Random Intervals Research Articles

The influence of substitution strategies on the physical match performance of elite female field hockey players

ABSTRACT The study aims were to i) compare movement demands of different substitution strategies for each position in elite female field hockey and ii) compare the effect substitutions had on match intensities post-substitution. During 19 international matches, movement data from 26 international players were collected. Substitution strategies were categorised into 5-min, 7.5-min, 10-min, at random intervals and full quarter play. Small reductions in work-rate were found from quarter 1 to 4 across positions despite running intensities not differing between quarters. Work-rate was greater for starting players during the opening minute and was also higher when compared to players coming off the bench. However, players coming off the bench had higher running intensities during this first minute with higher work-rates after minute two compared to the quarter average. Small differences in work-rate were found between substitution strategies for strikers and midfielders, with no differences in intensity for any position. In conclusion, a decrease in work-rate during the match suggests fatigue, but because intensity was maintained throughout, pacing strategies could be present. Although the substitution strategy utilised did not produce substantial results, the “first-minute-rush-effect” was confirmed as players coming off the bench were able to match and, in some cases, increase the work-rate.

Hybrid uncertainty propagation based on multi-fidelity surrogate model

There always exist multiple uncertainties including random uncertainty, interval uncertainty, and fuzzy uncertainty in engineering structures. In the presence of hybrid uncertainties, the hybrid uncertainty propagation analysis can be a challenging problem, which suffers from the computational burden of double-loop procedure when numerical simulation techniques are employed. In this work, a novel method for efficient hybrid uncertainty propagation analysis with the three types of uncertainties is proposed. Generally, multi-fidelity surrogate models, such as Co-Kriging, can greatly improve the computational efficiency by leveraging information from a low-fidelity model to build a high-fidelity approximate model. However, the traditional multi-fidelity surrogate model methods always calculate the hybrid uncertainty propagation result by combining with several numerical simulation techniques. This process can introduce post-processing errors unless unlimited number of samples are used, which is impossible in engineering application. In order to address this issue, the analytical solutions of the output mean and output variance are derived based on the Co-Kriging, and the resulting mean and variance are both random variables. Moreover, a new adaptive framework is established to strengthen the estimation accuracy of the hybrid uncertainty propagation result, by combining the augmented expected improvement function and the derived mean random variable. Several applications are introduced to demonstrate the effectiveness of the proposed method for solving hybrid uncertainty propagation problems.

Nonparametric density estimation over its unknown support for right censored data

The long-standing problem of nonparametric density estimation of the lifetime of interest over its unknown support, when only right-censored observations are available, is considered. Two cases, when the support of the lifetime of interest is a proper subset of the support of censoring variable and when the supports are the same, are explored. An orthogonal series density estimation, over a random interval defined by a specially chosen sequence of order statistics, is proposed and rates of the mean integrated squared error convergence are evaluated.

People with degenerative cervical myelopathy have impaired reactive balance during walking

BackgroundPeople with degenerative cervical myelopathy are known to have impaired standing balance and walking abilities, but less is known about balance responses during walking. Research questionThe aim of this project was to assess reactive balance impairments during walking in people with degenerative cervical myelopathy (PwDCM). We hypothesized that center of mass motion following perturbations would be larger in PwDCM and gluteus medius electromyographic amplitude responses would be decreased in PwDCM. MethodsReactive balance responses were quantified during unanticipated lateral pulls to the waist while treadmill walking. Walking biomechanics data were collected from 10 PwDCM (F=6) and 10 non-myelopathic controls (F=7) using an 8 camera Vicon System (Vicon MX T-Series). Electromyography was collected from lower limb muscles. Participants walked on an instrumented treadmill and received lateral pulls at random intervals and in randomized direction at 5% and 2.5% body mass. Participants walked at 3 prescribed foot placements to control for effects of the size of base of support. ResultsAs compared with controls, the perturbation-related positional change of the center of mass motion (ΔCOM) was increased in PwDCM (p=0.001) with similar changes in foot placement (p>0.05). Change in gluteus medius electromyography, however, was less in PwDCM than in controls (p<0.001). SignificanceAfter experimentally controlling step width, people with mild-to-moderate degenerative cervical myelopathy at least 3 months following cervical spine surgery have impaired reactive balance during walking likely coupled with reduced gluteus medius electromyographic responses. Rehabilitation programs focusing on reactive balance and power are likely necessary for this population.

