Miracle-dz/UniFAR-FTUs huggingface dataset:
TRANSTHEORETICAL MODEL FOR EXERCISE: MEASURE REDEVELOPMENT AND ASSESSING THE ROLE OF BARRIERS IN A DIVERSE POPULATION

Despite its well-documented success in differentiating stage of change (SOC) for readiness for regular exercise among primarily White populations, the Transtheoretical Model (TTM) exercise constructs have shown inconsistent results in understudied populations, such as Black and Hispanic/Latinx adults (Spencer et al., 2006). This cross-sectional study attempts to understand this trend by considering barriers to regular exercise among these populations. This study describes the development and validation of a novel barriers construct, as well as adapted constructs of Self-Efficacy (SE) and Decisional Balance (DB) within the TTM framework. Black and Hispanic/Latinx adults (n = 450) were recruited to complete this study. Exploratory and confirmatory analyses produced one Pros and two Cons' scales for the DB inventory, two scales for the SE inventory, and three scales for the Barriers inventory. Expected patterns for SE and Pros by SOC were found, while the anticipated results for Cons were not found. It was expected that Barriers would decrease with increasing SOC, however change across SOC was not significant and the opposite trend was found. These findings suggest that barriers to regular exercise might be progressively realized as individuals progress through SOC or may not be important to the sample studied. They also suggest that traditional TTM constructs can be culturally tailored or improved by incorporating barriers to exercise without disrupting the frameworks’ expected outcomes.
Adversarial Attacks on Faster R-CNN: Design and Ablation Study

Most adversarial attack methods focus on image classifiers rather than the other more common models deployed in security-critical applications. This work focuses on the design of the attack on the object detection model, Faster Region-based Convolutional Neural Network (Faster R-CNN). To this end, we first formulate adversarial attacks against Faster R-CNN as a multi-objective optimisation problem. Second, we apply Projected Gradient Descent (PGD) to solve the defined optimisation problem. In addition, we conduct an ablation study on the loss terms of the adversarial attacks’ objective function. Finally, the proposed attack is evaluated on the object detection task MSCOCO2017. Our experimental results show that attacking the Fast R-CNN submodule or attacking both Fast R-CNN and backbone network submodules leads to the best attack results at our widely selected evaluation metrics, and our best attack can reduce the detection accuracy mAP@[. 5, . 95] by 17.4%.
STRATEGIC SPORTS PLANNING IN MALAYSIA AND QATAR

In the ‘11th Malaysia Plan 2016–2020’ targets sports and recreation for development fall under the Ministry of Youth and Sports. Each division and agency, as well as the Olympic Council has its own strategic plan. Somewhat differently, Qatar launched a sports plan, its Sports Sector Strategy 2011–2016 (2011), under the ‘Qatar National Vision 2030’. Both countries recently accounted for elite sporting performances at the 18th Asian Games in Jakarta-Palembang (2018). Malaysia won 7 gold medals and placed 14th overall. Qatar took 6 gold medals and 15th place. This article compares Malaysia and Qatar sports programs regarding vision, mission, strategies, outcomes and problems. A literature review and document analysis are herein offered with the hope of aiding Malaysia’s Ministry of Youth and Sports to develop proactive strategies with excellent outcomes.
Placelogging : mobile spatial annotation and its potential use to urban planners and designers

