Multiple Stations Research Articles

ZigBee-Based Self-Service Charging Framework for Plug-in Electric Vehicles

This research presents a self-service charging structure for plug-in type electric vehicles, which includes multiple charging stations, a data service centre, and a mobile communication service centre. Each charging station is equipped with ZigBee networking devices, a charging station computer monitoring centre, and several chargers. The ZigBee networking equipment establishes a wi-fi connection with a ZigBee terminal node device in the electric vehicle. Each charger comprises a charger information management unit, an IC magnetic card identification device, a touch screen, a printer, and a charging device. The proposed self-service charging structure overcomes the issues of unstable electrical connection and high networking complexity associated with both wired and wi-fi communication procedures. It enables user-friendly, safe, auto, and intelligent charging processes without the need for manual intervention. Additionally, the structure supports multiple charging service modes. The research also presents a self-service charging procedure for plug-in type electric vehicles.

Joint Optimization of Order Allocation and Rack Selection in the “Parts-to-Picker” Picking System Considering Multiple Stations Workload Balance

E-commerce companies generate massive orders daily, and efficiently fulfilling them is a critical challenge. In the “parts-to-picker” order fulfillment system, the joint optimization of order allocation and rack selection is a crucial problem. Previous research has primarily focused on these two aspects separately and has yet to consider the issue of workload balancing across multiple picking stations, which can significantly impact picking efficiency. Therefore, this paper studies a joint optimization problem of order allocation and rack selection for a “parts-to-picker” order picking system with multiple picking stations to improve order picking efficiency and avoid uneven workload distribution. An integer programming model of order allocation and rack selection joint optimization is formulated to minimize the racks’ total moving distance and to balance the orders allocated to each picking station. The problem is decomposed into three sub-problems: order batching, batch allocation, and rack selection, and an improved simulated annealing (SA) algorithm is designed to solve the problem. Two workload comparing operators and two random operators are developed and introduced to the SA iterations. Random instances of different scales are generated for experiments. The algorithm solutions are compared with those generated by solving the IP model directly in a commercial solver, CPLEX, and applying the first-come-first-serve strategy (FCFS), respectively. The numerical results show that the proposed algorithm can generate order allocation and rack selection solutions much more efficiently, where the moving distances of the racks are effectively reduced and the workloads are balanced among the picking stations simultaneously. The model and algorithm proposed in this paper can provide a scientific decision-making basis for e-commerce companies to improve their picking efficiency.

Share or not share, the analysis of energy storage interaction of multiple renewable energy stations based on the evolution game

With the increasing penetration of renewable energy, the traditional energy storage operation based on individual framework —users own and operate independently mode may become impracticable due to conflicting interests, insufficient utilization, and poor interoperability. Riding on the wave of the proliferation of sharing economy, storage energy sharing expands the existing storage energy without requiring costly and time-consuming infrastructure investments. However, the development path of shared energy storage (SES) mode is not clear due to the asymmetric decision-making of the owners of energy storage systems under bounded rationality. In this paper, the diffusion of the business model of SES among multiple renewable energy stations (the owners, RES) and its key factors are analyzed based on the evolutionary game. The goal is to maximize social welfare and ensure the continuous growth of the sharing market. The numerical simulation is carried out based on the data from northwest China to interpret the development path. The result shows that, in renewable energy cluster the stations with intermittent output or with the higher prediction accuracy are more willing to participate in sharing. The energy storage sharing mode fails when the energy storage capacity ratio of RES is less than 10%. While the high-level ratio (more than 30%) is not conducive to the diffusion of the sharing model in RESs with low power generation prediction accuracy.

