Miracle-dz/UniFAR-FTUs huggingface dataset:
Syllable weight and natural duration in textsetting popular music in English

Hayes & Kaun (1996) argue that the mapping of syllables onto a metrical grid in textsetting is sensitive to natural duration, not just categorical weight (heavy or light). Most of their evidence, however, derives the final lengthening effects, which admit of another possible analysis (Halle 2004). Drawing on a corpus of 2,371 popular songs in English, I confirm that even when one controls for final lengthening and other factors, the setting of syllables to a discrete grid is sensitive to natural duration. Moreover, onset effects reveal that the domain of weight for textsetting is not the syllable, rime, or vowel-to-vowel interval, but rather the interval between p-centers (perceptual centers). Finally, I argue that the textsetting grammar invokes both natural duration and categorical weight; weight mapping cannot be reduced to one or the other.
KnowLab at BioCreative VII Track 5 LitCovid: Ensemble of deep learning models from diverse sources for COVID-19 literature classification

—Classifying scientific literature into an abstract set of topics requires leveraging various sources from the publication and external knowledge. In the BioCreative VII LitCovid track on COVID-19 literature multi-label topic annotation, we applied state-of-the-art deep learning based document classification models (BERT, variations of HAN, CNN, LSTM) and each with a different combination of metadata (title, abstract, keywords, and journal), knowledge sources, pre-trained embedding, and data augmentation techniques. Several ensemble techniques were then used to combine individual model outputs for synergized predictions. We showed that a class-specific average ensembling of the pre-trained and task-specific models achieved the best micro-F 1 score in validation (90.31%) and testing (89.32%) sets in the experiments, beyond the medium (89.25%) and mean value (87.78%) of all 80 valid submissions. We summarize lessons learned from our work on this task.
Handwriting Thai Digit Recognition Using Convolution Neural Networks

Handwriting recognition research is mainly focused on deep learning techniques and has achieved a great performance in the last few years. Especially, handwritten Thai digit recognition has been an important research area including generic digital numerical information, such as Thai official government documents and receipts. However, it becomes also a challenging task for a long time. For resolving the unavailability of a large Thai digit dataset, this paper constructs our dataset and learns them with some variants of the CNN model; Decision tree, K-nearest neighbors, Alexnet, LaNet-5
Cerebellar and Prefrontal Structures Associated With Executive Functioning in Pediatric Patients With Congenital Heart Defects

Objective Children, adolescents, and young adults with congenital heart defects (CHD) often display executive dysfunction. We consider the prefrontal and cerebellar brain structures as mechanisms for executive dysfunction among those with CHD. Methods 55 participants with CHD (M age = 13.93) and 95 healthy controls (M age = 13.13) completed magnetic resonance imaging (MRI) of the brain, from which we extracted volumetric data on prefrontal and cerebellar regions. Participants also completed neuropsychological tests of executive functioning; their parents completed ratings of their executive functions. Results Compared to healthy controls, those with CHD had smaller cerebellums and lateral, medial, and orbital prefrontal regions, they performed more poorly on tests of working memory, inhibitory control, and mental flexibility, and their parents rated them as having poorer executive functions across several indices. Across both groups, there were significant correlations for cerebellar and/or prefrontal volumes with cognitive assessments of working memory, mental flexibility, and inhibitory control and with parent-completed ratings of task initiation, working memory, and planning/organization. Greater prefrontal volumes were associated with better working memory, among those with larger cerebellums (with group differences based on the measure and the prefrontal region). Greater prefrontal volumes were related to better emotional regulation only among participants with CHD with smaller cerebellar volumes, and with poorer inhibition and emotional regulation only among healthy controls with larger cerebellar volumes. Conclusion The cerebellum not only contributes to executive functioning among young individuals with CHD but may also modulate the relationships between prefrontal regions and executive functioning differently for pediatric patients with CHD vs. health controls.
SARS-CoV-2 reinfection trends in South Africa: analysis of routine surveillance data