Promoting a reputation for quality

AbstractI model a firm that invests in the quality of its product and influences how that quality is disclosed. The firm can promote its product; at random intervals it can disclose quality for a cost. At low reputations, promotion allows a firm to reestablish itself and the firm invests to take advantage of this. However, the ability to promote crowds out incentives for investment at high reputations generated by other information sources, in particular information that is generated whenever the firm is selling a low quality product, leading to reputation cycles in settings where high reputations would have otherwise persisted.

Adaptive Locomotion Transition Recognition With Wearable Sensors for Lower Limb Robotic Prosthesis

Locomotion transition recognition with external disturbances is a key issue in lower limb robotic prostheses. Redonning the prosthetic socket and individual differences are encountered frequently in daily life. They change the feature distribution and further fail the previously trained recognition model. To bridge the technical gap between laboratory validation and practical demands, researchers need to construct an adaptive recognition method with high accuracy, quick response, time-efficient calibration, and minimal interference to the human body. Our study developed an adaptive recognition algorithm based on two miniaturized inertial sensors (anterior thigh and forefoot) and a foot pressure sensor, which can easily be integrated into the prosthesis. The algorithm fused probability-based fuzzy classifiers, which took heuristic-based features as inputs with a dynamic-time-warping-based automatic template generation block. It enables the recognition model to quickly fit the parameters for an untrained mode after external disturbances. We validated the proposed adaptive recognition method on 13 healthy subjects, performing 18 motion transitions with random interday intervals and across user evaluation, both offline and online. Even with the data of one subject as the prior knowledge, the other subjects produced an average accuracy of 98.04% and an average transition time latency of −180.99 ms without manual training in interday and intersubject uses. We also tested the method on two subjects using robotic prostheses, one person with a transfemoral amputation and the other person with a transtibial amputation. The average accuracy was still as high as 98.06% (transfemoral) and 100% (transtibial) without manual calibration after seven days of intervals. The results proved that the adaptive recognition method is robust to sensor redonning and user changes. Compared with the state-of-the-art methods, our study makes a further step toward the practical use of robotic prostheses. Future studies will be conducted to implement the algorithm on the robotic prosthesis for more extensive tests.

The effects of visual and auditory synchrony on human foraging.

Can synchrony in stimulation guide attention and aid perceptual performance? Here, in a series of three experiments, we tested the influence of visual and auditory synchrony on attentional selection during a novel human foraging task. Human foraging tasks are a recent extension of the classic visual search paradigm in which multiple targets must be located on a given trial, making it possible to capture a wide range of performance metrics. Experiment 1 was performed online, where the task was to forage for 10 (out of 20) vertical lines among 60 randomly oriented distractor lines that changed color between yellow and blue at random intervals. The targets either changed colors in visual synchrony or not. In another condition, a non-spatial sound additionally occurred synchronously with the color change of the targets. Experiment 2 was run in the laboratory (within-subjects) with the same design. When the targets changed color in visual synchrony, foraging times were significantly shorter than when they randomly changed colors, but there was no additional benefit for the sound synchrony, in contrast to predictions from the so-called "pip-and-pop" effect (Van der Burg et al., Journal of Experimental Psychology, 1053-1065, 2008). In Experiment 3, task difficulty was increased as participants foraged for as many 45° rotated lines as possible among lines of different orientations within 10 s, with the same synchrony conditions as in Experiments 1 and 2. Again, there was a large benefit of visual synchrony but no additional benefit for sound synchronization. Our results provide strong evidence that visual synchronization can guide attention during multiple target foraging. This likely reflects the local grouping of the synchronized targets. Importantly, there was no additional benefit for sound synchrony, even when the foraging task was quite difficult (Experiment 3).

