Window Duration Research Articles

The Dial-a-Ride problem with meeting points: A problem formulation for shared demand–responsive transit

In this paper, a formulation for the Dial-a-Ride Problem with Meeting Points (DARPmp) is introduced. The problem consists of defining routes that satisfy trip requests between pick-up and drop-off points while complying with time window, ride time, vehicle load, and route duration constraints. A set of meeting points is defined, and passengers may be asked to use these meeting points as alternative pickup or drop-off points if this results in routes with lower costs. Incorporating meeting points into the DARP is achieved by formulating a mixed-integer linear program. Two preprocessing steps and three valid inequalities are introduced, which improve the computational performance when solving the DARPmp to global optimality. Two versions of the Tabu Search metaheuristic are proposed to approximate the optimal solution in large-scale networks due to the NP-hardness of DARPmp. Performing numerical experiments with benchmark instances, this study demonstrates the benefits of DARPmp compared to DARP in terms of reducing vehicle running costs.

Meal Timing and Sleep Health Among Midlife Mexican Women During the Early Stages of the COVID-19 Pandemic.

This study aimed to examine associations between meal timing habits and sleep health in midlife Mexican women. Data comprised 379 midlife Mexican women who participated in a phone survey conducted within the Early Life Exposures in Mexico to Environmental Toxicants (ELEMENT) project during the early stages of the COVID-19 pandemic. Women answered questions related to meal habits and sleep duration, latency, and quality. We used linear regression to investigate the associations between meal timing, frequency of meals/snacks, eating window (duration between first and last eating occasion of the day), duration between last meal, bedtime, sleep duration, and logistic regression to examine the associations between meal timing, sleep latency, and sleep quality, adjusting for confounders. Later timing of meals throughout the day, and a shorter interval between the last meal of the day and bedtime, were associated with prolonged sleep latency and worse sleep quality. Associations with sleep duration were mixed: a longer eating window and a later largest and last meal were each associated with shorter sleep duration, while a later first meal and a shorter interval between the last meal of the day and bedtime were associated with longer sleep duration. Meal timing habits are associated with sleep duration, latency, and quality in midlife women.

The cerebellum acts as the analog to the medial temporal lobe for sensorimotor memory

The cerebellum is critical for sensorimotor learning. The specific contribution that it makes, however, remains unclear. Inspired by the classic finding that for declarative memories, medial temporal lobe (MTL) structures provide a gateway to the formation of long-term memory but are not required for short-term memory, we hypothesized that for sensorimotor memories, the cerebellum may play an analogous role. Here, we studied the sensorimotor learning of individuals with severe ataxia from cerebellar degeneration. We dissected the memories they formed during sensorimotor learning into a short-term temporally-volatile component, that decays rapidly with a time constant of just 15 to 20 s and thus cannot lead to long-term retention, and a longer-term temporally-persistent component that is stable for 60 s or more and leads to long-term retention. Remarkably, we find that these individuals display dramatically reduced levels of temporally-persistent sensorimotor memory, despite spared and even elevated levels of temporally-volatile sensorimotor memory. In particular, we find both impairment that systematically worsens with memory window duration over shorter memory windows (<12 s) and near-complete impairment of memory maintenance over longer memory windows (>25 s). This dissociation uncovers a unique role for the cerebellum as a gateway for the formation of long-term but not short-term sensorimotor memories, mirroring the role of the MTL for declarative memories. It thus reveals the existence of distinct neural substrates for short-term and long-term sensorimotor memory, and it explains both the trial-to-trial differences identified in this study and long-standing study-to-study differences in the effects of cerebellar damage on sensorimotor learning ability.

Optimizing Real-Time MI-BCI Performance in Post-Stroke Patients: Impact of Time Window Duration on Classification Accuracy and Responsiveness.

