Variable Time Research Articles

Short-term trajectory prediction for individual metro passengers based on multi-level periodicity mining from semantic trajectory

Short-term trajectory prediction for individual metro passengers is an essential component of many location-based applications, such as bottleneck identification, anomalous trajectory detection, and emergency evacuation. Accurate and interpretable trajectory prediction in short-term remains challenging since the trajectory of individual metro passengers is influenced by time-varying and periodic mobility patterns. Moreover, it is still difficult for deep learning-based prediction approaches to intuitively explain the individual periodic mobility patterns captured by the black-box model. In this study, a long short-term memory method based on multi-level periodicity mining (LSTM-MP) from semantic trajectory is developed to achieve accurate and interpretable short-term trajectory prediction for individual metro passengers. The newly defined semantic trajectories of metro passengers that combine the semantic information about the geometric and operational characteristics of the metro with the raw trajectory data are constructed to help obtain fine-grained periodic mobility patterns of individual passengers. Multi-level periodicity which includes long-range, weekly-interval, and daily-interval periodicities, is calculated with variable time granularity and its polymerization from the constructed semantic trajectories. Finally, the characteristics of multi-level periodicity are considered by developing a principal trajectory attention mechanism to select the most relevant trajectories for prediction in the LSTM-MP framework. The prediction performance of LSTM-MP is compared with 2-order Markov chain and Naïve-LSTM algorithms using the large real-world anonymous passenger WiFi probe dataset. Experimental results show that LSTM-MP can predict when and where an individual will be in the future time series with an average accuracy of 82.3%. This demonstrates the effectiveness of LSTM-MP in the short-term trajectory prediction for individual metro passengers. Potential applications of the short-term trajectory prediction for individual metro passengers are also identified.

Isotopic tracing of leachate percolation from municipal solid waste dump sites to groundwater in diverse climatic zones of India.

Groundwater resources in tropical regions are largely dependent on recharge by rainwater infiltration through soil layers with variable time. However, the rainwater infiltration through soil is a serious concern in urban tropics where it interacts with landfills at the dumpsites, potentially contaminating adjoining groundwater. In this study, the stable isotopic compositions of oxygen and hydrogen (δ18O and δ2H, respectively) in groundwater and leachates, adjoining municipal dumpsites in urban tropics (Bangalore, Kolkata and Durgapur located in diverse rainfall zonation of India), were analyzed to investigate their recharge sources and trace the possible mixing of leachate contaminants under three diverse climatology. The measured values of δ18O and δ2H suggested that the groundwater in these sites reflects higher recharge by rainwater. However, the d-excess values indicated secondary effects suggesting the groundwater has experienced significant modifications. The end member analysis using δ18O-d-excess relation pinpointed an additional leachate contribution from adjoining dumpsites. The critical fraction of leachate infiltration to groundwater quantified using two component mixing model ranged between (i) 1 and 33% in Bangalore, (ii) 5 and 13% in Kolkata and (iii) 18 and 76% in Durgapur, with its variability dependent on seasonality and aquifer connectivity. This information is crucial for groundwater management to secure water quality and to quantify potential hydrological contaminants particularly in drier seasons and drier regions, when and where the demand for groundwater is high, respectively.

Participatory modeling for high complexity, multi‐system issues: challenges and recommendations for balancing qualitative understanding and quantitative questions

AbstractCommunity stakeholder participation can be incredibly valuable for the qualitative model development process. However, modelers often encounter challenges for participatory modeling projects focusing on high‐complexity, synergistic interactions between multiple issues, systems, and granularity. The diverse stakeholder perspectives and volumes of information necessary for developing such models can yield qualitative models that are difficult to translate into quantitative simulation or clear insight for informed decision‐making. There are few recommended best practices for developing high‐complexity, participatory models. We use an ongoing project as a case study to highlight three practical challenges for tackling high‐complexity, multi‐system issues with system dynamics tools. These challenges include balanced and respectful stakeholder engagement, defining boundaries and levels of variable aggregation, and timing and processes for qualitative/quantitative model integration. Our five recommendations to address these challenges serve as a foundation for further research on methods for developing translatable qualitative multi‐system models for informing actions for systemic change. © 2024 System Dynamics Society.

