Temporal Aspects Research Articles

Compartment model of strategy-dependent time delays in replicator dynamics.

Real-world processes often exhibit temporal separation between actions and reactions - a characteristic frequently ignored in many modelling frameworks. Adding temporal aspects, like time delays, introduces a higher complexity of problems and leads to models that are challenging to analyse and computationally expensive to solve. In this work, we propose an intermediate solution to resolve the issue in the framework of evolutionary game theory. Our compartment-based model includes time delays while remaining relatively simple and straightforward to analyse. We show that this model yields qualitatively comparable results with models incorporating explicit delays. Particularly, we focus on the case of delays between parents' interaction and an offspring joining the population, with the magnitude of the delay depending on the parents' strategy. We analyse Stag-Hunt, Snowdrift, and the Prisoner's Dilemma game and show that strategy-dependent delays are detrimental to affected strategies. Additionally, we present how including delays may change the effective games played in the population, subsequently emphasising the importance of considering the studied systems' temporal aspects to model them accurately.

STABILITY ANALYSIS OF WORM PROPAGATION IN WIRELESS SENSOR NETWORK MODEL UTILIZING FRACTAL-FRACTIONAL OPERATOR

Wireless sensor networks have been extensively studied for their potential applications in both civil and military domains. However, due to their inherent resource constraints, sensor nodes are highly susceptible to cyber threats, particularly worm attacks, which pose significant security challenges. This study explores the dynamics of potential worm attacks within wireless sensor networks using a compartmental epidemic model. The model analyzes the temporal evolution of worm propagation while effectively capturing both spatial and temporal aspects. We determine the reproduction number and equilibrium of the system, with the local stability assessed using the Jacobian matrix. The linear growth and Lipschitz conditions are used to establish the existence and uniqueness of the solution. Furthermore, the Hyers–Ulam stability of the proposed model is also evaluated within the context of the fractal-fractional operator. Finally, a numerical method is developed to investigate the dynamic behavior of the wireless sensor network model under fractal-fractional orders, providing valuable insights into its robustness and security against potential worm attacks.

Comparing Apparent Haptic Motion and Funneling for the Perception of Tactile Animation Illusions ona Circular Tactile Display.

Tactile animation illusions are used to display dynamic information with haptic cues. In this study, we investigate two forms of tactile animation illusions that leverage the Funneling effect and Apparent Haptic Motion (AHM) on a one-dimensional circular tactile display. We define new parameters for the description of AHM that describe both the temporal and spatial aspects of these animations: Angle per Actuator (APA) and Revolution Duration (RD). We present three user studies about the perception of angular animations produced with these effects. Our results show that people can interpret AHM animations regardless of the APA value and that they can interpret tactile animation illusions slower than one degree per second. We also showed that the participants' ability to discriminate angular animations improves proportionally with the angle presented.

Time-Series Analysis of Ball Carrier Open-Space (BCOS) in Association Football

Assessing team performance in association football (commonly known as football or soccer) is challenging due to the sport’s low-scoring nature and inherent unpredictability. While evaluating strategies based on space control and the creation of open spaces has been explored in the literature, the temporal aspect of space availability for the ball carrier remains under-explored. This work introduces a novel time-series performance evaluation metric, Ball Carrier Open Space (BCOS), which focuses on the temporal dynamics of space available to the ball carrier to assess team performance. Additionally, it presents a novel approach to quantify open space for the ball carrier using player data extracted from television footage. This work discuss on BCOS in defensive third, central third and attacking third and a machine learning model is developed to evaluate their significance and temporal patterns. Trained model achieved 80.7% accuracy in classifying match-winning performances, underscoring the significance of BCOS. Correlation analysis between temporal features and match outcomes further reveals that BCOS in central third and attacking third are more important for match winning outcomes, while first-half performance plays a more critical role in determining match results than second-half performance. Based on the results of the correlation analysis, this study proposes a weighted ball carrier open space (wBCOS) metric to assess team performance, assigning weights to BCOS in attacking third, central third and defensive third based on their contributions to positive match outcomes. A machine learning model trained using wBCOS achieved an 82.5% accuracy in classifying match-winning performances, surpassing the performance of any previously published match-winner classification model.