Brain-computer interfaces (BCIs) are promising tools for motor neurorehabilitation. Achieving a balance between classification accuracy and system responsiveness is crucial for real-time applications. This study aimed to assess how the duration of time windows affects performance, specifically classification accuracy and the false positive rate, to optimize the temporal parameters of MI-BCI systems. We investigated the impact of time window duration on classification accuracy and false positive rate, employing Linear Discriminant Analysis (LDA), Multilayer Perceptron (MLP), and Support Vector Machine (SVM) on data acquired from six post-stroke patients and on the external BCI IVa dataset. EEG signals were recorded and processed using the Common Spatial Patterns (CSP) algorithm for feature extraction. Our results indicate that longer time windows generally enhance classification accuracy and reduce false positives across all classifiers, with LDA performing the best. However, to maintain the real-time responsiveness, crucial for practical applications, a balance must be struck. The results suggest an optimal time window of 1-2 s, offering a trade-off between classification performance and excessive delay to guarantee the system responsiveness. These findings underscore the importance of temporal optimization in MI-BCI systems to improve usability in real rehabilitation scenarios.

Anatomy of a Spectrogram

This video provides a brief explanation of how spectrograms are constructed as a collection of individual spectra from consecutive points in time across a speech waveform. The difference between “narrow-band” and “wide-band” spectrograms is also explained based on choice analysis window duration. Three-dimensional animations of waveforms, spectra, and spectrograms are used throughout the video to demonstrate spectrographic representations of sound.

Sample-Specific MS/MS Methods in High-Throughput Mass Spectrometry.

The drug discovery process increasingly relies on high-throughput sample analysis to accelerate the identification of viable drug candidates. Recently, chromatographic-free high-throughput mass spectrometry (HT-MS) technologies have emerged, significantly increasing sample readout speed and enabling the analysis of large sample sets. These HT-MS platforms continuously acquire data from various samples into a single data file, presenting challenges in applying distinctive data acquisition methods to specific samples. This study introduces a novel approach that integrates real-time sample loading status to activate sample-specific MS/MS data acquisition methods on the high-throughput acoustic ejection mass spectrometry platform. Effective method switching and high signal reproducibility were demonstrated across different data acquisition window durations in multiple reaction monitoring (MRM), high-resolution MRM (MRMHR), and information-dependent acquisition modes. This advancement provides a user-friendly and robust solution to the method-setting challenges of HT-MS, expanding the implementation of HT-MS platforms in drug discovery and other high-throughput analytical applications.

Determining the Optimal Window Duration to Enhance Emotion Recognition Based on Galvanic Skin Response and Photoplethysmography Signals

Automatic emotion recognition using portable sensors is gaining attention due to its potential use in real-life scenarios. Existing studies have not explored Galvanic Skin Response and Photoplethysmography sensors exclusively for emotion recognition using nonlinear features with machine learning (ML) classifiers such as Random Forest, Support Vector Machine, Gradient Boosting Machine, K-Nearest Neighbor, and Decision Tree. In this study, we proposed a genuine window sensitivity analysis on a continuous annotation dataset to determine the window duration and percentage of overlap that optimize the classification performance using ML algorithms and nonlinear features, namely, Lyapunov Exponent, Approximate Entropy, and Poincaré indices. We found an optimum window duration of 3 s with 50% overlap and achieved accuracies of 0.75 and 0.74 for both arousal and valence, respectively. In addition, we proposed a Strong Labeling Scheme that kept only the extreme values of the labels, which raised the accuracy score to 0.94 for arousal. Under certain conditions mentioned, traditional ML models offer a good compromise between performance and low computational cost. Our results suggest that well-known ML algorithms can still contribute to the field of emotion recognition, provided that window duration, overlap percentage, and nonlinear features are carefully selected.

An Open-Label Phase II Study Assessing the Safety of Bilateral, Sequential Administration of Retinal Gene Therapy in Participants with Choroideremia: The GEMINI Study.

