Multiple Stations Research Articles

3D Airborne EM Forward Modeling Based on Finite-Element Method with Goal-Oriented Adaptive Octree Mesh

The finite-element (FE) method for three-dimensional (3D) airborne electromagnetic (AEM) modeling can flexibly simulate complex geological structures at high accuracy. However, it has low efficiency and high computational requirements. To solve these problems, one needs to generate meshes more reasonably. In view of this, we develop an adaptive octree meshing scheme for frequency-domain AEM modeling. The octree meshes have the characteristics of regularity and flexibility, while the adaptive algorithm can effectively refine the mesh locally. In our adaptive mesh generation, the posterior errors and weighted coefficients are used to construct the final weighted posterior errors. We verify the accuracy of our method by comparing its results with semi-analytical solutions for a half-space model. Furthermore, we use the spectral-element (SE) method and our method to calculate EM responses for an abnormal block model and compare their computational costs. The results show that our adaptive scheme has obviously technical advantages over SE method for AEM modeling with multiple frequencies and multiple survey stations. Finally, we calculate a model with complex geological structures to verify the feasibility of our algorithm in complex geological circumstances.

3D Smooth Trajectory Planning for UAVs under Navigation Relayed by Multiple Stations Using Bézier Curves

Navigation relayed by multiple stations (NRMS) is a promising technique that can significantly extend the operational range of unmanned aerial vehicles (UAVs) and hence facilitate the execution of long-range tasks. However, NRMS employs multiple external stations in sequence to guide a UAV to its destination, introducing additional variables and constraints for UAV trajectory planning. This paper investigates the trajectory planning problem for a UAV under NRMS from its initial location to a pre-determined destination while maintaining a connection with one of the stations for safety reasons. Instead of line segments used in prior studies, a piecewise Bézier curve is applied to represent a smooth trajectory in three-dimensional (3D) continuous space, which brings both benefits and complexity. This problem is a bi-level optimisation problem consisting of upper-level station routing and lower-level UAV trajectory planning. A station sequence must be obtained first to construct a flight corridor for UAV trajectory planning while the planned trajectory evaluates it. To tackle this challenging bi-level optimisation problem, a novel efficient decoupling framework is proposed. First, the upper-level sub-problem is solved by leveraging techniques from graph theory to obtain an approximate station sequence. Then, an alternative minimisation-based algorithm is presented to address the non-linear and non-convex UAV trajectory planning sub-problem by optimising the spatial and temporal parameters of the piecewise Bézier curve iteratively. Computational experiments demonstrate the efficiency of the proposed decoupling framework and the quality of the obtained approximate station sequence. Additionally, the alternative minimisation-based algorithm is shown to outperform other non-linear optimisation methods in finding a better trajectory for the UAV within the given computational time.

Selection of statistical technique for imputation of single site-univariate and multisite-multivariate methods for particulate pollutants time series data with long gaps and high missing percentage.

Monitoring air contaminants has become essential to exposure science, toxicology, and public health research. However, missing values are common while monitoring air contaminants, especially in resource-constrained settings such as power cuts, calibration, and sensor failure. In contaminants monitoring, evaluating existing imputation techniques for dealing with recurrent periods of missing and unobserved data are limited. The proposed study aims to perform a statistical evaluation of six univariate and four multivariate time series imputation methods. The univariate methods are based on inter-time correlation characteristics, and the multivariate approach considers muti-site to impute missing data. The present study retrieved data from 38 ground-based monitoring stations for particulate pollutants in Delhi for 4 years. For univariate methods, missing values were simulated under 0-20% (5%, 10%, 15%, and 20%), and high 40%, 60%, and 80% missing levels having long gaps. Before evaluating multivariate methods, input data underwent pre-processing steps: selecting the target station to be imputed, choosing covariates based on the spatial correlation between multiple sites, and framing a combination of target and neighbouring stations (covariates) under 20%, 40%, 60%, and 80%. Next, the particulate pollutants data of 1480 days is provided as input to four multivariate techniques. Finally, the performance of each algorithm was evaluated using error metrics. The results show that the long interval time series data and spatial correlation of multiple stations significantly improved outcomes for univariate and multivariate time series methods. The univariate Kalman_arima performs well for long-missing gaps and all missing levels (except for 60-80%), yielding low error and high R2 and d values. In contrast, multivariate MIPCA performed better than Kalman-arima for all target stations with the highest missing percentage.

