Inherent Temporal Variability Research Articles

Flow prediction via adaptive dynamic graph with spatio-temporal correlations

Flow prediction has shown its significance in optimizing and smoothing resource allocation. However, it still faces challenges due to inherent temporal variability and dynamic spatial correlations of flow data. Although many methods have been introduced to solve the problem, they often rely on predefined graphs to capture shared patterns, which is likely to overlook the critical spatiotemporal dependencies that are necessary for mid-term and long-term prediction tasks. To address the issue, a novel flow prediction model, termed Dynamic Graph Multi-Step Prediction via Long Short-Term Memory Network and Adaptive Learning (DMLSA), is proposed. DMLSA employs Node Adaptive Learning (NAL) to learn node-specific patterns without relying on predefined static adjacency matrices. Moreover, DMLSA utilizes Graph Adaptive Generation (GAG) to extract dynamic features from node attributes and automatically infer interdependencies among flow sequences, thus enhancing the ability to predict complex flow dynamics. Additionally, DMLSA integrates multi-head self-attention mechanisms with gated recurrent units, effectively capturing both long-term and short-term node dependencies, therefore allowing for more precise identification of spatiotemporal patterns at the individual node level. Extensive experiments on five datasets demonstrate that DMLSA outperformed existing baseline models in terms of accuracy and stability for multi-step prediction tasks.

Seeing and turbulence profile simulations over complex terrain at the Thai National Observatory using a chemistry-coupled regional forecasting model

Abstract This study utilized advanced numerical simulations with the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to predict anticipated astronomical seeing conditions at the Thai National Observatory (TNO). The study evaluated the effects of both gas-phase and aerosol-phase chemical processes in the Earth’s atmosphere, along with the impact of spatial and temporal resolution on model performance. These simulations were validated against measurements from the Differential Image Motion Monitor (DIMM) and the Slope Detection and Ranging (SLODAR) technique. Due to the inherent temporal variability of the DIMM observations, a 24-h moving average window was applied to both DIMM data and WRF-Chem model outputs. This reduced the percentage root-mean-square error in the comparison between the two data sets from 23 per cent to 11 per cent and increased the correlation coefficient from 0.21 to 0.59. Chemistry played a minor role during the study period, contributing 3.49 per cent to astronomical seeing. However, it did affect the model’s accuracy. Additionally, the study revealed that higher spatial and temporal resolution simulations did not necessarily improve the model’s accuracy. When compared to SLODAR observations of the refractive index structure constant (Cn2dh), the simulations captured altitude variations within ±25 per cent above 5 km and 25–50 per cent below 5 km. Dome seeing also played a role, contributing to around 90 per cent or more in the lowest altitude layer. The results emphasized the significance of seeing predictions in providing valuable insights into complex atmospheric phenomena and how to mitigate the effects of atmospheric turbulence on telescopes.

Systematic Approach for Investigating Temporal Variability in Production Systems to Improve Production Planning and Control

Including the inherent temporal variability in a production system in planning and control processes can ensure the fulfillment of the production schedule and increase key performance indicators. This benefits the sustainable and efficient use of the system. The current lack of consideration of this inherent temporal variability in production planning leads to optimistic estimates and calculations of planned values that cannot be met. To complete this information, the inherent temporal variability in a production system is investigated using a systematic approach. This approach detects, identifies, and quantifies inherent temporal variability and is applied to a data base created via an automated, event-driven procedure. The approach is tested in a smart factory laboratory. The results to date on improving production planning and control are promising as key performance indicators have been increased. There is still a need for action to ensure the fulfillment of the production schedule. Concluding, work on this topic has just begun, as can be seen from the discussion section. More data need to be collected and aggregated for future research. This publication is intended to motivate researchers to address this issue and better manage the existing uncertainty in production through the use of data.

A Proxy for the Upstream IMF Clock Angle Using MAVEN Magnetic Field Data

AbstractWithout an upstream monitor at Mars to provide a contemporaneous measurement of solar wind conditions, it is useful to have techniques of inferring the upstream solar wind conditions using downstream data. We develop a method to estimate the upstream interplanetary magnetic field (IMF) clock angle, defined as the orientation of the IMF vector in the plane perpendicular to the solar wind velocity, at Mars using magnetometer data from the Mars Atmosphere and Volatile Evolution (MAVEN) mission. The technique fits MAVEN magnetometer data from within the Martian magnetosheath to a model of the draped magnetic field direction in a coordinate system aligned with the motional electric field. The results provide a proxy for the clock angle that agrees with upstream measurements and reproduces the observed distribution of clock angles. Even if upstream observations are available for a given orbit, the proxy provides an estimate of the clock angle at the time when the spacecraft is in the magnetosheath, which may correct for inherent temporal variability, because the solar wind varies on timescales shorter than the 4.5‐hr MAVEN orbit. The clock angle proxy can be applied for any orientation of the MAVEN orbit, even when MAVEN does not traverse into the upstream solar wind.

