Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of goalkeepers’ offensive participation on team performance in the women Spanish La Liga: a multinomial logistic regression analysis

This study aimed to examine the effect of goalkeeper distribution on offensive team performance, during the 2018/2019 and 2019–2020 seasons of the Women Spanish La Liga. A total of 10,868 distributions, during 376 matches were analyzed by systematic observation. Two UEFA PRO coaches designed an ad hoc observation instrument “GOALDFOOT” and one observer coded the data after a training process. An intra-observer reliability kappa index of 0.94 was established. Results show how the offensive effectiveness of the goalkeepers was similar to outfield players, with 0.4% of possessions ending in a goal, 2.2% ending in an attempt on goal, with 79.4% ending unsuccessfully. The goalkeeper lost possession from their distribution 32.5% of the time. Multivariate analysis identified several predictors of goalkeepers’ distributions. The results show that teams classified in the middle zone of the final classification of the regular league had 1.2 times more probability of being successful compared with the lowest ranked teams (p < 0.05). Goalkeeper’s distribution beginning during Open play after a transition, represented an increase success rate of almost 3 times compared to being performed from a free kick (p < 0.05). Passes from outfield players to a goalkeeper made from distant zones to the own goal, decreased the probability of success (p < 0.001). The pitch location of the distribution outcome near to the opponent goal offered the best probability of success. In conclusion, the most effective offensive sequences occur with dynamic transitions initiated with short passes. This information can provide coaches and players with insights to improve the offensive performance of goalkeepers.
Traffic Flow Prediction Based on Two-Channel Multi-Modal Fusion of MCB and Attention

The accuracy of urban traffic flow prediction is influenced by nearby regional road network, historical traffic flow and seasonal climate, which has complex spatial and temporal dependence. In view of the above factors, we proposed a multi-modal traffic flow prediction model fusing road network, historical traffic flow and weather data. Firstly, the weighted spatio-temporal graph was constructed based on the traffic flow time series data, and the weighted STSGCN model was used to extract the spatio-temporal graph features. Secondly, the image sequence was constructed by road network, vehicle track and sensor position data, and the visual features were extracted by ResNet. Finally, based on the MCB and Attention two-channel multi-modal fusion model, the spatio-temporal graph features and the visual features of the image sequence were fused to obtain the aligned fusion vector. Finally, the aligned fusion vector was combined with the weather feature vector to complete the traffic flow prediction. The experimental results showed that the prediction results of our proposed model were better than those of other baseline models. At the same time, the ablation results also proved the effectiveness of each module in our proposed prediction model.
Neural‐Network Potential Simulation of Defect Formation Induced by Knock‐On Irradiation Damage in GaN

Understanding the irradiation damage mechanisms of GaN is of great importance for improving irradiation resistance and the ion implantation processes of GaN‐based devices. The understanding of the damage mechanisms, which are normally simulated using molecular dynamics (MD), is bottlenecked by the dilemma that ab initio MD is limited by relatively high computational cost, whereas classical MD suffers from low accuracy. In this paper, a global neural network (G‐NN) potential is constructed for GaN using random stochastic surface walking global optimization combined with global neural network potential (SSW‐NN). By benchmarking the properties of intrinsic defects and defect‐pairs, as well as the threshold displacement energies along different crystallographic directions, this potential is found to provide accuracy similar to ab initio calculations. Furthermore, based on the large‐scale simulations of the knock‐on process, Ga and N vacancies, as well as N interstitials are found to be the major generated defects, whereas only Ga vacancies are predicted in the previous device simulation studies that have been widely recognized. This potential provides an efficient and accurate tool to gain a fundamental understanding of the irradiation damage mechanisms of GaN and refine the parameters in the related device simulations.
Prediction of brown tide algae using improved Gramian angular field and deep learning based on laser-induced fluorescence spectrum

The frequent occurrence of algal blooms has seriously affected the marine environment and human production activities. Therefore, it is crucial to monitor the phytoplankton concentration in water bodies. In this study, a prediction method for brown tide algae using improved Gramian angular field (IGAF) and deep learning based on the laser-induced fluorescence spectrum was proposed. The method combined one-dimensional (1D) fluorescence spectrum with IGAF for image coding. The internal normalizing approach of the original Gramian angle field algorithm was upgraded from local to global, which can increase the difference between samples with various concentrations. Then, we established a novel technique that fully takes into account the Gramian angular difference field and Gramian angular summation field features, allowing it to control the main and sub-diagonal features and successfully convert 1D sequences into images by adding various weight factors. Using depthwise separable convolutional neural network to extract image features helps reduce model training parameters, paired with long short-term memory network to rapidly predict the concentration of brown tide. To confirm the actual performance of the given approach, ablation and contrast experiments were carried out, and the results showed that the method’s regression accuracy, R 2 was 97.8%, with the lowest mean square error and mean absolute error. This study investigated the transformation of 1D spectra into images using IGAF, which not only explored the application of the fluorescence spectrum image coding method for algal regression but also enabled the introduction of the potent benefits of deep learning image processing into the field of spectral analysis.
Fast time-varying phasor analysis in the balanced three-phase large electric power system