Stable operation process of earthquake early warning system based on machine learning: trial test and management perspective

Earthquake Early Warning (EEW) is an alert system, based on seismic wave propagation theory, to reduce human casualties. EEW systems mainly utilize technologies through both network-based and on-site methods. The network-based method estimates the hypocenter and magnitude of an earthquake using data from multiple seismic stations, while the on-site method predicts the intensity measures from a single seismic station. Therefore, the on-site method reduces the lead time compared to the network-based method but is less accurate. To increase the accuracy of on-site EEW, our system was designed with a hybrid method, which included machine learning algorithms. At this time, machine learning was used to increase the accuracy of the initial P-wave identification rate. Additionally, a new approach using a nearby seismic station, called the 1+ α method, was proposed to reduce false alarms. In this study, an on-site EEW trial operation was performed to evaluate its performance. The warning cases for small and large events were reviewed and the possibility of stable alert decisions was confirmed.

Reassessment of the declines in the largest freshwater lake in China (Poyang Lake): uneven trends, risks and underlying causes

Water level decline in the largest freshwater lake in China (Poyang Lake) has raised grave concerns and continuous debates regarding the proposal of a water control project. Previous hydrologic investigations on the water level decline in Poyang Lake were conducted mostly during recession season and typical dry years and hence lacked the comprehensive view of associated risks and possible spatial heterogeneity of trend during low water level periods. The present study reassessed the long-term trend and regime shift of low water level variations and their associated risks based on hydrological data across multiple stations in Poyang Lake during 1952–2021. The underlying causes of the trends of water level decline were further investigated. Results showed uneven trends and risks of water level variations in different seasons and across different lake regions. The water level of all five hydrological stations in Poyang Lake significantly dropped in the recession season, and the risks of water level decline evidently increased since 2003, which could be largely attributed to the water level drop in the Yangtze River. In terms of the dry season, clear spatial differences of long-term trend of water level were found, with water level in the central and southern lake regions significantly dropping, which was probably caused by dramatic bathymetry undercutting in the central and northern lake regions. In addition, the impacts of topographic changes became significant when water level of Hukou fell below 13.8 and 11.8m for the northern and southern lake regions, respectively. By contrast, water level in the northern lake region showed increasing trends in the dry season. In addition, only the occurrence time of water level under moderate risk significantly advanced for all stations except for Hukou. The present study could provide a full picture of low water level trends, associated risks across different lake regions and underlying causes in Poyang Lake, thus providing insights into adaptive water resources management.

Time sequence three-phase probabilistic power flow calculation for power system including traction station load of high-speed railway

The increasing traction station load of high-speed railway with single-phase, high-power, spatiotemporal mobility, and uncertainty has brought major impacts and challenges to the power system. The non-negligible characteristics are the spatial distribution of traction stations on high-speed railway lines, the time sequence of traction load moving with EMUs, as well as the nonparametric probabilistic features and correlation of traction load. A method of time sequence three-phase probabilistic power flow (3PPF) calculation for power system including traction station load of high-speed railway is proposed in this paper. Firstly, through the port transformation of traction power supply system (TPSS), the three-phase equivalent model of TPSS is established to construct an unbalanced three-phase power flow calculation model of grid with traction station load. Secondly, considering the spatial correlation, time sequence and non-parametric probabilistic feature of traction load, the time sequence probabilistic models of multiple traction station loads are built based on diffusion-based kernel density estimator (DKDE). Then, the algorithm of time sequence 3PPF calculation based on three-point estimation method (3PEM) is applied to deal with the computational efficiency and accuracy problems of 3PPF considering correlation and timing. Finally, the two modified three-phase power systems are employed for case studies in combination with the measured data of traction station of a high-speed railway. The validity and computing efficiency of the proposed method are verified.