Castleman Disease and Literature Review from Mankweng Hospital, Limpopo, South Africa

Castleman disease is a lymphoproliferative disorder and is easily confused with lymphoma or other lymphadenopathy. Castleman’s disease is divided into unicentric Castleman disease, which involved a single enlarged lymph node or region of lymph nodes, and multicentric Castleman disease, which involved multiple lymph node stations. In this paper we highlight the presentation of Castleman’s disease on a female patient to share our experience with literature review. Case Presentation: A 29 years old female patient was referred to our surgical outpatient clinic from the peripheral hospital with right & left painless neck cervical mass slowly growing which started 3 years ago along with a right breast lump and patient was RVD non-reactive. On physical examination she was severely wasted. She had a 4cm by 3cm mass of the right supraclavicular region & another mass of 3cm x 1 cm mass on the left supraclavicular area. Her abdominal examination revealed hepatomegaly which is extended 8 cm below the subcostal margin. Histology report of cervical mass concluded Reactive lymph nodes with features of hyaline vascular Castleman disease and Breast mass revealed fibroadenoma. Immunohistochemistry of cervical node biopsy for HHV8 is negative. Blood result of inflammatory markers: C - reactive protein was 459 mg/L. Conclusions: Owing to the rarity of this condition, Castleman Disease Collaborative Network (CDCN) guideline is important for assessment & treatment plan.

Improving Intra-Urban Prediction of Atmospheric Fine Particles Using a Hybrid Deep Learning Approach

Growing evidence links intra-urban gradients in atmospheric fine particles (PM2.5), a complex and variable cocktail of toxic chemicals, to adverse health outcomes. Here, we propose an improved hierarchical deep learning model framework to estimate the hourly variation of PM2.5 mass concentration at the street level. By using a full-year monitoring data (including meteorological parameters, hourly concentrations of PM2.5, and gaseous precursors) from multiple stations in Shanghai, the largest city in China, as a training dataset, we first apply a convolutional neural network to obtain cross-domain and time-series features so that the inherent features of air quality and meteorological data associated with PM2.5 can be effectively extracted. Next, a Gaussian weight calculation layer is used to determine the potential interaction effects between different regions and neighboring stations. Finally, a long and short-term memory model layer is used to efficiently extract the temporal evolution characteristics of PM2.5 concentrations from the previous output layer. Further comparative analysis reveals that our proposed model framework significantly outperforms previous benchmark methods in terms of the stability and accuracy of PM2.5 prediction, which has important implications for the intra-urban health assessment of PM2.5-related pollution exposures.

Bilateral comparison of irradiance scales between PMOD/WRC and PTB for longwave downward radiation measurements

In this work, comparison measurements are presented between two independently realised and characterised blackbody cavities which serve as irradiance standards, namely the well-established Tilted Bottom Cavity BB2007 and the new Hemispherical Blackbody (HSBB). Both are used to realise the unit for thermal infrared irradiance. The BB2007 at the Physikalisch-Meteorologisches Observatorium Davos/World Radiation Center has long provided the reference for longwave downward radiation within the Baseline Surface Radiation Network. Longwave downward radiation is constantly measured at multiple stations around the world using specific broadband infrared radiometers with a hemispherical acceptance angle, for example pyrgeometers. The HSBB, developed at the Physikalisch-Technische Bundesanstalt (PTB) in recent years, was specifically designed to calibrate radiometers with a hemispherical acceptance angle which measure longwave downward radiation. The HSBB is directly traceable to the Radiation Temperature Scale of PTB and, in turn, via this scale to the SI. Comparison measurements between the BB2007 and HSBB in this work were carried out with three transfer instruments: the dedicated Temperature Stabilised Radiation Thermometer, an Infrared Integrating Sphere (IRIS) instrument and a Kipp and Zonen CG4 pyrgeometer. The results show good agreement with respect to the target irradiance uncertainty of W m−2 provided by the HSBB. This study thus supports and validates the traceability of atmospheric longwave downward radiation to the SI linking measurements performed with the World Infrared Standard Group of pyrgeometers to the traceability of the reference blackbody BB2007 using IRIS instruments as the transfer standard.

Hydrological status of the Dagu River Basin and management suggestions based on soil and water assessment tool multi-station calibration