With the rise of 'Mobile 2.0,' digital telecommunications are beginning to provide deep resources of information to mobile city dwellers. Particularly striking among recent applications is placelogging, defined here as the practice of digitally annotating physical places with user-generated text and media accessible onsite to other mobile users. Placelogging presents potential as a means of engaging citizens with each other and with their environment, as well as distributing authority over defining what Kevin Lynch called "the image of the city. " But as an emerging practice, its uniqueness as a mode of communication distinct from other forms of spatial annotation has not yet been proved. To investigate the uniqueness of placelogging, the thesis first establishes a taxonomy of spatial annotation through which to consider the different forms, functions, and trends in official adoption of a wide range of annotating media. Next, annotations made through various media, including placelogging projects [murmur] and Yellow Arrow, are catalogued in two neighborhoods each of Toronto and New York City. A preliminary methodology is defined to analyze and compare trends in distribution, placement and content of annotations. Placelogs are found to distinguish themselves by annotating a wide range of public and private places, identified as what Margaret Crawford calls the 'everyday space' of city residents, with predominantly subjective, first-person content, which is described as marking 'everyday time. ' Participant interviews and research on related technologies are used to support claims that placelogging could be used to identify sites of shared meaning in the city as well as to foster place attachment, claim to space and social connections among participants. Uses in community development are considered through three cases of implementation: [murmur] in Toronto, Yellow Arrow in Copenhagen and Proboscis' Urban Tapestries in London. Uses for revealed meanings are proposed in preservation, identification of development priorities and sensitivity of response in urban development. Lynch's visual image of the city is revisited as a 'meaning image' that encourages design practices of 'interactive ethnography' and 'sometimes' urban design. Finally, unfamiliarity with and differential access to technology are considered in the context of public uses for placelogging and its derived data. Thesis Supervisor: Lawrence J. Vale Title: Professor and Department Head, Department of Urban Studies and Planning
Variability of test match cricket and the effects of match location on physical demands in male seam bowlers

ABSTRACT The physical demands of test match cricket in seam bowlers during fielding are currently unknown. Similarly, analysis of between-match variability and the effects of playing home vs. away is required. Nine international male seam bowlers across 28 test matches (n = 9 home; n = 19 away) were investigated over five years (2015–2019). Seam bowlers wore global positioning sensors during match play fielding to quantify physical demands. Absolute and relative (per hour) distances covered in five velocity bands, total distance, and number of accelerations and decelerations were assessed for each match. Coefficient of variation (CV%) and smallest worthwhile change were used to calculate between-match variability. Mixed linear modelling was used to analyse home vs away matches. Seam bowlers covered up to 50 km, with maximal durations of >21 hours during test match fielding. Small between-match CV% (8.3) were found for maximal velocity with large (CV% = 21–192) between-match variability across most other variables. Greater distances were covered at 15–20 km·h−1 (p = 0.02) and >25 km·h−1 (p = 0.04) when playing at home. The results demonstrated substantial, highly variable physical demands. Practitioners should adapt training retrospectively to the match demands encountered and should anticipate that match intensity may be higher during home matches.
UAV Swarm Exploration With Byzantine Fault Tolerance

Swarm robots need Byzantine fault tolerance to ensure their tasks can still be complete even if malfunctioning or malicious robots exist. Nevertheless, advancement in implementing Byzantine fault tolerance is not suitable for the UAV swarm exploration task. This study aims to contribute to this growing area of research. We presented an implementation of Byzantine fault tolerance for swarm robots, proposing a theoretically more effective and flexible approach than previous approaches. We integrated a multi-agent foraging algorithm with a BFT consensus algorithm for the UAV swarm exploration task, providing a method that can be generalized to similar multi-agent cooperative tasks. We developed a ROS-based framework to incorporate such algorithms into swarm robots, facilitating robotics development and testing. Empirical results of the UAV swarm exploration task imply our approach’s advantages.
A sensorless approach for tracking control problem of tubular linear synchronous motor

As well-known, linear motors are widely applied to various industrial applicationsdue to their abilities in providing directly straight movement without auxiliary mechanical transmissions. This paper addresses the sensorless control problem of tubular linear synchronous motors, which belong to a family of permanent magnet linear motor. To be specific, a novel velocity observer is proposed to deal with an unmeasurable velocity problem, and asymptotic convergence of the observer error is ensured. Unlike other studies on sensorless control methods for linear motors, our proposed observer is designed by regrading unknown disturbance load in the tracking control problem whereas considering theoretical demonstrations. By adjusting controller parameters properly, the position and velocity tracking error converge in arbitrary small values. Finally, the effectiveness of the proposed method is verified in two illustrative examples.
On-Chip Millimeter-Wave DGS Based Bandstop Filter in 0.18-μm CMOS Process