An Off-Grid Compressive Sensing Algorithm Based on Sparse Bayesian Learning for RFPA Radar

In the application of Compressive Sensing (CS) theory for sidelobe suppression in Random Frequency and Pulse Repetition Interval Agile (RFPA) radar, the off−grid issues affect the performance of target parameter estimation in RFPA radar. Therefore, to address this issue, this paper presents an off−grid CS algorithm named Refinement and Generalized Double Pareto (GDP) distribution based on Sparse Bayesian Learning (RGDP−SBL) for RFPA radar that utilizes a coarse−to−fine grid refinement approach, allowing precise and cost−effective signal recovery while mitigating the impact of off−grid issues on target parameter estimation. To obtain a high-precision signal recovery, especially in scenarios involving closely spaced targets, the RGDP−SBL algorithm makes use of a three−level hierarchical prior model. Furthermore, the RGDP−SBL algorithm efficiently utilizes diagonal elements during the coarse search and exploits the convexity of the grid energy curve during the fine search, therefore significantly reducing computational complexity. Simulation results demonstrate that the RGDP−SBL algorithm significantly improves signal recovery performance while maintaining low computational complexity in multiple scenarios for RFPA radar.

Random/Cognitive Hybrid Uncertainty Analysis of Ship Multitasking Cabin Layout

Identifying the optimal cabin layout is an important way to improve the efficiency of ship systems and ensure the efficient circulation of personnel and materials. The ship task state refers to the state maintained by the joint actions of ship machinery and equipment, cargo, and personnel when facing different jobs and tasks during operation. A cabin layout that facilitates multitasking states can improve the efficiency of collaboration between systems and ensure the operation of the ship. The demand for human flow and logistics is different in multitasking states. To express the demand in mathematical form, there is a certain random uncertainty in the numerical quantification of the demand. Thus, to better meet the needs of different states, the coefficient values of each state can be integrated using special methods. Ensuring values’ initial preference to the greatest extent inevitably produces a degree of cognitive uncertainty. Therefore, uncertainty analysis is necessary for cabin layout design to be used for multitasking states. In this paper, a deterministic optimization platform of cabin layout in multitasking states is obtained. Adjacent and circulating strength coefficients are obtained through numerical quantization of the demand for human flow and logistics. The random uncertainty in the input values of two coefficients was represented by random variables, and the cognitive uncertainty was represented by interval variables. In order to solve the problem of two types of variables, a random-interval hybrid uncertainty model was established. Through random intervalization and interval randomization, three cases, of random variables, interval variables, and random variables and interval variables, were studied. The probability distribution of the model function was used to evaluate the influence of different compositions of uncertainty parameters on the robustness of the cabin layout scheme. The necessity and effectiveness of uncertainty analysis in multitasking cabin layout are discussed below.

A novel active learning Gaussian process modeling-based method for time-dependent reliability analysis considering mixed variables

The purpose of time-dependent reliability analysis is evaluating the reliability of a system or component within a specified timeframe. Several approaches have been suggested for addressing time-dependent reliability challenges, including analytical methods and methods based on response surrogate models. As a representative of Machine Learning methods, the Active Learning Gaussian process model has gained more and more popularity in time-dependent reliability analysis. In practical applications, certain uncertainties may not be adequately represented by random variables following deterministic distributions. In contrast, these uncertainties can be easily described by interval variables. This article introduces an innovative method based on Active Learning Gaussian process modeling for time-dependent reliability analysis, encompassing both random and interval variables. The initial step involves transforming stochastic processes into random parameters through spectral decomposition. Moreover, a new sampling strategy to manage mixed uncertainties is proposed, where the candidate samples of mixed variables with positive maximum extreme responses and negative minimum extreme responses are identified. These samples are utilized to construct and renew an extreme Gaussian process model. Lastly, we present an improved confidence-based learning function to accelerate the learning process, which is designed to locate the optimal random input and interval input for updating the extreme Gaussian process model. One mathematical example and two engineering examples are conducted to verify the performance of the proposed method. Results illustrate that our method offers high accuracy while reducing the evaluations of true performance functions in addressing time-dependent reliability issues under mixed uncertainties.