Objective: To examine whether SARS-CoV-2 reinfection risk has changed through time in South Africa, in the context of the emergence of the Beta and Delta variants Design: Retrospective analysis of routine epidemiological surveillance data Setting: Line list data on SARS-CoV-2 with specimen receipt dates between 04 March 2020 and 30 June 2021, collected through South Africa's National Notifiable Medical Conditions Surveillance System Participants: 1,551,655 individuals with laboratory-confirmed SARS-CoV-2 who had a positive test result at least 90 days prior to 30 June 2021. Individuals having sequential positive tests at least 90 days apart were considered to have suspected reinfections. Main outcome measures: Incidence of suspected reinfections through time; comparison of reinfection rates to the expectation under a null model (approach 1); empirical estimates of the time-varying hazards of infection and reinfection throughout the epidemic (approach 2) Results: 16,029 suspected reinfections were identified. The number of reinfections observed through the end of June 2021 is consistent with the null model of no change in reinfection risk (approach 1). Although increases in the hazard of primary infection were observed following the introduction of both the Beta and Delta variants, no corresponding increase was observed in the reinfection hazard (approach 2). Contrary to expectation, the estimated hazard ratio for reinfection versus primary infection was lower during waves driven by the Beta and Delta variants than for the first wave (relative hazard ratio for wave 2 versus wave 1: 0.75 (95% CI: 0.59-0.97); for wave 3 versus wave 1: 0.70 (95% CI: 0.55-0.90)). Although this finding may be partially explained by changes in testing availability, it is also consistent with a scenario in which variants have increased transmissibility but little or no evasion of immunity. Conclusion: We conclude there is no population-wide epidemiological evidence of immune escape and recommend ongoing monitoring of these trends.
Purchasing high-cost medical equipment in hospitals: A systematic review

Objectives: To systematically review academic literature for empirical studies on any processes, procedures, methods or approaches to purchasing high-cost medical equipment within hospitals in high-income countries. Design: Systematic review Methods: On 13 August 2020, we searched the following from inception: Cost-Effectiveness Analysis Registry, EconLit and ProQuest Dissertations & Theses A&I via ProQuest, Embase, MEDLINE, and MEDLINE in Process via Ovid SP, Google and Google Scholar, Health Management and Policy Database via Ovid SP, IEEE Xplore Digital Library, International HTA Database, NHS EED via CRD Web, Science Citation Index-Expanded, Conference Proceedings Citation Index-Science, and Emerging Sources Citation Index via Web of Science, Scopus, and Zetoc conference search. Studies were included if they described the approach to purchasing (also known as procurement or acquisition) of high-cost medical devices and/or equipment conducting within hospitals in high-income countries between 2000-2020. Studies were screened, data extracted, and summarised. Results: Of 9437 records, 24 were included, based in 12 different countries and covering equipment types ranging from surgical robots to MRI scanners and orthopaedic implants. Study types included descriptions of processes taking place within or across hospitals (n=14), out of which three reported cost savings; empirical studies in which hospital records or participant data were analysed (n=8), and evaluations or pilots of proposed purchasing processes (n=2). Studies mainly highlight the importance of multidisciplinary involvement (especially clinical engineers and clinicians) in purchasing decision-making to balance technical, financial, safety and clinical aspects of device selection, and the potential of increasing evidence-based decisions using approaches ranging from hospital-based health technology assessments, ergonomics, to conducting user trials of the device in use before purchase. Conclusions: We highlight the lack of rigorous empirical work on this topic, calling for more intervention based and empirical work to advance the evidence base in this domain to advance knowledge, policy and practice.
The SUMO Challenge