Lectura y almacenamiento de recursos bibliográficos empleando tecnología RFID de ultra alta frecuencia con internet de las cosas

Libraries supervisors have the administrative duty of control and keep safe their inventories, such as books, documents, manuals, and theses, facilitating access to their users to this information and helping them loan and return, internal or external, in case it’s permitted. We wanted to design and build a prototype for a device capable of complementing these tasks in a low cost. We go through the processes in the design of a prototype for a simultaneous Radio Frequency Identification Reader using Ultra High Frequencies with Internet of Things technologies for data storage and communication between multiple stations, to do so we use a decision matrix method to select hardware tools, and software programming languages and libraries, needed to build such device, comparing, and weighting the characteristics that fit our needs. The resulting hardware selection was the Raspberry Pi and the RedBoard with a 59,7% and 12,4% respectively, and NodeJS with WebSockets for the software selection with a 53,8%. Finally, we present the designed architecture and a working prototype of the device that was built. Obtained response times from the reader and the database storage were as low as 10 milliseconds and as high as 20 milliseconds.

Mechanism Analysis of the Strong Coastal Current Zone and Abrupt Strong–Current Phenomena in Spring and Summer in the Yangjiang Sea Area of Western Guangdong in the Northwest of the South China Sea

Residual current analysis of multiple stations’ periodic observational data for sea currents, and multiple voyages, multiple seasons from 2018 to 2022, revealed the existence of a strong southwest current zone, 20–30 m underwater and within the coastal current area of Yangjiang, western Guangdong, in the northwest region of the South China Sea. The velocity of the residual currents in the strong–current zone was 38.2–100.3 cm/s. Observational data for wind, sea currents, salinity and tide from multiple coastal stations in the spring (from March 2019 to May 2019) and summer (from June 2019 to August 2019) of 2019 demonstrated the existence of abrupt strong currents in the coastal current sea area of Yangjiang, western Guangdong. Analyses of continuous sequence hydro meteorological data for the Yangjiang area indicated that wind stress was the main factor determining the direction of sea currents; increases in the near-shore water level produce a westward geostrophic current and a wind current in the shallow sea area; their joint effect was the key factor determining the velocity of the coastal current in this sea area. In spring and summer, when Pearl River runoff into the sea reaches a peak, and under the action of the northeast wind, the water level on the west coast of Guangdong rose. This created a barotropic pressure–gradient force from the shore to the outer sea, generating a southwestward geostrophic current in the same direction as the wind-driven current. The joint action of the wind-driven and geostrophic currents then generated a sudden southwestward coastal current.

PM2.5 Concentration Prediction Method Based on Temporal Attention Mechanism and CNN-LSTM

Accurately predicting PM2.5 concentration can effectively avoid the harm caused by heavy pollution weather to human health. In view of the non-linearity, time series characteristics, and the problem of large multi-step prediction errors in PM2.5 concentration data, a method combining Long Short-term Memory Network and Convolutional Neural Network with Time Pattern Attention mechanism (TPA-CNN-LSTM) is proposed. The method uses historical PM2.5 concentration data, historical meteorological data, and surrounding station data to predict the future 6-hour PM2.5 concentration of air quality monitoring stations. Firstly, CNN is used to obtain the spatial characteristics between multiple stations, secondly, LSTM is added after CNN to extract the temporal changes of non-linear data, and finally, to capture the key features of temporal information, Temporal Pattern Attention mechanism (TPA) is added. TPA can automatically adjust weights based on the input of each time step, and select the most relevant time step for prediction, thereby improving the accuracy of the model. An example analysis is conducted on the measured data of Beijing's air quality stations in 2018, and compared with other mainstream algorithms. The results show that the proposed model has higher prediction accuracy and performance.

Deep Learning Approaches for Numerical Modeling and Historical Reconstruction of Water Quality Parameters in Lower Seine

Water quality monitoring is essential for managing water resources and ensuring human and environmental health. However, obtaining reliable data can be challenging and costly, especially in complex systems such as estuaries. To address this problem, we propose a novel deep learning-based approach that uses limited available data to accurately estimate and reconstruct critical water quality variables, such as electrical conductivity, dissolved oxygen, and turbidity. Our approach included two tasks, numerical modeling and historical reconstruction, and was applied to the Seine River in the Normandy region of France at four quality stations. In the first task, we evaluated four deep learning approaches (GRU, BiLSTM, BiLSTM-Attention, and CNN-BiLSTM-Attention) to numerically simulate each variable for each station under different input data selection scenarios. We found that incorporating the quality data with the water level data collected at the various stations into the input data improved the accuracy of the water quality data simulation. Combining water levels from multiple stations reliably reproduced electrical conductivity, especially at stations near the sea where tidal fluctuations control saltwater intrusion in the area. While each model had its strengths, the CNN-BiLSTM-Attention model performed best in complex tasks with dissimilar input trends, and the GRU model outperformed other models in simple monitoring tasks with similar input-target trends. The second task involved automatically searching the optimal configurations for completing the missing historical data in sequential order using the modeling task results. The electrical conductivity data were filled before the dissolved oxygen data, which were in turn more reliable than the turbidity simulation. The deep learning models accurately reconstructed 15 years of water quality data using only six and a half years of modeling data. Overall, this research demonstrates the potential of deep learning approaches with their limitations and discusses the best configurations to improve water quality monitoring and reconstruction.

