
Using data widely available at the time of patients' hospital presentations, we developed a stroke prediction model with high sensitivity and reasonable specificity. The prediction algorithm uses variables that are routinely collected by providers and payers and might be useful in underresourced hospitals with limited availability of sensitive diagnostic tools or incomplete data-gathering capabilities.
Many predators produce dormant offspring to escape harsh environmental conditions, but the evolutionary stability of this adaptation has not been fully explored. Like seed banks in plants, dormancy ...provides a stable competitive advantage when seasonal variations occur, because the persistence of dormant forms under harsh conditions compensates for the increased cost of producing dormant offspring. However, dormancy also exists in environments with minimal abiotic variation—an observation not accounted for by existing theory. Here it is demonstrated that dormancy can out‐compete perennial activity under conditions of extensive prey density fluctuation caused by overpredation. It is shown that at a critical level of prey density fluctuations, dormancy becomes an evolutionarily stable strategy. This is interpreted as a manifestation of Parrondo's paradox: although neither the active nor dormant forms of a dormancy‐capable predator can individually out‐compete a perennially active predator, alternating between these two losing strategies can paradoxically result in a winning strategy. Parrondo's paradox may thus explain the widespread success of quiescent behavioral strategies such as dormancy, suggesting that dormancy emerges as a natural evolutionary response to the self‐destructive tendencies of overpredation and related biological phenomena.
Many predators produce dormant offspring to escape harsh conditions, but only at the cost of less offspring. The evolutionary stability of this costly dormancy is explained by Parrondo's paradox. Although both active and dormant forms of dormancy‐capable predators individually lose to perennially active predators, alternating between these losing strategies ensures better recovery after overpredation, resulting in a strategy that wins.
Certified training must be provided for lay vision screeners prior to their conduct of a vision screening programme. However, the effectiveness of trained lay screeners does deteriorate over time. ...This study aims to evaluate the effectiveness of a recertification vision screening training module using the KieVision
Preschool Vision Screening Kit for preschool teachers in Malaysia.
This was a randomised control trial. Fifty-nine preschool teachers previously enrolled in a Knowledge Transfer Programme were divided into a Study Group (
= 31) to receive recertification training and a Control Group (
= 28) to attend briefing sessions. Subjects was then asked to perform vision screening on 15 preschool children aged 4 years old-6 years old at their respective premises, then verified by optometrists after 2 weeks from the initial screening on the same children.
A total of 894 children were screened, with the Study Group and Control Group screened 49.7% and 50.3%, respectively. There was higher validity in vision screening findings from the Study Group (sensitivity = 66.7%, positive predictive value (PPV) = 61.5%) compared to the Control Group (sensitivity = 36.0 %, PPV = 40.9%).
Teachers who received recertification training were more competent in detecting children's vision impairment using KieVision
Preschool Vision Screening Kit. Thus, timely recertification training should be emphasised to ensure sustainable consistency and reliability of vision screening programmes conducted by lay vision screeners.
Because of the inherent difficulty in differentiating two olefins, the development of metal-catalyzed asymmetric cyclization of 1,6-dienes remains challenging. Herein, we describe the first ...rhodium(III)-catalyzed asymmetric borylative cyclization of cyclohexadienone-tethered mono-, 1,1-di-, and (E)-1,2-disubstituted alkenes (1,6-dienes), affording optically pure cis-bicyclic skeletons bearing three or four contiguous stereocenters with high yields (25–93%), and excellent diastereoselectivities (>20:1 dr) and enantioselectivities (90–99% ee). This mild catalytic approach is generally compatible with a wide range of functional groups, which allows several facile conversions of the cyclization products. Furthermore, on the basis of our SAESI-MS experiment and computational study, a Rh(I)/(III) catalytic cycle is proposed in this tandem reaction, and the Rh(I) active species catalyzes the overall transformation via sequential oxidative addition of B2pin2, olefin insertion, cyclizing conjugate addition, and reductive elimination. The irreversible conjugate addition determines the overall regioselectivity of borylative cyclization, and the ring strain favors the formation of 5,6-bicyclic structure. This highlights the control of ring strain in diene cyclizations, which provides a useful basis for future reaction designs.
Eye movement analysis is critical to studying human brain phenomena such as perception, cognition, and behavior. However, under uncontrolled real-world settings, the recorded gaze coordinates ...(commonly used to track eye movements) are typically noisy and make it difficult to track change in the state of each phenomenon precisely, primarily because the expected change is usually a slower transient process. This paper proposes an approach, Improved Naive Segmented linear regression (INSLR), which approximates the gaze coordinates with a piecewise linear function (PLF) referred to as a hypothesis. INSLR improves the existing NSLR approach by employing a hypotheses clustering algorithm, which redefines the final hypothesis estimation in two steps: (1) At each time-stamp, measure the likelihood of each hypothesis in the candidate list of hypotheses by using the least square fit score and its distance from the k-means of the hypotheses in the list. (2) Filter hypothesis based on a pre-defined threshold. We demonstrate the significance of the INSLR method in addressing the challenges of uncontrolled real-world settings such as gaze denoising and minimizing gaze prediction errors from cost-effective devices like webcams. Experiment results show INSLR consistently outperforms the baseline NSLR in denoising noisy signals from three eye movement datasets and minimizes the error in gaze prediction from a low precision device for 71.1% samples. Furthermore, this improvement in denoising quality is further validated by the improved accuracy of the oculomotor event classifier called NSLR-HMM and enhanced sensitivity in detecting variations in attention induced by distractor during online lecture.