Application of a Variable Weight Time Function Combined Model in Surface Subsidence Prediction in Goaf Area: A Case Study in China

To attain precise forecasts of surface displacements and deformations in goaf areas (a void or cavity that remains underground after the extraction of mineral resources) following coal extraction, this study based on the limitations of individual time function models, conducted a thorough analysis of how the parameters of the model impact subsidence curves. Parameter estimation was conducted using the trust-region reflective algorithm (TRF), and the time function models were identified. Then we utilized a combined model approach and introduced the sliding window mechanism to assign variable weights to the model. Based on this, the combined model was used for prediction, followed by the application of this composite prediction to engineering scenarios for the dynamic forecasting of surface movements and deformations. The results indicated that, in comparison with DE, GA, PSO algorithms, the TRF exhibited superior stability and convergence. The parameter models obtained using this method demonstrated a higher level of predictive accuracy. Moreover, the predictive precision of the variable-weight time function combined model surpassed that of corresponding individual time function models. When employing six different variable-weight combination prediction models for point C22, the Weibull-MMF model demonstrated the most favorable fitting performance, featuring a root mean square error (RMSE) of 32.98 mm, a mean absolute error (MAE) of 25.66 mm, a mean absolute percentage error (MAPE) of 7.67%; the correlation coefficient R2 reached 0.99937. These metrics consistently outperformed their respective individual time function models. Additionally, in the validation process of the combined model at point C16, the residuals were notably smaller than those of individual models. This reaffirmed the accuracy and reliability of the proposed variable-weight combined model. Given that the variable-weight combination model was an evolution from individual time function models, its applicability extends to a broader range, offering valuable guidance for the dynamic prediction of surface movement and deformation in mining areas.

Analyzing the Factors Influencing Time Delays in Korean Railroad Accidents

Railroads play a pivotal role in the Korean national economy, necessitating a thorough understanding of factors influencing accidents for effective mitigation strategies. Unlike prior research focused on accident frequency and severity, this study delves into the often-overlooked aspect of time delays resulting from railroad accidents. Analyzing 15 years of nationwide data (2008–2022), encompassing 3244 human-related and 3350 technical events, this research identifies key factors influencing delay likelihood and duration. Factors considered include event type, season, train type, location, operator size, person type involved, facility type, and causes. Despite an overall decrease in events, variable delay times highlight the need to comprehend specific contributing factors. To address excess zeros, the study employs a two-stage model and a zero-inflated negative binomial (ZINB) model, alongside artificial neural networks (ANNs) for non-linear pattern recognition. Human-related delays are influenced by event types, seasons, and passenger categories, exhibit nuanced impacts. Technical-related delays are influenced by incident types and facility involvement. Regarding model performance, the ANN models outperform regression-based models consistently in all cases. This study emphasizes the importance of considering both human and technical factors in predicting and understanding railroad accident delays, offering valuable insights for formulating strategies to mitigate service disruptions associated with these incidents.

Physics-informed neural nets for control of dynamical systems

Physics-informed neural networks (PINNs) incorporate established physical principles into the training of deep neural networks, ensuring that they adhere to the underlying physics of the process while reducing the need for labeled data, since the desired output is not a prerequisite for physics-informed training. For modeling systems described by Ordinary Differential Equations (ODEs), traditional PINNs typically take continuous time as an input variable and produce the solution to the corresponding ODE. However, in their original form, PINNs neither accommodate control inputs nor do they effectively simulate variable long-range intervals without experiencing a significant decline in prediction accuracy. In this context, this work introduces a novel framework known as “Physics-Informed Neural Nets for Control” (PINC). PINC presents an innovative PINN-based architecture tailored to control problems, capable of simulating longer-range time horizons that are not predetermined during training. This increased flexibility sets it apart from traditional PINNs. The variable simulation time is achieved by adding inputs to the PINC network that convey the initial condition and the control signal for a particular time interval. Simulating variable long-range intervals involves running the PINC net across a sequence of shorter intervals. In this autoregressive process, the network predictions are linked in a self-feedback mode, with the initial state (input) of the next interval set to the last predicted state (network output) of the previous interval. We showcase the effectiveness of our proposal in identifying and controlling three nonlinear dynamic systems: the Van der Pol oscillator, the four-tank system, and an electric submersible pump. Crucially, these experiments demonstrate that learning the dynamics of these systems can be achieved without relying on any sample collected from the actual process, and it offers faster inference speed compared to numerical simulations.