An abelian way approach to study random extended intervals and their ARMA processes

&lt;abstract&gt;&lt;p&gt;An extended interval is a range $ A = [\underline{A}, \overline{A}] $ where $ \underline{A} $ may be bigger than $ \overline{A} $. This is not really natural, but is what has been used as the definition of an extended interval so far. In the present work we introduce a new, natural, and very intuitive way to see an extended interval. From now on, an extended interval is a subset of the Cartesian product $ {\mathbb R}\times {\mathbb Z}_2 $, where $ {\mathbb Z}_2 = \{0, 1\} $ is the set of directions; the direction $ 0 $ is for increasing intervals, and the direction $ 1 $ for decreasing ones. For instance, $ [3, 6]\times\{1\} $ is the decreasing version of $ [6, 3] $. Thereafter, we introduce on the set of extended intervals a family of metrics $ d_\gamma $, depending on a function $ \gamma(t) $, and show that there exists a unique metric $ d_\gamma $ for which $ \gamma(t)dt $ is what we have called an "adapted measure". This unique metric has very good properties, is simple to compute, and has been implemented in the software $ R $. Furthermore, we use this metric to {define variability for random extended intervals. We further study extended interval-valued ARMA} time series and prove the Wold decomposition theorem for stationary extended interval-valued times series.&lt;/p&gt;&lt;/abstract&gt;

Effect of instructions on the microstructure of human schedule performance.

Three experiments examined the effect of instructions on human free-operant performance on random ratio (RR) and random interval (RI) schedules. Both rates of responding, and the microstructure of behavior, were explored to determine whether bout-initiation and within-bout responding may be controlled by different processes. The results demonstrated that responding in acquisition (Experiments 1 and 2) and extinction (Experiment 3) was impacted in line with given instructions. During acquisition, rates were higher on RR compared to RI for accurate and minimal instructions. During extinction, rates decreased when there were minimal instructions. However, instructions had a greater impact on within-bout responding, than they did on bout-initiation responding. Overall rates of responding, and within-bout rates, varied in line with the nature of the instructions, but bout-initiation responding did not (Experiments 1 and 2). Resistance to extinction was increased by instructions in terms of overall responding and within-bout rates, but not in terms of bout-initiation rates (Experiment 3). These data are consistent with the hypothesis that bout-initiation responding may be less impacted by instructions than within-bout responding, speculatively, the former is stimulus-driven, automatic/habitual, and less accessible to conscious processing. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Integrable discretization of linear and logistic equations under random intervals with higher-order accuracy

Integrable discretization of linear and logistic equations under random intervals with higher-order accuracy

LGSNet: A Two-Stream Network for Micro- and Macro-Expression Spotting With Background Modeling

Micro- and macro-expression spotting in an untrimmed video is a challenging task, due to the mass generation of false positive samples. Most existing methods localize higher response areas by extracting hand-crafted features or cropping specific regions from all or some key raw images. However, these methods either neglect the continuous temporal information or model the inherent human motion paradigms (background) as foreground. Consequently, we propose a novel two-stream network, named Local suppression and Global enhancement Spotting Network (LGSNet), which takes segment-level features from optical flow and videos as input. LGSNet adopts anchors to encode expression intervals and selects the encoded deviations as the object of optimization. Furthermore, we introduce a Temporal Multi-Receptive Field Feature Fusion Module (TMRF <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula> M) and a Local Suppression and Global Enhancement Module (LSGEM), which help spot short intervals more precisely and suppress background information. To further highlight the differences between positive and negative samples, we set up a large number of random pseudo ground truth intervals (background clips) on some discarded sliding windows to accomplish background clips modeling to counteract the effect of non-expressive face and head movements. Experimental results show that our proposed network achieves state-of-the-art performance on the CAS(ME) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> , CAS(ME) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula> and SAMM-LV datasets.

