Temporal Approach Research Articles

Street Experiments Across EU Cities: An Exploratory Study on Leveraging Data for Urban Mobility Impact Evaluation

European cities are under pressure to be at the forefront of climate neutrality while providing inclusive, safe, and sustainable urban mobility. Street experiments are being adopted to accelerate this transition, yet assessing their impact remains challenging. This study addresses this gap by providing an evidence-based impact assessment of street experiments. The research builds on insights from 20 European cities, including 13 from the EU Cities Mission, regarding expected goals and current evaluation barriers. A preliminary quasi-experimental spatial and temporal approach is proposed and further enriched through the identification of the most relevant mobility domains and indicators addressed by cities. An exploration of data collection technologies is undertaken to meet the cities’ needs, culminating in the design of a portable and easy-to-install laboratory, the Labkit, for in situ and non-intrusive evaluation of public space interventions. The Labkit is tested and validated in an open area with a constant flow of pedestrians, cyclists, e-scooters, and vehicles. The results of this testing process, along with feedback from cities regarding the methodological approach and potential indicators, are analysed. The study concludes with a discussion of the opportunities and limitations of data-driven approaches for urban mobility impact assessment and the proposal of future research directions.

Collaborative multiview time series modeling for vehicle maintenance demand prediction

Accurate prediction of vehicle maintenance demands is crucial for sustaining vehicle use, optimizing performance, and minimizing ownership costs. However, current methods only predict maintenance demand for specific vehicle components and lack the capability to offer a comprehensive prediction of all maintenance demands. Furthermore, predicting vehicle maintenance demand must incorporate the impacts of various essential maintenance projects on subsequent demands. To address these challenges, we propose an innovative method for predicting vehicle all maintenance demands based on collaborative multiview time series modeling. Leveraging the interdependencies among vehicle maintenance projects across various time periods, we employ a temporal dependency learning approach utilizing a multi-attention mechanism. To enhance the interaction between distinct time points and temporal dependencies, we developed a dependency-aware learning algorithm that effectively integrates and weighs the information and dependencies at each time step, thereby improving the model’s ability to capture the complex relationships among maintenance projects over time. To capture the significant impact of key maintenance projects on future demands, we propose a module that leverages both long short-term memory networks and the attention mechanism. Experimental results on actual vehicle maintenance records confirm that the proposed model outperforms existing methods, demonstrating its efficacy and applicability in predicting vehicle maintenance demand.

Leveraging Convolutional Neural Networks for Predicting Symptom Escalation in Chemotherapy Patients: A Temporal Resampling Approach.

This paper introduces a novel approach for predicting symptom escalation in chemotherapy patients by leveraging Convolutional Neural Networks (CNNs). Accurate forecasting of symptom escalation is crucial in cancer care, as it enables timely interventions and enhances symptom management, ultimately improving patients' quality of life during treatment. The analytical dataset consists of daily self-reported symptom logs from chemotherapy patients, capturing a variety of symptoms such as nausea, fatigue, and pain. However, the data was significantly imbalanced, with approximately 84% of entries showing no symptom escalation. To address this issue and enhance the model's ability to identify symptom escalation, the data was resampled into varying interval lengths, ranging from 3 to 7 days. This resampling allows the model to detect notable changes in symptom severity over different time frames. The study's results show that shorter intervals (3 days) delivered the best performance, achieving an accuracy of 79%, a precision of 85%, a recall of 79%, and an F1 score of 82%. As the interval length increased, both accuracy and recall declined, though precision remained relatively consistent. These findings illustrate the capability of CNN-based models to capture temporal patterns in symptom progression effectively. Incorporating such predictive models into digital health platforms could empower healthcare providers to offer more personalized, proactive care, allowing for earlier interventions that may reduce symptom severity and improve adherence to treatment.

Space cannot substitute for time in the study of the ecosystem services-human wellbeing relationship

Space cannot substitute for time in the study of the ecosystem services-human wellbeing relationship

Parkinson's disease tremor prediction towards real-time suppression: A self-attention deep temporal convolutional network approach.