Research on decision making of intelligent vehicle based on composite priority experience replay

To address the problems of underutilization of samples and unstable training for intelligent vehicle training in the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, a TD3 algorithm based on the Composite Prioritized Experience Replay (CPR-TD3) mechanism is proposed. It considers experience immediate reward value and Temporal Difference error (TD-error) based and respectively to construct priorities to rank the samples. Subsequently composite average ranking of the samples to recalculate the priorities for sampling, uses the collected samples to train the target network. Then introduces the minimum lane change distance and the variable headway time distance to improve the reward function. Finally, the improved algorithm is proved to be effective by comparing it with the traditional TD3 on the highway scenario, and the CPR-TD3 algorithm improves the training efficiency of intelligent vehicles.

An end-to-end machine learning approach with explanation for time series with varying lengths

AbstractAn accurate prediction of complex product quality parameters from process time series by an end-to-end learning approach remains a significant challenge in machine learning. A special difficulty is the application of industrial batch process data because many batch processes generate variable length time series. In the industrial application of such methods, explainability is often desired. In this study, a 1D convolutional neural network (CNN) algorithm with a masking layer is proposed to solve the problem for time series of variable length. In addition, a novel combination of 1D CNN and class activation mapping (CAM) technique is part of this study to better understand the model results and highlight some regions of interest in the time series. As a comparative state-of-the-art unsupervised machine learning method, the One-Nearest Neighbours (1NN) algorithm combined with dynamic time warping (DTW) was used. Both methods are investigated as end-to-end learning methods with balanced and unbalanced class distributions and with scaled and unscaled input data, respectively. The FastDTW and DTAIDistance algorithms were investigated for the DTW calculation. The data set is made up of sensor signals that was collected during the production of plastic parts. The objective was to predict a quality parameter of plastic parts during production. For this research, the quality parameter will be a difficult or only destructively measurable parameter and both methods will be investigated for their applicability to this prediction task. The application of the proposed approach to an industrial facility for producing plastic products shows a prediction accuracy of 83.7%. It can improve the reverence method by approximately 1.4%. In addition to the slight increase in accuracy, the CNN training time was significantly reduced compared to the DTW calculation.

Survival models: a tool for managing and analyzing the incidence of rust in irrigated coffee trees

Studies related to the characteristics and health of coffee make it possible to manage and enhance the product, directly influencing its quality. Such studies are crucial for strategic marketing reasons, encompassing cost reduction, loss, and production optimization. In addition to these aspects, there are concerns about improving product quality. Thus, studying the variable time until the development of coffee rust, considering different irrigation systems, is relevant, given that this disease causes early leaf fall and branch drying, diminishing both production and product quality. In light of the above, the objective is to demonstrate that the main survival analysis models can be employed as instruments for managing and analyzing the incidence of coffee rust, considering different irrigation systems. The analysis revealed that the drip irrigation system was more effective than the others.

Existence and Properties of the Solution of Nonlinear Differential Equations with Impulses at Variable Times

In this paper, a class of nonlinear ordinary differential equations with impulses at variable times is considered. The existence and uniqueness of the solution are given. At the same time, modifying the classical definitions of continuous dependence and Gâteaux differentiability, some results on the continuous dependence and Gâteaux differentiable of the solution relative to the initial value are also presented in a new topology sense. For the autonomous impulsive system, the periodicity of the solution is given. As an application, the properties of the solution for a type of controlled nonlinear ordinary differential equation with impulses at variable times is obtained. These results are a foundation to study optimal control problems of systems governed by differential equations with impulses at variable times.

