Random Intervals Research Articles

Analysis of pedestrian crossing violations at an overpass location by using a logit model

Jaywalking significantly increases accident risks compared to marked crosswalks. Overpasses are often installed to address this, but violations continue. This study examines pedestrian behavior at overpass locations, focusing on 438 individuals recorded on video. Factors like parked vehicles, gender, age, group size, mobile phone distraction, carrying items, the number of waiting pedestrians upon arrival, waiting times, and traffic volume were analyzed. Since jaywalking is more frequent during off-peak hours when traffic is not congested, observations were made at random intervals during these times. A binary logit model was used to identify the factors influencing jaywalking. The study found that points of interest near overpasses significantly affect pedestrian behavior. Additionally, parked vehicles near the overpass reduce crossing distance, encouraging jaywalking. Policy recommendations include implementing parking restrictions and strategically locating overpasses to align with pedestrian movement patterns and nearby points of interest.

Stroke impairs the proactive control of dynamic balance during predictable treadmill accelerations.

We maintain balance during gait using both proactive and reactive control strategies. Damage to the brain from a stroke impairs reactive balance, but little is known about how a stroke impacts proactive control during walking. Identifying stroke-related impairments to proactive control could inform the design of interventions to improve balance and reduce fall risk. Therefore, we determined if people with and without stroke differ in their ability to use proactive strategies during predictable treadmill accelerations. Groups of 14 adults with and without stroke walked with single-belt accelerations at random (every one to five strides) and regular (every three strides) intervals. We quantified the effects of the perturbations as changes to center of mass (COM) speed and used mechanical leg work to quantify the proactive strategies to slow the COM. Participants without stroke reduced peak COM speed better than those with stroke when perturbations were at regular intervals (-0.023 versus -0.001 m/s; p=0.007). They also reduced positive leg work more during the perturbation step than the group post-stroke (-7.7% versus -1.9%; p=0.004). Together, these results demonstrate that the sensorimotor impairments resulting from a stroke impair the ability to generate effective proactive adjustments to reduce the destabilizing effects of predictable perturbations.

A Highly Efficient Compressive Sensing Algorithm Based on Root-Sparse Bayesian Learning for RFPA Radar

Off-grid issues and high computational complexity are two major challenges faced by sparse Bayesian learning (SBL)-based compressive sensing (CS) algorithms used for random frequency pulse interval agile (RFPA) radar. Therefore, this paper proposes an off-grid CS algorithm for RFPA radar based on Root-SBL to address these issues. To effectively cope with off-grid issues, this paper derives a root-solving formula inspired by the Root-SBL algorithm for velocity parameters applicable to RFPA radar, thus enabling the proposed algorithm to directly solve the velocity parameters of targets during the fine search stage. Meanwhile, to ensure computational feasibility, the proposed algorithm utilizes a simple single-level hierarchical prior distribution model and employs the derived root-solving formula to avoid the refinement of velocity grids. Moreover, during the fine search stage, the proposed algorithm combines the fixed-point strategy with the Expectation-Maximization algorithm to update the hyperparameters, further reducing computational complexity. In terms of implementation, the proposed algorithm updates hyperparameters based on the single-level prior distribution to approximate values for the range and velocity parameters during the coarse search stage. Subsequently, in the fine search stage, the proposed algorithm performs a grid search only in the range dimension and uses the derived root-solving formula to directly solve for the target velocity parameters. Simulation results demonstrate that the proposed algorithm maintains low computational complexity while exhibiting stable performance for parameter estimation in various multi-target off-grid scenarios.

A theoretical study on evaluating brain tumor changes in tumor treating fields therapy by impedance detection.

TTFields is a novel FDA-approved technology utilized for treating glioblastoma multiforme (GBM) within the brain. Presently, the effectiveness of therapy is evaluated through MRI imaging at random two-month intervals. Electrical impedance is an important and effective parameter for reflecting changes in tissue properties. In TTFields treatment for brain tumors, electrodes attached to the scalp deliver electric field energy to the tumor region. We hypothesize that these electrodes can also serve as sensors to detect impedance changes caused by tumor alterations in real time, thus continuously assessing the effectiveness of the treatment. In this work, we propose and scrutinize this hypothesis by conducting an in silico study to confirm the potential feasibility of the proposed concept. Our results indicate that the impedance amplitude change measured between opposing TTFields electrode arrays utilizing voltage and frequency of 50 V and 200 kHz (typical TTFields treatment parameters), has enough resolution (> 1mm) and Signal-to-Noise Ratio (> 40 dB) to evaluate tumor size change in the head. The impedance detection technique may be a significant augmentation to TTFields cancer treatment, enabling the continuous evaluation of safety and efficacy throughout the procedure.