Traditional phasor representation of sinusoidal signals, the standard analytical tool for power system stability analysis, is limited by the quasistationary assumption on the speeds of the phasor states. This paper provides a rigorous formulation of a time-varying phasor representation for the balanced three-phase large power system with no restrictions on the speeds. Power balance equations become a set of differential equations in the phasor dynamic states and singularly perturbed behavior of the resulting dynamics is explored. >
Harmonizing work safety and health practices towards technician’s productivity in the workplace: A modified theory of planned behaviour

Abstract Several technicians have been exposed to danger and varying degree of risk, exposed to diverse grades of occupational and health hazard, injuries and death which may arise from accidents. Several factors that cause these accidents range from inadequate experience, short job tenure, insufficient training, etc. Therefore, this study harmonizes safety and health practices at work towards technician’s productivity in the workplace. A total of 1,080 registered technicians in workplaces in Lagos State, South-West region, Nigeria, were used as sample in this study. The samples were selected using purposive sampling technique. Exploratory and confirmatory factor analyses were used during the empirical analysis to achieve the model fits. The study shows that there is a significant influence of the health and safety practice constructs (job safety, coworker’s safety, supervisor’s safety and management of safety practices) on the construct of theory of planned behaviour (attitude, subjective norms, perceived behavioural control and intentions) to further enhance technician safety and health practices/safety behaviour. The result from the study indicates that school-based occupational safety and health education may contribute to future reduction in job-related injury, to ensure that safe and healthy work becomes important to the student experience.
A robust H-infinity controller design approach for neutral systems with time-varying delay and plant and controller uncertainties

This paper is concerned with a state feedback controller design method for neutral systems with a time-varying delay, considering uncertainties in the plant parameters, as well as in controller gain. The uncertainties are in additive form, affecting both the system matrices of the plant and the controller gain. The uncertainties that are assumed admissible are time-varying and norm-bounded. The neutral system is also subject to external disturbances. A robust stabilizing H-infinity state-feedback controller is synthesized under several conditions that are presented in the form of matrix inequalities. A new generalized type of Jensen integral inequality has been introduced for utilization in the derivation of the aforementioned results, which could thus have been relaxed via that approach. A feasible solution set is obtained using the well-known cone complementarity technique by solving a non-linear minimization problem subject to linear matrix inequalities. A numerical example with case studies concludes the present work. The results of the minimum achievable attenuation rate indicate considerable improvement in comparison with those reported in the literature.
3D Radiative Transfer in Weakly Inhomogeneous Medium. Part II: Discrete Ordinate Method and Effective Algorithm for Its Inversion

Abstract The forward discrete ordinate method (DOM) has been reformulated to include effects of a weak inhomogeneity of a medium. The modification is based on an expansion of the direct beam source term. This treatment of the source term is similar to the gradient correction (GC) method, presented in the first part of the paper for the diffusion approximation. The same requirement on the scales of variations applies to the current method: length of horizontal variations of optical properties of the medium should be large in comparison to the mean radiative transport length. The modification has another important advantage in that it permits obtaining particular solutions for both infrared and direct beam radiative transfer in one computational step. An effective algorithm for solving inverse radiative transfer problems has been developed following the above reformulation. This algorithm modifies the DOM using Newton’s iterative scheme in order to find a solution of the inverse problem. The algorithm conve.
Excitation and ionization of the hydrogen atom by intense femtosecond chirped laser pulses

The excitation and ionization processes of the hydrogen atom by intense femtosecond chirped laser pulses are investigated by solving numerically the three-dimensional time-dependent Schrodinger equation. We study, in three distinct cases, the interaction of the atomic system with chirped pulses of different central carrier frequencies that correspond to some harmonics of a Ti–sapphire laser. We analyze, in each case, the mechanism of the population transfer from the ground to the excited electronic states and show how it differs depending on the sign of the chirp parameter. We also show that the ionization yield is sensitive to the chirp parameter when resonant or near-resonant transitions are induced by the chirped pulses. The latter are efficient to control the excitation process by inducing selective population transfer when compared with unchirped pulses.
Accessibility conditions of MIMO nonlinear control systems on homogeneous time scales