Accurate prediction of Parkinson's disease tremor (PDT) is crucial for developing assistive technologies; however, this is challenging due to the nonlinear, stochastic, and nonstationary characteristics of PDT, which substantially vary among patients and their activities. Moreover, most models only have one-step prediction capabilities, which causes delays in real-time applications. This paper proposes a self-attention deep temporal convolutional network (SADTCN) model for the real-time prediction of hand-arm PDT signals from different activities and joint angular motions. The SADTCN can capture both short- and long-term dependencies and complex temporal and spatial dynamics of PDT signals and hence, can effectively adapt to varying tremor characteristics. The performance of the proposed model is evaluated using experimental hand-arm PDT data. The results show that the SADTCN outperforms existing deep learning (DL) models by accurately predicting varying tremor amplitudes and frequencies multi-step ahead. Moreover, we performed spectrum analysis on the measured and predicted signal using the short-time Fourier transform (STFT) as a measure of potential active tremor control and found that SADTCN can accurately determine the transience of tremor amplitude in frequency and time. Finally, we run the Wilcoxon signed-rank statistical test and the results show a statistically significant improvement in the proposed model over the other DL models in all conditions. Therefore, the SADTCN can overcome the nonstationary, nonlinear, and stochastic nature of PDT to perform multi-step prediction with high accuracy, robustness, and generalizability in unseen testing data.

A Signal Temporal Logic approach for task-based coordination of multi-aerial systems: A wind turbine inspection case study

A Signal Temporal Logic approach for task-based coordination of multi-aerial systems: A wind turbine inspection case study

Temporal-multimodal consistency alignment for Alzheimer's cognitive assessment prediction.

As one of the most prevalent neurodegenerative disorders, Alzheimer's disease (AD) severely impacts human thinking and behavior. Early and accurate prediction of cognitive decline is crucial for timely AD intervention. However, most existing prognostic methods hardly explore the underlying association among longitudinal data from different modalities in disease progression, thus the predictive ability of current models is still quitelimited. We propose the unifying Multi-Modality fusion with DUal-gRanularity Alignment framework (MM-DURA) to simultaneously model longitudinal correlations and modalities interactions for cognitive assessment forecasting. Our proposed framework leverages temporal MRI scans, time-aligned clinical diagnostics, and genomic data as inputs to forecast multiple cognitive assessmentscores. We propose a novel coarse-to-fine feature representation learning approach to ascertain the congruence between modalities at both the subject and visit granularities. This method ensures the alignment of multimodal data pertaining to individual subjects and captures the temporal progression of these modalities. Additionally, we design a hierarchical multimodality fusion (HMF) block that can effectively exploit the interrelationships and dependencies among modalities. Lastly, we employ an LSTM-based regression head with the fused multimodality embedding as input to forecast the future status of cognitiveability. We validate our method on the public Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and investigate the optimal hierarchical structure for modality fusion. The whole dataset includes 707 subjects participating in the ADNI1, ADNIGO, and ADNI2 studies. All subjects underwent longitudinal examinations with an average study period of approximately 14 months. The subject-level split for training, validation, and testing sets is 0.75:0.05:0.20. The proposed MM-DURA framework demonstrates superior performance, achieving remarkable RMSE values of 1.099 for CDRSB, 5.601 for ADAS-Cog, 2.051 for MMSE, 6.504 for RAVLT, and 3.447 for FAQ cognitive assessments forecasting. These results outperform all six comparison methods, including two state-of-the-art multimodal temporal modeling approaches. Comprehensive ablation experimental results affirm the effectiveness of longitudinal modeling with temporal-multimodal alignment, highlighting its clinical potential for cognitive assessment prediction. Visualizations of key brain regions and SNP significance analysis also provide substantialinterpretability. In this work, we proposed a novel framework that unifies multimodality fusion with dual-granularity alignment for cognitive assessment forecasting. Our approach utilizes a temporal-multimodal consistency alignment strategy, which effectively synchronizes various modalities within a unified latent space. Furthermore, the innovative HMF block we developed capitalizes on the inherent relationships and dependencies between modalities to optimize data integration. Extensive numerical results on five cognitive assessment scores, supported by detailed visualizations demonstrate the superior performance of our approach compared to existing methods. Our code has been released, and it is available at https://github.com/IcecreamArtist/MM_DURA.