Soil and water assessment tool (SWAT) model was used to calculate the runoff, nitrogen and phosphorus emissions in the Dagu River Basin from 2002 to 2012 and provided preliminary suggestions on environmental protection measures. Calibration on hydrological watershed parameters was obtained from data obtained from multiple representative stations. Cluster analysis was used to group all stations, and the parameters of different representative stations were applied to the entire geographical area to reflect the hydrological conditions of the basin. Additionally, the spatiotemporal changes and pollution source characteristics of nitrogen and phosphorus were analyzed. Anthropogenic sources—chemical fertilizer loss and livestock breeding—accounted for 49.9–29.6% of total nitrogen (TN) and 41.4% and 40.3% of total phosphorus (TP), respectively. Non-point sources accounted for 85.8% of the TN and 89.4% of the TP. Six landscape management practices were set up to evaluate the reduction effect on non-point source pollution loads, and comprehensive measures were proposed to reduce TN (TP) by ~ 70%. According to water quality model, the response relationship between river control and nutrient distribution in Jiaozhou Bay is calculated. Based on the findings, we propose an optimal control scheme for non-point source pollution in the Dagu River Basin and other similar basins.

Single-station quad-constellation GNSS regional ionospheric modelling for navigation

Conventionally, regional ionospheric models (RIMs) are established using GNSS at multiple stations. However, this will severely rely on the availability of the local GNSS observation network. In this study, a single-station quad-constellation GNSS regional ionospheric modelling (SQG-RIM) approach is proposed by establishing the RIM with the GPS/GLONASS/BDS/Galileo observations at a single station. To facilitate its application, an algorithm is further proposed to divide the entire modelling region into the core area and the non-core area. Compared with the global ionospheric map (GIM), the SQG-RIM in the core area can increase the ionospheric error correction rate by about 9%. Further, the SQG-RIM is applied to single point positioning (SPP) using a differentiated weighting scheme for different signal-passing areas. Test results indicate that the three-dimensional quad-constellation SPP accuracies are improved by 57% and 52% after applying the SQG-RIM as compared to the Klobuchar and GIM models, respectively.

Castleman disease: a rare case in a young woman.

Castleman disease is a rare lymphoproliferative disorder characterized by benign enlargement of lymph nodes. It is divided into unicentric disease, which involves a single enlarged lymph node, and multicentric disease, which affects multiple lymph node stations. In this report, we describe a rare case of a 28-year-old female patient with an unicentric Castleman disease. Computed tomography and magnetic resonance imaging revealed a well-circumscribed large mass in the left neck, characterized by intense homogenous enhancement and suspected for a malignant disease. The patient underwent an excisional biopsy for definitive diagnosis of unicentric Castleman disease and ruled out malignant conditions.

Seismic Monitoring of Novaya Zemlya: Progress, Challenges, and Prospects

AbstractThe permanent seismic stations of the European Arctic maintain a detection threshold of around magnitude 2 for events on and around Novaya Zemlya. Events above magnitude 3 are clearly observed by multiple stations at regional and far-regional distances and, with improved travel-time models, can be located with high accuracy. The monitoring capability for smaller magnitude events is dominated by the small aperture seismic arrays ARCES and SPITS. We review the properties of Novaya Zemlya seismic signals on key stations and discuss how empirical signal processing may enhance detection and interpretation of future events in the region. We present a joint probabilistic location for 21 low-magnitude events between 1986 and 2020 in which the joint probability distribution for all events simultaneously exploits both constraints on earlier events from stations no longer in operation and constraints on newer events from more recently deployed stations. Advances in signal processing, enhanced exploitation of archive data, new permanent stations, and comparative multiple event analysis will all contribute both to a more robust and sensitive detection capability and higher confidence in signal interpretation.

Multi-station automatic classification of seismic signatures from the Lascar volcano database

Abstract. This study was aimed to build a multi-station automatic classification system for volcanic seismic signatures such as hybrid, long period, tremor, tectonic, and volcano–tectonic events. This system was based on a probabilistic model made using transfer learning, which has, as the main tool, a pre-trained convolutional network named AlexNet. We designed five experiments using different datasets with data that were real, synthetic, two different combinations of these (combined 1 and combined 2), and a balanced subset without synthetic data. The experiment presented the highest scores when a process of data augmentation was introduced into processing sequence. Thus, the lack of real data in some classes (imbalance) dramatically affected the quality of the results, because the learning step (training) was overfitted to the more numerous classes. To test the model stability with variable inputs, we implemented a k-fold cross-validation procedure. Under this approach, the results reached high predictive performance, considering that only the percentage of recognition of the tectonic events (TC) class was partially affected. The results obtained showed the performance of the probabilistic model, reaching high scores over different test datasets. The most valuable benefit of using this technique was that the use of volcano seismic signals from multiple stations provided a more generalizable model which, in the near future, can be extended to multi-volcano database systems. The impact of this work is significant in the evaluation of hazard and risk by monitoring the dynamic evolution of volcanic centers, which is crucial for understanding the stages in a volcano’s eruptive cycle.