Defected Ground Structure (DGS) based on-chip bandstop filter (BSF) design is proposed for millimeter-wave applications. In the proposed structure, a capacitively loaded T-shape resonator is embedded in the original DGS resonator, which forms two high quality (<inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula>-) factor small loop resonators. Moreover, this structure, in combination with the feedline and series capacitor, independently realizes the position of two transmission poles: one at each side of the parallel resonance without increasing the layout size. The proposed BSF presents a sharp scattering (S-) parameter response due to the appearance of two transmission poles. As a result, the loaded <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula>-factor and negative group delay of the BSF are improved. The prototype of the proposed BSF is fabricated in 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> Complementary Metal-Oxide-Semiconductor (CMOS) process and measured. The measurement result shows a return loss of 1.78 dB at 53.2 GHz center stopband frequency with the negative group delay of 161 pS. The measurement results also agree well with the electromagnetic simulation results. Without pads, the active area of the prototyped BSF is only 0.024 mm<sup>2</sup>.
Biomarker Changes in Response to a 12-Week Supplementation of an Oral Nutritional Supplement Enriched with Protein, Vitamin D and HMB in Malnourished Community Dwelling Older Adults with Sarcopenia

Malnutrition and sarcopenia commonly overlap and contribute to adverse health outcomes. Previously, chronic supplementation with two oral nutritional supplements (ONS), control (CONS) and experimental ONS enriched with protein, vitamin D and β-hydroxy β-methylbutyrate (HMB) (EONS), improved muscle strength and quality in malnourished sarcopenic older adults, with EONS demonstrating early strength benefits at 12 weeks. To understand the underlying biological mechanisms contributing to the observed early strength benefits of EONS, we examined serum biomarker changes in response to 12-week supplementation. Serum samples (EONS (n = 90) and CONS (n = 103)) collected at baseline and 12 weeks were analyzed. Biomarkers (n = 243) were measured using multiplexed immunoassay, commercial immunoassays and ELISAs. Sixty markers were excluded with levels below assay detection limits. Sixteen biomarkers significantly changed in response to both interventions including nutritional and metabolic markers. Thirteen biomarkers significantly changed in response to EONS but not CONS. Increases in immunoglobulins, myoglobin, total protein, vitamin E and magnesium were observed with EONS. Inflammation-related ferritin and osteopontin decreased, while soluble receptors for cytokines increased, suggesting decreased inflammation. Sex hormone-binding globulin associated with sarcopenia also decreased with EONS. Biomarkers reflective of multiple biological systems were impacted by nutritional intervention in sarcopenic older adults. Incremental biomarker changes were observed in response to EONS containing HMB that possibly link to improvements in skeletal muscle health.
Reduced Rare-Earth Flux-Switching Machines for Traction Applications

There has been growing interest in electrical machines that reduce or eliminate rare-earth material content. Traction applications are among the key applications where reducing cost and, hence, reduction of rare-earth materials are key requirements. This paper will assess the potential of different variants of flux-switching machines (FSMs) that either reduce or eliminate rare-earth materials in the context of traction applications. Two designs use different grades of dysprosium-free permanent magnets (PMs), and the third design is a wound-field variant that does not include PMs at all. A detailed analysis of all three designs in comparison to the required set of specifications will be presented. The key opportunities and challenges will be highlighted. The impact of the high pole-count/frequency of the FSMs will also be evaluated. Experimental results for one of the designs with dysprosium-free PMs will also be presented.
Pre-Training-Based Grammatical Error Correction Model for the Written Language of Chinese Hearing Impaired Students