AI-KODA score application for cleanliness assessment in video capsule endoscopy frames

Background Currently, there is no automated method for assessing cleanliness in video capsule endoscopy (VCE). Our objectives were to automate the process of evaluating and collecting medical scores of VCE frames according to the existing KOrea-CanaDA (KODA) scoring system by developing an easy-to-use mobile application called artificial intelligence-KODA (AI-KODA) score, as well as to determine the inter-rater and intra-rater reliability of the KODA score among three readers for prospective AI applications, and check the efficacy of the application. Method From the 28 patient capsule videos considered, 1539 sequential frames were selected at five-minute intervals, and 634 random frames were selected at random intervals during small bowel transit. The frames were processed and shifted to AI-KODA. Three readers (gastroenterology fellows), who had been trained in reading VCE, rated 2173 frames in duplicate four weeks apart after completing the training module on AI-KODA. The scores were saved automatically in real time. Reliability was assessed for each video using estimate of intra-class correlation coefficients (ICCs). Then, the AI dataset was developed using the frames and their respective scores, and it was subjected to automatic classification of the scores via the random forest and the k-nearest neighbors classifiers. Results For sequential frames, ICCs for inter-rater variability were ‘excellent’ to ‘good’ among the three readers, while ICCs for intra-rater variability were ‘good’ to ‘moderate’. For random frames, ICCs for inter-rater and intra-rater variability were ‘excellent’ among the three readers. The overall accuracy achieved was up to 61% for the random forest classifier and 62.38% for the k-nearest neighbors classifier. Conclusions AI-KODA automates the process of scoring VCE frames based on the existing KODA score. It saves time in cleanliness assessment and is user-friendly for research and clinical use. Comprehensive benchmarking of the AI dataset is in process.

Measuring mind wandering with experience sampling during task performance: An item response theory investigation

The tendency for individuals to mind wander is often measured using experience sampling methods in which probe questions embedded within computerized cognitive tasks attempt to catch episodes of off-task thought at random intervals during task performance. However, mind-wandering probe questions and response options are often chosen ad hoc and vary between studies with extant little guidance as to the psychometric consequences of these methodological decisions. In the present study, we examined the psychometric properties of several common approaches for assessing mind wandering using methods from item response theory (IRT). IRT latent modeling demonstrated that measurement information was generally distributed across the range of trait estimates according to when probes were presented in time. Probes presented earlier in time provided more information about individuals with greater tendency to mind wandering than probes presented later. Furthermore, mind-wandering ratings made on a continuous scale or using multiple categorical rating options provided more information about individuals’ latent mind-wandering tendency – across a broader range of the trait continuum – than ratings dichotomized into on-task and off-task categories. In addition, IRT provided evidence that reports of “task-related thoughts” contribute to the task-focused dimension of the construct continuum, providing justification for studies conceptualizing these responses as a kind of task-related focus. Together, we hope these findings will help guide researchers hoping to maximize the measurement precision of their mind wandering assessment procedures.

On the number variance of sequences with small additive energy

For a real-valued sequence (xn)n=1∞, denote by SN(ℓ) the number of its first N fractional parts lying in a random interval of size ℓ:=L/N, where L=o(N) as N→∞. We study the variance of SN(ℓ) (the number variance) for sequences of the form xn=αan, where (an)n=1∞ is a sequence of distinct integers. We show that if the additive energy of the sequence (an)n=1∞ is bounded from above by N3−ε/L2 for some ε>0, then for almost all α, the number variance is asymptotic to L (Poissonian number variance). This holds in particular for the sequence xn=αnd,d≥2 whenever L=Nβ with 0≤β<1/2.

Random interval schedule of reinforcement influences punishment resistance for cocaine in rats

In an animal model of compulsive drug use, a subset of rats continues to self-administer cocaine despite footshock consequences and is considered punishment resistant. We recently found that punishment resistance is associated with habits that persist under conditions that typically encourage a transition to goal-directed control. Given that random ratio (RR) and random interval (RI) schedules of reinforcement influence whether responding is goal-directed or habitual, we investigated the influence of these schedules on punishment resistance for cocaine or food. Male and female Sprague Dawley rats were trained to self-administer either intravenous cocaine or food pellets on a seeking-taking chained schedule of reinforcement, with the seeking lever requiring completion of either an RR20 or RI60 schedule. Rats were then given four days of punishment testing with footshock administered at the completion of seeking on a random one-third of trials. For cocaine-trained rats, the RI60 schedule led to greater punishment resistance (i.e., more trials completed) than the RR20 schedule in males and females. For food-trained rats, the RI60 schedule led to greater punishment resistance (i.e., higher reward rates) than the RR20 schedule in female rats, although male rats showed punishment resistance on both RR20 and RI60 schedules. For both cocaine and food, we found that seeking responses were suppressed to a greater degree than reward rate with the RI60 schedule, whereas response rate and reward rate were equally suppressed with the RR20 schedule. This dissociation between punishment effects on reward rate and response rate with the RI60 schedule can be explained by the nonlinear relation between these variables on RI schedules, but it does not account for the enhanced resistance to punishment. Overall, the results show greater punishment resistance with the RI60 schedule as compared to the RR20 schedule, indicating that schedules of reinforcement are an influencing factor on resistance to negative consequences.