Choroideremia, an incurable, progressive retinal degeneration primarily affecting young men, leads to sight loss. GEMINI was a multicenter, open-label, prospective, two-period, interventional Phase II study assessing the safety of bilateral sequential administration of timrepigene emparvovec, a gene therapy, in adult males with genetically confirmed choroideremia (NCT03507686, ClinicalTrials.gov). Timrepigene emparvovec is an adeno-associated virus serotype 2 vector encoding the cDNA of Rab escort protein 1, augmented by a downstream woodchuck hepatitis virus post-transcriptional regulatory element. Up to 0.1 mL of timrepigene emparvovec, containing 1 × 1011 vector genomes, was administered by subretinal injection following vitrectomy and retinal detachment. The second eye was treated after an intrasurgery window of <6, 6-12, or >12 months. Each eye was followed at up to nine visits over 12 months. Overall, 66 participants received timrepigene emparvovec, and 53 completed the study. Visual acuity (VA) was generally maintained in both eyes, independent of intrasurgery window duration, even after bilateral retinal detachment and subretinal injection. Bilateral treatment was well tolerated, with predominantly mild or moderate treatment-emergent adverse events (TEAEs) and a low rate of serious surgical complications (7.6%). Retinal inflammation TEAEs were reported in 45.5% of participants, with similar rates in both eyes; post hoc analyses found that these were not associated with clinically significant vision loss at month 12 versus baseline. Two participants (3.0%) reported serious noninfective retinitis. Prior timrepigene emparvovec exposure did not increase the risk of serious TEAEs or serious ocular TEAEs upon injection of the second eye; furthermore, no systemic immune reaction or inoculation effect was observed. Presence of antivector neutralizing antibodies at baseline was potentially associated with a higher percentage of TEAEs related to ocular inflammation or reduced VA after injection of the first eye. The GEMINI study results may inform decisions regarding bilateral sequential administration of other gene therapies for retinal diseases.

Interference with Signaling Track Circuits Caused by Rolling Stock: Uncertainty and Variability on a Test Case

The demonstration of compliance of rolling stock against disturbance limits for railway signaling, and in particular track circuits, is subject to a large deal of variability, caused by the diverse values of the electrical parameters of the railway line and resulting transfer functions, as well as the operating conditions of the rolling stock during tests. Instrumental uncertainty is evaluated with a type B approach and shown to be much less than the experimental variability. Repeated test runs in acceleration, coasting, cruising, and braking conditions are considered, deriving both max-hold (spread) and sample (or experimental) standard deviation curves compared to the respective mean values (type A approach to the evaluation of uncertainty, as defined in of the Guide to the Uncertainty in Measurement. The major source of variability affecting a significant portion of the spectrum is caused by the superposed oscillations of the onboard LC filter, for which different choices of the transformation window duration are discussed. The test runs and the acquired data covered, overall, 1 day of tests along about 300 km of the Italian 3 kV DC railway network.

Monthly Runoff Prediction for Xijiang River via Gated Recurrent Unit, Discrete Wavelet Transform, and Variational Modal Decomposition

Neural networks have become widely employed in streamflow forecasting due to their ability to capture complex hydrological processes and provide accurate predictions. In this study, we propose a framework for monthly runoff prediction using antecedent monthly runoff, water level, and precipitation. This framework integrates the discrete wavelet transform (DWT) for denoising, variational modal decomposition (VMD) for sub-sequence extraction, and gated recurrent unit (GRU) networks for modeling individual sub-sequences. Our findings demonstrate that the DWT–VMD–GRU model, utilizing runoff and rainfall time series as inputs, outperforms other models such as GRU, long short-term memory (LSTM), DWT–GRU, and DWT–LSTM, consistently exhibiting superior performance across various evaluation metrics. During the testing phase, the DWT–VMD–GRU model yielded RMSE, MAE, MAPE, NSE, and KGE values of 245.5 m3/s, 200.5 m3/s, 0.033, 0.997, and 0.978, respectively. Additionally, optimal sliding window durations for different input combinations typically range from 1 to 3 months, with the DWT–VMD–GRU model (using runoff and rainfall) achieving optimal performance with a one-month sliding window. The model’s superior accuracy enhances water resource management, flood control, and reservoir operation, supporting better-informed decisions and efficient resource allocation.

Cell-to-cell heterogeneity in ion channel conductance impacts substrate vulnerability to arrhythmia.

