Miracle-dz/UniFAR-FTUs huggingface dataset:
Biomechanics of Volleyball Players' Run-Up and Take-Off Link under Deep Learning

In volleyball, the correct approach and start (including the number of steps and stride speed) are a prerequisite for all technical movements to attack. It can not only improve the horizontal speed of the athlete, but also properly convert the total speed into vertical speed, so that the hitting point is improved and the ball speed is accelerated. To explore the biomechanical characteristics of lower limb movements in the run-up and take-off stage of volleyball spiking, this paper takes four male volleyball players from the Physical Education College of X University as the research objects to analyze the kinematics and dynamics of the run-up process and the take-off process. This paper uses the precise recognition method under the background of deep learning to accurately capture the movements of the research object. This paper discusses the effects of time, speed, distance, knee, and hip parameters (angle, joint muscle torque, and power) on the effect of spiking techniques. It is expected to provide reference for the diagnosis, guidance, and muscle strength training of this special technical movement. The research results show that the horizontal speed of No. 2 athlete is 3.62 m/s and the vertical speed is 2.71 m/s when he takes off. The landing time is 0.375 s and the lift-off time is 0.16 s. The torque and power of the knee joint changed greatly during the take-off process, and the change of the hip joint was small.
An IoT-based System for Landslides Warnings Using a Real Time Slope Monitoring Model

ABSTRACTThis work presents an IoT-based system for remote monitoringof landslides. The solution uses a Wireless Underground SensorNetwork (WUSN), a cloud computing platform and an App to implementa real time monitoring scheme. The sensor network runson Arduino components connected through Wi-Fi modules, andis responsible to collect soil moisture rates. Then, the collecteddata is sent to a cloud computing environment in order to ensurea robust and secure storage. Moreover, the cloud platform runsa model for triggering an alarm when a potential landslide is detected.Finally, an App aimed to data visualization in real time andlandslides warning is also presented. The proposed work enables areal time end to end solution, once it starts with the data collectedby the humidity sensors and finishes with the data visualization bythe citizens, which is very suitable for Smart Cities and Internetof Things (IoT) environments. The system was evaluated throughreal tests and simulated scenarios. The results show the sensornetwork accurately measured the soil moisture and the landslidesmonitoring model was able to send warnings consistently.
Cognitive flexibility in beginning decoding and encoding

Abstract The development of beginning decoding and encoding skills is influenced by linguistic skills as well as executive functions (EFs). These higher-level cognitive processes include working memory, inhibition, and cognitive flexibility, and individual differences in these EFs have been shown to contribute to early academic learning. The present study extends the prior research on EFs by examining the relationship between one type of EF, cognitive flexibility, and decoding and encoding development in English-speaking kindergarteners with limited alphabet knowledge. Pooling data from two cohorts of kindergarten children who took part in a brief phonics intervention (N = 125 from 23 classrooms at one U.S. public school), we estimated the unique effect of cognitive flexibility on decoding and spelling gains, controlling for potential confounds. Results showed that initial cognitive flexibility significantly positively predicted word-level decoding and spelling gains (uniquely explaining an average of approximately 5% of the variance in gains for these measures), but the effect on decoding gains was stronger for children with lower incoming alphabet skills (5-7 letters or fewer). These findings are consistent with the earlier research on EFs and reading acquisition with older children, and also indicate that greater alphabetic skills may compensate for lower initial EF in decoding development for children learning alphabetic languages.
A Novel Approach for the Selection of Power-Generation Technology Using a Linguistic Neutrosophic CODAS Method: A Case Study in Libya

Rapid increases in energy demand and international drive to reduce carbon emissions from fossil fuels have led many oil-rich countries to diversify their energy portfolio and resources. Libya is one of these countries, and it has recently become interested in utilizing its renewable-energy resources in order to reduce financial and energy dependency on oil reserves. This paper introduces an original multicriteria decision-making Pairwise-CODAS model in which the modification of the CODAS method was made using Linguistic Neutrosophic Numbers (LNN). The paper also suggests a new LNN Pairwise (LNN PW) model for determining the weight coefficients of the criteria developed by the authors. By integrating these models with linguistic neutrosophic numbers, it was shown that it is possible to a significant extent to eliminate subjective qualitative assessments and assumptions by decision makers in complex decision-making conditions. The LNN PW-CODAS model was tested and validated in a case study of the selection of optimal Power-Generation Technology (PGT) in Libya. Testing of the model showed that the proposed model based on linguistic neutrosophic numbers provides objective expert evaluation by eliminating subjective assessments when determining the numerical values of criteria. A sensitivity analysis of the LNN PW-CODAS model, carried out through 68 scenarios of changes in the weight coefficients, showed a high degree of stability of the solutions obtained in the ranking of the alternatives. The results were validated by comparison with LNN extensions of four multicriteria decision-making models.
High Frequency Modeling and Experimental Analysis for Implementation of Impedance-based Structural Health Monitoring

