Miracle-dz/UniFAR-FTUs huggingface dataset:
A Sensor-Based Feedback Device Stimulating Daily Life Upper Extremity Activity in Stroke Patients: A Feasibility Study

This study aims to evaluate the feasibility and explore the efficacy of the Arm Activity Tracker (AAT). The AAT is a device based on wrist-worn accelerometers that provides visual and tactile feedback to stimulate daily life upper extremity (UE) activity in stroke patients. Methods: A randomised, crossover within-subject study was conducted in sub-acute stroke patients admitted to a rehabilitation centre. Feasibility encompassed (1) adherence: the dropout rate and the number of participants with insufficient AAT data collection; (2) acceptance: the technology acceptance model (range: 7–112) and (3) usability: the system usability scale (range: 0–100). A two-way ANOVA was used to estimate the difference between the baseline, intervention and control conditions for (1) paretic UE activity and (2) UE activity ratio. Results: Seventeen stroke patients were included. A 29% dropout rate was observed, and two participants had insufficient data collection. Participants who adhered to the study reported good acceptance (median (IQR): 94 (77–111)) and usability (median (IQR): 77.5 (75–78.5)-). We found small to medium effect sizes favouring the intervention condition for paretic UE activity (η2G = 0.07, p = 0.04) and ratio (η2G = 0.11, p = 0.22). Conclusion: Participants who adhered to the study showed good acceptance and usability of the AAT and increased paretic UE activity. Dropouts should be further evaluated, and a sufficiently powered trial should be performed to analyse efficacy.
Nuclear data measurement activities at CIAE

China Institute of Atomic Energy (CIAE) is one of the most important bases for nuclear data measurement in China. In this paper, the nuclear data measurement activities at CIAE in recent years are presented. The secondary neutron emission DX and DDX measurement, the integral experiment for nuclear data benchmarking, the excitation function measurement, the neutron induced fission yields measurement and the gamma production yields measurement carried out in recent years are introduced. Furthermore, the progress of some new facilities and proposed plans (e.g. A Gamma ray Total Absorption Facility (GTAF) for (n,γ) reaction cross section measurement, A 3 He detector array for (n,2n) reaction cross section measurement, the back streaming white neutron beam for nuclear data measurement at China Spallation Neutron Source (CSNS)) are also presented.
Using Thromboelastography to Treat Patients with COVID-19-Associated Coagulopathy: A Case Report from the Front Lines

There has been increasing evidence to support that COVID-19 infection is associated with hypercoagulable complications due to a systemic inflammatory response indirectly activating the coagulation cascade. This phenomenon has been termed, COVID-19-associated coagulopathy. Uniquely, trauma patients are highly susceptible to thromboembolic events. Monitoring for adequate chemical prophylaxis against these events is difficult, especially with superimposed COVID-19 infection. Although thromboelastography (TEG) has been utilized to assess clotting status in medical and surgical patients with ongoing bleeding, there has been limited research to investigate the value of TEG in evaluating clotting status of trauma patients affected by COVID-19. This case is the first to describe a COVID-19 positive trauma patient, who developed a DVT after splenectomy, who was on subtherapeutic DVT prophylaxis as assessed with anti-Xa assay monitoring and TEG analysis. This case supports that further utilization of TEG in COVID-19-positive trauma patients may be beneficial in identifying those patients at higher risk of thromboembolic complications, as well as assisting in DVT prophylaxis dosing, especially when confounding factors make identifying this risk challenging.
Bobsleigh start interval times and three-dimensional motion analysis of the lower limb joints in preparation for the 2018 Pyeongchang Winter Olympics