NEURAL ORDINARY DIFFERENTIAL EQUATIONS FOR TIME SERIES RECONSTRUCTION

Context. Neural Ordinary Differential Equations is a deep neural networks family that leverage numerical methods approaches for solving the problem of time series reconstruction, given small amount of unevenly distributed samples.&#x0D; Objective. The goal of the following research is the synthesis of a deep neural network that is able to solve input signal reconstruction and time series extrapolation task.&#x0D; Method. The proposed method exhibits the benefits of solving time series extrapolation task over forecasting one. A model that implements encoder-decoder architecture with differential equation solving in latent space, is proposed. The latter approach was proven to demonstrate outstanding performance in solving time series reconstruction task given a small percentage of noisy and uneven distributed input signals. The proposed Latent Ordinary Differential Equations Variational Autoencoder (LODE-VAE) model was benchmarked on synthetic non-stationary data with added white noise and randomly sampled with random intervals between each signal.&#x0D; Results. The proposed method was implemented via deep neural network to solve time series extrapolation task.&#x0D; Conclusions. The conducted experiments have confirmed that proposed model solves the given task effectively and is recommended to apply it to solving real-world problems that require reconstructing dynamics of non-stationary processes. The prospects for further research may include the process of computational optimization of proposed models, as well as conducting additional experiments involving different baselines, e. g. Generative Adversarial Networks or attention Networks.

Intersection Planning for Multilane Unmanned Aerial Vehicle Traffic Management

Unmanned aerial system (UAS) traffic management of airspace is a domain that demands strategic management of unmanned aerial vehicles (UAVs) for smooth and conflict-free movement in the uncharted low-altitude G airspace. In the context of the previously proposed CORRIDRONE structure, UAV traffic has to be organized in a shared volume of airspace connecting two or more corridors for a network of multilane corridors, resulting in the formation of aerial intersections. In this work, an intersection planning algorithm is proposed that aims to provide no-conflict paths to the UAVs inside the intersection volume. Paths are modeled as a function of the lanes involved in the transition, and conflict resolution is achieved by changing lanes. Optimized solutions are found among the conflicted UAV paths, such that only a few paths need modifying, optimizing the number of lane changes and time spent in the intersection. Simulation results, including random starting time intervals, various UAV sizes, corridor sizes, and differing numbers of lanes in intersecting corridors, are presented to demonstrate the concepts discussed in the paper.

Sex and individual differences in the effect of chronic low-dose ethanol on behavioral strategy selection.

The development of an alcohol use disorder (AUD) involves impaired behavioral control and flexibility. Behavioral inflexibility includes an inability to shift behavior in response to changes in behavioral outcomes. Low levels of ethanol drinking may promote the formation of inflexible, habitual reward seeking, but this may depend on the timing of ethanol exposure in relation to learning. The goal of this study was to determine whether a history of low-dose ethanol exposure promoted contingency-insensitive sucrose seeking and altered behavioral strategy selection. Male and female C57BL/6J mice were trained to perform a response (lever press) for sucrose on two different reinforcement schedules: one that is thought to promote inflexible responding (random interval) and one that maintains flexible responding (variable ratio [VR]). Following instrumental training each day, mice were exposed to saline or low-dose ethanol (0.5 g/kg; i.p.) either proximal (1 h after) or distal (4 h after) to learning. Mice were then tested for sensitivity to changes in contingency in a contingency degradation test. A history of low-dose ethanol exposure shifted behavioral strategy selection, as measured by reward tracking behavior, but this depended on sex and reinforcement schedule history. Both male and female mice used different strategies depending on the reinforcement schedule, but only males exhibited ethanol-induced shifts in strategy selection. A history of low-dose ethanol exposure did not impact contingency sensitivity in males but promoted insensitivity in females specifically on the VR lever. Female mice show distinct behavioral effects of repeated, low-dose ethanol exposure as compared to males, with sex differences in the use of reward tracking strategies to guide behavior. Future studies should investigate sex differences in the neural consequences of chronic low-dose ethanol exposure that may underlie behavioral changes.