•Proposed INSLR method improves gaze estimation in noisy real-world settings.•INSLR outperforms NSLR in denoising gaze signals from various datasets.•INSLR enhances oculomotor event classification accuracy.•Effectiveness in minimizing gaze prediction errors from low-precision devices.•Validates INSLR’s denoising quality and sensitivity to attention variations.
The traditional fingerprinting-based positioning approach usually requires a laborious training phase to collect the measurements in an environment, which is a challenge for applications involving ...large buildings. In this paper, we propose a novel approach to fuse similarity-based sequence and dead reckoning to track the positions of users in wireless indoor environments. The reference fingerprinting map is constructed without the need for training and is based upon the geometrical relationships of the transmitters, whose positions are known and can be obtained offline. The fingerprint used for online positioning is represented by a ranked sequence of transmitters based on the measured received signal strength (RSS), which is referred to as RSS sequence in this paper. The similarities between this sequence and the reference fingerprints are computed and embedded into a particle filter to locate a user. The displacement estimation from inertial measurement unit is then integrated into the particle filter to track a mobile user. Moreover, the proposed approach can be easily extended to include other sources of sensors. In this paper, frequency modulation radio signal measurements are used as the example to fuse with Wi-Fi measurements to achieve a better tracking accuracy. Extensive experiments are also conducted to evaluate the proposed approach.
As a special soil widely existing in world, loess engineering properties are often disturbed by water and salt. Hence, the influence of water content and salt content on the conductivity properties ...of loess was analyzed using the electrical resistivity of loess obtained by LCR digital bridge tester in this study. Loess electrical resistivity with different water content (8–20%) and NaCl content (0–6%) was obtained at test frequencies of 100 Hz, 1 kHz, and 10 kHz. Results show that loess electrical resistivity exhibited an exponential function with a change in water content. As water content increased, loess electrical resistivity decreased significantly. When water content exceeded the plastic limit, loess electrical resistivity decreased slowly. When NaCl content around 2%, the increase of ion content in conductive path of loess enhanced loess conductivity. When NaCl content reached 6%, the conductive capacity of the loess tended to reach its maximum, and the resistivity slowly decreased and stabilized. There was a nonlinear functional relation between loess electrical resistivity and test frequency. As the test frequency increased, the number of ions that could be used to form a conductive path increased, and loess electrical resistivity decreased. In addition, three paths model of loess electrical resistivity and double-layer structure can well explain above phenomena. This research can provide theoretical basis for electrical resistivity technology to predict water content and salt content, and valuable reference for large-scale field application of electrical resistivity observation technology.
This paper introduces a progressive assist-as-needed (pAAN) controller into our custom-made lower limb exoskeleton system. This control strategy can enhance the active participation of subjects. The ...controller can dynamically estimate a subject's input (voluntary joint torque) based on electromyography (EMG) without calibrations. The EMG-torque relationship learning is unsupervised. The zero-error estimation of the subject's input is guaranteed by a progressive learning strategy. The adaptive controller adjusts the control inputs of motors to complete predefined trajectories. Online torque estimation and adaptive motion control are both realised in the pAAN controller. Additionally, some practical problems of EMG application, caused by time-varying property of EMG signals and electrode displacement, would be avoided. From the simulation and experimental studies, our pAAN controller can predict the subject's input well, and the exoskeleton helps subjects move precisely. Active participation of subjects is achieved during training.
Alzheimer’s disease (AD) is associated with a very large burden on global healthcare systems. Thus, it is imperative to find effective treatments of the disease. One feature of AD is the accumulation ...of neurotoxic β-amyloid peptide (Aβ). Aβ induces multiple pathological processes that are deleterious to nerve cells. Despite the development of medications that target the reduction of Aβ to treat AD, none has proven to be effective to date. Non-pharmacological interventions, such as physical exercise, are also being studied. The benefits of exercise on AD are widely recognized. Experimental and clinical studies have been performed to verify the role that exercise plays in reducing Aβ deposition to alleviate AD. This paper reviewed the various mechanisms involved in the exercise-induced reduction of Aβ, including the regulation of amyloid precursor protein cleaved proteases, the glymphatic system, brain-blood transport proteins, degrading enzymes and autophagy, which is beneficial to promote exercise therapy as a means of prevention and treatment of AD and indicates that exercise may provide new therapeutic targets for the treatment of AD.
PDF