Towards a comprehensive view on evolutionary refugia in West African rainforests

West African rainforests are amongst the world’s premier biodiversity hotspots. Over time, the size and distribution of these rainforests have changed significantly due to fluctuations in climate. However, compared to other biodiversity hotspots, our understanding of potential West African rainforest refugia remains relatively limited. Studies from different disciplines have provided valuable insights into refugia location, age and evolutionary role. Fossil pollen data and comparative biogeography studies have revealed cycles of rainforest contraction and expansion linked to aridification and have used these patterns to identify large-scale refugia. Phylogeographic studies mostly corroborated the existence and locality of large-scale refugia, but also unearthed the putative presence of microrefugia; smaller isolated regions that jointly form larger refuge zones (macrorefugia). Moreover, some refugia seem to harbour deep evolutionary lineages, potentially reflecting long-term stability, whereas others may have been stable during more recent aridification cycles. Phylogeographic splits are congruent across species, but asynchronous and frequently align with both climatically unstable regions and landscape features, such as rivers. These temporal and biogeographic aspects have been further explored using demographic and Species Distribution Modelling (SDM). Across various rainforest taxa, these methods show that populations and suitable habitat retracted and expanded, promoting macro-evolutionary change. Climatic fluctuations thus appear to have played an important role in shaping West African biodiversity. Overall, research shifted from identifying refugia to quantifying their role in driving evolutionary change. However, the scarcity of studies linking detailed changes in demography with SDM limits our ability to make general statements regarding refugia dynamics, predict species response to climate change and prioritise future climate refugia. Palaeoclimatic cycles of aridification led to the fragmentation and contraction of West African rainforests. Several refugia (e.g. Mount Nimba, Cape Three Points, Cape Palmas and Niger Delta) have been repeatedly identified using pollen and biogeographic data, but phylogeographic analyses point to the presence of microrefugia within and beyond these regions. Some microrefugia may have sustained species persistence since the Miocene (i.e. Fouta Djallon and Ghana), while others supported species persistence during more recent aridification cycles (Pleistocene), suggesting refugia may have operated at different time-scales. Refugia are not the sole driver of diversification, but played a key role in driving diversification of rainforest-dependent species in combination with other factors like river barriers. Future research would benefit from investigating a broader range of taxa, exploring lesser-known regions (e.g. Fouta Djallon or Niger Delta) and adopting landscape demography approaches to achieve a comprehensive understanding refugia dynamics in varying environments.

Dynamical intracranial EEG functional network controllability localizes the seizure onset zone and predicts the epilepsy surgical outcome

Objective. Seizure onset zone (SOZ) localization and SOZ resection outcome prediction are critical for the surgical treatment of drug-resistant epilepsy but have mainly relied on manual inspection of intracranial electroencephalography (iEEG) monitoring data, which can be both inaccurate and time-consuming. Therefore, automating SOZ localization and surgical outcome prediction by using appropriate iEEG neural features and machine learning models has become an emerging topic. However, current channel-wise local features, graph-theoretic network features, and system-theoretic network features cannot fully capture the spatial, temporal, and neural dynamical aspects of epilepsy, hindering accurate SOZ localization and surgical outcome prediction.Approach. Here, we develop a method for computing dynamical functional network controllability from multi-channel iEEG signals, which from a control-theoretic viewpoint, has the ability to simultaneously capture the spatial, temporal, functional, and dynamical aspects of epileptic brain networks. We then apply multiple machine learning models to use iEEG functional network controllability for localizing SOZ and predicting surgical outcomes in drug-resistant epilepsy patients and compare with existing neural features. We finally combine iEEG functional network controllability with representative local, graph-theoretic, and system-theoretic features to leverage complementary information for further improving performance.Main results. We find that iEEG functional network controllability at SOZ channels is significantly higher than that of other channels. We further show that machine learning models using iEEG functional network controllability successfully localize SOZ and predict surgical outcomes, significantly outperforming existing local, graph-theoretic, and system-theoretic features. We finally demonstrate that there exists complementary information among different types of neural features and fusing them further improves performance.Significance. Our results suggest that iEEG functional network controllability is an effective feature for automatic SOZ localization and surgical outcome prediction in epilepsy treatment.

Future Path Presentation to Passengers of an Autonomous Wheelchair Using Vibrotactile Feedback

While autonomous wheelchairs reduce the burden on passengers, automation can make it difficult for them to anticipate the future path of the wheelchair, potentially causing anxiety or discomfort due to unexpected movements. In this study, we define “path” as the geometric trajectory of the wheelchair position, without considering temporal aspects. Providing passengers with information about this future path is crucial, particularly when multiple pedestrians or obstacles are present. Previous studies have primarily focused on presenting only the direction in which the wheelchair turns. In this study, we propose a path presentation method that conveys both the direction and width of turns by varying the duration of haptic apparent motion according to the turning width. The results from the evaluation experiment showed that presenting the future path, including the extent of avoidance maneuvers, improved user understanding and offered a slightly greater sense of security compared to methods that presented only directional information or no feedback at all.

