Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Statistical Parametric Mapping as a statistical method for more detailed insights in swimming: a systematic review

Swimming is a time-based sport and hence strongly dependent from velocity. Most studies about swimming refer to velocity as discrete variable, i.e., 0-D (no time dimension). However, it was argued that using swimming velocity as a continuous variable (1-D, with time dimension) with Statistical Parametric Mapping (SPM) can bring deeper and detailed insights about swimming performance. Therefore, the aim of this study was to perform a systematic review about the current body of knowledge of using Statistical Parametric Mapping in a swimming context. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used to identify relevant articles. After screening, nine articles related to Statistical Parametric Mapping (SPM) analysis in swimming were retained for synthesis. Results showed that four articles (44.4%) aimed to understand the kinematics, isokinetic joint torque or electromyographic (EMG) pattern of the swimmer’s shoulder either on land or during front crawl trials. Two articles (22.2%) focused on understanding the swimming velocity while performing the breaststroke stroke. One article (11.1%) analyzed the swimmers’ propulsion at front-crawl stroke, another one (11.1%) compared swimming velocity during a complete stroke cycle in young swimmers of both sexes as a discrete variable and as a continuous variable. Also, one article (11.1%) analyzed the underwater undulatory velocity. In an EMG context, some findings verified in SPM are not possible to be discovered with traditional 0-D statistical methods. Studies about swimming velocity (breaststroke, freestyle, and underwater undulatory velocity) and propulsion (front-crawl) also highlighted the SPM advantages in comparison to traditional statistical methods. By using SPM, researchers were able to verify specifically where within the stroke cycle significant differences were found. Therefore, coaches can get more detailed information to design specific training drills to overcome hypothetical handicaps.
Resilient, but for how long? The relationships between temperament, burnout, and mental health in healthcare workers during the Covid-19 pandemic

Introduction Dispositional traits of wellbeing and stress-reaction are strong predictors of mood symptoms following stressful life events, and the COVID-19 pandemic introduced many life stressors, especially for healthcare workers. Methods We longitudinally investigated the relationships among positive and negative temperament group status (created according to wellbeing and stress-reaction personality measures), burnout (exhaustion, interpersonal disengagement), COVID concern (e.g., health, money worries), and moral injury (personal acts, others’ acts) as predictors of generalized anxiety, depression, and post-traumatic stress symptoms in 435 healthcare workers. Participants were employees in healthcare settings in North Central Florida who completed online surveys monthly for 8 months starting in October/November 2020. Multidimensional Personality Questionnaire subscale scores for stress-reaction and wellbeing were subjected to K-means cluster analyses that identified two groups of individuals, those with high stress-reaction and low wellbeing (negative temperament) and those with the opposite pattern defined as positive temperament (low stress-reaction and high wellbeing). Repeated measures ANOVAs assessed all time points and ANCOVAs assessed the biggest change at timepoint 2 while controlling for baseline symptoms. Results and Discussion The negative temperament group reported greater mood symptoms, burnout, and COVID concern, than positive temperament participants overall, and negative participants’ scores decreased over time while positive participants’ scores increased over time. Burnout appeared to most strongly mediate this group-by-time interaction, with the burnout exhaustion scale driving anxiety and depression symptoms. PTSD symptoms were also related to COVID-19 health worry and negative temperament. Overall, results suggest that individuals with higher stress-reactions and more negative outlooks on life were at risk for anxiety, depression, and PTSD early in the COVID-19 pandemic, whereas individuals with positive temperament traits became more exhausted and thus more symptomatic over time. Targeting interventions to reduce mood symptoms in negative temperament individuals and prevent burnout/exhaustion in positive temperament individuals early in an extended crisis may be an efficient and effective approach to reduce the mental health burden on essential workers.
Genome resources and whole genome resequencing of Phytophthora rubi isolates from red raspberry

Phytophthora rubi is a primary causal agent of Phytophthora root rot and wilting of raspberry (Rubus idaeus L.) worldwide. The disease is a major concern for raspberry growers in Canada and USA. To date, no information is available on genomic diversity of P. rubi population from raspberry in Canada. Using a PCR-free library prep with dual-indexing for an Illumina HiSEQX running a 2x150 bp configuration, we generated whole genome sequence data of P. rubi isolates (n = 25) recovered during 2018 to 2020 from nine fields, four locations and four cultivars of raspberry growing areas of British Columbia, Canada. The assembled genome of 24 isolates of P. rubi averaged 8,541 scaffolds, 309× coverage, and 65,960,000 bp. We exploited single nucleotide polymorphisms (SNPs) obtained from whole genome sequence data to analyze the genome structure and genetic diversity of the P. rubi isolates. Low heterozygosity among the 72% of pathogen isolates and standardized index of association revealed that those isolates were clonal. Principal component analysis, discriminant analysis of principal component, and phylogenetic tree revealed that P. rubi isolates clustered with the raspberry specific cultivars. This study provides novel resources and insight into genome structure, genetic diversity, and reproductive biology of P rubi isolated from red raspberry. The availability of the P. rubi genomes also provides valuable resources for future comparative genomic and evolutionary studies for oomycetes pathogens.
Correlation analysis between subtest scores of CERAD-K and a newly developed tablet computer-based digital cognitive test (Inbrain CST)