Performance and phenotypic stability of maize hybrids containing exotic introgressions in multi‐environment trials

AbstractMaize (Zea mays L.) grown in the US Midwest contains only a small fraction of the genetic diversity present in the species. Maize populations from other parts of the world may contain genetic variation that could be used to improve or increase the diversity of US germplasm. This study was conducted to assess the performance and phenotypic stability of hybrids containing diverse exotic introgressions across North American environments. Doubled haploid (DH) lines were created by the Germplasm Enhancement of Maize project from backcross 1 families that used 27 open‐pollinated populations from Latin America as donor parents and PHZ51, a non‐Stiff Stalk inbred developed in Iowa, as the recurrent parent. DH lines were testcrossed with LH195, a Stiff Stalk inbred developed in Iowa, and hybrid field trials were performed at 24 environments in the United States and Canada. Experimental hybrids had variable flowering time, plant and ear height, and test weight, but none had significantly greater yield than the PHZ51 × LH195 hybrid. The slopes of linear reaction norm models were significantly lower for the experimental hybrids than the US‐adapted reference hybrids for three traits: yield, growing degree units (GDU) to anthesis, and GDU to silking. The results suggest that unfavorable alleles and phenotypic stability should be considered when exotic open‐pollinated populations are used in US maize breeding.

Exploring the exponential sensitivity of risk perception in the COVID-19 pandemic.

Individual's risk perception regarding specific hazards is a dynamic process that evolves over time. This study analyzed the relationship between the number of COVID-19 cases and the South Korean public's risk perceptions from the outset of the pandemic to the recent past. More than 70 repeated cross-sectional surveys were conducted biweekly to measure individuals' risk perception. An autoregressive integrated moving average with explanatory variable time series analysis was used to characterize the relationship between the number of COVID-19 cases and level of risk perceptions. It revealed that individuals' risk perception and the number of COVID-19 cases were not linearly related but were logarithmically correlated. This finding can be understood as a psychic numbing effect, suggesting that people's perception of risk is not linear but rather exponentially sensitive to changes. The findings also revealed a significant influence of individuals' trust in local governments on their risk perceptions, highlighting the substantial role played by local governments in direct risk management during the COVID-19 pandemic.

Productivity benefits of cereal-legume cover crop mixtures under variable soil nitrogen and termination times

Mixtures of cereal and legume cover crops can merge the benefits of these two functional groups and sometimes increase biomass production and ecosystem services compared to monocultures. However, the multifunctionality and productivity of cover crop mixtures depend on environmental and management factors that influence species composition. We hypothesized that (i) the species composition in the cereal/legume cover crop mixtures will change in response to soil N level and growth stage, (ii) cereal/legume mixtures will accumulate more aboveground biomass and N and produce greater leaf area index and canopy cover than monocultures, particularly at low soil N levels and with late termination, and (iii) cereal/legume mixtures will reduce the risk of high C:N ratios associated with late termination of a cereal cover crop. We evaluated cereal/legume cover crop mixtures and monocultures in a long-term maize (Zea mays L.) N rate study at the University of Kentucky. The study had four inorganic N rates applied to maize since 1970 (0, 84, 168, 336 kg N ha-1) and three winter cover crop treatments (cereal rye (Secale cereale L.) monoculture, crimson clover (Trifolium incarnatum L.) monoculture, rye/clover mixture). We evaluated the cover crops from fall 2020 through spring 2022. We collected repeated measurements of cover crop aboveground biomass, N content, leaf area index, and canopy cover. At the final sampling in late April or early May, we found that the cover crop mixture produced more biomass than both monocultures at intermediate N input rates, while low and high N input rates resulted in similar biomass levels as monoculture clover and rye, respectively. The treatment without N fertilizer application produced proportionally more clover in the mixture than in higher fertilizer rates. Clover in monoculture did not show significant differences in biomass among the N fertilizer rates, while rye increased its growth proportionally to the applied N. Early termination represented more biomass for rye in mixture, while late termination favored clover growth. The cereal/legume mixtures had moderate C:N ratios relative to both monocultures across N rates and sampling dates. The results of this study suggest that cover crop mixtures may perform equally or better than monoculture across different N levels and termination timings.

Variable time delay estimation in continuous industrial processes

Digital sensors and machine learning enable efficiency improvements in production processes, through process monitoring, anomaly detection, soft sensing, and process control. However, the development of such solutions requires several data preprocessing steps. In continuous processes, a crucial part of the data preparation is adjusting for time delays between different sensors. This is necessary to ensure that each sensor measurement relate to the same volume of materials going through various processing steps.This study provides an overview of data-driven methods for estimating time lags between sensors in continuous processes. The methods are assessed in a large simulation study, on data sets with different sample sizes, model complexities and autocorrelation functions. Our results shows that most methods work well if the relationships are close to linear, but more flexible metrics like distance correlation and maximum information coefficient are needed in more complex systems. Finally, we present a real industrial example to illustrate some real-world aspects of the variable time delay estimation process.