Spatial-temporal modeling of urban resilience and risk to earthquakes

In the face of burgeoning urbanization, cities and residential areas are increasingly vulnerable to diverse hazards. This research develops a spatial-temporal assessment of Bojnord City’s earthquake risk and resilience, concentrating on the morning, evening, and nighttime periods. After a thorough assessment of the literature, the research identified seven key criteria and 27 sub-criteria that address important aspects. Fuzzy logic was utilized for data standardization and the DANP approach was employed to weight the criteria in order to appropriately assess resilience and risk. The IO and OWA models were used to integrate these criteria, and the results showed notable spatial and temporal disparities in risk and resilience. The results highlight the significance of integrating both temporal and spatial aspects in risk assessments and urban resilience evaluations to enhance the effectiveness of disaster management plans. The findings demonstrate that some regions are always at high risk and low resilience, regardless of the time of day, emphasizing the necessity of focused, continuous disaster preparedness plans. This approach not only validates the consideration of the dynamic criteria but also provides a replicable methodology for other cities facing similar seismic threats.

The temporal dimension of teacher learning in a video-based professional development program: An ecological perspective

ABSTRACT Background Attending and responding to when and where teachers are with respect to change—the temporal aspects of teacher learning—can yield more effective professional development (PD) efforts. Toward this end, we conceptualize phases of learning in a PD program, and how these phases are shaped by teacher learning ecologies. Methods Conceptually, we build on the Adaptive Cycles framework (Gunderson & Holling, 2002) to represent this complexity, and empirically, we build on video-based PD conversations. Using interaction analysis methods, we ground our model by investigating learning in the PD through the phases of the Adaptive Cycles. Findings We offer a way of capturing complexity while still enabling analysis. Empirical investigation shows how the teams differed in their starting point, learning experiences, and available resources. We distinguish phases in which video-based reflection was more salient in honing teachers’ pedagogical judgments, and phases through which invoking the variety of resources was more significant. Contribution These findings provide tools for centering temporality and bringing it, in a more substantive way, into consideration in design, facilitation, and analysis of teacher PD, toward enhanced responsiveness of PD providers to the rhythms of teacher learning and the teachers’ experiences of this learning.

Geography Education and Earth Science: Bridging Concepts in Chinese High School Teaching

The teaching of earth science in Chinese senior high school geography has faced significant challenges, particularly in the compulsory module “The History of the Earth.” Despite being part of the 2019 curriculum reform, current teaching practices often fail to emphasize the geographical nature of the subject, instead treating it as a mix of geology, biology, and historical facts. This paper addresses this challenge by proposing a framework that maintains a geographical focus while teaching earth science concepts. The framework consists of three integrated approaches. First, it introduces a structured method for teaching geological time through an “Eon → Era → Period → Year” progression, helping students build a comprehensive understanding of temporal scales from a geographical perspective. Second, it demonstrates how to effectively use the “Trade Space for Time” method alongside stratigraphic analysis, enabling students to connect spatial and temporal aspects of geographical phenomena. Third, it develops geographical thinking by following the logical pathway of “Fossil → Biology → Environment,” ensuring that biological evidence is interpreted within a geographical context. For successful implementation, the paper recommends two key strategies: managing interdisciplinary content while maintaining geography’s core focus, and emphasizing geographical characteristics throughout specific teaching content. This approach ensures that “The History of the Earth” fulfills its role in developing students’ geographical literacy while contributing to their broader understanding of earth science within China’s senior high school curriculum.

Measurement of the Prompt Neutron Decay Constant in a Zero-Power Reactor Using a Novel 3D Detector System

Analyzing neutron noise in zero-power reactors provides information about integral parameters, such as the prompt decay constant α . This information can be used to validate codes that predict said parameters, or time-dependent behavior, particularly reactor transients. The CROCUS zero-power research reactor has been widely used for conducting noise experiments and has recently been enhanced with a new neutron detection system SAFFRON. This system includes an array of about 150 miniature neutron detectors evenly dispersed throughout the core, allowing for three-dimensional (3D) spatial detection capabilities. We conducted a neutron noise experiment using the 3D array to test this new system to determine the prompt decay constant α . Our results, derived from the Rossi- α method, aligned with the predictions from the Serpent 2 Monte Carlo code, which utilized the Lawrence Livermore National Laboratory FREYA library for fission. The agreement was within 1 σ of the estimated uncertainties. We notably observed that based on previous experiments, using all 150 detectors does not achieve the levels of detection efficiency (in counts per fission) expected to be necessary to observe the fission chain decay, yet we still observe the decay with comparatively high statistical significance. We therefore show experimentally that the spatial extent of the detection system is an important parameter in the prediction of the success of the neutron noise technique. We also explored the cross-spectral density method for different combinations of SAFFRON detectors, which gave consistent results with the Rossi- α method. In addition, uncertainty estimation methods, including the bootstrap method, were applied to SAFFRON, addressing both temporal and spatial aspects. We discuss the benefits of using bootstrap methods for estimating the uncertainty of the prompt decay constant, which we found often underestimated when using the covariance matrix from a standard nonlinear fit. The results also indicate that SAFFRON is a powerful measurement tool that can be expanded for use in active perturbation experiments, 3D flux maps, and other high-resolution reactor physics experiments.