Determining whether an ectopic depolarization will lead to a self-perpetuating arrhythmia is of critical importance in determining arrhythmia risk, so it is necessary to understand what factors impact substrate vulnerability. This study sought to explore the impact of cell-to-cell heterogeneity in ion channel conductance on substrate vulnerability to arrhythmia by measuring the duration of the vulnerable window in computational models of one-dimensional cables of ventricular cardiomyocytes. We began by using a population of uniform cable models to determine the mechanisms underlying the vulnerable window phenomenon. We found that in addition to the known importance of GNa, the conductances GCa,L and GKr also play a minor role in determining the vulnerable window duration. We also found that a steeper slope of the repolarizing action potential during the vulnerable window correlated with a shorter vulnerable window duration in uniform cables. We applied our understanding from these initial simulations to an investigation of the vulnerable window in heterogeneous cable models. The heterogeneous cables displayed a great deal of intra-cable variation in vulnerable window duration, highly sensitive to the cardiomyocytes in the local environment of the ectopic stimulus. Coupling strength modulated not only the magnitude of the vulnerable window duration but also the extent of intra-tissue variability in vulnerable window duration.NEW & NOTEWORTHY We investigate the impact of cell-to-cell heterogeneity in ion channel conductance on substrate vulnerability to arrhythmia by measuring the vulnerable window duration in computational cardiomyocyte cable models. We demonstrate a wide range of intra-cable variability in vulnerable window duration (VWD) and show how this is changed by ion channel block and coupling strength perturbations.

Critical speed models of high-resolution speed-duration profiles describe peak running demands in soccer

The purpose of this study was to characterize critical speed (CS) models for summarizing high-resolution speed-duration profiles from GPS tracking data obtained from soccer players. GPS data from 15 male NCAA Division I soccer players were collected during practices and games over a 6-week period. Moving averages of the speed data were computed for each file for duration windows spanning 0.1 to 600 seconds at 0.1-second resolution. Speed-duration profiles for each session and for the entire sampling period (“global”) were generated for each player by selecting the maximal mean speeds for each duration. Four models were fit to the profiles: the two-parameter CS (CS2) model, the three-parameter CS (CS3) model, the omni-domain speed-duration (OmSD) model, and the five-parameter logistic (5PL) model. The 5PL, CS3, and OmSD models exhibited similar goodness of fits, and all outperformed the CS2 model. Similar CS estimates were obtained for each model, whereas maximum speed ( Smax) estimates were lower for OmSD compared to the 5PL. Players exhibited a range of parameter values for CS, D′, and Smax. Smax and CS estimated from session-specific speed-duration profiles were on average higher for games compared to practices. We conclude that CS models are useful for empirically describing speed-duration profiles and for assessing peak running demands for soccer practices and games. The proposed approach could help coaches design practice activities to better mimic game demands.

Does continuous glucose monitoring influence adherence to time-restricted eating?

Obesity is a significant health issue in Aotearoa; effective and pragmatic strategies to facilitate weight loss are urgently required. Growing recognition of the circadian rhythm’s impact on metabolism has popularised diets like time-restricted eating (TRE)(1). The 16:8 TRE method involves limiting food intake to an 8-hour daily eating window and can lead to weight loss without other substantial changes to diet(2). Nonetheless, TRE requires accountability and tolerating hunger for short periods. Continuous glucose monitors (CGM) are small wearable biofeedback devices that measure interstitial glucose levels scanned via smartphones. By providing immediate feedback on the physiological effects of eating and fasting, CGM use may promote adherence to TRE(3). This pilot study aimed to 1) investigate how CGM affects adherence to TRE and 2) assess the feasibility of CGM use while undertaking TRE. This two-arm randomised controlled trial enrolled healthy adults from Dunedin, assigning them to TRE-only or TRE+CGM groups for 14 days. Successful adherence to TRE was defined a priori as maintaining an 8-hour eating window on 80% of days. CGM feasibility was defined a priori as scanning the glucose monitor thrice daily on 80% of days. Secondary outcomes included well-being, anthropometry, glucose levels, and overall TRE and CGM experiences via semi-structured interviews. Twenty-two participants were randomised into two groups: TRE-only (n = 11) and TRE+CGM (n = 11, with n = 2 excluded from analysis post-randomisation for medical reasons). Participants had a diverse range of ethnicities, the mean age was 32 (+/-14.9) years, and 55% were female. The TRE+CGM group adhered to the 8-hour eating window for an average of 10.0 days (range 2-14) compared with 8.6 days (range 2-14) in the TRE-only group. Both groups had similar mean eating window durations of 8.1 hours. Five (56%) participants in the TRE+CGM group achieved the a priori criteria for TRE adherence, compared to 3 (27%) in the TRE-only group. Participants in the TRE+CGM group performed an average of 8.2 (+/-5.6) daily scans, with n = 7 (78%) of participants meeting the a priori CGM feasibility criteria. Neither group reported consistent adverse psychological impacts in DASS-21 and WHO-5 scores. Interviews highlighted that CGM increased hunger tolerance during fasting as participants felt reassured by their normal glucose levels. CGM aided TRE accountability by acting as a biological tracker of food intake. Participants reported that TRE led to improved energy and self-efficacy, a more productive daily routine, and healthier food choices. Promisingly, 72% of participants would use CGM and undertake TRE in future. This study demonstrates that using CGM while undertaking TRE is feasible and can improve adherence by enhancing hunger tolerance and accountability. Overall, participants experienced increased awareness of eating habits and physiological mechanisms. Over the longer term, this simple and synergistic approach may be a helpful weight loss strategy.

