Station Management Research Articles

Spatial distribution characteristics of soil water-salt gradients in the ecological buffer zone of arid zone lakes and their influencing factors

Soil salinization exerts a severe impact on environmental degradation that threatens the structure and ecological functions of lake wetland. To study the characteristics of soil water-salt in the typical plant communities in the ecological buffer zone of the arid lake wetland, three zones were selected lakeshore as the starting point in Stone House, Bird Island, and Duck Bay management stations of Lake Ebinur wetland, August 2016 and 2017. These zones, including the waterward radiation zone, the water level variation zone, and the landward radiation zone, to investigate the variation patterns of soil water-salt in different zones and soil depths (0–5, 5–10, 10–20, 20–40, 40–60 cm). The results showed that: the soil pH in different plant communities in zones were all alkaline with weak varianbility. Soil moisture and total salt content were moderately variable. According to the Xinjiang saline-alkali classification standards, soils in the three zones were classified as non-salinized soil to mildly salinized soil, heavily salinized soil to saline soil, and non-salinized soil to heavily salinized soil, respectively; soil pH and moisture content in different plant communities of the zones tended to increase along with soil deepens and salt content decreased. The differences in soil pH and moisture between different soil profiles in the same zone were not so significant, but the differences in total salt content were bigger. The soil moisture and total salt content among different zones within the same soil profile remained prominent. The relationship between soil pH, moisture content, and total salt content in waterward radiation zone and water level variation zone was complex, showing an unclear pattern. In the landward radiation zone, soil pH and total salt content were negatively correlated with moisture, while soil pH was positively correlated with total salt content. The study demonstrated that the changes in soil water-salt in Lake Ebinur Wetlands were mainly influenced by structural factors (microtopography, soil characteristics, vegetation, parent material, hydrology), followed by random factors (irrigation, soil improvement, cultivation practices, and human activities).

Evaluation of the performance of the vehicular ad hoc network protocols in the case of V2I and EV2I communications

Vehicular ad hoc network (VANET) is an intelligent technology that enables efficient communication, secure data transmission, and traffic management. The purpose of routing protocols in the VANET network is to route data between vehicles (V2V) and vehicles-to-infrastructure (V2I). Recently, researchers have shown interest in designing effective routing protocols for the VANET network, as not all existing protocols are suitable for all traffic scenarios. Electric vehicles (EVs) are increasingly being adopted and integrated into intelligent transportation systems (ITS). Developed countries are actively promoting sustainable transportation solutions to increase energy efficiency and reduce carbon emissions. Therefore, this research presents an EV charging station (CS) management scheme based on communication between EVs and RSUs, with performance evaluation simulated using VANET network protocols. In this study, the G-MDORA, MDORA, and geographical routing protocol (GRP) protocols were modified to accommodate V2I communication, and RSUs were distributed along the roadmap. Additionally, a scheme for managing electric vehicle CS was presented, focusing on the communication between electric vehicles and RSUs in the EV2I context. Performance was evaluated using the G-MDORA, GRP, and MDORA protocols, considering factors such as throughput, communication overhead, packet delivery ratio, and end to end delay.

A two-step passenger flow anomaly detection scheme based on machine learning methods

Subway is an important transportation means for residents due to its large volume, punctuality and environmental friendliness. However, weather factors, sports events, concerts and some unexpected events can lead to a surge or abnormality in passenger flow, which brings enormous pressure to the management of stations and passenger flow guidance. Inspired by this, this paper formulates the abnormal passenger flows into different categories in terms of the characteristics and periodical trends, and proposes a two-step abnormal detection scheme to identify the anomalies and their type, and locate abnormal positions. First, two abnormal passenger flows recognition methods based on Jensen–Shannon divergence, dynamic time warping, and density-based spatial clustering of applications with noise are established to identify the station-level abnormal passenger flow. Then, a triple standard deviation algorithm based on sliding window is further proposed to identify the abnormal type and position. Real-world smart card data of the Beijing subway in China, and the manual mutation data of the real data are employed to evaluate effectiveness of our framework. The results show that our two-step scheme is superior to the state-of-the-art algorithms, which can detect out and locate abnormal passenger flows with various characteristics. On more mutation data, this paper discusses the performances on various anomalies of different types of stations in depth, which further indicates our framework is robust and effective in practice.