Formative Objective Structured Clinical Examinations (OSCEs) as an Assessment Tool in UK Undergraduate Medical Education: A Review of Its Utility.

The Objective Structured Clinical Examination (OSCE) is a globally established clinical examination; it is often considered the gold standard in evaluating clinical competence within medicine and other healthcare professionals' educations alike. The OSCE consists of a circuit of multiple stations testing a multitude of clinical competencies expected of undergraduate students at certain levels throughout training. Despite its widespread use, the evidence regarding formative renditions of the examination in medical training is highly variable; thus, its suitability as an assessment has been challenged for various reasons. Classically, Van Der Vleuten's formula of utility has been adopted in the appraisal of assessment methods as means of testing, including the OSCE. This review aims to provide a comprehensive overview of the literature surrounding the formative use of OSCEs in undergraduate medical training, whilst specifically focusing on the constituents of the equation and means of mitigating factors that compromise its objectivity.

Deep Reinforcement Learning for UAV Routing in the Presence of Multiple Charging Stations

Deploying Unmanned Aerial Vehicles (UAVs) for traffic monitoring has been a hotspot given their flexibility and broader view. However, a UAV is usually constrained by battery capacity due to limited payload. On the other hand, the development of wireless charging technology has allowed UAVs to replenish energy from charging stations. In this paper, we study a UAV routing problem in the presence of multiple charging stations (URPMCS) with the objective of minimizing the total distance traveled by the UAV during traffic monitoring. We present a deep reinforcement learning based method, where a multi-head heterogeneous attention mechanism is designed to facilitate learning a policy that automatically and sequentially constructs the route, while taking the energy consumption into account. In our method, two types of attentions are leveraged to learn the relations between monitoring targets and charging station nodes, adopting an encoder-decoder-like policy network. Moreover, we also employ a curriculum learning strategy to enhance generalization to different numbers of charging stations. Computational results show that our method outperforms conventional algorithms with higher solution quality (except for exact methods such as Gurobi) and shorter runtime in general, and also exhibits strong generalized performance on problem instances with different distributions and sizes.

A data-driven framework for medium-term electric vehicle charging demand forecasting

The rapid phase-in of electric vehicles (EV) will cause unprecedented issues with managing the supply of electricity and charging stations. It is in the interest of utility providers and everyday consumers to be able to plan for peak charging times, and related congestion. While past work has been done for localized, short-term forecasting, it has not included longer term forecasting, or considered the relationships between multiple stations. Importantly, past work has also not offered a framework for dataset construction and evaluated different dataset features. We propose a methodology to forecast demand at public EV charging stations, and use it to explore the potential of data-driven models to predict demand up to one week in advance. Our strategy includes selecting parameters for formatting a dataset given a list of charging events, a way to consider flexible prediction horizons, and deployment of deep and supervised learning-based models. To the best of our knowledge, ours is the first study to propose machine learning to forecast medium-term public EV charging demand, to exploit weather and other features at public charging stations, and to forecast demand at multiple stations and the entire network. We validated our approach using data from eleven stations over three years from Scotland, UK. Our method outperforms the benchmark time series method, and predicts network demand with a symmetric mean absolute percentage error (SMAPE) of 5.9% and a mean absolute error (MAE) of 124.7 kWh, or less than twelve percent of average daily demand.