Optimal Configuration of Extreme Fast Charging Stations Integrated with Energy Storage System and Photovoltaic Panels in Distribution Networks

Extreme fast charging (XFC) for electric vehicles (EVs) has emerged recently because of the short charging period. However, the extreme high charging power of EVs at XFC stations may severely impact distribution networks. This paper addresses the estimation of the charging power demand of XFC stations and the design of multiple XFC stations with renewable energy resources in current distribution networks. First, a Monte Carlo (MC) simulation tool was created utilizing the EV arrival time and state-of-charge (SOC) distributions obtained from the dataset of vehicle travel surveys. Various impact factors are considered to obtain a realistic estimation of the charging power demand of XFC stations. Then, a method for determining the optimal energy capacity of the energy storage system (ESS), ESS rated power, and size of photovoltaic (PV) panels for multiple XFC stations in a distribution network is presented, with the goal of achieving an optimal configuration. The optimal power flow technique is applied to this optimization so that the optimal solutions meet not only the charging demand but also the operational constraints related to XFC, ESS, PV panels, and distribution networks. Simulation results of a use case indicate that the presented MC simulation can estimate approximate real-world XFC charging demand, and the optimized ESS and PV units in multiple XFC stations in the distribution network can reduce the annual total cost of XFC stations and improve the performance of the distribution network.

A Two-Stage Optimization Algorithm of the Train Traction Energy Consumption in Urban Rail Transit

The optimization of the train traction energy consumption in urban rail transit is a nonlinear optimization problem, and its solution is a very difficult task. This article focuses on the optimization of train traction energy consumption on the line with multiple stations. First, a single-objective nonlinear energy-saving optimization model is established for a given line and total travel time. Second, a two-stage optimization algorithm is proposed to solve this model. In the first stage, for a given interval and travel time, an optimization model of the traction energy consumption is established by using the distance discretization method. A dichotomous iterative algorithm based on energy consumption is proposed to search the optimal traction energy consumption. By calling this algorithm repeatedly, the energy–time curve of each interval can be obtained. In the second stage, according to these energy–time curves, the total travel time of a train on the line is allocated to each interval one by one in the form of time slices. And finally the optimal travel time and optimal traction energy consumption of the train on each interval are obtained. This method does not need to set the train operation mode sequence in advance but adaptively selects the energy-saving operation mode according to the line constraints and the train parameters. The up-direction from Yizhuang to Tongjinan of the Beijing Metro Yizhuang line in China is selected as the test section. The experimental results demonstrate the effectiveness and computational efficiency of our proposed methods and algorithms. Moreover, the dichotomous iterative algorithm runs so fast that it can be used in real-time driver advisory systems or automatic train operation systems.

NIGHTTIME LIGHT: A POTENTIAL PROXY FOR LOCAL NOCTURNAL URBAN HEAT ISLAND INTENSITY IN SEOUL

ABSTRACT Nighttime light (NTL) imagery provides valuable information for monitoring urban development, human activity, and, consequently, urban thermal environments. However, only a few studies have attempted to directly draw linkages between NTL and urban heat island (UHI) formation. Establishing associations between NTL and UHI indicators could potentially ease the process of identifying heightened thermal environments and thus pinpointing urban hotspots in need of policy interventions and strategic mitigation measures. In the present study, through clustering and correlation analysis, we study ties between NTL, surface urban heat island intensity (SUHII) and atmospheric urban heat island intensity (AUHII) in Seoul. NTL data was derived from the Suomi National Polar-Orbiting Partnership-Visible and Infrared imager/Radiometer Suite (SNPP-VIIRS), while SUHII data was obtained from the moderate resolution imaging spectroradiometer (MODIS) satellite instrument. Additionally, AUHII was computed from archived weather data collected from multiple automatic weather stations (AWSs) in Seoul. Our results show that NTL is highly positively correlated with SUHII (R > 0.86, p < 0.05) and AUHII (R > 0.68, p < 0.05). The findings highlight the potential usage of NTL as a proxy for UHI formation, simplifying the process of identifying heightened thermal areas in Seoul that may require rapid policy intervention.