ERP evidence of attentional somatosensory processing and stimulus-response coupling under different hand and arm postures.

We investigated (1) the effects of divided and focused attention on event-related brain potentials (ERPs) elicited by somatosensory stimulation under different response modes, (2) the effects of hand position (closely-placed vs. separated hands) and arm posture (crossed vs. uncrossed forearms) on the attentional modulation of somatosensory ERPs, and (3) changes in the coupling of stimulus- and response-related processes by somatosensory attention using a single-trial analysis of P300 latency and reaction times. Electrocutaneous stimulation was presented randomly to the thumb or middle finger of the left or right hand at random interstimulus intervals (700-900 ms). Subjects attended unilaterally or bilaterally to stimuli in order to detect target stimuli by a motor response or counting. The effects of unilaterally-focused attention were also tested under different hand and arm positions. The amplitude of N140 in the divided attention condition was intermediate between unilaterally attended and unattended stimuli in the unilaterally-focused attention condition in both the mental counting and motor response tasks. Attended infrequent (target) stimuli elicited greater P300 in the unilaterally attention condition than in the divided attention condition. P300 latency was longer in the divided attention condition than in the unilaterally-focused attention condition in the motor response task, but remained unchanged in the counting task. Closely locating the hands had no impact, whereas crossing the forearms decreased the attentional enhancement in N140 amplitude. In contrast, these two manipulations uniformly decreased P300 amplitude and increased P300 latency. The correlation between single-trial P300 latency and RT was decreased by crossed forearms, but not by divided attention or closely-placed hands. Therefore, the present results indicate that focused and divided attention differently affected middle latency and late processing, and that hand position and arm posture also differently affected attentional processes and stimulus-response coupling.

텍스트 마이닝을 활용한 감정 비율 단어 그래프

Unstructured data consisting of natural language such as SNS, papers, and questionnaire subjective question answers are generally compared or visualized using text mining. When analyzing text data by setting a random interval representing a time such as year, quarter, month, and day, it may be necessary to determine which interval has the most data, which sentiment words are used a lot for each interval, and how much text data in a particular interval is written negatively than positive. As a way to solve this problem, this study proposes an sentiment ratio word graph that can deliver the above three information at once. An sentiment ratio word graph is a graph created by dividing text data by a random interval standard representing time such as year, quarter, month, and day of the week and then assigning the sentiment score in the sentiment dictionary to the text data. When visualizing an sentiment ratio word graph, if you also use the pie coefficient to display words, you can further convey information about which words are most relevant to the sentiment word in a particular interval.

O031 The Inter-stimulus Interval Effect in the Psychomotor Vigilance Task

Abstract Introduction The Psychomotor Vigilance Task (PVT) is a widely used measure of the effects of sleep deprivation on vigilant attention. The test requires a rapid response to a stimulus occurring at random inter-stimulus intervals (ISIs) between 2 and 10 seconds following a previous response. A few studies have found an ISI effect where shorter ISIs (2-4s) have slower reaction times (RTs) than longer ISIs (8-10s). How this ISI effect is impacted by circadian timing has yet to be investigated. Method This study compared the ISI effect at 3 time points coinciding with 5, 23 (circadian trough), and 29 hours of wakefulness. Data were taken from 16 healthy participants during a 30 hour period of sleep deprivation. Results A repeated-measures (3 x 3) ANOVA (3 time points, 3 ISIs) found that RTs were slower and lapses (RT&amp;gt;500msec) more frequent in short ISIs compared with medium and long ISIs. RTs were also slower and lapses more frequent at the circadian trough compared to both the early testing session and after 29h of total sleep deprivation. Discussion Increased lapses in shorter ISIs could be explained by an involuntary mental “rest period” after responding, that gets longer and more frequent under sleep pressure from both circadian and homeostatic sleep pressure. These findings have implications for interpreting PVT results and their application to everyday tasks, such as driving. Further research could involve neuroimaging the brain’s responses during different ISIs to confirm the mental ‘rest period’ following a response in the PVT task.