Prediction-based early warning system for overflow of people in metro stations

As cities rapidly urbanize, handling the management of crowded transportation hubs, notably metro stations, has become an immediate concern. High passenger traffic can lead to severe risks, including stampedes. While many past passenger flow forecasting systems aim to enhance prediction accuracy, the inherently noisy nature of passenger flow data makes it challenging for existing technologies to provide stable and precise predictions, making passenger flow management based solely on these models risky. This paper introduces a novel system that mitigates this risk by integrating a predictive model with managerial methods. The proposed management framework formulates a unique model for each station, determining a risk deviation coefficient grounded in the station's historical prediction accuracy. Station management is then holistically executed based on this coefficient and the predictive model. This paper employs the LSTM model for station-specific passenger flow prediction and defines the risk-related parameter , taking prediction accuracy into account. This adjusted LSTM prediction is then utilized to proactively streamline resource allocation, targeting improved passenger safety and overall station experience.

Analisis Respon Konsumen Terhadap Antrean Yang Terjadi Di SPBU 3440506 Cipatik Kecamatan Cihamplas Kabupaten Bandung Barat

This study aims to analyze consumer responses to the queue phenomenon that occurs at Cipatik Public Fuel Filling Station (SPBU). Queues at gas stations are often a major concern for consumers, affecting customer satisfaction and the operational performance of gas stations. This study identifies the main factors that influence consumer response to queues, focusing on queue cases, waiting time, and consumer feelings regarding service. The research method used a quantitative approach through a consumer survey involving a representative sample of Cipatik gas station users. Data was collected through a questionnaire covering aspects such as consumer perception of service speed, queue efficiency, and overall satisfaction level. The results of data analysis show that there is a lack of consumer satisfaction with the queues that occur at Cipatik Service Station, West Bandung Regency. Therefore, it is necessary to improve queue management by the gas station. The addition of filling pump lines needs to be addressed so that the service level can increase. In addition, shorter waiting times and a more efficient service experience are considered by consumers as key factors in increasing their satisfaction. The findings of this study provide valuable insights for Cipatik Service Station management and relevant stakeholders to improve queue management and increase customer satisfaction. The practical implications of this study can assist policy makers in developing more effective operational strategies to address queuing issues and improve consumer experience at the gas station.

Distributed energy management of electric vehicle charging stations based on hierarchical pricing mechanism and aggregate feasible regions

With the rapid development of electric vehicle charging stations, effective management of them has become challenging due to the high uncertainty of electric vehicles, the pricing mechanisms of charging stations, and their coupling with distribution networks. To address these challenges, this paper proposes a two-stage framework for energy management at charging stations. In the first stage, a resource allocation model considering the profits of distribution systems, charging stations, and electric vehicle users is established based on the aggregate feasible power regions of charging stations. The aggregate feasible region is obtained based on the combination of Minkowski summation and the data-driven method, which can preserve the privacy of electric vehicle data and reduce the computational burden. In the second stage, a novel hierarchical pricing mechanism is developed, which encompasses both the clearing price between charging stations and distribution networks and the retail electricity price between charging stations and electric vehicle users. Notably, charging stations participate in the power clearing of distributed networks based on the aggregate feasible power region, while a two-stage robust pricing strategy is established between electric vehicle users and charging stations. The model is finally optimized through a distributed coordination mechanism with a clear physical interpretation. The simulation results show that the proposed aggregation method enables charging stations to achieve a total economic profit at least 1.76 % higher than three competitive methods. The hierarchical pricing mechanism allows charging stations to achieve total economic profits 18.60 % and 2.94 % higher than those in the centralized dispatch and price-taker modes, respectively, while simultaneously reducing operating costs for the distributed network by 25.96 % and 27.99 %.