Estimating Effectiveness of Identifying Human Trafficking via Data Envelopment Analysis

Transit monitoring is a preventive approach used to identify possible cases of human trafficking before exploitation while an individual is in transit or before crossing a border. Transit monitoring is often conducted by nongovernmental organizations (NGOs) that train staff to identify and intercept suspicious activity. Love Justice International (LJI) is a well-established NGO that has been conducting transit monitoring for years along the Nepal-India border at multiple monitoring stations. In partnership with LJI, we developed a system that uses data envelopment analysis (DEA) to help LJI decision makers evaluate the performance of these stations at intercepting potential human trafficking victims given the amount of resources (staff, etc.) available and make specific operational improvement recommendations. Our model consists of 91 decision-making units from seven stations over 13 quarters and considers three inputs, four outputs, and three homogeneity criteria. Using this model, we identified efficient stations, compared rankings of station performance, and recommended strategies to improve efficiency. To the best of our knowledge, this is the first application of DEA in the anti-human trafficking domain. History: This paper was refereed. Funding: This material is based upon work supported by the National Science Foundation [Grant CMMI-1841893].

Design and In Situ Additive Manufacturing of Multifunctional Structures

Multifunctional structures (MFSs) integrate diverse functions to achieve superior properties. However, conventional design and manufacturing methods—which generally lack quality control and largely depend on complex equipment with multiple stations to achieve the integration of distinct materials and devices—are unable to satisfy the requirements of MFS applications in emerging industries such as aerospace engineering. Motivated by the concept of design for manufacturing, we adopt a layer regulation method with an established optimization model to design typical MFSs with load-bearing, electric, heat-conduction, and radiation-shielding functions. A high-temperature in situ additive manufacturing (AM) technology is developed to print various metallic wires or carbon fiber-reinforced high-melting-point polyetheretherketone (PEEK) composites. It is found that the MFS, despite its low mass, exceeds the stiffness of the PEEK substrate by 21.5%. The embedded electrics remain functional after the elastic deformation stage. Compared with those of the PEEK substrate, the equivalent thermal conductivity of the MFS beneath the central heat source area is enhanced by 568.0%, and the radiation shielding is improved by 27.9%. Moreover, a satellite prototype with diverse MFSs is rapidly constructed as an illustration. This work provides a systematic approach for high-performance design and advanced manufacturing, which exhibits considerable prospects for both the function expansion and performance enhancement of industrial equipment.

Beneficial effects of postoperative radiotherapy for IIIA‑N2 non‑small cell lung cancer after radical resection analysed using the propensity score‑matching method.

The objective of the present study was to investigate the role of postoperative radiotherapy (PORT) after radical resection of stage IIIA-N2 non-small cell lung cancer (NSCLC). Subgroups of patients who benefited from PORT were evaluated. A retrospective review of 288 consecutive patients with resected pIIIA-N2 NSCLC at Beijing Chest Hospital (Beijing, China) was performed. Of these patients, 61 received PORT. The 288 patients were divided into PORT and non-PORT groups according to the treatment received. The baseline characteristics of the two patient groups were balanced using propensity score-matching (PSM; 1:1 matching). In total, 60 patients in the PORT group and 60 patients in the non-PORT group were matched. After PSM, the median survival time of the matched patients was 53 months. The 1-, 3- and 5-year overall survival (OS) rates of the PORT patient group were 95.0, 63.2 and 48.2%, respectively, while those of the non-PORT group were 86.7, 58.3 and 34.5%, respectively, and there was no significant difference between the two groups (P=0.056). The 5-year local recurrence-free survival (LRFS) rate in the PORT group was significantly improved (P=0.001). The effects of PORT on OS and LRFS rates were analysed in patients with different clinicopathological features. For subgroups with multiple N2 stations, N2 positive lymph nodes ≥4 and squamous cell carcinoma, PORT significantly increased the OS and LRFS rates (P<0.05). In conclusion, there was no statistically significant improvement in the 5-year OS rate with PORT overall, but there may be subgroups, such as patients with multiple N2 stations, N2 positive nodes ≥4 and squamous cell carcinoma histology, that could be explored as potentially benefitting from improved 5-year OS and LRFS rates with PORT.