When to Use What: A Comparison of Three Approaches to Quantify Relationships Among Ecosystem Services

Sustainable landscape management requires accurately identifying the trade-offs and synergies among ecosystem services (ES). Three commonly utilized approaches to quantify ES trade-off/synergy relationships include the space-for-time approach, landscape background-adjusted space-for-time approach, and temporal trend approach. However, the similarities and differences among these three approaches in identifying ES relationships in the same area remain unclear. Thus, we conducted a case study in the rapidly urbanizing Yangtze River Delta region, comparing the three approaches based on annual data spanning from 2001 to 2020 for 12 types of ES. We found that: (1) the ES trade-off/synergy relationships detected by the three approaches exhibit significant divergence, with only 1.45% consistency among the 66 pairs of ES relationships. (2) All three approaches can overlook ES trade-offs, miss ES synergies, and erroneously detect interactions where none exist. (3) The mechanisms contributing to the misidentification of ES relationships by the three approaches include: neglecting the underlying assumptions of different approaches, insufficient time interval length, short time series of ES data, data aggregation effects, non-linear changes in ESs, time lag effects of ES relationships, among others. Our results indicate that each of the three approaches has its own advantages and disadvantages in identifying ES relationships. Prior to selecting an approach for identifying relationships between ESs in a specific study area, careful consideration of the availability of time series data, the characteristics of the chosen ES type, and thorough examination of the underlying assumptions and uncertainties of each approach are imperative.

Modeling Delayed Causal Effects in Complex Systems: Advances in Temporal Causal Analysis

This comprehensive article examines the challenges and advancements in modeling delayed causal effects within complex systems. The article explores various analytical techniques, from neural approaches to automated delay discovery, highlighting their applications across industrial, healthcare, and energy sectors. The article investigates implementation considerations including data collection, model selection, and validation strategies, while examining the evolution of temporal causal analysis through emerging technologies. The article demonstrates significant improvements in prediction accuracy, process optimization, and pattern recognition through advanced temporal modeling approaches, offering valuable insights for future developments in causal AI systems.

Physics-constrained coupled neural differential equations for one dimensional blood flow modeling.

Physics-constrained coupled neural differential equations for one dimensional blood flow modeling.

A modified approach for treating zygomatic arch fracture with plate fixation: a retrospective study

BackgroundZygomatic complex fractures are prevalent among maxillofacial bone injuries due to the prominence of the zygomatic arch, which can significantly impact facial aesthetics and the ability to open the mouth. Although non-surgical interventions are available for mild cases, severe fractures necessitate surgical intervention. The surgical repair of zygomatic arch fractures poses a risk of injuring the temporal branch of the facial nerve (TBFN). This study aimed to develop a modern modification to reduce the probability of common complications.MethodWe conducted a retrospective study involving 163 patients at the Department of Oral & Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, spanning from March 2021 to June 2024. Our study introduced a novel modification of the temporal hairline approach for the treatment of these fractures.ResultsAmong the patients who underwent the modified approach (n = 38), there were no instances of TBFN injuries. In contrast, the traditional approach (n = 125) resulted in 17 cases of nerve-related complications.ConclusionOur findings indicate that the modified temporal hairline approach offers distinct advantages in minimizing the risk of TBFN injury with minimally noticeable scarring, while ensuring that the fracture is securely repaired. This provides a new safe surgical approach option for the osteosynthesis of zygomatic arch fractures.

Temporal adjustment approach for high-resolution continental scale modeling of soil organic carbon

Open-source legacy data available for training soil organic carbon (SOC) models are limited and not uniformly distributed in space or time. While some process-based models predict SOC changes, most of the large-scale data-driven SOC modeling efforts overlook temporal shifts. Accounting for the expected temporal drift allows us to increase the accuracy of dataset available for machine learning models. Here we present an approach for creating proximity-based distance matrices using the legacy data available in contiguous US (CONUS) and generating spatially resolved temporal shift projections that adjust observations to the target date. The approach was evaluated by comparing SOC observations projected to two reference years, SOC1980 and SOC2020 and without temporal adjustment (SOCno−adj). Stocks of SOC projections showed significant differences between SOCno−adj and SOC2020. Baseline estimate of SOC stocks in CONUS croplands (top 1 m) were higher based on SOCno−adj (14.49 Pg C) compared to SOC2020 (13.29 Pg C), for pasture lands 15.49 Pg (SOCno−adj) and 14.22 Pg C (SOC2020), for forest lands at 39.52 Pg C (SOCno−adj) and 40.83 Pg C (SOC2020). The study results confirmed the validity of our methodology, and its capability to enhance SOC stock projections effectively with temporal adjustments. Potential users of this study’s outcomes include many stakeholders involved in carbon incentive programs, including farmers, scientists, policy makers, and industry partners.