Community Radio Influence and Project Interventions During the Covid-19 Period

The Ministry of Health in Nairobi, Kenya confirmed the first case of COVID-19 (Corona Virus) on the 12th of March 2020 since the outbreak in China in December of 2019. COVID-19 was declared a pandemic and as a result negatively affected the lives of communities globally. Job loss, mental illness, uncertainty, deaths, domestic violence, denial, misconceptions of the existence of COVID-19 and depressed economies are some of the characteristic features of nations during the pandemic. The purpose of this study was to explore the role of community radio in community engagement in response to the COVID-19 pandemic among Voi and Marsabit marginalised communities in Kenya. The study objectives were to determine the influence of Sifa FM community radio station programmes on community development in Voi and Marsabit communities during the COVID-19 period; to establish the COVID-19 intervention projects conducted by Sifa FM community radio stations in Kenya that promote community development initiatives in Marsabit and Voi; to assess the challenges encountered when engaging in COVID-19 intervention projects by Sifa FM Voi and Marsabit community radio stations in Kenya to promote community engagement; and to identify risks and gaps encountered by Sifa FM Voi and Marsabit community radio stations during the COVID-19 period in Kenya. The study adopted the Empowerment Theory. 212 respondents participated in this study and consisted of four groups: The Sifa FM station manager, 11 community leaders and 100 community members in both Voi and Marsabit respectively. Purposive sampling was used to select community leaders and Sifa FM radio station manager, while snowball sampling was adopted to select community members residing in Voi and Marsabit communities. Mixed research design was adopted for this study. Data was collected using two interview guides and a semi-structured questionnaire. Data was collected between July and October 2021. Quantitative data was captured using SPSS version 20 software while qualitative data was analysed using thematic and content analysis.Findings revealed that Sifa FM community radio station programmes aired during the COVID-19 period contributed to increased levels of social and economic development in Voi and Marsabit communities. Social development was achieved to some extent through educating Sifa FM listeners on COVID-19 which influenced community health. Programmes about economic activities that were aired on Sifa FM provided insight that would impact the living standards of community members and economic development. Sifa FM contributed significantly to social-economic development of Voi and Marsabit communities during the COVID-19 period. Findings further revealed that Sifa FM contributed to several project interventions where the highest number of respondents supported public awareness on COVID-19 interventions as the priority. Results indicated that more can be done to increase the scope of outreach for project interventions from Sifa FM. There are challenges which were encountered by Voi and Marsa bit communities such as distribution of low-quality masks. Gaps were identified such as creating awareness of COVID-19. One risk associated with COVID-19 in Voi and Marsabit was job loss at a percentage of 84%

Conflict Management in Organizational Communication

This research aims to investigate the necessity of conflict management for fostering organizational health. A healthy organization not only survives environmental changes but also adapts and develops in the long term according to established visions and missions. Utilizing the In-depth Interview and documentation methods, primary and secondary data sources were employed, including literature and internet resources. The qualitative data analysis involved data reduction and presentation. Findings reveal conflicts among operators at a fuel station, specifically concerning discipline, lack of responsibility, and insufficient cooperation in scheduling holidays. Interviews with a station supervisor highlight frequent conflicts, creating discomfort in the workplace. The study suggests the need for proactive conflict resolution strategies in fuel station management to enhance overall organizational effectiveness. Highlights : Strategic Importance of Conflict Management: Effective conflict resolution strategies are crucial for sustaining organizational health and fostering adaptability amid environmental changes. Operator Dynamics in Fuel Stations: Insights into conflicts among fuel station operators, including issues related to discipline, responsibility, and collaboration in holiday scheduling. Proactive Strategies for Organizational Effectiveness: Recommendations for fuel station management to implement proactive conflict resolution measures, ensuring a harmonious and productive workplace environment Keywords: Conflict Management, Organizational Health, Fuel Station Operations, Qualitative Inquiry, Workplace Dynamics

Hub Node Identification in Urban Rail Transit Network Evolution Using a Ridership-Weighted Network

With the development of the urban rail transit network (URTN), the network structure and performance have changed, and the node importance has also been redistributed. However, little research has been done on how hub nodes change as the network develops over a lengthy period. Moreover, most hub node identification methods only focus on the analysis of topological networks or single-dimension measurements, resulting in inaccurate identification results. To overcome the above limitations, a novel method of hub node identification is proposed. Based on the ridership-weighted network model, the node centrality and reliability are aggregated to quantify the weighted comprehensive importance of the nodes. Furthermore, network invulnerability measurement is used to demonstrate the effectiveness of the proposed method. This method is applied to the Xi’an Urban Rail Transit Network (XURTN) from 2011 to 2021. With the XURTN’s development, its connectivity, balance, and fault tolerance have improved. After the basic network skeleton was formed, the number and proportion of hub nodes increased steadily. By comparing the spatial characteristics of the identified hub nodes over two successive periods, it can be found that the evolution direction of the hub nodes is correlated with the type of new lines and coincides also with the development direction of the urban area. In addition, the node orders of the proposed method have a greater impact on the network vulnerability, in which the network-weighted efficiency [Formula: see text] decreases faster and more dramatically, that is, 1.17%–45.75% more than that of other methods. Overall, this study provides a basis for the URTN and station planning and management.