A promising structural health monitoring (SHM) method for implementation on real world structures is impedance-based health monitoring. An in-service system is envisioned to include on board processing and perhaps wireless transfer of data. Ideally, a system could be produced as a slap-on or automatically installed addition to a structure. The research presented in this dissertation addresses issues that will help make such a system a reality. Although impedance-based SHM does not typically use an analytical model for basic damage identification, a model is necessary for more advanced features of SHM, such as damage prognosis, and to evaluate system parameters when installing on various structures. A model was developed based on circuit analysis of the previously proposed low-cost circuit for impedance-based SHM in combination with spectral elements. When a three-layer spectral element representing a piezoceramic bonded to a base beam is used, the model can predict the large peaks in the impedance response due to resonances of the bonded active sensor. Parallel and series connections of distributed sensor systems are investigated both experimentally and with the developed model. Additionally, the distribution of baseline damage metrics is determined to assess how the large quantities of data produced by a monitoring system can be handled statistically. A modification of the RMSD damage metric has also been proposed that is essentially the squared sum of the Z-statistic for each frequency point. Preferred excitation frequencies for macro-fiber composite (MFC) active sensors are statistically determined for a long composite boom under development for use in rigidizable inflatable space structures. Acknowledgements First, I would like to express my gratitude to my advisor, Dr. Daniel Inman, for his help and support. Sincere thanks are also due to Drs. Don Leo, M.P. Singh, Harry Robertshaw, and Gyuhae Park for being on my doctoral committee. Dr. Park deserves special recognition for not only his advice on the impedance-based structural health monitoring, but also for convincing me to pursue my Ph.D. I would like to thank all of my colleagues who have made the last several years at CIMSS enjoyable. I would like to acknowledge the financial support of the Goodson Professorship, NASA Graduate Student Researcher Program, National Science Foundation and Extreme Diagnostics, Inc. Finally, I am especially thankful for the steadfast support of my parents and fiancée, Abby Turpyn. I would have never made it without their constant encouragement.
Supporting Parker Solar Probe mission with Goode Solar Telescope at Big Bear Solar Observatory

New Jersey Institute of Technology (NJIT) has built, and now operates the 1.6-meter Goode Solar Telescope (GST) at the Big Bear Solar Observatory (BBSO), which was the highest-resolution solar telescope built in the U.S. in a generation. GST observations have been put in a campaign mode to support the NASA Parker Solar Probe (PSP) mission during its perihelia in 2019, 2020, 2021 and 2022, by providing unique high-resolution observations and critically complementary information on the photosphere and chromosphere at the source regions of solar wind acceleration and heating of solar corona. This paper presents the acquisition, processing, and archiving of high-resolution imaging spectroscopy and polarimetry data generated with GST instruments, as well as the preliminary scientific results.
How Can We Fight Partisan Biases in the COVID-19 Pandemic? AI Source Labels on Fact-checking Messages Reduce Motivated Reasoning

ABSTRACT Upon a surge of misinformation surrounding COVID-19, fact-checking has received much attention as a tool to fight the rampant misinformation. However, such correction efforts have faced challenges from partisans’ biased information processing. For example, partisans trust or distrust a fact-checking message based on whether the message benefits or harms their supporting party. To minimize such politically biased processing of corrective health information, this experimental study examined how different source labels of fact-checkers (human experts vs. AI vs. user consensus) affect partisans’ perceived credibility of fact-checking messages about COVID-19. Our findings showed that AI and user consensus (vs. human experts) source labels on fact-checking messages significantly reduced partisan-based motivated reasoning in evaluating fact-checking message credibility.
DEVELOPMENT OF PREDICTION MODELS USING LINEAR ADAPTIVE FILTERS FOR SOME PROPERTIES OF A NEWLY DEVELOPED THERMO- REGULATED BRICK

The development of prediction models using linear adaptive filters for some properties of a newly developed thermo-regulated brick has been carried out. The research started with the development of the bricks using anthill clay a material known for its thermoregulation property. The bricks were subjected to various tests to ensure that they conform to specifications and standard for thermo-regulated bricks and also to ensure that the thermal conductivity does not differ so much from that of bricks from virgin anthill clay. The tests data upon which the developed prediction models in this work were based were those of water absorption test, compression test and by extension abrasive index. Two models were developed for the prediction of water absorption and abrasive index properties of the developed thermo-regulated bricks. Plots of the predicted values of water absorption and the actual values of water absorption on the same graph showed a tight fitting curve indicating a high degree of accuracy for the prediction model. The plots of the predicted values of abrasive index and the actual values of abrasive index on the same graph showed a tight fit at some points and variations at other points. The performance evaluation of the prediction models showed that the model for the prediction of water absorption had a mean square error of 8.3521E- 4 and the model for the prediction of abrasive index had a mean square error of 0.0225. The performance evaluation has indicated that the two models have not done badly, though the accuracy of the water absorption model is higher than that of the abrasive index.
e-mission: An Open-Source, Smartphone Platform for Collecting Human Travel Data