A necessary and sufficient accessibility condition for the set of nonlinear higher order input-output (i/o) delta differential equations is presented. The accessibility definition is based on the concept of an autonomous element that is specified to the multi-input multi-output systems. The condition is presented in terms of the greatest common left divisor of two left differential polynomial matrices associated with the system of the i/o delta-differential equations defined on a homogenous time scale which serves as a model of time and unifies the continuous and discrete time. We associate the subspace $\mathcal{H}_{\infty}$ of the vector space of differential one-forms with the considered system. This subspace is invariant with respect to taking delta derivatives. The relation between $\mathcal{H}_\infty$ and the element of a left free module over the ring of left differential polynomials is presented. The presented accessibility condition provides a basis for system reduction, i.e. for finding the transfer equivalent minimal accessible representation of the set of the i/o equations which is a suitable starting point for constructing an observable and accessible state space realization. Moreover, the condition allows to check the transfer equivalence of nonlinear systems, defined on homogeneous time scales.
Motivations for game stream spectatorship: A content analysis of Danmaku on Bilibili

This article explores the motivations of viewers who watch Danmaku game streams on Bilibili, a Chinese streaming platform, by analysing Danmaku comments (弹幕, danmu). Drawing upon and extending findings from previous research, I establish a framework that synthesizes different types of Danmaku (emotion-expression, information, social, and entertainment) with viewer motivations (knowledge-sharing, entertaining, and communicating). To demonstrate how it works, this study employs content analysis based on virtual observation. After examining 37,331 Danmaku comments across 9 streams in three different categories (Esports, Speedrun, and Let’s Play), it is found that the primary motive for Danmaku game stream viewing is to communicate, and the distribution of motivations varies depending on the stream category. The article addresses a gap in the research of Chinese game streaming, providing empirical insights into the behaviours and motivations of viewers on these platforms. Danmaku is identified as a prominent feature of Chinese game streaming that offers a distinctive perspective for studying viewer motivations within the context of Chinese online video culture.
Virtual angle‐based adaptive control for trajectory tracking and balancing of ball‐balancing robots without velocity measurements

This article proposes a virtual angle‐based adaptive control method for trajectory tracking and balancing of ball‐balancing robots without velocity measurements. The trajectory tracking and balancing control of ball‐balancing robots is challenging due to underactuation and uncertain nonlinearities. The hierarchical control strategy, which designs the control system using a linear combination of trajectory tracking and balancing errors, can be a solution. However, it has a local minimum problem where the convergence of tracking and balancing errors is not guaranteed even though the linear combination error is zero. Therefore, a virtual angle‐based control method is presented, which can solve the underactuation problem without the local minimum issue. In addition, a neural network‐based observer is developed to estimate the velocity information of ball‐balancing robots with uncertainties, and the input saturation problem is considered. It is proven that all tracking and balancing errors are bounded and can be made arbitrarily small in the Lyapunov sense. Finally, simulation results are provided to verify the effectiveness of the proposed control system.
Estimation of an Unknown Distributed Heat Source in the Inverse Heat Conduction Problem

This article presents a hybrid numerical method to predict the unknown space and time dependent strength of heat source in one-dimensional inverse heat conduction problems. The hybrid numerical algorithm is based on the Borukhov-Vabishchevich (2000) and Newton’s iterative method. The spatial distribution of the heat source strength is represented a priori as the series forms of the known function and the unknown coefficient in the parameter estimation can be adjusted from the improperly initial distribution to the acceptable one automatically using the Newton's iterative method. To confirm the validity and efficiency of the present method, two comparative examples are presented. Comparisons are made between the present estimations and the exact solutions, and the agreement is found out to be generally good.
An Exploration of Impostor Syndrome in STEM and STEM Self-Efficacy in Adolescent Learners from a Teacher’s Perspective

Over the years, there has been a surge in the demand for a proficient STEM (Science, Technology, Engineering, and Math) workforce to occupy the proliferating STEM job vacancies worldwide. The STEM workforce must be expanded in order to fill this gap. However, the reputation of STEM learning as being arduous has proven to be a deterrent to students’ interest in the pursuit of STEM careers. In an expounding qualitative study, three STEM teachers were interviewed for the purpose of examining STEM Impostor Syndrome (IS) and STEM Self-Efficacy (S-SE) from a teacher’s perspective. Findings suggest that teachers are sentient of the manifestation of STEM IS among students. Teachers noted that STEM IS poses a significant challenge for their interactions with students in the classroom, and also suggested that STEM IS is inversely proportional to S-SE.
Predictive modelling to determine the attainable moisture content of Alstonia boonei wood using a solar kiln dryer