Deployment of battery-swapping stations: Integrating travel chain simulation and multi-objective optimization for delivery electric micromobility vehicles

As the delivery sector increasingly relies on electric micromobility vehicles (EMVs), the urgency for efficient battery-swapping infrastructure becomes critical. This study develops a comprehensive framework for predicting battery-swapping demand for delivery EMVs (DEMVs) based on an activity-based travel chain simulation model and devises a multi-objective optimization model for the strategic placement of battery-swapping stations. The simulation model integrates submodules such as the EMV generation and attraction model, Agent-based EMV travel chain, and EMV and battery-swapping behavior model to capture the nuanced travel patterns and battery-swapping demand. Leveraging the Non-dominated Sorting Genetic Algorithm II (NSGA-II), the study optimizes the network design of battery-swapping stations considering both construction and travel costs. A case study in Nanjing City, representative of the diverse delivery sector's operations, substantiates the simulation's accuracy, maps out the spatiotemporal distribution of swapping demand, and analyzes the Pareto optimal set derived from the optimization model. Sensitivity analysis focuses on the facility planning model, assessing how uncertainties in swapping demand and battery charging rates within stations impact operational efficacy. This research melds demand forecasting with infrastructure optimization, providing actionable insights for the planning and management of battery-swapping stations.

Research on intelligent energy management method of multifunctional fusion electric vehicle charging station based on machine learning

The machine-learning based approach to energy management of multifunctional charging stations that meets the needs in the context of "carbon neutrality". The method takes multifunctional charging station as the research object, considers the uncertainty elements such as market price signal, charging load, and the change of photovoltaic power generation, and aims at maximizing the comprehensive operation efficiency of charging station in the decision cycle, realizes the prediction of power generation and load power by using deep learning method, and obtains the optimal energy management strategy by reinforcement learning, and realizes intelligent management and operation of multifunctional charging station. A typical case study is presented and simulation analysis is carried out for typical cases to verify the correctness and effectiveness of the method. It is shown that the method can effectively cope with the uncertain elements in the operation of multifunctional charging stations, reduce the hardware configuration and manual operation and maintenance costs of charging stations while ensuring safety constraints, and effectively improve the energy utilization efficiency and economy of the whole charging station.

Comprehensive Evaluation Model for Competitiveness of Mass Media Companies in the IoT Sensor Networks

Mass media companies can be elaborated as organizations that manipulate technological components for their various departments such as movie studios, publishing houses, radio and television station management teams that impact a large range of audience via vast communication strategies. The companies have also been referred to as media conglomerates, media groups, or media houses further illustrating their grasp over the global markets and their revenue structures. The largest media companies such as Apple, Disney, and Comcast among others, offer products and services to users that are diverse individuals as well as large organizations leading to significant revenues as well as challenges that have been further explored in a comprehensive manner in the study. IoT utilizes wireless networks that are without infrastructure to install a huge number of wireless sensors that track system, physical, and environmental conditions. If you're wanting to integrate WSN into your business, our highly driven and experienced engineers can give you an all-encompassing solution.

An Assessment of the Impact of Partial Commercialization on Financial Performance of Public Enterprises (State-Owned Enterprises) in Nigeria

This study investigated the impact of partial commercialization on the financial performance of public enterprises (state- owned enterprises) in Nigeria. This study employed both expo factor research, and descriptive research designs. Population of this study is made up of all NTA stations in Nigeria as at 30th June, 2023. Secondary source of data is collected from the financial statements of the NTA covering a period of 2001 to 2021 financial years. This study used both descriptive and inferential analyses. The study found that partial commercialization has positive significant influence on the pattern of expenditures of NTA, The study equally finds that Share of Federal Accounts Allocation Committee (FAAC)-statutory has negative significant while Aid and grant has positive significant impact on interest earned by NTA and Finally, the study discovers that there is no significant relationship between both share of Federal Accounts Allocation Committee (FAAC)-statutory allocation, and Aid and grant on non-tax revenue generated by NTA in respect to financial performance, impliedly meaning there is no significant impact between the partial commercialization and non-tax revenue generated by NTA. The study therefore recommended that the government enhance its funding allocation for Aid and Grants by increasing the provision of television and studio equipment, transmitters, outside broadcasting vans, masts, and generators to the Nigerian Television Authority (NTA) and the analysis ultimately reveals that the establishment of additional NTA stations at the local level has resulted in the depletion of people and material resources within the organisation. Consequently, this has placed a greater strain on the management of NTA stations and has diminished their financial performance. As a result, the report suggests that in light of the digitization of NTA, the government should consider reducing the number of NTA stations to solely encompass state capitals and Abuja. This strategic move aims to effectively curtail the operational costs associated with managing 116 stations.

Lessons from the deployment and management of public handwashing stations in response to the COVID-19 pandemic in Kenya: A cross-sectional, observational study.