GPS-equipped smartphones provide new methods to collect data about travel behavior, including travel survey apps that incorporate automated location sensing. Previous approaches to this have involved proprietary or one-off tools that are inconsistent and difficult to evaluate. In contrast, e-mission is an open-source, extensible software platform that consists of (a) an app for survey participants to install on their Android or iOS smartphones and (b) cloud-hosted software for managing the collected data. e-mission collects continuous location data, user-initiated annotations, and responses to contextual, platform initiated survey questions. New studies can be set up using the existing University of California, Berkeley, infrastructure with no additional coding, or the platform can be extended for more complex projects. This paper reviews the requirements for smartphone travel data collection, describes the architecture and capabilities of the e-mission platform, and evaluates its performance in a pilot deployment. The results show that the platform is usable, with over 150 installations in a month; stable, with over 85% of users retaining it for more than 3 days; and extensible, with interface and survey customizations accomplished in a little over a week of full-time work by a transportation engineering researcher. We hope that e-mission will be a useful tool for app-based data collection and will serve as a catalyst for related research.
Mixed finite element methods for flow in porous media

Mixed nite element discretizations for problems arising in ow in porous medium applications are considered. We rst study second order elliptic equations which model single phase ow. We consider the recently introduced expanded mixed method. Combined with global mapping techniques, the method is suitable for full conductivity tensors and general geometry domains. In the case of the lowest order Raviart-Thomas spaces, quadrature rules reduce the method to cell-centered nite diierences, making it very eecient computationally. We consider problems with discontinuous coeecients on multiblock domains. To obtain accurate approximations, we enhance the scheme by introducing Lagrange multiplier pressures along subdomain boundaries and coeecient discontinuities. This modiication comes at no extra computational cost, if the method is implemented in parallel, using non-overlapping domain decomposition algorithms. Moreover, for regular solutions, it provides optimal convergence and discrete superconvergence for both pressure and velocity. We next consider the standard mixed nite element method on non-matching grids. We introduce mortar pressures along the non-matching interfaces. The mortar space is chosen to have higher approximability than the normal trace of the velocity spaces. The method is shown to be optimally convergent for all variables. Superconvergence for the subdomain pressures and, if the tensor coeecient is diagonal , for the velocities and the mortar pressures is also proven. We also consider the expanded mixed method on general geometry multiblock domains with non-matching grids. We analyze the resulting nite diierence scheme and show superconvergence for all variables. EEciency is not sacriiced by adding the mortar pressures. The computational complexity is shown to be comparable to the one on matching grids. Numerical results are presented, that verify the theory. iii We nally consider mixed nite element discretizations for the nonlinear multi-phase ow system. The system is reformulated as a pressure and a saturation equation. The methods described above are directly applied to the elliptic or parabolic pressure equation. We present an analysis of a mixed method on non-matching grids for the saturation equation of degenerate parabolic type. Acknowledgments To my wife Maria I would like to express my deep thanks to my advisor Mary Wheeler for her guidance and support. She has been both a mentor and a friend to me throughout my years at Rice. I also thank my former advisor Raytcho Lazarov, who is responsible for a great part of what I have achieved in my life. I am especially in debt to Todd Arbogast, the collaboration with …
The impact of ICT characteristics on students’ learning outcomes during COVID-19 lockdown period: The crucial role of techno eustress perception

BACKGROUND: The COVID-19 crisis led to an unprecedented acceleration of digital learning. It pushed many institutions to abruptly switch to fully online learning modes from face-to-face learning. Prior studies show that higher IT demands can cause challenge or hindrance stressors, depending on how the digital technology characteristics are perceived by the end-user. However, there is a gap in our knowledge regarding how ICT characteristics can lead to positive stress appraisals in a remote learning environment. OBJECTIVE: This paper leverages the person-environment fit and technostress literature to examine how usefulness and reliability as demand-ability stressors of ICT tools can positively impact learning outcomes among remote learning students. Techno eustress perceptions are evaluated as a crucial mechanism for theorizing the positive impact. METHODS: We used the survey method, sampling students (N = 82) during the lockdown period to test this model. RESULTS: Our findings highlight the ICT characteristic of usefulness as salient in contributing to student learning outcomes as it promotes techno eustress. CONCLUSIONS: This is the first study to demonstrate a positive impact of ICT characteristics on student learning outcomes via techno eustress perceptions.
Does Food Intake of Australian Toddlers 12–24 Months Align with Recommendations: Findings from the Australian Feeding Infants and Toddlers Study (OzFITS) 2021