This study aimed to provide data to improve the technique of Korean bobsledders. To this end, we measured the start interval times of bobsledders with different performance levels and performed a motion analysis of the lower limb joints during the start interval. We divided 12 Korean bobsledders into a superior group and an inferior group before measuring the interval times and performing the motion analysis of the lower limb joints at the start of the bobsleigh. The start interval times showed a statistically significant difference between the superior and inferior groups (p < .05). The motion analysis of the lower limb joints revealed significant differences in hip flexion and extension, and in ankle dorsiflexion, plantar flexion, and supination (p < .05). Based on these differences, we deduced that the superior bobsledders achieved superior start times by using movements that focus more on horizontal changes in the center of gravity than on vertical changes, and movements that facilitate a longer stride.
The uniform boundedness of networked control systems based on event-triggered mechanism

Networked control systems(NCSs) with continuous plant was described by a discrete model. A new model of NCSs that involves the network conditions, state and event-triggered communication strategy is proposed. A sufficient condition of stability for NCSs neglecting the state error was derived by Lyapunov method and linear matrix inequality approach. Moreover, a state feedback controller was designed to ensure the NCSs with uniform event-triggered to be uniformly bounded. Finally, a numerical example is provided to illustrate the effectiveness of the proposed method.
Predictive Identification of At-Risk Students: Using Learning Management System Data

This paper describes a neural network model that can be used to detect at-risk students failing a particular course using only grade book data from a learning management system. By analyzing data extracted from the learning management system at the end of week 5, the model can predict with an accuracy of 88% whether the student will pass or fail a specific course. Data from the grade books from all course shells from the Spring 2022 semester (N = 22,041 rows) were analyzed, and four factors were found to be significant predictors of student success/failure: the current course grade after the fifth week of the semester and the presence of missing grades in weeks 3, 4, and 5. Several models were investigated before concluding that a neural network model had the best overall utility for the purpose of an early alert system. By categorizing students who are predicted to fail more than one course as being generally at risk, we provide a metric for those who use early warning systems to target resources to the most at-risk students and intervene before students drop out. Seventy-four percent of the students whom our model classified as being generally at risk ended up failing at least one course.
Multivariate posterior inference for spatial models with the integrated nested Laplace approximation

The integrated nested Laplace approximation (INLA) is a convenient way to obtain approximations to the posterior marginals for parameters in Bayesian hierarchical models when the latent effects can be expressed as a Gaussian Markov random field. In addition, its implementation in the R‐INLA package for the R statistical software provides an easy way to fit models using the INLA in practice in a fraction of the time that other computer‐intensive methods (e.g. Markov chain Monte Carlo methods) take to fit the same model. Although the INLA provides a fast approximation to the marginals of the model parameters, it is difficult to use it with models that are not implemented in R‐INLA. It is also difficult to make multivariate posterior inference on the parameters of the model as the INLA focuses on the posterior marginals and not the joint posterior distribution. We describe how to use the INLA within the Metropolis–Hastings algorithm to fit complex spatial models and to estimate the joint posterior distribution of a small number of parameters. We illustrate the benefits of this new method with two examples. In the first, a spatial econometrics model with two auto‐correlation parameters (for the response and the error term) is considered. This model is not currently available in R‐INLA, and multivariate inference is often required to assess dependence between the two spatial auto‐correlation parameters in the model. Furthermore, the estimation of spillover effects is based on the joint posterior distribution of a spatial auto‐correlation parameter and a covariate coefficient. In the second example, a multivariate spatial model for several diseases is proposed for disease mapping. This model includes a shared specific spatial effect as well as disease‐specific spatial effects. Dependence on the shared spatial effect is modulated via disease‐specific weights. By inspecting the joint posterior distribution of these weights it is possible to assess which diseases have a similar spatial pattern.
Solving the bi-objective capacitated p-median problem with multilevel capacities using compromise programming and VNS