An unexpected cervical unicentric Castleman disease disguising as a lymph node metastasis of papillary thyroid carcinoma

The onset of cervical lymph nodes could be a turning point in the oncologic management of patients previously treated for differentiated thyroid cancer. Its accurate diagnosis may be challenging. A 56-year-old female patient was diagnosed with papillary thyroid carcinoma (PTC). The initial surgical procedure included total thyroidectomy and prophylactic bilateral central-compartment neck dissection. The pathologic examination featured a 3-centimeter left-sided encapsulated follicular variant of PTC, without vascular invasion nor lymph nodes (LN) metastases. The tumor was staged pT2N0M0 and considered as ATA low risk. We obtained a radioiodine remnant ablation following a cumulative dose of 100 mCi. The response to therapy was excellent: indetectable stimulated thyroglobulin (TgOFF), negative anti-thyroglobulin auto-antibodies (TgAB), and negative imaging. After a 10-year remission, follow-up neck ultrasonography detected solid left-sided lymphadenopathy of level III measuring 86 × 31 mm with slightly concomitant rising TgOFF and positive TgAB, suggesting a possible PTC LN metastasis. The patient underwent surgical resection with favorable outcomes. The recurrence of PTC in the absence of aggressive histological features is unlikely (< 5% of cases). The onset of enlarged cervical lymphadenopathy with biochemical signs of cancer activity in patients with underlying PTC suggests a disease relapse. The pathologic examination revealed a unicentric plasmacytic variant of Castleman disease (UCD), with no evidence of malignancy. Castleman disease (CD) represents a cluster of rare and heterogeneous disorders with distinctive LN histopathological abnormalities. CD can occur in a single LN station [unicentric CD] or involve multiple LN stations with systemic symptoms [multicentric CD]. Mediastinum (29%) and abdominal (21%) sites are the most commonly implicated. Since cervical UCD can potentially mimic PTC lymph metastasis, the distinction between these two conditions is challenging. Only biopsy and histological proof could establish a conclusive diagnosis, allowing appropriate management for each scenario.

Scheduling Drones for Ship Emission Detection from Multiple Stations

Various port cities and authorities have established emission control areas (ECAs) to constrain ships’ fuel usage in a specified offshore geographical range. However, these ECA policies involve high costs and have low monitoring and regulation enforcement efficiencies. In this study, a meeting model was used to investigate the drone-scheduling problem by considering the simultaneous movements of drones and ships. Set-covering integer linear programs were developed to formulate the assignments of drones to ships, and a model and solution algorithm were devised to determine the moving times and meeting positions for particular drones and ships. The proposed models and algorithms were employed and verified in experiments. The flying times for the datasets with three drone base stations were shorter than those with two. More drones resulted in shorter flying distances. The use of the meeting model enabled the acquirement of shorter flying times and distances than when it was not used. The datasets with more ships had longer flying times and distances, with almost linear relationships. The sensitivity of the effect of varying 5% of the ships’ speeds on the flying time metrics was less than 1%, affecting the flying distance by about 4–5%. Accelerating the drones was more effective towards optimizing the drones’ flying distances than times. Numerical studies showed that the consideration of simultaneous movements in the model allowed for a reduction in the drones’ flying distances and increased efficiency. Based on the modeling and experimental studies, managerial implications and possible extensions are discussed.

Multiple Monitoring Stations in Big Cities: First Example of Three Spore Traps in Rome