Modelling of wood drying is important for predicting the performance and efficiency of solar dryers and to optimise the drying process during each season of the year. The attainable moisture content (MC) of Alstonia boonei wood was studied when dried in a laboratory-scale solar kiln. Meteorological data (temperature, relative humidity, wind speed/direction and solar radiation) were observed over a period of 31 days. For the purpose of this study, three of the variables: temperature, relative humidity and solar radiation—were used for mathematical modelling of the drying process. The average attainable MC observed over a 31-day drying period was divided into a 70:30 dataset, representing calibrating and validating sets. Several regression models were formulated using the calibrating set. The optimal model was selected based on higher values of R 2 and R, and lower standard error, after which validation was done using the remaining dataset (validating set) by performing tests of bias and percentage bias and a student’s t-test. To meet the required criteria for a suitable model, values of the validating parameters must be low and have p-values that denote significance. The log polynomial model MC = −16 + 3.99 ln(SR2) + 1.49 ln(T2) + 5.80 ln(H2) was judged best for computing the attainable MC of A. boonei wood using a solar dryer across the whole year in the study area (Ondo State, Nigeria). The computational results showed fair agreement between the predicted and measured MC, which established the validity of the model and its suitability for application when drying low-density wood in the range of 340–370 kg/m3.
Distributed Computing for Efficient Data Processing and Storage

: Distributed computing is now widely recognized as a key infrastructure component for effective data management. The exponential rise of big data produced by businesses and individuals has increased the need for scalable and affordable methods of managing and analyzing this data. This paper discusses the problems and potential solutions associated with big data processing and storage, as well as the history, definition, and architectures of distributed computing. We also give some instances of data collection methods frequently used in the field of distributed computing and talk about the various tools and technologies utilized in this area. The topic of distributed computing is rapidly developing, thus it's crucial to think about how these tools will affect society and ethics. Issues of privacy, security, and bias must be carefully considered for the appropriate use of distributed computing. Researchers and practitioners in this field have a duty to ensure the proper and ethical use of these technologies given the enormous potential of distributed computing to revolutionize the way we process and store data.
Strengthening and plastic events in crystals: The role of dislocation surface sources

Mechanical deformation of nanopillars displays features that are distinctly different from bulk single crystal behavior: Yield strength increases with decreasing size and plastic deformation comes together with strain bursts or/and stress drops (depending on loading conditions). The character of the phenomenon is standing as a paradox: While these events resemble the universal noise heard in bulk crystals using acoustic emission (AE) techniques, they emerge primarily with decreasing size and increasing strength in nanopillars. In this paper, we present a realistic but minimal discrete dislocation plasticity model for the elasto-plastic deformation of nanopillars that is consistent with the main experimental observations of nano pillar compression experiments and provides a clear insight to this paradox. The model naturally transitions between the typical progressive behavior of nanopillars to a behavior that resembles bulk mesoscale plasticity, upon alteration of a single parameter: the density of surface dislocation sources. Strengthening as well as flow stress fluctuations are observed when surface sources are predominant, whereas the fluctuations are universal, across a range of sample width and aspect ratio, when surface sources are absent. In this way, our model provides a route of unifying statistical aspects of mechanical deformation across scales.
Laser-Formed Sensors with Electrically Conductive MWCNT Networks for Gesture Recognition Applications

Currently, an urgent need in the field of wearable electronics is the development of flexible sensors that can be attached to the human body to monitor various physiological indicators and movements. In this work, we propose a method for forming an electrically conductive network of multi-walled carbon nanotubes (MWCNT) in a matrix of silicone elastomer to make stretchable sensors sensitive to mechanical strain. The electrical conductivity and sensitivity characteristics of the sensor were improved by using laser exposure, through the effect of forming strong carbon nanotube (CNT) networks. The initial electrical resistance of the sensors obtained using laser technology was ~3 kOhm (in the absence of deformation) at a low concentration of nanotubes of 3 wt% in composition. For comparison, in a similar manufacturing process, but without laser exposure, the active material had significantly higher values of electrical resistance, which was ~19 kOhm in this case. The laser-fabricated sensors have a high tensile sensitivity (gauge factor ~10), linearity of >0.97, a low hysteresis of 2.4%, tensile strength of 963 kPa, and a fast strain response of 1 ms. The low Young’s modulus values of ~47 kPa and the high electrical and sensitivity characteristics of the sensors made it possible to fabricate a smart gesture recognition sensor system based on them, with a recognition accuracy of ~94%. Data reading and visualization were performed using the developed electronic unit based on the ATXMEGA8E5-AU microcontroller and software. The obtained results open great prospects for the application of flexible CNT sensors in intelligent wearable devices (IWDs) for medical and industrial applications.