A bi-objective optimisation using a compromise programming approach is proposed for the capacitated p-median problem in the presence of the fixed cost of opening facility and several possible capacities that can be used by potential facilities. As the sum of distances between customers and their facilities and the total fixed cost for opening facilities are important aspects, the model is proposed to deal with those conflicting objectives. We develop a mathematical model using integer linear programming (ILP) to determine the optimal location of open facilities with their optimal capacity. Two approaches are designed to deal with the bi-objective capacitated p-median problem, namely compromise programming with an exact method and with a variable neighbourhood search based matheuristic. New sets of generated instances are used to evaluate the performance of the proposed approaches. The computational experiments show that the proposed approaches produce interesting results.
Analysis of the opinions of individuals on the COVID-19 vaccination on social media

The COVID-19 pandemic continues to threaten public health globally. To develop effective interventions and campaigns to raise vaccination rates, policy makers need to understand people's attitudes towards vaccination. We examine the perspectives of people in India, the United States, Canada, and the United Kingdom on the administration of different COVID-19 vaccines. We analyse how public opinion and emotional tendencies regarding the COVID-19 vaccines relate to popular issues on social media. We employ machine learning algorithms to forecast thoughts based on the social media posts. The prevailing emotional tendency indicates that individuals have faith in immunisation. However, there is a likelihood that significant statements or events on a national, international, or political scale influence public perception of vaccinations. We show how public health officials can track public attitudes and opinions towards vaccine-related information in a geo-aware manner, respond to the sceptics, and increase the level of vaccine trust in a particular region or community.
Relative and Incremental Value Relevance of Traditional and Real Performance Measures: Evidence from Palestine Manufacturing Listed Companies

Purpose: This study seeks to test both incremental and relative value relevance of the Economic Value-Added (EVA) as a real performance indicator and (EPS) as a traditional performance indicator in the context of the Palestine Exchange (PEX) listed manufacturing companies. Theoretical framework: Previous research provided mixed evidence about the superiority of EVA over the traditional performance measures in predicting market value; some studies supported the superiority of EVA (Zimmerman, 1997; Shishanya et al., 2020), while others denied that superiority (Biddle et al., 1997; Agnatia & Amalia, 2018). Testing the value relevance of EVA compared to EPS was rarely conducted in Palestine. Design/methodology/approach: Seventy company-year observations were gathered from 10 of 13 manufacturing PEX-listed companies over seven years (2015-2021); the study employed regression and correlation analysis. Findings: The study found a significant relationship between EPS and stock returns while concluding an insignificant relationship between EVA and stock returns. The study also found that EPS beats EVA. However, the findings showed that EVA adds incremental information content to EPS’s interpretation of stock returns. Research, Practical & Social implications: The study outcomes help and direct the investors and analysts, in assessing the company’s performance and predicting stock returns; it also helps policymakers and regulators in directing and updating reporting requirements. Originality/value: This study adds to the literature on the value relevance of real and traditional performance indices in Palestine. Further research is recommended on other sectors and the whole market.
L1 Regression using Lewis Weights Preconditioning and Stochastic Gradient Descent

We present preconditioned stochastic gradient descent (SGD) algorithms for the $\ell_1$ minimization problem $\min_{x}\|A x - b\|_1$ in the overdetermined case, where there are far more constraints than variables. Specifically, we have $A \in \mathbb{R}^{n \times d}$ for $n \gg d$. Commonly known as the Least Absolute Deviations problem, $\ell_1$ regression can be used to solve many important combinatorial problems, such as minimum cut and shortest path. SGD-based algorithms are appealing for their simplicity and practical efficiency. Our primary insight is that careful preprocessing can yield preconditioned matrices $\tilde{A}$ with strong properties (besides good condition number and low-dimension) that allow for faster convergence of gradient descent. In particular, we precondition using Lewis weights to obtain an isotropic matrix with fewer rows and strong upper bounds on all row norms. We leverage these conditions to find a good initialization, which we use along with recent smoothing reductions and accelerated stochastic gradient descent algorithms to achieve $\epsilon$ relative error in $\tilde{O}(nnz(A) + d^{2.5} \epsilon^{-2})$ time with high probability, where $nnz(A)$ is the number of non-zeros in $A$. This improves over the previous best result using gradient descent for $\ell_1$ regression. We also match the best known running times for interior point methods in several settings. Finally, we also show that if our original matrix $A$ is approximately isotropic and the row norms are approximately equal, we can give an algorithm that avoids using fast matrix multiplication and obtains a running time of $\tilde{O}(nnz(A) + s d^{1.5}\epsilon^{-2} + d^2\epsilon^{-2})$, where $s$ is the maximum number of non-zeros in a row of $A$. In this setting, we beat the best interior point methods for certain parameter regimes.
Lagged Exact Bayesian Online Changepoint Detection