Dynamic and Static Strength Analysis of 5056 Aluminum Alloy Fabricated by Wire-Arc Additive Manufacturing

This article presents the results of experimental studies on the dynamic and static strength of commercial aluminum alloy 5056 manufactured by wire-arc additive manufacturing (WAAM). The main objective is to evaluate the utilization potential of this technology for manufacturing parts for operation under shock loads. The dynamic tensile strength of the material was investigated with a modified Kolsky method, implemented by a split Hopkinson pressure bar. A comparative analysis of the strength characteristics of materials manufactured by WAAM and conventional cold-rolling methods was carried out using a structurally temporal approach with the incubation time criterion. The results showed that the aluminum alloy obtained by WAAM demonstrates comparable strength levels to that of cold-rolled material. The findings suggest that WAAM can be a competitive alternative for producing high-strength aluminum alloys for operation under shock loads.

Convergence of a particle Monte Carlo algorithm for scalar conservation laws

Abstract The subject of this paper is a Monte Carlo algorithm for scalar conservation laws proposed in [L. Pareschi and M. Seaïd, A new Monte Carlo approach for conservation laws and relaxation systems, Computational Science—ICCS 2004. Part II, Lecture Notes in Comput. Sci. 3037, Springer, Berlin 2004, 276–283]. The algorithm is a stochastic particle method based on a probabilistic interpretation of the Jin–Xin relaxation formulation of conservation laws. We prove convergence as the number of particles approaches infinity, and the spatial and temporal mesh sizes approach zero, assuming that the number of particles approaches infinity at a rate sufficiently high compared to the rate that the mesh size approaches zero. For the case where the solution can take either sign, our version of the algorithm is novel. We present two numerical examples as evidence of convergence.

Assessing sargassum pressure on coastal habitats using a spatial and temporal approach at the territorial scale

Assessing sargassum pressure on coastal habitats using a spatial and temporal approach at the territorial scale

Scleral Fixation of an Intraocular Lens With Gore-Tex® Suture From a Temporal Approach.

Scleral Fixation of an Intraocular Lens With Gore-Tex® Suture From a Temporal Approach.

Temporal transferability assessment of injury severity models for single-vehicle and multi-vehicle crashes at highway ramp areas.

Temporal transferability assessment of injury severity models for single-vehicle and multi-vehicle crashes at highway ramp areas.

Analysis of ischemic stroke biomarkers based on non-targeted metabolomics

Biomarkers for ischemic stroke (IS) are yet to fulfill clinical requirements. This study used non-targeted metabolomics to investigate differential metabolites and metabolic pathways in plasma and brain tissue following IS, with the aim of identifying new potential biomarkers and therapeutic targets. Twelve Tibetan miniature pigs were randomly assigned to a model- or sham-operation group. An electrocoagulation-based anterior temporal approach was employed to occlude the middle cerebral artery, thereby creating a model for IS. Plasma and brain tissue samples were collected 36 h post-surgery and analyzed using liquid chromatography-mass spectrometry. Principal component and partial least squares discriminant analyses were used to screen for differential metabolites and exclude exogenous metabolites at p<0.05. Compounds were classified according to the HMDB (Human Metabolome Database), and subjected to KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway and VIP (variable importance in projection) analyses. Plasma metabolomics revealed that 86 metabolites were upregulated while 149 were downregulated, with (Z)-3-oxo-2-(2-pentenyl)-1-cyclopentylacetic acid, trans-cinnamic acid and cinnamoylglycine determined to be significant metabolites. Fifty-eight differential metabolites were upregulated in brain tissue and 53 were downregulated, with 2,3-dihydroflavon-3-ol, guanidinoacetic acid (GAA), N-acetyl-D-tryptophan, oxidized glutathione, 2-hydroxyquinoline, and N-acetyl-L-aspartate (NAA) identified as significant metabolites. Organic acids and derivatives, lipids and lipid-like molecules, organoheterocyclic compounds, and organic oxygen compounds were found to be common compound categories among the top five types of compound in both plasma and brain tissue. Common metabolic pathways in plasma and brain tissue include amino acid metabolism, digestive system, cancer overview, and lipid metabolism pathways, with the (Z)-3-oxo-2-(2-pentenyl)-1-cyclopentylacetic acid, GAA, oxidized glutathione, and NAA metabolites serving as potential biomarkers. This study provides a theoretical foundation for the early screening and development of clinical treatment strategies for IS.