Grammatical error correction has been considered as an application closely related to daily life and an important shared task in many prestigious competitions and workshops. The neural machine translation with an encoder-decoder architecture containing language models has been the fundamental solution for the grammatical error correction. Whereas Grammatical error correction task on texts of hearing impaired people or its solution has not been seen yet, and common Grammatical error correction tasks are suffering several challenges, such as insufficient training data, insufficient accuracy due to the unsatisfactory capacity of extracting semantic and grammatical patterns. Under these circumstances, we proposed a novel encoder-decoder architecture based on multi-head self-attention along with multiple strategies, which excels at extracting deep representations from the corrupted sentences of hearing impaired students and further reconstructing the sentences into grammatical ones. Via the re-ranking strategy, our model can correct various kinds of errors including spelling and complex syntax errors. The ablation experiments prove that the semantic extracting of self-attention mechanism excluding the position encoding with the word order shuffle operation can significantly learn the hearing impaired students’ sentence patterns whose word order is quite different from the ones of hearing people and improve the correction scores. The pre-training can enhance the restoring efficiency of sentence structure in the decoding process. The comparison experiments with baseline models show that our model obtains superior performance either in the hearing impaired students’ grammatical error correction or in a common grammatical error correction shared task.
Numerical Computational Heuristic Through Morlet Wavelet Neural Network for Solving the Dynamics of Nonlinear SITR COVID-19

The present investigations are associated with designing Morlet wavelet neural network (MWNN) for solving a class of susceptible, infected, treatment and recovered (SITR) fractal systems of COVID-19 propagation and control. The structure of an error function is accessible using the SITR differential form and its initial conditions. The optimization is performed using the MWNN together with the global as well as local search heuristics of genetic algorithm (GA) and active-set algorithm (ASA), i.e., MWNN-GA-ASA. The detail of each class of the SITR nonlinear COVID-19 system is also discussed. The obtained outcomes of the SITR system are compared with the Runge-Kutta results to check the perfection of the designed method. The statistical analysis is performed using different measures for 30 independent runs as well as 15 variables to authenticate the consistency of the proposed method. The plots of the absolute error, convergence analysis, histogram, performance measures, and boxplots are also provided to find the exactness, dependability and stability of the MWNN-GA-ASA.
Contrast-Enhanced CT-Based Radiomics for the Differentiation of Nodular Goiter from Papillary Thyroid Carcinoma in Thyroid Nodules

Background Papillary thyroid carcinoma (PTC) and nodular goiter (NG) represent the most commonly malignant and benign diseases of thyroid nodules and are often confused in diagnosis. CT examination has a certain diagnostic value for the diagnosis of suspected malignant thyroid nodules. The application of machine learning to radiomics features provides a new diagnostic approach, which has been widely used in ultrasound examination of the thyroid, but there are few literatures on CT examination. Purpose To explore the efficacy of a diagnostic model aided by machine learning for preoperative differentiation of nodular goiter and papillary thyroid carcinoma thyroid nodules on the basis of 3D arterial-phase contrast-enhanced computed tomography (CECT) features. Materials and Methods We collected the data of 193 NG and 214 PTC thyroid nodules from 407 patients in CT examinations. Together with the pathologist findings and radiology diagnosis, we built a radiomics model using the 1218 features extracted from the arterial phase of CECT images. By comparing the diagnostic performance of the radiomics model with that of the clinical diagnosis, we assessed the performance of the radiomics model. Results The radiomics model was developed based on multivariable logistic regression with the optimal 12 radiomics features after feature dimension reduction. The radiomics model performed well on the classification accuracy of the PTC and NG thyroid nodules in the training group and validation group. Conclusion The radiomics model based on the 3D arterial phase of CECT features performed better than the group of experienced radiologists in differentiating NG and PTC thyroid nodules.
Deep Reinforcement Learning Based Autonomous Racing Car Control With Priori Knowledge