Using epistemic network analysis to examine the cognitive engagement process in online collaborative learning.

Using epistemic network analysis to examine the cognitive engagement process in online collaborative learning.

Theta-Gamma Decoupling - A neurophysiological marker of impaired reward processing in Parkinson's disease.

Theta-Gamma Decoupling - A neurophysiological marker of impaired reward processing in Parkinson's disease.

Evaluation of Soundboard Damping Variations on Physical and Perceptual Aspects of the Sound of a Steel-String Acoustic Guitar

To make guitars with high sonic quality, it is essential to understand the relationship between the physical characteristics of the selected materials and the acoustic characteristics of the final instrument and how they will be perceived. The selection of wood for the soundboard is a crucial step in the guitar-making process. One relevant physical characteristic is internal material damping, about which, however, only little is known in comparison to other characteristics such as density and Young’s modulus. The present study investigated the relationship of soundboard damping to both physical and perceptual aspects of guitar sound. Three similar steel-string guitars were built with the damping of their soundboards varying between low, mid and high. Measurements of the transfer function and the plucked tone decay of the final guitars were carried out. A listening test and a playing test were conducted. The participants were asked to rate preference and seven sound attributes comprising loudness and both spectral and temporal aspects. To prepare the listening test, a short sequence was recorded by a professional guitarist with each guitar. The results suggest that varying soundboard damping had no practically relevant influence on the physical and perceptual aspects of the sound of the guitar used in this study.

Multi-User Activity Recognition Using Plot Images Based on Ambiental Sensors

Artificial intelligence has increasingly taken over various aspects of daily life, resulting in the proliferation of smart devices and the development of smart living and working environments. One significant domain within this technological advancement is human activity recognition, which includes a broad spectrum of applications such as patient monitoring and supervision of children’s activities. In this research, we endeavor to design a human activity recognition system that effectively analyzes multi-user data through a machine learning framework centered on graphical plot images. The proposed methodology uses a PIR sensor-based system. This system uses a two-stage process; the first one involves generating new image datasets as density map images and graphical representations based on the Kyoto CASAS multi-user dataset. In the second stage, the generated data are provided to a sequential convolutional neural network, which predicts the 16 activities developed by two users. To generate the new datasets, we only used data from ambient sensors, which were organized in windows. We tested many types of window dimensions and extra features such as temporal aspect and the limitation of two activities in one window. The neural network was optimized by increasing the deconvolutional layers and adding the AdamW optimizer. The results demonstrate the viability of this method, evidencing an accuracy rate of 83% for multi-user activity and an accuracy rate of 99% for single-user activity. This study successfully achieved its objective of identifying an efficient activity recognition methodology and a data image representation. Furthermore, future enhancements are anticipated by integrating data sourced from PIR sensors, with information gathered from user-personal devices such as smartphones. This approach is also applicable to real-time recognition systems.

Groundwater Recharge of Fractured Rock Aquifers in SE Australia Is Episodic and Controlled by Season and Rainfall Amount

AbstractSustainable management of groundwater resources requires a comprehensive understanding of groundwater recharge; including when and under what conditions groundwater recharge occurs. However, recharge is one of the least understood hydrologic processes. Here we show how event‐scale rainfall recharge thresholds vary over a year using a novel network of subterranean drip loggers installed in caves, mines, and tunnels to observe groundwater recharge events. These cannot be used to directly estimate groundwater recharge volumes, but instead detail temporal aspects, such as the rainfall amount required to trigger recharge. We describe how these thresholds vary over time and space from a range of geological, environmental, and climatic conditions. At our Southeast Australian sites, median rainfall recharge thresholds of 10–20 mm in 48 hr were needed to activate recharge. Rainfall events of this magnitude are infrequent, they are expected to change with climate change, and they are fundamentally important for informing groundwater recharge.

Comparing temporal and aggregated network descriptions of fluid transport in the Mediterranean Sea.