(1) Background: Food-based dietary guidelines promote population health and well-being through dietary patterns that reduce chronic disease risk while providing adequate energy and nutrients. In Australia, recommended dietary patterns based on servings per day from the five food groups—fruits, vegetables, cereals and grains, meats and alternatives, and dairy—have been developed for toddlers 1–2 years of age. However, no study has assessed the intake of the five food groups in this age group nationally. (2) Aim: To compare daily servings and the percentage of energy from the five food groups and discretionary foods in toddlers 1–2 years old to the Australian Dietary Guidelines. (3) Methods: Dietary intake was assessed using a one-day food record for 475 toddlers. (4) Results: Apart from fruit and dairy, servings of the five food groups were below the recommendations. Two-thirds of toddlers did not consume enough vegetables, and only 10% consumed the recommended number of servings for cereals and grains. On average, toddlers consumed only half the recommended servings of meat and alternatives. Nearly all toddlers (89%) consumed discretionary foods, which accounted for ~12% of total energy. Forty-five percent of toddlers received breastmilk. On average, breastfed toddlers consumed fewer servings from the five food groups than non-breastfed toddlers. Dairy contributed 20% of daily energy in all toddlers; however, this food group accounted for 13% in breastfed and 32% in non-breastfed toddlers on the day of the food record. (4) Conclusions: Compared to the recommendations, alignment with the servings of the five food group foods was not achieved by most toddlers, except for fruit and dairy. Discretionary foods may have displaced nutritious family foods. Consistent with Australian Infant Feeding Guidelines, many toddlers in this study continued to receive breastmilk but the recommended dietary patterns do not include breastmilk. Dietary modeling, including breastmilk as the primary milk source, is urgently needed, along with practical advice on incorporating breastmilk in a toddler’s diet while optimizing food consumption.
Therapeutic Suggestions During General Anesthesia Reduce Postoperative Nausea and Vomiting in High-Risk Patients – A Post hoc Analysis of a Randomized Controlled Trial

Postoperative nausea and vomiting (PONV) are one of the most adverse events after general anesthesia, a distressing experience, and pose a risk to the patient. Despite advances in drug prophylaxis and PONV treatment, the incidence remains high and additional non-pharmacological treatments are needed. In this post hoc analysis of a recently published double-blind multicenter randomized controlled trial on the efficacy of intraoperative therapeutic suggestions on postoperative opioid dosage, we analyzed the effects of intraoperative therapeutic suggestions on PONV. We focus on patients with a high risk of PONV (Apfel risk score of 3–4) and distinguished early (first two postoperative hours) and delayed PONV (2–24 h). A total of 385 patients with a moderate or high risk for PONV were included. The incidence of early and delayed PONV was reduced (22.7–18.3 and 29.9–24.1%, respectively), without statistical significance, whereas in high-risk patients (n = 180) their incidence was nearly halved, 17.2 vs. 31.2% (p = 0.030) and 20.7 vs. 34.4% (p = 0.040), corresponding to a number needed to treat of 7 to avoid PONV. In addition, there was a significant reduction in PONV severity. In a multivariate logistic regression model, assignment to the control group (OR 2.2; 95% CI: 1.1–4.8) was identified as an independent predictor of the occurrence of early PONV. Our results indicate that intraoperative therapeutic suggestions can significantly reduce the incidence of PONV in high-risk patients. This encourages the expansion of therapeutic suggestions under general anesthesia, which are inexpensive and virtually free of side effects. Clinical Trial Registration: German Clinical Trials Register, https://drks.de, registration number: DRKS00013800.
Associations between Workers’ Health and Working Conditions: Would the Physical and Mental Health of Nonregular Employees Improve If Their Income Was Adjusted?

Precarious employment can negatively affect health, but workers may be healthy if they earn enough income. This study uses equivalent disposable income and examines the interaction between income classes and employment types to clarify whether workers’ health improves as the income classes rise. In Japan, nonstandard workers, called nonregular employees, have remained high since 2013. Therefore, using data from the national cross-sectional Comprehensive Survey of Living Conditions 2013, an official survey performed in Japan, we targeted a sample of employees aged 18 to 45 who graduated during the economic recession. Our final sample included 8282 employees (4444 males and 3838 females). The health (general and mental) status indicators used the dichotomized self-rated health and scores of the K6 questionnaires scored in the national survey. The association between income and health was almost proportional. Female workers tended to improve their health as their income class increased; however, this tendency was not observed in male workers, especially nonregular employees. Although the associations were weakened by added income information on the regression models, nonregular employees always showed inferior health to regular employees. The health status of nonregular employees, especially female nonregular employees, is statistically significantly lower than that of regular employees, even when the economic class is similar. In conclusion, improving low incomes for nonregular employees could improve health challenges, but income alone may not result in the same health status for regular and nonregular employees.