A cloud model-based method for passenger flow control at subway stations: A real-world case study

In the condition of large passenger flow, subway station managers take measures of passenger flow control organization for reducing high safety operation risks at subway stations. The volume of passenger flow in urban railway network operation continues to increase and the Congestion of passenger flow is very high. Passenger flow control measures can greatly give birth to the pressure of transportation and ensure an urban rail transit system’s safe operation. In this paper, we develop a cloud model-based method for passenger flow control, which extends the four-level risk-control grade of a large passenger flow at facilities by considering its fuzzy and stochastic characteristics. Then, an efficient passenger flow control strategy for subway stations is made, where the control time and locations are simultaneously determined. Finally, a station in the Beijing subway is studied to test the validity of the proposed approach. The results show that the time of maximum queuing length is much shorter and the density of passenger flow is lower than existing methods in practice. With the in-depth study of complex network controllability, many studies have applied to control judgment and real network optimization. This paper analyzes the cloud-model-based method for passenger flow control at subway stations and therefore a new method can be incorporated for developing and optimizing control strategies. A few researchers have attempted to find the solution to the problem of crowding risk classification and the passenger flow control strategy. The focus of some studies simultaneously solves the passenger flow control with multiple stations.

IG-Net: An Interaction Graph Network Model for Metro Passenger Flow Forecasting

The urban metro system accommodates significant travel demand and alleviates traffic congestion. Improving metro operational efficiency can increase the metro operator revenue and promote the development of robust urban transportation. To achieve this goal, passenger flow forecasting is a crucial and well-recognized task in metro operation. However, passenger flow forecasting is a challenging task as there exist many unquantifiable factors in resident travel. To address this problem, we propose an innovative model named Interaction Graph Network (IG-Net) to perform passenger flow forecasting at the station level, capable of capturing the non-Euclidean relationships between stations. Three kinds of inter-station interaction graphs are developed to model these inter-station interactions: connectivity, similarity, and temporal correlation graphs. Moreover, we apply multiple channels of graph convolutional neural networks to capture interaction representations and develop a multi-task learning architecture across multiple stations. The proposed IG-Net achieved better performance than the benchmark models when forecasting passenger flow over multiple stations, based on experiments with the Suzhou metro. Finally, we identify the significant effects of interaction graph combinations and multi-task loss functions via further experimentation.

A Day-Ahead Scheduling Model of Power Systems Incorporating Multiple Tidal Range Power Stations

With the global trend to exploit more renewable energy sources, tidal range energy has been gaining more attention recently. This power generation technology is expected to reduce the share of fossil fuels and provide flexibility to the power system. With a pioneering tidal range generation project just granted in Wales and more proposals planned in Great Britain, it is important to study how the incorporation of multiple tidal range power stations will affect power system operation. In this paper, the role of tidal range energy generation in the future Great Britain power system is investigated based on a day-ahead scheduling model of power system incorporating multiple tidal range power stations. In the proposed model, tidal range power stations situated at different sites operate flexibly and in coordination, supporting the power system to reach the minimum operating cost. A case study based on the Great Britain electricity transmission system in 2030 with one tidal barrage and one tidal lagoon was investigated. The results showed that the coordination of flexible tidal range power stations can reduce the power system's operating cost. Furthermore, the energy-storage feature of tidal range power stations can act as a stable source of flexibility in the power system.

Generative adversarial networks for detecting contamination events in water distribution systems using multi-parameter, multi-site water quality monitoring.

Contamination events in water distribution networks (WDNs) can have a huge impact on water supply and public health; increasingly, online water quality sensors are deployed for real-time detection of contamination events. Machine learning has been used to integrate multivariate time series water quality data at multiple stations for contamination detection; however, accurate extraction of spatial features in water quality signals remains challenging. This study proposed a contamination detection method based on generative adversarial networks (GANs). The GAN model was constructed to simultaneously consider the spatial correlation between sensor locations and temporal information of water quality indicators. The model consists of two networks-a generator and a discriminator-the outputs of which are used to measure the degree of abnormality of water quality data at each time step, referred to as the anomaly score. Bayesian sequential analysis is used to update the likelihood of event occurrence based on the anomaly scores. Alarms are then generated from the fusion of single-site and multi-site models. The proposed method was tested on a WDN for various contamination events with different characteristics. Results showed high detection performance by the proposed GAN method compared with the minimum volume ellipsoid benchmark method for various contamination amplitudes. Additionally, the GAN method achieved high accuracy for various contamination events with different amplitudes and numbers of anomalous water quality parameters, and water quality data from different sensor stations, highlighting its robustness and potential for practical application to real-time contamination events.