Pattern Separation and Pattern Completion Within the Hippocampal Circuit During Naturalistic Stimuli.

Pattern separation and pattern completion in the hippocampus play a critical role in episodic learning and memory. However, there is limited empirical evidence supporting the role of the hippocampal circuit in these processes during complex continuous experiences. In this study, we analyzed high-resolution fMRI data from the "Forrest Gump" open-access dataset (16 participants) using a sliding-window temporal autocorrelation approach to investigate whether the canonical hippocampal circuit (DG-CA3-CA1-SUB) shows evidence consistent with the occurrence of pattern separation or pattern completion during a naturalistic audio movie task. Our results revealed that when processing continuous naturalistic stimuli, the DG-CA3 pair exhibited evidence consistent with the occurrence of the pattern separation process, whereas both the CA3-CA1 and CA1-SUB pairs showed evidence consistent with pattern completion. Moreover, during the latter half of the audio movie, we observed evidence consistent with a reduction in pattern completion in the CA3-CA1 pair and an increase in pattern completion in the CA1-SUB pair. Overall, these findings improve our understanding of the evidence related to the occurrence of pattern separation and pattern completion processes during natural experiences.

P0367 A Clustering approach to discriminate slow and rapid biologics switchers in difficult-to-treat Crohn’s Disease patients

Abstract Background CD patients exhibit highly variable responses to biologics. While some patients achieve sustained remission with only one biologic over the course of their disease, other will require the sequencing of multiple biologics (“difficult-to-treat” patients) for optimal disease control. The aim of this study was to delineate the profile of treatment regimen of the biological exposed CD patients with luminal disease. Methods Among CD patients diagnosed between 1999 and 2019 with B1 phenotype at diagnosis, 203 patients who maintained this phenotype at maximum follow-up (FU, median 11y [8–15]) were selected, based on inclusion criteria. All events were recorded from date of diagnosis to maximum FU, from disease characteristics to treatment adaptations. A temporal clustering approach using k-means was applied to determine biologics treatment regimen profiles. Patient profiles were compared using the Dynamic Time Warping (DTW) similarity measure, considering 3 successive cumulative biologic exposures (for primary/secondary non response only). Clusters number was chosen to balance maximizing silhouette score while ensuring sufficient individuals per cluster. Cumulative biologic exposures were summarized as cluster profiles, represented by their corresponding barycenters computed with soft-DTW distance. Clustering approach was performed using Python (tslearn). Results The 203 patients were clustered in 5 distinct profiles, based on their cumulative biologics exposure in the first 10 years after CD diagnosis (Figure 1). Patients in Cluster 1 were not exposed to biologics (n=55). Patients in Cluster 2 were treated late with one biologic (n=51) while patients in Cluster 3 were treated early with one biologic (n=53). Patients from Cluster 4 (n=23) and Cluster 5 (n=21) were both treated early with multiple biologics. Patients from Cluster 2 were exposed to biologics later after CD diagnosis than patients from Cluster 3 to 5 (Median 5.7y [3.6–6.9] vs 0.7y [0.2-1.8], p&amp;lt;0.0001), as also shown by their lower biological exposure compared to those of Cluster 3 to 5 (median 48% [37-58] vs 80% [60-89], p&amp;lt;0.0001). Most interestingly, patients from Cluster 5 required faster biologics changes compared to patients from Cluster 4 (Figure 2). In Cluster 5 (rapid switchers), patients were exposed earlier compared to patients in Cluster 4 (Slow Switchers) to the first (p&amp;lt;0.005), second (P&amp;lt;0.005) and third biologic (p=0.01), introducing the concept of rapid and slow biologic switchers among CD patients. Conclusion This clustering approach highlights the highly variable pattern of response to biologics in luminal CD patients. Furthermore, this study discriminatessingle and multiple biologics-exposed patients, in whom we identified slow and rapid biologic switchers.