Introduction The prevalence of Alzheimer’s disease (AD) and other dementias is increasing; therefore, identifying individuals at risk for dementia is crucial. Traditional neuropsychological assessments are expensive and time-consuming; however, computerized cognitive testing is becoming popular in clinical and research settings, particularly during the COVID-19 pandemic. This study aimed to investigate the correlation between the computerized cognitive test, Inbrain cognitive screening test (CST), and the traditional neuropsychological battery, the consortium to establish a registry for Alzheimer’s disease assessment packet (CERAD-K). Methods We enrolled 166 participants from five districts in Republic of Korea, including cognitively unimpaired individuals and those with mild cognitive impairment (MCI) diagnosed by experienced neurologists. We used the Inbrain CST and CERAD-K to evaluate the cognitive function of the participants, and the scores of each subtest of the Inbrain CST and CERAD-K were compared. Results A significant correlation was found between the Inbrain CST and CERAD-K subtests. Furthermore, multivariate analysis revealed a significant correlation between the Inbrain CST and the CERAD-K test pairs after adjusting for age, educational level, and sex. Discussion In conclusion, this study demonstrates that the Inbrain CST is a reliable tool for detecting cognitive impairment in cognitively unimpaired individuals and patients with MCI, because it has a high correlation and agreement with CERAD-K. Therefore, the Inbrain CST can be a useful, time-efficient, and cost-effective computer-based cognitive test for individuals at risk for cognitive impairment.
Survey of algorithms for partially observable Markov decision processes

A partially observable Markov decision process(POMDP) is an extension of a Markov decision process(MDP),which can partially keep the state of the system under observation.The applied potential for POMDP remains largely unrealized due to lack of tractable solution methodologies.The POMDP algorithms can divide into the approximate algorithms and the exact algorithms,and the exact algorithms are the base of the approximate algorithms.The exact and approximate algorithms for solving discrete-time,finite POMDP over finite horizon are summarized.In the end the reasons why POMDP problems are intractable and the future research directions are proposed.
Interacting with Large Language Models: A Case Study on AI-Aided Brainstorming for Guesstimation Problems

Designing cooperative AI-systems that do not automate tasks but rather aid human cognition is challenging and requires human-centered design approaches. Here, we introduce AI-aided brainstorming for solving guesstimation problems, i.e. estimating quantities from incomplete information, as a testbed for human-AI interaction with large language models (LLMs). In a think-aloud study, we found that humans decompose guesstimation questions into sub-questions and often replace them with semantically related ones. If they fail to brainstorm related questions, they often get stuck and do not ﬁnd a solution. Therefore, to support this brainstorming process, we prompted a large language model (GPT-3) with successful replacements from our think-aloud data. In follow-up studies, we tested whether the availability of this tool improves participants’ answers. While the tool successfully produced human-like suggestions, participants were reluctant to use it. From our ﬁndings, we conclude that for human-AI interaction with LLMs to be successful AI-systems must complement rather than mimic a user’s associations.
Design and Optimization of a Stacked Three-Layer X-ray Detector for Multi-Spectral Medical Imaging Applications

Dual-Energy (DE) imaging is a powerful technique in x-ray imaging that increases the ability to detect specific materials in a cluttered background, and is used to more accurately visualize anatomical features and diagnose abnormalities. Single-shot imaging has arisen as one of the dominant techniques used to obtain DE images, but has the intrinsic drawback of a loss of x-ray photons in a metal mid-filter. I propose a new design of a stacked three-layer x-ray detector for DE single-shot imaging that addresses this fundamental drawback. Each layer consists of its own scintillator of individual thickness and an underlying thin-film-transistor-based flat-panel. Three images are obtained simultaneously in the detector during the same x-ray exposure, and thus the imaging system is immune to motion artifacts. The detector operation is two-fold: a conventional radiography image can be obtained by combining all three layers’ images, while a DE subtraction image can be captured from the front and back layers, where the middle layer acts as a filter that helps achieve spectral separation. Two sample imaging tasks that can particularly benefit from this new detector design are selected. These are iodinated vessel visualization in coronary angiography and calcified nodule detection in pulmonary radiology. I proceed to optimize the detector parameters and imaging system configuration to best fit these two tasks by obtaining the best possible contrast to noise ratio per root entrance exposure. To achieve this, I develop an analytical model by adapting well-established theoretical foundations to fit this new detector design. These results are compared to a conventional DE temporal subtraction detector and a typical single-shot DE subtraction detector with a metal mid-filter, both of which underwent a similar analytical optimization process. The findings are then validated using advanced Monte Carlo simulations for all optimized detector configurations. The analytical and simulation results indicate that the proposed detector performs comparably as a DE imager to established single-shot detectors. It is therefore clear that this new design is an improvement on the state-of-the-art since it provides an additional feature at no performance cost. Given the performance expected from these initial results and the recent decrease in price for digital x-ray detectors, the simplicity of the three-layer stacked imager approach appears promising to usher in a new generation of multi-spectral digital x-ray imaging.
Designing optimal double-sampling plan based on process capability index