We introduce the Scene Understanding and Modeling (SUMO) challenge with the goal of studying and evaluating the performance of RGBD-to-3D semantic scene modeling algorithms. Challenge participants are tasked to derive a complete, instance-based 3D representation of a scene given an RGB-D representation. The complete representation should highlight geometric instances, appearance, and semantics. This document describes the details of the challenge tasks, including scene elements of interest, data format, and metrics.
Application Analysis on PSO Algorithm in the Discrete Optimization Problems

Particle Swarm Optimization (PSO) is kind of algorithm that can be used to solve optimization problems. In practice, many optimization problems are discrete but PSO algorithm was initially designed to meet the requirements of continuous problems. A lot of researches had made efforts to handle this case and varieties of discrete PSO algorithms were proposed. However, these algorithms just focus on the specific problem, and the performance of it significantly degrades when extending the algorithm to other problems. For now, there is no reasonable unified principle or method for analyzing the application of PSO algorithm in discrete optimization problem, which limits the development of discrete PSO algorithm. To address the challenge, we first give an investigation of PSO algorithm from the perspective of spatial search, then, try to give a novel analysis of the key feature changes when PSO algorithm is applied to discrete optimization, and propose a classification method to summary existing discrete PSO algorithms.
Pedestrian detection algorithm based on improved muti-scale feature fusion

Aiming at the problem of false detection and missed detection of small targets and occluded targets in the process of pedestrian detection, a pedestrian detection algorithm based on improved multi-scale feature fusion is proposed. First, for the YOLOv4 multi-scale feature fusion module PANet, which does not consider the interaction relationship between scales, PANet is improved to reduce the semantic gap between scales, and the attention mechanism is introduced to learn the importance of different layers to strengthen feature fusion; then, dilated convolution is introduced. Dilated convolution reduces the problem of information loss during the downsampling process; finally, the K-means clustering algorithm is used to redesign the anchor box and modify the loss function to detect a single category. The experimental results show that the improved pedestrian detection algorithm in the INRIA and WiderPerson data sets under different congestion conditions, the AP reaches 96.83% and 59.67%, respectively. Compared with the pedestrian detection results of the YOLOv4 model, the algorithm improves by 2.41% and 1.03%, respectively. The problem of false detection and missed detection of small targets and occlusion has been significantly improved.
Empirical Analysis of Construction Enterprise Information Systems: Assessing System Integration, Critical Factors, and Benefits

Attaining higher levels of system integration is seen as the primary goal of construction enterprise information systems (CEIS). Increased system integration resulting from CEIS implementation is expected to lead to numerous benefits. These benefits encompass information technology infrastructure and strategic, operational, organizational, and managerial aspects of the firm. By adopting CEIS, firms seek tangible and intangible benefits, such as cost reduction, improved productivity, enhanced efficiency, and business growth. Through the use of statistical analysis, this study quantifies the critical success factors that impact CEIS integration and the ensuing benefits. Furthermore, it analyzes the effects of system integration on CEIS induced benefits. It also investigates the impact of CEIS strategy on CEIS induced benefits and identifies the relationship between CEIS strategy and system integration. Finally, it assesses the effects of CEIS induced benefits on user satisfaction and provides a CEIS impleme.
Inverse method for flow stress parameters identification of tube bulge hydroforming considering anisotropy

The objective of this paper is to determine the behaviour parameters of hydroformed tubular materials of annealed 25CrMo4 steel seamless tubes. An inverse identification procedure has been developed to determine the flow stress parameters from experimental free bulge and simple tensile tests. Two inverse identification strategies are proposed. In the first one, the tubular material behaviour is assumed isotropic, and then the parameters of the effective stress-strain relationship are derived from experimental bulge height versus the forming pressure. In the second one, the planar anisotropy of Hill'48 yield criterion is considered. Firstly, the simple tensile test is used to fit the isotropic strain hardening law. Secondly, the anisotropic parameter is identified by inverse technique from the experimental bulge test. The obtained results are compared with experimental measurements to validate the proposed strategies. It is proven that inverse identification methods could be a better alternative to analytical methods for flow stress parameters determination. Moreover, the anisotropy parameter has shown a large effect on the hydroformed tube responses, particularly on the wall thickness. Consequently, this material anisotropy should be considered within the flow stress identification of tube bulge tests.
Design and implementation of endogenous security container based on union file system