Identifying changes in the generative process of sequential data, known as changepoint detection, has become an increasingly important topic for a wide variety of fields. A recently developed approach, which we call EXact Online Bayesian Changepoint Detection (EXO), has shown reasonable results with efficient computation for real time updates. However, when the changes are relatively small, EXO starts to have difficulty in detecting changepoints accurately. We propose a new algorithm called $\ell$-Lag EXact Online Bayesian Changepoint Detection (LEXO-$\ell$), which improves the accuracy of the detection by incorporating $\ell$ time lags in the inference. We prove that LEXO-1 finds the exact posterior distribution for the current run length and can be computed efficiently, with extension to arbitrary lag. Additionally, we show that LEXO-1 performs better than EXO in an extensive simulation study; this study is extended to higher order lags to illustrate the performance of the generalized methodology. Lastly, we illustrate applicability with two real world data examples comparing EXO and LEXO-1.
Deep-Learning-Based Human Chromosome Classification: Data Augmentation and Ensemble

Object classification is a crucial task in deep learning, which involves the identification and categorization of objects in images or videos. Although humans can easily recognize common objects, such as cars, animals, or plants, performing this task on a large scale can be time-consuming and error-prone. Therefore, automating this process using neural networks can save time and effort while achieving higher accuracy. Our study focuses on the classification step of human chromosome karyotyping, an important medical procedure that helps diagnose genetic disorders. Traditionally, this task is performed manually by expert cytologists, which is a time-consuming process that requires specialized medical skills. Therefore, automating it through deep learning can be immensely useful. To accomplish this, we implemented and adapted existing preprocessing and data augmentation techniques to prepare the chromosome images for classification. We used ResNet-50 convolutional neural network and Swin Transformer, coupled with an ensemble approach to classify the chromosomes, obtaining state-of-the-art performance in the tested dataset.
A critical evaluation of inter-laboratory data on total, elemental and isotopic carbon in the carbonaceous particle reference material

A primary purpose of this report is to provide documentation for the new isotopic and chemical particulate carbon data for certified, reference and information values in Tables 12 and 13 of the Certificate of Analysis. Supporting this is a critical review of underlying international intercomparison data and methodologies, provided by 18 teams of analytical experts from 11 institutions. Key results of the intercomparison are: (1) a new, Certified Value for total carbon (TC) in SRM 1649a; (2) 14C Reference Values for total carbon and a number of organic species, including 8 individual PAH; and (3) elemental carbon Information Values for 13 analytical methods applied to elemental carbon (EC). Elemental carbon comprised a special focus of the intercomparison. The results are diverse, reflecting the confounding of methodological-matrix effect artifacts, and information on different regions of the black carbon spectrum representing more or less refractory forms of this universal product of combustion. These data can be described in terms of three clusters, centered at EC/TC ratios of 0.075, 0.28, and 0.46. Availability of both chemical and 14C speciation data for SRM 1649a holds great promise for improved analytical insight through comparative analysis (e.g., fossil/biomass partition in EC compared to PAH), and through the application of the principle of isotopic mass balance.
On $H_\infty$ Sliding Mode Control Under Stochastic Communication Protocol