(1) Background: Rome is a municipality with an area of 1287 km2 and presents floristic-vegetational complexity that is reflected in the composition of aerospora, which are responsible for pollinosis. The presence of airborne pollen can be detected by pollen monitoring. The large extent of the city’s territory makes it possible to verify possible changes in pollen composition in different sites of the city. With this in mind, a study was conducted to assess the differences in airborne pollen concentration, considering phenological and production indicators at three different sites in the city. (2) Methods: Pollen data of eight taxa were considered, Alnus spp., Castanea sativa Miller, Cupressaceae-Taxaceae, Olea europaea L., Platanaceae, Poaceae, Quercus spp., and Urticaceae, during 2020 and 2021, using three monitoring samplers. The airborne pollen concentration and the seasons of the three centers were calculated and compared with each other. (3) Results: The diversity between the three samplers shows a phenological succession in accordance with the microclimatic diversity present in the city. The heterogeneity of the airborne pollen concentration reflects the floristic-vegetational diversity, while qualitative and quantitative parameters indicate a homogeneous flowering trend reflecting the seasonality of the various species. (4) Conclusions: The present work and the Italian geographic context suggest the need for a greater number of sampling points to guarantee a true localization of the data. Having several sampling stations also contributes to the protection of health and green areas, which are difficult to manage, conserve, and maintain.

A Hybrid Deep Learning Model for Multi-Station Classification and Passenger Flow Prediction

Multiple station passenger flow prediction is crucial but challenging for intelligent transportation systems. Recently, deep learning models have been widely applied in multi-station passenger flow prediction. However, flows at the same station in different periods, or different stations in the same period, always present different characteristics. These indicate that globally extracting spatio-temporal features for multi-station passenger flow prediction may only be powerful enough to achieve the excepted performance for some stations. Therefore, a novel two-step multi-station passenger flow prediction model is proposed. First, an unsupervised clustering method for station classification using pure passenger flow is proposed based on the Transformer encoder and K-Means. Two novel evaluation metrics are introduced to verify the effectiveness of the classification results. Then, based on the classification results, a passenger flow prediction model is proposed for every type of station. Residual network (ResNet) and graph convolution network (GCN) are applied for spatial feature extraction, and attention long short-term memory network (AttLSTM) is used for temporal feature extraction. Integrating results for every type of station creates a prediction model for all stations in the network. Experiments are conducted on two real-world ridership datasets. The proposed model performs better than unclassified results in multi-station passenger flow prediction.

Stability of a cascade system with multiple stations

Stability of a cascade system with multiple stations

Feature Extraction and Prediction of Water Quality Based on Candlestick Theory and Deep Learning Methods

In environmental hydrodynamics, a research topic that has gained popularity is the transmission and diffusion of water pollutants. Various types of change processes in hydrological and water quality are directly related to meteorological changes. If these changing characteristics are classified effectively, this will be conducive to the application of deep learning theory in water pollution simulation. When periodically monitoring water quality, data were represented with a candlestick chart, and different classification features were displayed. The water quality data from the research area from 2012 to 2019 generated 24 classification results in line with the physics laws. Therefore, a deep learning water pollution prediction method was proposed to classify the changing process of pollution to improve the prediction accuracy of water quality, based on candlestick theory, visual geometry group, and gate recurrent unit (CT-VGG-GRU). In this method, after the periodic changes of water quality were represented by candlestick graphically, the features were extracted by the VGG network based on its advantages in graphic feature extraction. Then, this feature and other scenario parameters were fused as the input of the time series network model, and the pollutant concentration sequence at the predicted station constituted the output of the model. Finally, a hybrid model combining graphical and time series features was formed, and this model used continuous time series data from multiple stations on the Lijiang River watershed to train and validate the model. Experimental results indicated that, compared with other comparison models, such as the back propagation neural network (BPNN), support vector regression (SVR), GRU, and VGG-GRU, the proposed model had the highest prediction accuracy, especially for the prediction of extreme values. Additionally, the change trend of water pollution was closer to the real situation, which indicated that the process change information of water pollution could be fully extracted by the CT-VGG-GRU model based on candlestick theory. For the water quality indicators DO, CODMn, and NH3-N, the mean absolute errors (MAE) were 0.284, 0.113, and 0.014, the root mean square errors (RMSE) were 0.315, 0.122, and 0.016, and the symmetric mean absolute percentage errors (SMAPE) were 0.022, 0.108, and 0.127, respectively. The established CT-VGG-GRU model achieved superior computational performance. Using the proposed model, the classification information of the river pollution process could be obtained effectively and the time series information could also be retained, which made the application of the deep learning model to the transmission and diffusion process of river water pollution more explanatory. The proposed model can provide a new method for water quality prediction.