Examining the bidirectional ripple effects in the NFT markets: Risky center or hedging center?

This study introduces a novel bidirectional ripple effect method to identify the risky center, hedging center, and duration of ripple effects. This method is used to examine the static and dynamic ripple effects among NFTs using idiosyncratic volatility measures. The findings indicate that, overall, correlations and bidirectional ripple effects among NFTs are prominent over the sample period. Only a few NFTs are significant ripple centers. Decentraland is a significant risky center, while CryptoVoxels serves as a reliable hedging center. The outcomes of rolling window tests and durations reveal that the central role of NFTs varies over time. The findings also show that ripple effects have significant durations. These conclusions hold considerable importance for NFT investors in making investment choices and managing risks.

An ontological approach to the detection of anomalies in vehicular ad hoc networks

In a vehicular environment, by becoming connected, vehicles are subject to more threats in comparison to traditional information systems, with the difference that, as a cyber-physical system, anomalies and intrusions could have repercussions in the physical world. In this work, we have developed an ontological anomaly-detection approach (OADA). The anomalies studied in this work mainly concern: network scans, DNS tunnel attacks, and telemetry data anomalies. Our contribution relates to a study of the attributes of interest for the algorithm used during the detection phase, namely the hierarchical temporal memory algorithm (HTM). The packets exchanged by the vehicle are grouped in instant description windows. These windows are then analyzed to extract a set of attributes. These are linked to the properties of network traffic, such as flow or latency. They are subject to the process of detecting anomalies and intrusions, carried out thanks to the algorithm with HTM. For each entry, the algorithm produces a score that allows us to decide if a window is abnormal and to lift an alert if that is the case. We evaluated our system using a communications and anomalies emulation tool. We use the corpus of data produced thanks to Autobot. We seek to determine from among the best scores of Matthews correlation coefficient (MCC) and Detection efficiency score (DES) which were the parameters for which HTM detects all anomalies with the greatest possible coverage. The obtained results prove that HTM can detect all anomalies for each window duration.

Speaker Profiling Based on the Short-Term Acoustic Features of Vowels

Speech samples can provide valuable information regarding speaker characteristics, including their social backgrounds. Accent variations with speaker backgrounds reflect corresponding acoustic features of speech, and these acoustic variations can be analyzed to assist in tracking down criminals from speech samples available as forensic evidence. Speech accent identification has recently received significant consideration in the speech forensics research community. However, most works have utilized long-term temporal modelling of acoustic features for accent classification and disregarded the stationary acoustic characteristics of particular phoneme articulations. This paper analyzes short-term acoustic features extracted from a central time window of English vowel speech segments for accent discrimination. Various feature computation techniques have been compared for the accent classification task. It has been found that using spectral features as an input gives better performance than using cepstral features, with the lower filters contributing more significantly to the classification task. Moreover, detailed analysis has been presented for time window durations and frequency bin resolution to compute short-term spectral features concerning accent discrimination. Using longer time durations generally requires higher frequency resolution to optimize classification performance. These results are significant, as they show the benefits of using spectral features for speaker profiling despite the popularity of cepstral features for other speech-related tasks.

Broadleaf tree phenology and springtime wildfire occurrence in boreal Canada.