This paper is concerned with the sliding mode control (SMC) problem for a class of uncertain discrete-time systems subject to unmatched external disturbances and communication constraints. In order to reduce the bandwidth usage between the controller and the actuators, the stochastic communication protocol (SCP) is utilized to determine which actuator should be given the access to the network at a certain instant. A key issue of the addressed problem is to design both the sliding surface and the sliding mode controller under the SCP scheduling. An updating rule on actuator input is first introduced and then a token-dependent SMC law is designed. Sufficient conditions are established for the resultant SMC systems such that not only the reachability with a sliding domain around the specified sliding surface is ensured, but also the stochastic stability with a prescribed <inline-formula><tex-math notation="LaTeX">$H_\infty$</tex-math></inline-formula> performance level is guaranteed. Based on these conditions, a set of coupled matrix inequalities is given to acquire the token-dependent parameter matrices in the proposed SMC law. Finally, a numerical example is presented to illustrate the effectiveness of the proposed <inline-formula><tex-math notation="LaTeX">$H_\infty$</tex-math></inline-formula> SMC scheme under the SCP scheduling.
Visual Inspection Method of Ceramic Bottle Surface Defects Based on Niblack Optimization

In order to improve the efficiency and accuracy of the defect detection of ceramic bottle production line, a method of visual inspection of ceramic bottle surface defects based on threshold segmentation Niblack optimization algorithm is been proposed. The CLAHE interpolation image enhancement algorithm is used to improve the image contrast, and then the local threshold is obtained by calculating the local mean and standard deviation in the domain window to realize the segmentation of the defective region. Then the minimum gray value difference is introduced and whether there are defects in images are determined to the center pixel gray value range. The characteristics of the defect target are extracted, and the defect classifier is used to classify the defects according to the principle of support vector machine. The experimental results show that the optimization algorithm can realize the effective segmentation of the defect area and reduce the false detection rate by reducing the influence of background noise on the segmentation results with an accuracy rate of 90.8%.
Current crowding and self-heating effects in AlGaN-based flip-chip deep-ultraviolet light-emitting diodes

We thoroughly explored the physical origin of the efficiency decrease with increasing injection current and current crowding effect in 280 nm AlGaN-based flip-chip deep-ultraviolet (DUV) light-emitting diodes (LEDs). The current spreading length was experimentally determined to be much smaller in DUV LEDs than that in conventional InGaN-based visible LEDs. The severe self-heating caused by the low power conversion efficiency of DUV LEDs should be mainly responsible for the considerable decrease of efficiency when current crowding is present. The wall-plug efficiency of the DUV LEDs was markedly enhanced by using a well-designed p-electrode pattern to improve the current distribution.
Plant Pathologist- A Machine Learning Diagnostician for the Plant Disease

Crop infection in terms of bacteria, fungus and other viruses is an unrelenting issue and loos of food due to this is a key challenge faced by farmers for many decades. Proper identification of this disease may prevent this loss. This study describes precision agriculture in terms of a mobile application called ‘plant pathologist’ for detecting plant diseases that forecast their occurrence using machine learning and artificial intelligence. A combination of computer vision and deep learning algorithms were used to evaluate images of plant leaves and compared them to a database of known diseases in order to make a quick and accurate diagnosis. The development process involved the identification of the research issue and the objectives, the gathering and pre-processing of data, the choice and training of the model, and the model evaluation through this quantitative methodology. The convolutional neural network (CNN) was trained on a sizable dataset of annotated plant disease photos as part of the machine learning model utilized in the mobile app. The CNN was trained to recognise the visual characteristics of photos of healthy and diseased plants and classify them according to several disease types. To ensure adaptability and precise identification of plant diseases, the app has been trained on a specific type. With hyperparameter tuning and cross-validation, the best-performing CNN model was chosen for the model selection procedure. The mobile application, which offers thorough information on plant disease diagnosis is then updated with the chosen model. This offers farmers, botanists, and plant enthusiasts an invaluable resource to accurately identify plant diseases through machine learning.