Spatial and temporal variation in subtidal molluscan diversity amongst temperate estuarine habitats

AbstractEffective management of marine ecosystems is enhanced when detailed information on biodiversity is available. Key information to underpin management actions and conservation planning includes relationships between species assemblages and environmental gradients, and information on species distributions. We conducted a subtidal biodiversity assessment of surface‐dwelling subtidal molluscs in eight a priori defined habitat types using underwater visual censuses to quantitatively explore relationships between molluscan assemblages, and their correlation with benthic habitats and abiotic variables. In addition, variations in diversity were examined for two key habitat types (areas dominated by Dendronephthya australis and by filter feeders) over a period of 15 months to examine temporal change. We found that molluscs form distinct assemblages within subtidal habitats, but that assemblages within key habitats show inherent temporal variability. Regional (gamma) diversity of molluscs was found to result from a combination of: (i) within habitat alpha diversity, which increased with habitat complexity; (ii) between habitat beta diversity, with significant differences in molluscan assemblages amongst habitats with differing benthic growth, substrate type, and depth; and (iii) temporal beta diversity, with significant changes detected in molluscan assemblages over time. The results demonstrate how habitats and abiotic variables (principally depth and substrate type) combine to contribute to molluscan biodiversity in temperate estuaries, and illustrate the value of these factors as surrogates for surface‐dwelling subtidal molluscs in conservation planning.

Functional Imaging Biomarkers: Potential to Guide an Individualised Approach to Radiotherapy

The identification of robust prognostic and predictive biomarkers would transform the ability to implement an individualised approach to radiotherapy. In this regard, there has been a surge of interest in the use of functional imaging to assess key underlying biological processes within tumours and their response to therapy. Importantly, functional imaging biomarkers hold the potential to evaluate tumour heterogeneity/biology both spatially and temporally. An ever-increasing range of functional imaging techniques is now available primarily involving positron emission tomography and magnetic resonance imaging. Small-scale studies across multiple tumour types have consistently been able to correlate changes in functional imaging parameters during radiotherapy with disease outcomes. Considerable challenges remain before the implementation of functional imaging biomarkers into routine clinical practice, including the inherent temporal variability of biological processes within tumours, reproducibility of imaging, determination of optimal imaging technique/combinations, timing during treatment and design of appropriate validation studies.

Single-cell analysis reveals heterogeneity in onset of transgene expression from synthetic tetracycline-dependent promoters.

Synthetic promoters have been designed for mammalian cells to achieve both temporal and quantitative control over transgene expression without interfering with the endogenous cellular network. Routine applications of synthetic expression systems are based on steady-state measurements of gene expression while the mechanism by which these steady-states are realised at the single-cell level has not been investigated. We focused on the elucidation of the kinetics of doxycycline-controlled synthetic modules as a paradigm. Following gene expression in single cells, we observed a gradual increase of transgene expression within the first 48 h after activation, as determined by flow cytometry. Time-lapse microscopy revealed that the onset of transgene expression was highly variable in individual cells. Interestingly, a bidirectional cassette design showed significantly reduced cell-to-cell heterogeneity in expression. Of note, the influence of the cell cycle seems to be negligible, since the onset of expression correlates with cell division in only a minor fraction of the cell population. In contrast, rapid and synchronous transgene expression could be realized using a posttranslational regulation system that relies on ligand-induced stabilization of a tagged protein. Thus, the inherent temporal variability of transcriptionally regulated synthetic transgene expression systems has to be considered for kinetic and correlative experimental applications.

Streamflow variability and optimal capacity of run‐of‐river hydropower plants

The identification of the capacity of a run‐of‐river plant which allows for the optimal utilization of the available water resources is a challenging task, mainly because of the inherent temporal variability of river flows. This paper proposes an analytical framework to describe the energy production and the economic profitability of small run‐of‐river power plants on the basis of the underlying streamflow regime. We provide analytical expressions for the capacity which maximize the produced energy as a function of the underlying flow duration curve and minimum environmental flow requirements downstream of the plant intake. Similar analytical expressions are derived for the capacity which maximize the economic return deriving from construction and operation of a new plant. The analytical approach is applied to a minihydro plant recently proposed in a small Alpine catchment in northeastern Italy, evidencing the potential of the method as a flexible and simple design tool for practical application. The analytical model provides useful insight on the major hydrologic and economic controls (e.g., streamflow variability, energy price, costs) on the optimal plant capacity and helps in identifying policy strategies to reduce the current gap between the economic and energy optimizations of run‐of‐river plants.