Variable time headway spacing strategy for connected vehicles platoon based on sliding mode control

In complex traffic environments, the preceding vehicle in the platoon often experiences frequent acceleration and deceleration due to changing traffic conditions, resulting in fluctuations in the velocity of the following vehicles. While the current spacing strategy can help the platoon regain a stable state during the fluctuation of vehicle flow, it does not effectively suppress the velocity and acceleration fluctuations of the following vehicles during the adjustment process. This paper proposes a novel variable time headway (VTH) spacing strategy, which employs the neighboring vehicle velocity ratio to depict the relative variations in velocity and considers the impact of the preceding vehicle’s acceleration. As a result, the fluctuations of the preceding vehicle can be precisely characterized allowing the following vehicles to respond promptly and accurately to the changes in the behavior of the preceding vehicle. Simultaneously, a robust low communication dependency (RLCD) system based on the third-order dynamic model is proposed to mitigate the impact of complexity and reliability of information flow topology (IFT) on platoon stability in the Vehicular Ad hoc NETwork (VANET). Based on the proposed VTH strategy, a robust coupled sliding mode controller for the platoon is designed under the low communication dependency requirement. Furthermore, by introducing dynamic sliding mode control into the coupled sliding mode surface, a smoother controller input is obtained while ensuring the string stability of the vehicle platoon. The numerical simulation results demonstrate conclusively that the proposed strategy effectively reduces velocity tracking errors and acceleration fluctuations for connected vehicles (CVs) in complex traffic environments. The fluctuations of the preceding vehicles can be more effectively absorbed, and the overall tracking performance of the vehicle platoon is enhanced.

Diagnostic journey for individuals with fibrous dysplasia / McCune albright syndrome (FD/MAS)

BackgroundReducing delayed diagnosis is a significant healthcare priority for individuals with rare diseases. Fibrous Dysplasia/ McCune Albright Syndrome (FD/MAS) is a rare bone disease caused by somatic activation mutations of NASA. FD/MAS has a broad clinical phenotype reflecting variable involvement of bone, endocrine and other tissues, distribution and severity. The variable phenotype is likely to prolong the diagnostic journey for patients further.AimTo describe the time from symptom onset to final diagnosis in individuals living with FDMAS.MethodsWe used the UK-based RUDY research database (www.rudystudy.org), where patients self-report their diagnosis of FD/MAS. Participants are invited to complete the diagnostic journey based on the EPIRARE criteria.Results51 individuals diagnosed with FD/MAS were included in this analysis. Among them, 70% were female, and the median age was 51.0 years (IQR 34.5–57.5]. 12 (35%) individuals reported McCune Albright Syndrome, 11 (21.6%) craniofacial and 11(21.6%) for each of poly- and mono-ostotic FD and 6 (11.8%) did not know their type of FD/MAS. Pain was the commonest first symptom (58.8%), and 47.1% received another diagnosis before the diagnosis of FD/MAS. The median time to final diagnosis from the first symptom was two years with a wide IQR (1,18) and range (0–59 years). Only 12 (23.5%) of individuals were diagnosed within 12 months of their first symptoms. The type of FD/MAS was not associated with the reported time to diagnosis. Significant independent predictors of longer time to final diagnosis included older current age, younger age at first symptom and diagnosis after 2010.ConclusionIndividuals with FDMAS have a variable time to diagnosis that can span decades. This study highlights the need for further research on how to improve diagnostic pathways within Orthopaedic and Ear, Nose and Throat (ENT)/Maxillofacial services. Our data provides a baseline to assess the impact of novel NHS diagnostic networks on reducing the diagnostic odyssey.

N95 respirator hybrid decontamination method using Ultraviolet Germicidal Irradiation (UVGI) coupled with Microwave-Generated Steam (MGS).