Quality Assessment Method for Insulator Sample Bank Based on Sampling Theory

Abstract Insulator strings are important components in power systems to support and isolate transmission lines. Due to long-term exposure to the natural environment, insulator strings are susceptible to damage, such as self-explosion, broken umbrella skirts, and galvanic corrosion, which is one of the most common failures. Once damaged, it can lead to widespread power outage, which affects people’s life and industrial production, so it is necessary to inspect and replace the damaged insulator strings regularly. This thesis proposes a quadratic sampling based approach to assess the quality of insulator string sample banks. Through simple random sampling, estimation and interval estimation of the sample pool, the pass rate and average number of labels of the original sample pool are initially assessed and compared with the usage requirements to decide whether to carry out a second sampling or not; the second sampling used SADS as an evaluation metric to assess the performance of the insulator string sample bank.

Human causality detection and judgment with unsignaled and signaled delayed outcomes.

Four experiments examined human ratings of causal effectiveness, and ability to detect causal relationships, in a nonverbal paradigm. Participants responded on a concurrent random interval, extinction schedule. In the presence of one stimulus, responses produced an outcome (triangle flash); in the presence of the other stimulus, they did not. Following making a judgment of causal effectiveness, two further stimuli were presented simultaneously with one another, and participants had to select one depending on which of the previous two stimuli were associated with effective responses. In all experiments, immediate outcomes were associated with higher causal ratings and better causal detection than outcomes delayed by 3 s. A signal inserted between response and outcome improved ratings and detection (Experiments 2 and 4), even when it was contiguous with the response but not the outcome (Experiments 2 and 3). Stimuli associated with both components (marking cues) did not impact judgments or detection (Experiment 3). Stimuli signaling the availability of an outcome if a response was made (signaled reinforcement) did not improve causal judgments, but did improve detection of stimuli associated with the outcome (Experiment 4). Responses during the delay interfered with detection of the actual relationship when delays were unsignaled (Experiments 1-4), but not with fully or briefly signaled delays (Experiments 2-4), or with signaled reinforcement (Experiment 4). The results suggest a delay stimulus serves to signal the response has been successful and demark the delay period by serving a discriminative function. These findings mirror those seen in nonhuman conditioning. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A reinforcement schedule with two types of temporal information

Decades of research on reinforcement schedules have demonstrated that temporal information regarding the arrival or nonarrival of biologically significant events controls animal behavior. The fixed interval (FI) schedule, which is a time-based reinforcement schedule, suggests that responses are regulated by the time elapsed since the last reinforcement. This raises the question of how behavior is controlled when two distinct temporal cues regarding the availability of reinforcers are simultaneously presented. We modified the random interval (RI) schedule to create a reinforcement schedule incorporating two types of temporal information: the time elapsed since the last reinforcement, and the inter-reinforcement interval (IRI). Through long-term operant conditioning in rats, we examined how temporal information controls responses. When two temporal cues were available, we found that behavior was influenced by each cue. Moreover, we discovered that the effects of these cues could be analytically separated within the dynamics of response rates. The findings revealed that in controlling behavior, living organisms can utilize two temporal cues rather than relying on a single, clear cue.•We modified the random interval (RI) schedule to create a reinforcement schedule incorporating two types of temporal information.•When two temporal cues were available, we found that behavior was influenced by each cue.•Rats can utilize two temporal cues rather than relying on a single, clear cue.

Maxima over random time intervals for heavy-tailed compound renewal and Lévy processes

We derive subexponential tail asymptotics for the distribution of the maximum of a compound renewal process with linear component and of a Lévy process, both with negative drift, over random time horizon τ that does not depend on the future increments of the process. Our asymptotic results are uniform over the whole class of such random times. Particular examples are given by stopping times and by τ independent of the processes. We link our results with random walk theory.