Development of a Body Sensor Network to Detect Motor Patterns of Epileptic Seizures

The objective of this study was the development of a remote monitoring system to monitor and detect simple motor seizures. Using accelerometer-based kinematic sensors, data were gathered from subjects undergoing medication titration at the Beth Israel Deaconess Medical Center. Over the course of the study, subjects repeatedly performed a predefined set of instrumental activities of daily living (iADLs). During the monitoring sessions, EEG and video data were also recorded and provided the gold standard for seizure detection. To distinguish seizure events from iADLs, we developed a template matching algorithm. Considering the unique signature of seizure events and the inherent temporal variability of seizure types across subjects, we incorporated a customized mass-spring template into the dynamic time warping algorithm. We then ported this algorithm onto a commercially available internet tablet and developed our body sensor network on the Mercury platform. We designed several policies on this platform to compare the tradeoffs between feature calculation, raw data transmission, and battery lifetime. From a dataset of 21 seizures, the sensitivity for our template matching algorithm was found to be 0.91 and specificity of 0.84. We achieved a battery lifetime of 10.5 h on the Mercury platform.

Persistence of elevated rabies prevention costs following post-epizootic declines in rates of rabies among raccoons ( Procyon lotor)

Determining the benefits to cost relationships among different approaches to rabies control and prevention has been hindered by the inherent temporal variability in the dynamics of disease among wildlife reservoir hosts and a tangible and objective measure of the cost of rabies prevention. A major and unavoidable component of rabies prevention programs involves diagnostic testing of animals and the subsequent initiation of appropriate public health responses. The unit cost per negative and positive diagnostic test outcome can be reasonably estimated. This metric when linked to methodologies subdividing the epizootic process into distinct temporal stages provided the requisite detail to estimate benefits derived from rabies control strategies. Oral rabies vaccine (ORV), for prevention of the raccoon-associated variant of rabies, has been distributed in Ohio and adjoining states in an effort to develop an immune barrier to the westward spread of epizootic raccoon rabies. The costs of ORV delivery have been quantified. Herein, the cost structures required to assess the benefits accrued by prevention were developed. A regression model was developed effectively predicting ( r 2 = 0.70) the total number of rabies diagnostic tests performed by 53 counties in five northeastern (NE) states from 1992 to 2001. Five temporal stages sufficed to capture the range of variability in the raccoon rabies epizootic process. Unit costs, dollars per diagnostic test outcome, were calculated for negative and positive results from published reports. Ohio counties were matched to NE counties based on similar socioeconomic characters. A “pseudo-epizootic” of raccoon rabies was introduced into Ohio and the costs savings from ORV were derived as the excess costs imposed by epizootic spread throughout the state. At 46 km/year (range modeled, 30–60 km/year), the pseudo epizootic spread, and reached the enzootic stage, in all Ohio counties by year 13 (range modeled, 11–17 years). Cumulative excess costs for Ohio ranged between $11 and $21 million; counties of low socioeconomic status experienced the greatest relative excess costs. The costs for rabies prevention activities reached apices during the epizootic stage of raccoon rabies (2.7–10.8 times baseline) an unforeseen finding indicated elevated costs persisted (1.7–7.2 times baseline) into the enzootic stage.

Auto-correlation analysis of ocean surface wind vectors

The nature of the inherent temporal variability of surface winds is analyzed by comparison of winds obtained through different measurement methods. In this work, an auto-correlation analysis of a time series data of surface winds measuredin situ by a deep water buoy in the Indian Ocean has been carried out. Hourly time series data available for 240 hours in the month of May, 1999 were subjected to an auto-correlation analysis. The analysis indicates an exponential fall of the autocorrelation in the first few hours with a decorrelation time scale of about 6 hours. For a meaningful comparison between satellite derived products andin situ data, satellite data acquired at different time intervals should be used with appropriate ‘weights’, rather than treating the data as concurrent in time. This paper presents a scheme for temporal weighting using the auto-correlation analysis. These temporal ‘weights’ can potentially improve the root mean square (rms) deviation between satellite andin situ measurements. A case study using the TRMM Microwave Imager (TMI) and Indian Ocean buoy wind speed data resulted in an improvement of about 10%.