The Coronavirus Disease 2019 (COVID-19) pandemic has induced a critical supply of personal protective equipment (PPE) especially N95 respirators. Utilizing respirator decontamination procedures to reduce the pathogen load of a contaminated N95 respirator can be a viable solution for reuse purposes. In this study, the efficiency of a novel hybrid respirator decontamination method of ultraviolet germicidal irradiation (UVGI) which utilizes ultraviolet-C (UV-C) rays coupled with microwave-generated steam (MGS) against feline coronavirus (FCoV) was evaluated. The contaminated 3M 1860 respirator pieces were treated with three treatments (UVGI-only, MGS-only, and Hybrid-UVGI + MGS) with variable time. The virucidal activity was evaluated using the TCID50 method. The comparison of decontamination efficiency of the treatments indicated that the hybrid method achieved at least a pathogen log reduction of 4 logs, faster than MGS and UVGI. These data recommend that the proposed hybrid decontamination system is more effective comparatively in achieving pathogen log reduction of 4 logs.

Carbonaceous catalysts (biochar and activated carbon) from agricultural residues and their application in production of biodiesel: A review

Carbonaceous catalysts obtained from agricultural residue could have potential in the production of biofuels such as biodiesel. This review paper discusses the preparation conditions (temperature, heating rate, hold time, inert gas flow rate, etc play key roles in development of textural characteristics of the catalysts) and functionalization methods of biochar and activated carbon derived from agricultural residues and their application to produce biodiesel. Research works reported in achieving maximum yield of biodiesel in terms of variable precursors, alcohol-to-oil ratio, reaction time and temperatures have been profoundly tabulated. Effect of textural properties of the biochar and activated carbon (such as surface area, total pore volume, average pore size, and functional group attached with the catalyst) on the biodiesel yield are examined. Studies on Regeneration and reusing of the spent catalysts are carefully inspected. The economic evaluation studies for the biochar and activated carbon and the applications of these for biodiesel production are scrutinized. Finally, the strategies to increase biomass and catalyst productivity, future prospect and research directions to enhance biofuel/biodiesel production and for the development of biochar and activated carbon from agricultural residues for sustainable biodiesel production is suggested.

Variable practice is superior to self-directed training for laparoscopic simulator training: a randomized trial

BackgroundMastering laparoscopy is challenging—it requires specific psychomotor skills which are difficult to obtain in the operating room without potentially compromising patient safety. Proficiency-based training programs using virtual reality simulators allow novices to practice and develop their skills in a patient-safe learning environment. Variable practice leads to stronger retention and skills transfer in a non-surgical setting. The objective of this trial was to investigate if variable practice was superior to self-directed training.MethodsA randomized trial where participants (n = 36) were randomized to proficiency-based laparoscopic simulator training of basic skills using either variable practice or self-directed training, followed by a transfer test with proficiency-based training on a procedural task (a salpingectomy). All participants returned after a period of 3–5 weeks to perform a retention test. Results: The mean time to proficiency for the basic skills tasks were 119 min (SD: 93) for the variable practice group versus 182 min (SD: 46) for the self-directed training group (p = 0.015). The time to reach proficiency during the transfer test was 103 min (SD: 57) versus 183 min (SD: 64) for the variable practice group versus the self-directed training group, respectively (p < 0.001). The mean time to proficiency for the retention test was 51 min (SD: 26) and 109 min (SD: 53) for the variable practice group and self-directed training group, respectively (p < 0.001).ConclusionVariable practice is superior to self-directed training for proficiency-based laparoscopic training. With variable time to practice proficiency is reduced, there is higher transfer to a procedural task, and retention is improved.

On the nonlinearity of the foreperiod effect

One of the frequently employed tasks within the implicit timing paradigm is the foreperiod task. The foreperiod is the time interval spanning from the presentation of a warning signal to the appearance of a target stimulus, during which reaction time trajectory follows time uncertainty. While the typical approach in analyzing foreperiod effects is based on linear approximations, the uncertainty in the estimation of time, expressed by the Weber fraction, implies a nonlinear trend. In the present study, we analyzed the variable foreperiod reaction times from a relatively large sample (n = 109). We found that the linear regression on reaction times and log-transformed reaction times poorly fitted the foreperiod data. However, a nonlinear regression based on an exponential decay function with three distinctive parameters provided the best fit. We discussed the inferential hazards of a simplistic linear approach and demonstrated how a nonlinear formulation can create new opportunities for studies in implicit timing research, which were previously impossible.