Hybrid uncertainty propagation analysis of nonlinear systems in the frequency domain based on multi-scale random interval moment method

To quantify the impact of hybrid uncertainty (random and interval parameters) on the frequency-domain nonlinear dynamic response, the multi-scale method (MSM) and the random interval moment method (RIMM) are combined to establish a new uncertainty propagation analysis method, called the multi-scale random interval moment method (MS-RIMM). RIMM is used to describe the hybrid uncertainty, while MSM is used to determine the frequency-domain nonlinear dynamic response. The statistical characteristics (i.e., the expectation value and variance) of the amplitude-frequency response of the nonlinear system with hybrid uncertainties are derived. Furthermore, the accuracy and effectiveness of the proposed method are verified by comparing the results with those obtained using the multi-scale Monte Carlo simulation method (MS-MCSM). Overall, the results of this study can serve as a useful reference for the hybrid uncertainty propagation analysis of nonlinear systems and for predicting the frequency-domain nonlinear dynamic response.

Forecasting interval‐valued returns of crude oil: A novel kernel‐based approach

AbstractThis paper proposes a novel kernel‐based generalized random interval multilayer perceptron (KG‐iMLP) method for predicting high‐volatility interval‐valued returns of crude oil. The KG‐iMLP model is constructed by utilizing the distance based on a kernel function, which outperforms the conventional Euclidean distance. Additionally, the optimal kernel function is estimated using the variance–covariance matrix of the prediction error, contributing to a better understanding of the overall characteristics of interval‐valued data. The introduction of the kernel function renders the algorithms used for estimating machine learning parameters ineffective. Therefore, this paper further proposes a backward distance of accumulative error propagation algorithm to estimate both the kernel function and model parameters, which provides a feasible approach for utilizing kernel function in interval neural networks. In the empirical analysis of weekly and daily returns of WTI crude oil, the superior predictive performance of the proposed method is demonstrated, enabling stable and accurate predictions for both point values and interval values. The model exhibits consistent outstanding performance across different network structures, showcasing the potential of KG‐iMLP for crude oil price forecasting.

Synchronization through frequency shuffling.

A wide variety of engineered and natural systems are modeled as networks of coupled nonlinear oscillators. In nature, the intrinsic frequencies of these oscillators are not constant in time. Here, we probe the effect of such a temporal heterogeneity on coupled oscillator networks through the lens of the Kuramoto model. To do this, we shuffle repeatedly the intrinsic frequencies among the oscillators at either random or regular time intervals. What emerges is the remarkable effect that frequent shuffling induces earlier onset (i.e., at a lower coupling) of synchrony among the oscillator phases. Our study provides a novel strategy to induce and control synchrony under resource constraints. We demonstrate our results analytically and in experiments with a network of Wien Bridge oscillators with internal frequencies being shuffled in time.

Unsupervised feature based algorithms for time series extrinsic regression

Time Series Extrinsic Regression (TSER) involves using a set of training time series to form a predictive model of a continuous response variable that is not directly related to the regressor series. The TSER archive for comparing algorithms was released in 2022 with 19 problems. We increase the size of this archive to 63 problems and reproduce the previous comparison of baseline algorithms. We then extend the comparison to include a wider range of standard regressors and the latest versions of TSER models used in the previous study. We show that none of the previously evaluated regressors can outperform a regression adaptation of a standard classifier, rotation forest. We introduce two new TSER algorithms developed from related work in time series classification. FreshPRINCE is a pipeline estimator consisting of a transform into a wide range of summary features followed by a rotation forest regressor. DrCIF is a tree ensemble that creates features from summary statistics over random intervals. Our study demonstrates that both algorithms, along with InceptionTime, exhibit significantly better performance compared to the other 18 regressors tested. More importantly, DrCIF is the only one that significantly outperforms a standard rotation forest regressor.

Quantifying Cigarette and e-Cigarette Marketing Exposure Among Chinese Adolescents Using Ecological Momentary Assessment.