Ocean currents exhibit strong time dependence at all scales that influences physical and biochemical dynamics. Network approaches to fluid transport permit to address explicitly how connectivity across the seascape is affected by the spatiotemporal variability of currents. However, such temporal aspect is mostly neglected, relying on a static representation of the flow. We here investigate the role of current variability on networks describing physical transport across the Mediterranean basin. We first focus on degree distributions and community structure comparing ensembles of temporal networks that explicitly resolve time dependence and their aggregated, i.e., time-averaged, counterparts. Furthermore, we explore the implications of the two approaches in a simple reaction dispersal model for a generic tracer. Our analysis evidences that aggregation induces structural network changes that cannot be easily avoided, not even introducing a pruning of the aggregated adjacency matrix. We also highlight that, depending on the time scales considered, the importance of the temporal features of the networks can vary significantly. Finally, we find that the tracer evolution obtained from a temporal dispersal kernel cannot be always approximated by aggregated adjacency matrices, in particular during transients of the dynamics.

A Data-Synthesis-Driven Approach to Recognize Urban Functional Zones by Integrating Dynamic Semantic Features

Urban functional zones (UFZs) are related to people’s daily activities. Accurate recognition of UFZs is of great significance for an in-depth understanding of the complex urban system and optimizing the urban spatial structure. Emerging geospatial big data provide new ideas for humans to recognize urban functional zones. Point-of-interest (POI) data have achieved good results in the recognition of UFZs. However, since humans are the actual users of urban functions, and POI data only reflect static socioeconomic characteristics without considering the semantic and temporal features of dynamic human activities, it leads to an incomplete and insufficient representation of complex UFZs. To solve these problems, we proposed a data-synthesis-driven approach to quantify and analyze the distribution and mixing of urban functional zones. Firstly, representation learning is used to mine the spatial semantic features, activity temporal features, and activity semantic features that are embedded in POI data and social media check-in data from spatial, temporal, and semantic aspects. Secondly, a weighted Stacking ensemble model is used to fully integrate the advantages between different features and classifiers to infer the proportions of urban functions and dominant functions of each urban functional zone. A case study within the 5th Ring Road of Beijing, China, is used to evaluate the proposed method. The results show that the approach combining dynamic and static features of POI data and social media data effectively represents the semantic information of UFZs, thereby further improving the accuracy of UFZ recognition. This work can provide a reference for uncovering the hidden linkages between human activity characteristics and urban functions.

ATP-Regulated Formation of Transient Peptide Amphiphiles Superstructures.

Self-assembly of biotic systems serves as inspiration for the preparation of synthetic supramolecular assemblies to mimic the structural, temporal, and functional aspects of living systems. Despite peptide amphiphiles (PAs) being widely studied in the context of biomimetic and bioactive functional nanomaterials, very little is currently known about the reversible and spatiotemporal control of their hierarchical self-assemblies. Here, it is shown that PA-based supramolecular nanofibers can transiently form superstructures, through binding with oppositely charged adenosine triphosphate (ATP), leading to charge screening and stabilization of bundled nanofibers. Enzymatic hydrolysis of ATP to adenosine monophosphate and phosphates causes the disassembly of the superstructures and recovery of individual nanofibers. The lifetime of superstructures can be controlled by adjusting the concentration of either ATP or enzyme. The role that the formation of bundled PA nanofibers has on chemical reactivity and catalysis is also evaluated. It is observed that superstructuration is responsible for downregulation in the PA activity, which can then be recovered by gradual disassembly of the bundles. These results demonstrate the potential of reversible and controlled hierarchical self-assembly to modulate the reactivity and catalysis of peptide nanostructures.

Replicating Host-Microbiome Interactions: Harnessing Organ-on-a-Chip and Organoid Technologies to Model Vaginal and Lung Physiology.

Organ-on-a-chip (OOC) and organoid technologies are at the forefront of developing sophisticated in vitro systems that replicate complex host-microbiome interactions, including those associated with vaginal health and lung infection. We explore how these technologies provide insights into host-microbiome and host-pathogen interactions and the associated immune responses. Integrating omics data and high-resolution imaging in analyzing these models enhances our understanding of host-microbiome interactions' temporal and spatial aspects, paving the way for new diagnostic and treatment strategies. This review underscores the potential of OOC and organoid technologies in elucidating the complexities of vaginal health and lung disease, which have received less attention than other organ systems in recent organoid and OCC studies. Yet, each system presents notable characteristics, rendering them ideal candidates for these designs. Additionally, this review describes the key factors associated with each organ system and how to choose the technology setup to replicate human physiology.