Although broadleaf tree species of the boreal biome have a lower flammability compared to conifers, there is a period following snow melt and prior to leaf flush (i.e., greenup), termed the "spring window" by fire managers, when these forests are relatively conducive to wildfire ignition and spread. The goal of this study was to characterize the duration, timing, and fire proneness of the spring window across boreal Canada and assess the link between these phenologicalvariables and the incidence of springtime wildfires. We used remotely sensed snow cover and greenup data to identify the annual spring window for five boreal ecozones from 2001 to 2021 and then compared seasonality of wildfire starts (by cause) and fire-conducive weather in relation to this window, averaged over the 21-year period. We conducted a path analysis to concomitantly evaluate the influence of the spring window's duration, the timing of greenup, and fire-conducive weather on the annual number and the seasonality of spring wildfires. Results show that the characteristics of spring windows vary substantially from year to year and among geographic zones, with the interior west of Canada having the longest and most fire-conducive spread window and, accordingly, the greatest springtime wildfire activity. We also provide support for the belief that springtime weather generally promotes wind-driven, rather than drought-driven wildfires. The path analyses show idiosyncratic behavior among ecozones, but, in general, the seasonality of the wildfire season is mainly driven by the timing of the greenup, whereas the number of spring wildfires mostly responds to the duration of the spring window and the frequency of fire-conducive weather. The results of this study allows us to better understand and anticipate the biome-wide changes projected for the northern forests of North America.

Assessing the timing and the duration of exposure to air pollution on cardiometabolic biomarkers in patients suspected of coronary artery disease

Air pollution can affect cardiometabolic biomarkers in susceptible populations, but the most important exposure window (lag days) and exposure duration (length of averaging period) are not well understood. We investigated air pollution exposure across different time intervals on ten cardiometabolic biomarkers in 1550 patients suspected of coronary artery disease. Daily residential PM2.5 and NO2 were estimated using satellite-based spatiotemporal models and assigned to participants for up to one year before the blood collection. Distributed lag models and generalized linear models were used to examine the single-day-effects by variable lags and cumulative effects of exposures averaged over different periods before the blood draw. In single-day-effect models, PM2.5 was associated with lower apolipoprotein A (ApoA) in the first 22 lag days with the effect peaking on the first lag day; PM2.5 was also associated with elevated high-sensitivity C-reactive protein (hs-CRP) with significant exposure windows observed after the first 5 lag days. For the cumulative effects, short- and medium-term exposure was associated with lower ApoA (up to 30wk-average) and higher hs-CRP (up to 8wk-average), triglycerides and glucose (up to 6 d-average), but the associations were attenuated to null over the long term. The impacts of air pollution on inflammation, lipid, and glucose metabolism differ by the exposure timing and durations, which can inform our understanding of the cascade of underlying mechanisms among susceptible patients.

The Community Stress-Drop Validation Study—Part I: Source, Propagation, and Site Decomposition of Fourier Spectra

AbstractAs part of the community stress-drop validation study initiative, we apply a spectral decomposition approach to isolate the source spectra of 556 events occurred during the 2019 Ridgecrest sequence (Southern California). We perform multiple decompositions by introducing alternative choices for some processing and model assumptions, namely: three different S-wave window durations (i.e., 5 s, 20 s, and variable between 5 and 20 s); two attenuation models that account differently for depth dependencies; and two different site amplification constraints applied to restore uniqueness of the solution. Seismic moment and corner frequency are estimated for the Brune and Boatwright source models, and an extensive archive including source spectra, site amplifications, attenuation models, and tables with source parameters is disseminated as the main product of the present study. We also compare different approaches to measure the precision of the parameters expressed in terms of 95% confidence intervals (CIs). The CIs estimated from the asymptotic standard errors and from Monte Carlo resampling of the residual distribution show an almost one-to-one correspondence; the approach based on model selection by setting a threshold for misfit chosen with an F-ratio test is conservative compared to the approach based on the asymptotic standard errors. The uncertainty analysis is completed in the companion article in which the outcomes from this work are used to compare epistemic uncertainty with precision of the source parameters.

A Dust Detection Database for the Inner Heliosphere Using the Parker Solar Probe Spacecraft

A database of in situ dust impact detections made by the Parker Solar Probe spacecraft is created to facilitate studies of interplanetary dust dynamics in the inner heliosphere. A standardized dust detection methodology is established and tested for validity. Individual impact detections are included in the database, and are used to derive dust impact rates. Impact rates are corrected for effects related to high-amplitude plasma waves and undercounting due to finite detection window duration. These corrections suggest that: (i) most dust impacts on Parker Solar Probe are consistent with a random process; and (ii) the true dust impact rate may be ∼50% greater than the impact rate determined using uncorrected data for certain portions of the orbit, especially near perihelion.