ABSTRACT Process capability analysis is applied to monitor the process quality. Process capability can be quantified by process capability index. These indices have wide application in quality control methods and acceptance sampling plans. In this paper, we introduce a double-sampling plan based on process capability index. In this type of scheme, under a decision rule and with the specified rejection and acceptance numbers, the second sample is selected and the decision of rejection or acceptance is made based on the information obtained from two samples. The purpose of this scheme is to reduce the average sample number in order to reduce the time and cost of sampling. A comparison study has been conducted in order to evaluate the performance of proposed method in comparison with classical single sampling plans.
The International Pulsar Timing Array: second data release

In this paper, we describe the International Pulsar Timing Array second data release, which includes recent pulsar timing data obtained by three regional consortia: the European Pulsar Timing Array, the North American Nanohertz Observatory for Gravitational Waves, and the Parkes Pulsar Timing Array. We analyse and where possible combine high-precision timing data for 65 millisecond pulsars which are regularly observed by these groups. A basic noise analysis, including the processes which are both correlated and uncorrelated in time, provides noise models and timing ephemerides for the pulsars. We find that the timing precisions of pulsars are generally improved compared to the previous data release, mainly due to the addition of new data in the combination. The main purpose of this work is to create the most up-to-date IPTA data release. These data are publicly available for searches for low-frequency gravitational waves and other pulsar science.
Tempers Rising: The Effect of Heat on Spite

The relationship between heat and harmful outcomes is well documented, with research connecting various adverse economic outcomes to the climate. In the presence of increasing global warming and climate change, understanding why the climate leads to negative economic outcomes is essential for forming peaceful institutions of the future. We study how behavioral economic outcomes change in the presence of heat through a lab experiment involving 1,110 observations conducted in five different countries. This paper specifically focuses on the social preference outcome of spite. We find that increased time exposure to the treatment effect of heat is required to elicit an individual’s spiteful behavior. Our results also suggest heterogeneity in this effect with a particular difference along gender and income consistency. We deploy novel methods to analyze heterogeneity using a machine-learning causal forest and Sorted Group Average Treatment Effect (GATES). Acknowledgements: Thank you to Professor Wydick, Professor Anttila-Hughes, Professor Hobbs Bruce, Professor Cassar! Thanks Nikita for making this thesis template, you can find the link here.
Explanation through behavior: a human-inspired framework for explainable robotics

—Can robots’ observable behavior give human observers insights into their hidden internal processes? This paper examines this idea with the aim of developing a framework for explainability in robotics. This framework constitutes a promising avenue for the development of intuitive human-robot interactions in which people would use the same brain mechanisms on which they rely to understand other humans during social interactions.
Investigation of Photocathodes Based on Phosphorus Doped Nanocrystalline Diamond Films

The report presents the results of a study of photo-cathodes based on phosphorus-doped nanocrystalline diamond films grown on conductive silicon substrates using the CVD method. The quantum efficiency of such cathodes was studied under the action of laser radiation with a wavelength of 266 nm and pulse duration of 15 ns. It has been found that the quantum efficiency increases with a decrease in the thickness of the diamond film, reaching a maximum at a thickness of 100 nm. In this case, the quantum efficiency was found to be 2.4 $\cdot 10^{-5}$ electrons per photon.
Adaptive Active Fusion of Camera and Single-Point LiDAR for Depth Estimation

Depth sensing is an important problem in many applications, such as autonomous driving, robotics, and automation. This article presents an adaptive active fusion method for scene depth estimation by using a camera and a single-point light detection and ranging (LiDAR) sensor. An active scanning mechanism is proposed to guide laser scanning based on critical visual and saliency features, and the convolutional spatial propagation network (CSPN) is designed to generate and refine full depth map from the sparse depth scans. The active scanning mechanism generates a depth mask by using log-spectrum saliency detection, Canny edge detection, and uniform sampling, which indicate critical regions that require a high resolution of laser scanning. To reconstruct a full depth map, the designed CSPN network extracts affinity matrices from the sparse depth scans, while reserving global spatial information in the images. The performance of proposed method was evaluated and compared with the state-of-the-art methods on the NYU depth dataset v2 (NYUv2) and the experiment demonstrated its outperformance in reconstruction accuracy and robustness to measurement noise. The proposed method was also evaluated in real-world scenarios.
Subwavelength sound focusing using a time-reversal acoustic sink