In the community of artificial intelligence, re-searchers have devoted much effort to the application of deep reinforcement learning algorithms for autonomous driving. Under deep reinforcement learning framework, it is important for the racing car agent to interact with its external environment to accumulate enough driving experience. However, the inter-action process is usually inefficient, risky and time-consuming. Furthermore, it is a common problem in relevant studies that brake policy is difficult to master. In this paper, we adopt some priori knowledge about vehicle dynamics to design the brake force and update it to the actor-critic network by soft-learning strategy. In addition, some effective strategies are developed to improve the training efficiency and control performance. The Open Racing Car Simulator(TORCS) is adopted to evaluate our algorithm. The simulation results show the effectiveness of our proposed algorithm with better learning efficiency, robustness and generalization performance.
New Defected Microstrip Structure Bandstop Filter

Defected microstrip structure (DMS) has similar properties with defected ground structure (DGS) but without any leakage through the ground plane. In this report, the stop-band performance of DMS is studied, and new single band, dual-band and tri-band bandstop fllters with DMS are proposed. The dual-band and tri-band bandstop fllters are fabricated, and the measured results are similar to the simulation. The new DMS bandstop fllters have advantages of good frequency selectivity, low loss and simple circuit topology, and simultaneously, has a miniature circuit size of no more than 30mm £ 1:2mm.
Personalized Risk-Based Screening Design for Comparative Two-Arm Group Sequential Clinical Trials

Personalized medicine has been emerging to take into account individual variability in genes and environment. In the era of personalized medicine, it is critical to incorporate the patients’ characteristics and improve the clinical benefit for patients. The patients’ characteristics are incorporated in adaptive randomization to identify patients who are expected to get more benefit from the treatment and optimize the treatment allocation. However, it is challenging to control potential selection bias from using observed efficacy data and the effect of prognostic covariates in adaptive randomization. This paper proposes a personalized risk-based screening design using Bayesian covariate-adjusted response-adaptive randomization that compares the experimental screening method to a standard screening method based on indicators of having a disease. Personalized risk-based allocation probability is built for adaptive randomization, and Bayesian adaptive decision rules are calibrated to preserve error rates. A simulation study shows that the proposed design controls error rates and yields a much smaller number of failures and a larger number of patients allocated to a better intervention compared to existing randomized controlled trial designs. Therefore, the proposed design performs well for randomized controlled clinical trials under personalized medicine.
Predictive Value of the Baseline and Early Changes in Blood Eosinophils for Short-Term Mortality in Patients with Acute Respiratory Distress Syndrome

Background Eosinophils play an essential role in the pathogenesis of acute respiratory distress syndrome (ARDS). We aimed to assess the association between the baseline blood eosinophils, eosinophil changes during the first week in the intensive care unit (ICU) and short-term patient outcomes. Methods All patients meeting the Berlin definition of ARDS from the Medical Information Mart for Intensive Care IV database were retrospectively analyzed. We used logistic regression, Kaplan–Meier survival and random forest analysis to determine the association between the baseline eosinophils and short-term mortality. Then the trends in eosinophils over time were compared between the survivors and non-survivors using a generalized additive mixed model (GAMM), which is a common approach used for analysis of repeated measurement data. Area under the receiver-operating characteristic curve (AUC) was used to evaluate the predictive value. Results A total of 1685 patients were included, and the 30-day mortality was 25.1%. Kaplan–Meier analysis showed that patients with high baseline eosinophils (>0.3%) had lower 30-day mortality (p < 0.001). Random forest model selected the baseline eosinophils as an important factor associated with 30-day mortality. Multivariable logistic regression analysis identified high baseline eosinophils as an independent factor for 30-day mortality (OR 0.743, 95% CI 0.568–0.970). The GAMM result showed that the levels of eosinophils were increased in both survival and non-survival groups, and the between-group differences increased over time, with an average of 0.154 daily after adjusting for confounders. The AUC of changes in eosinophils within the first week was significantly higher than that of baseline eosinophils. Conclusion There is a negative association between the baseline eosinophils and short-term mortality in ARDS patients, and the differences in eosinophils increased over time between the survivors and non-survivors. Higher increase in eosinophils is associated with decreased short-term mortality, and dynamic monitoring of eosinophils could better predict the survival of ARDS patients.