In order to solve the increasing attacks on container file system and the IO errors of containers in big data processing scenarios in cloud computing environment, a scheme based on the idea of heterogeneous redundancy in endogenous security and transformation of container union file system was proposed to improve the security and fault tolerance of containers. Based on the above scheme, experiments are carried out on Docker, the most popular container technology, and OverlayFS, the most representative union file system. The experimental results show that this scheme can improve the security and fault tolerance of containers on the premise of ensuring availability, and realize the endogenous security of containers.
Subsurface geological model of Malay basin using free air anomaly

The aim of gravity survey is to assist in the detection and delineation of subsurface geological features such as salt domes and faults. In this study, free air anomaly (FAA) data was adopted for mapping and modelling process to delineate subsurface geological features and basement depth in Malay Basin. FAA is the measured gravity anomaly after a free air correction is applied, and it is used for elevation correction. The data of FAA in this study is obtained from Earth Gravitational Model (EGM) 2008 released by the National Geospatial-Intelligence Agency (NGA)-EGM Development Team. Oasis Montaj software was used in the mapping and modelling process whereby the base map which constructed by the Oasis Montaj is used to form the FAA map of Malay Basin. Typically, the positive anomaly is associated with the high-density intrusion at the base of the crust, while in contrast (negative anomaly), it is related to the sedimentary basin in the upper crust. On top of that, the regional-residual anomaly, total horizontal derivative (THD) and 3D Euler Deconvolution enhanced maps were produced and interpreted to acquire comprehensive insight of subsurface geological features. To conclude, this study showed 5% deviation as compared to previous reported works and the deepest basement depth encountered is 14.5 km.
A Method of Single-terminal MMC Single-pole Grounding Protection for DC Distribution Network Based on Multi-class Similarity Analysis

Based on the analysis model of single-pole to ground fault occurred in single-ended MMC DC distribution network, the characteristics of single-pole to ground fault are studied. The similarity of positive and negative current between the fault feeder and the non-fault feeder, the similarity of fault current between the fault feeder and the non-fault feeder, and the similarity of positive and negative current sum between the two feeders are analyzed. Based on the differences among the three types of similarity, a new method of single-pole grounding protection for DC distribution network with MMC based on multi-type similarity analysis is proposed. The simulation results show that this method can accurately determine the fault feeder, and has the characteristics of strong anti-interference ability, simple calculation and easy implementation. It can effectively solve the problem of single-pole grounding protection of DC distribution network and effectively improve the safety and reliability of power supply of DC distribution network.
Application of Three-Dimensional Animation in Mechanical Control Mechanism

With the development of the times and the advancement of science and technology, the development of 3D animation has gradually been applied in various fields. In the field of mechanical control, three-dimensional animation has gradually become a very important application. In the actual application process, the use of three-dimensional animation effects cannot be applied in the field of mechanical control only by imagination. It must be applied in practice from the perspective of mechanical control. Analyze from its own stability and external factors to realize its application value. The purpose of this paper is to study the application of 3D animation in mechanical control mechanism. This article will take mechanical control as an entry point to discuss the application of three-dimensional animation in its mechanism. The research is carried out from two aspects. The first is the application of the robot arm. Based on the kinematic path planning of the shortest time priority strategy, the shorter the motion time of the robot arm, the faster the speed, and the vibration is more difficult to avoid. This is the vibration of the flexible arm. Improve its work efficiency, lay the foundation for more complex path planning problems; create conditions for lighter and faster robotic arm applications. The second is the application of the mechanical foot. The proposed ankle joint control strategy based on the lateral and forward cycle matching can be combined with the forward plane walking control strategy under the stable initial gait of the robot to realize the humanlike virtual prototype of the biped robot. Dynamic walking for analysis. Experimental investigations have shown that in the process of normal walking, there is a dynamic swing process and a static support process. The swing process refers to the lifting of the foot to move forward or backward, and the supporting process refers to the immobilization of one foot to the other. One fulcrum on each foot allows it to move forwards and backwards. Among them, the dynamic swing process accounts for 40% of the whole process, and the static support process accounts for 60%. In general, based on these data, the three-dimensional robotic arm and three-dimensional robotic foot can be better studied.
Application of Random Region Augmentation Algorithm in Deep Learning