Addressing cigarette and e-cigarette use in China is key to reducing the global tobacco epidemic. Marketing exposure is one causal factor for adolescent smoking and e-cigarette use. Currently, China restricts cigarette and e-cigarette ads in public places and online; however, there may not be full policy compliance. We collected real-time data in the natural environment to estimate how much and where Chinese adolescents-a group susceptible to smoking and e-cigarette use-are exposed to cigarette and e-cigarette marketing to inform policy responses. In June 2022, we conducted a 7-day ecological momentary assessment (EMA) study with 15-16-year-olds (n = 96) across eight Chinese cities. Participants completed up to 42 EMA surveys (six per day), sent at random intervals outside of school hours. In each survey, participants reported whether they saw (1) displays and (2) ads in the past hour (none, cigarette, e-cigarette, both) in the past hour. We also captured the source of cigarette/e-cigarette ad exposure. Most participants were exposed to cigarette and/or e-cigarette displays (89.6%) or ads (79.2%) more than 7 days. On average, participants reported past-hour exposure to displays 12.7 times and past-hour exposure to ads 10.8 times over the week. The most common sources of cigarette ads were public places (eg, kiosks, supermarkets); the most common sources of e-cigarette ad exposure were social media/internet or e-cigarette stores. Findings highlight the need to enhance enforcement of restrictions on cigarette and e-cigarette ads in public places and online in China and extend restrictions to ban displays. Marketing exposure is a causal factor in youth smoking and e-cigarette use. We used EMA to estimate cigarette and e-cigarette display and ad exposure among Chinese adolescents. On average, participants reported past-hour exposure to cigarette and/or e-cigarette displays 13 times and past-hour exposure to cigarette and/or e-cigarette ads 11 times more than 1 week. Most saw ads in public places and online. Results suggest strengthening implementation of China's ban on cigarette and e-cigarette ads in public places and online and banning product displays. These are policy responses that can contribute to reducing adolescent cigarette and e-cigarette uptake in China.

Total sleep deprivation effects on the attentional blink.

The Attentional Blink (AB) is a phenomenon that reflects difficulty in detecting or identifying the second of two successive targets (T1 and T2) that are presented in rapid succession, between 200-500ms apart. The AB involves indicators of attentional and temporal integration mechanisms related to the early stages of visual processing. The aim of this study was to identify the effects of 24-h of sleep deprivation (total sleep deprivation, TSD) on the attentional and temporal integration mechanisms of the AB. Twenty-two undergraduate students were recorded during five successive days, in these three conditions: baseline (two days), TSD (one day), and recovery (two days). Each day, at around 12:00h, participants responded to a Rapid Serial Visual Presentation task (RSVP) that presented two targets separated by random intervals from 100 to 1000ms. The attentional mechanisms were assessed by the AB presence, the AB magnitude, and the AB interval, while the temporal integration mechanisms were evaluated by lag-1 sparing and order reversal responses. TSD negatively affected the attentional mechanisms, which is expressed by an overall reduction in performance, an extended AB interval, and a reduced AB magnitude. TSD also negatively affected the temporal integration mechanisms, manifested by an absence of lag-1 sparing and an increase in order reversals. These results suggest that people are still able to respond to two successive stimuli after 24h without sleep. However, it becomes more difficult to respond to both stimuli because the attentional and temporal integration mechanisms of the AB are impaired.

A retrospective study on the immobilisation of five captive primate species at Uganda Wildlife Conservation Education Centre, Uganda

AbstractThis retrospective study examined data from 108 primates consisting of 41 olive baboons (Papio anubis), 30 colobus monkeys (Colobus guereza), six debrazza monkeys (Cercopithecus neglectus), 26 red‐tailed monkeys (Cercopithecus ascanius), and five ruwenzori colobus monkeys (Colobus angolensis ruwenzorii). These primates underwent annual health assessments, involving the administration of an initial intramuscular dose ketamine (5 mg/kg) plus medetomidine (0.05 mg/kg). Additional intramuscular top‐ups of ketamine (5 mg/kg) plus medetomidine (0.05 mg/kg) were given as needed. Monitoring started when the animals were placed on the examination table in dorsal recumbency and continued until their return to the enclosures. During this period, various physiological parameters, such as heart rate (beats per minute), respiration rate (breaths per minute), peripheral oxygen saturation (SpO2, %) and rectal temperature (°C), were recorded at random intervals. To reverse immobilisation, atipamezole was administered intramuscularly at five times the medetomidine dose. Additionally, the study measured various induction and recovery times.

Stochastic resetting with refractory periods: pathway formulation and exact results

We look into the problem of stochastic resetting with refractory periods. The model dynamics comprises diffusive and motionless phases. The diffusive phase ends at random time instants, at which the system is reset to a given position—where the system remains at rest for a random time interval, termed the refractory period. A pathway formulation is introduced to derive exact analytical results for the relevant observables in a broad framework, with the resetting time and the refractory period following arbitrary distributions. For the paradigmatic case of Poissonian distributions of the resetting and refractory times, in general with different characteristic rates, closed-form expressions are obtained that successfully describe the relaxation to the steady state. Finally, we focus on the single-target search problem, in which the survival probability and the mean first passage time to the target can be exactly computed. Therein, we also discuss optimal strategies, which show a non-trivial dependence on the refractory period.