Time-reversal mirrors (TRM) have been developed since 1986 in order to focus ultrasonic transient waves in complex media. In the last few years, the properties of TR of acoustic fields have been studied in many different areas. Nevertheless, few applications of TR have been developed in audible range acoustics. The aim of this paper is to demonstrate the concept of time-reversal acoustic sink (TRAS) in audible frequency regime, in order to overcome the diffraction limit imposed by the TRM focusing. The major difference between the TRAS and TRM experiments in ultrasonics and audible range is the ratio between the wavelength and the size of the transducers and objects on which the focusing is achieved. The audible range experiment are lead in Fresnel field (near field), whereas the ultrasonic experiments are lead in Fraunhoffer field (far field). We present the first experimental results with a TRAS in this frequency range. The focusing behaviour in a reverberation room using different transient sounds and frequency domains are investigated and discussed, showing that one can take advantage of reverberation in order to achieve subwavelength sound focusing using a single-element TRM. We report that a focal spot of a seventh of a wavelength has been recorded using the TRAS techniques in audible range, compared to the half wavelength obtained with normal TRM processing. A promising application of a numerical TRAS-method in acoustic imaging and localization of acoustic and vibrational sources is presented.
Mitigation of Overvoltages in Low Voltage Network by Distributed Battery Energy Storage System

The growing demand for implementing small renewable energy sources, photovoltaic or wind sources, in the distribution system is characteristic of the last decade. On the one hand, the distribution system operator (DSO) needs to meet this growing demand while being responsible for the reliable and safe operation of the network. DSO is therefore forced to analyze the risks associated with growing distributed production, such as overvoltages in the network, and determine their maximum allowed installed capacity, hosting capacity. This paper uses the stochastic methodology and probability power flow calculations to analyze the photovoltaic hosting capacity (PVHC) of an existing rural low-voltage (LV) system in the Slovak Republic. The outcomes show that overvoltages are the primary limiting factor in an analyzed network, and the PVHC depends on the permissible probability of their violation. The article further analyses the possible ways of increasing PVHC by gradual penetration of small battery energy storage systems. The results show that BESS should be considered only as a supplementary device, which will always positively affect the size of the PVHC if it is used to maximize the utilization of photovoltaic generation at the point of consumption.
Preliminary Aerodynamic Simulation of a Flying Car Concept

The pursuit of flight has never ceased, and flying cars are gradually becoming a reality. However, only a few designs can accommodate both ground travel and aerial flight. As a result, aerodynamic research on flying cars is relatively scarce. This paper presents a novel concept of a flying car. The concept transforms between flight mode and vehicle mode through the rotation and folding of rotors and wings. Using aerodynamic simulation methods, the aerodynamic characteristics of the flying car in four states, including ground travel, single body, vertical takeoff and landing, and forward flight, under different incoming flow speeds are studied. The simulation results provide guidance for the design of the flying car concept.
On PV Hosting Capacity of Distribution Networks

This paper provides a summary of the research regarding photovoltaic hosting capacity of the distribution networks in Finnish circumstances. Characteristic to these circumstances is high and diverse seasonal variations of electricity demand and photovoltaic generation. The paper presents key findings of the hosting capacity and discusses different methods and approaches when analyzing the hosting capacity questions both from the distribution systems operator and the prosumer point of view.
NIR-VGGNet19: A Novel Deep Convolutional Neural Network for Pinus NIR Spectra Classification

Wood is an indispensable non-renewable resource and plays an important role in our life. So, it is a necessity to apply accurate classification techniques. In this paper, a novel model of deep one-dimensional convolutional neural network NIR-VGGNet19 is proposed to classify seven pius wood by combining near-infrared spectroscopy. NIR-VGGNet19 uses a deep convolutional neural network to automatically extract features, eliminate noise, and solve the problem of spectral overlapping peaks; initially eliminates random noise by adding $1\times 7$ convolutional kernels; then uses an attention mechanism to extract more accurate features. According to the results, when NIR-VGGNet19 was tested against other methods, NIR-VGGNet19 had the highest classification accuracy, achieving 98.41% on the test set. In contrast, the accuracy of LeNet, AlexNet, VGGNet-19, ResNet-34, back propagation neural network, and support vector machine were 70.95%, 92.54%, 93.02%, 96.67%, 76.19%, and 71.26%, respectively. Thus, it suggests that the NIR-VGGNet19 can improve the accuracy of classifying wood of the homogeneous genus.