In the field of computer vision, the collection and sorting of image data is the core driving force. However, the current data collection work cannot perfectly collect the image data of each actual landing scene. The purpose of the data augmentation algorithm is to increase the diversity of the data set and improve the robustness of the model. Traditional data augmentation methods include geometric augmentation and color augmentation, mainly including flipping, rotating, cropping, translation, stretching, zooming, adding noise, blurring, Dropout, Cutout, color jittering. Traditional data augmentation methods have certain limitations, and the effect is not obvious. Based on the idea of Cutout algorithm, this paper proposes the RRA augmentation algorithm, which divides four quadrant regions in the image, and randomly selects the ROI region in each region, and is different from the Cutout algorithm directly discarding the region, but randomizing the region Enhance the color, and finally do geometric augmentation processing on the overall image. Compared with the original single data augmentation operation, the algorithm improves precision by 7%, and recall improves by 7%.
Fuel Cell Systems Explained

Preface. Foreword to the first edition. Acknowledgements. Abbreviations. Symbols. Introduction. Efficiency and Open Circuit Voltage. Operational Fuel Cell Voltages. Proton Exchange Membrane Fuel Cells. Alkaline Electrolyte Fuel Cells. Direct Methanol Fuel Cells. Medium and High Temperature Fuel Cells. Fuelling Fuel Cells. Compressors, Turbines, Ejectors, Fans, Blowers, and Pumps. Delivering Fuel Cell Power. Fuel Cell Systems Analysed. Appendix 1: Change in Molar Gibbs Free Energy Calculations. Appendix 2: Useful Fuel Cell Equations. Index.
Research on Wafer Surface Defect Pattern Detection Method Based on Incremental Learning

Wafer manufacturing is an important step in quality control and analysis in the semiconductor industry. The defect pattern classification algorithm of wafer maps has received extensive attention from academia and industry. At present, most methods for detecting wafer surface defect patterns focus on static data model classification and analysis. However, in the production process, static data models cannot satisfy the dynamic analysis of wafer defect patterns in the form of streaming data. In this regard, this paper proposes a wafer surface defect pattern detection method based on incremental learning. Our experiment uses Resnet as the backbone network, and the data set uses the WM811K wafer data set. Experiments have proved that our method can achieve better classification accuracy in the field of wafer defect detection, which provides the possibility for continuous learning of wafer defects in the future.
Numerical Simulation on Wind Environment of the Square with High-Rises

A SST turbulence model is applied to solve the Reynolds Averaged Navier-Stokes Equations in numerically simulating the 3D steady wind flow field around the square with high-rise buildings in atmospheric boundary layer. The aerodynamic prediction was supported by velocity data collected in field measurement of 240 points from an campus square. Then a detailed parametric study is performed on velocity distribution. The parameters include wind attack angle, green belt length and the distance between green belts. The analysis results show that numerical simulation is a feasible way to study the wind environment around square and the distributing of velocity. The green belts accelerates the air flow parallel to it. The effect of acceleration depends mainly on the distance between the green belts rather than the length of the green belts. When the wind is southeast or northwest, the maximums velocity in pedestrian height are between buildings. The curved green belts can improve the wind environment rather than linear green belts. The research provides a foundation for the wind resistance