During the COVID-19 pandemic in Kenya, 5,311 handwashing stations were distributed by the National Business Compact Coalition (NBCC) to help combat the virus. This study evaluated 316 of these stations across five counties, assessing functionality, usability, and accessibility. Quantitative data, including spot checks and surveys, revealed that 83.9% of the evaluated stations were functional, with paid caretakers, which is associated with higher functionality rates. Qualitative insights underscored challenges such as inadequate signage and limited soap and water availability, particularly affecting individuals with disabilities. Despite initial success, only 61% of stations remained functional 6-8 months post-distribution, often due to relocation by caretakers. Future distributions should prioritize long-term support for caretakers to sustain station functionality. This study highlights the importance of ongoing monitoring and support for public handwashing facilities in pandemic response efforts.

Data-Driven Energy Management of an Electric Vehicle Charging Station Using Deep Reinforcement Learning

Data-Driven Energy Management of an Electric Vehicle Charging Station Using Deep Reinforcement Learning

Research on Key Technologies of Remote Operation and Maintenance System for Substation Automation Equipment

Abstract With the help of automation technology, the efficiency and reliability of substation operations are synchronized. However, the equipment will operate under abnormal problems for a long time. If the problem is not resolved promptly, the consequences will be unimaginable. For timely detection of problems, this paper proposes a method for the construction of remote operation and maintenance systems. The remote operation and maintenance system is mainly composed of two-factor authentication, responsibility area authentication, and permission authentication. The security of remote operations and maintenance operations is guaranteed with the support of these key technologies. This paper enhances the safety and stability of automation equipment by offering remote wake-up, remote reset, historical information retrieval, and forwarding table monitoring services in the operation and maintenance service module. Adopting IoT technology, the core management module of remote management of master stations is constructed to ensure the safety of remote operations and maintenance data interaction between master stations and sub-stations. Simulation experiments are being conducted to evaluate the performance of IoT technology in remote operations and maintenance interactions at the main station. The energy consumption of the data interaction module of substation automation equipment is 0.19 and 0.012 mA for 1 hour and 1 day, respectively, for the equipment to operate for 5-10 days, where the advantages of IoT technology are reflected. The processing time of the operation and maintenance personnel under the traditional operation and maintenance and remote operation and maintenance modes were analyzed through comparative experiments, and the processing time of the device dead time was reduced from 175.46 minutes to 4.356 minutes after using the remote operation and maintenance system, and the efficiency of the operation and maintenance personnel was improved.

Battery Swapping Station Management With Heterogeneous Battery Functionality Degradation

Battery Swapping Station Management With Heterogeneous Battery Functionality Degradation

Decentralized Pricing Mechanism for Traffic and Charging Station Management of EVs in Smart Cities

Decentralized Pricing Mechanism for Traffic and Charging Station Management of EVs in Smart Cities

Using AI-Powered Vehicle Identification System in Gas Stations (AI-VIS)

In many countries around the world, retail fuel sales have to be recorded and monitored with specific vehicle information such as license plate by government institutions and station managers. Different hardware methods are utilized to achieve this goal such as UHF (Ultra high frequency) vehicle identification tags installed on the vehicles. To extract data from the tags, RFID-UHF antennas need to be installed on the nozzle for the recognition of vehicles today, which implies an increase in hardware costs per vehicle. Additionally, the electronic waste generated by the hardware used for vehicle recognition hurts the environment. In this study, the aim is to provide a comprehensive solution that enhances the modern automotive world's efficiency, security, and convenience. The core objective of this study is to design and implement a cutting-edge Vehicle Identification System (VIS) that leverages the power of Artificial Intelligence and Computer Vision. The proposed system has the ability to recognize various critical attributes of vehicles at gas stations, including the vehicle make, license plate, vehicle type, color, and fueling information. The system utilizes advanced Image Processing and Deep Learning techniques to achieve precise identification and classification, improving security, and law enforcement.

AHP 분석기법을 활용한 산지 태양광 발전시설 점검기준 개발

Recently, mountain photovoltaic power stations have caused sediment-related disasters, such as soil erosion and landslides. Efforts to mitigate these issues include strengthening installation slope standards and restructuring systems through mandatory inspections. In response to these challenges, this study aimed to select inspection items for the ongoing management of mountain photovoltaic power stations, and to establish standards for their inspection and overall management. Initially, inspection items for mountain photovoltaic power stations were categorized into three major groups, eight intermediate categories, 17 minor categories, and 58 detailed categories based on spatial criteria. Using the Analytic Hierarchy Process(AHP) analysis method to calculate the weight for each item, 'slope' was found to directly influence mountain landslide disasters, and items related to 'erosion' and 'drainage facilities' carried a high weight. Subsequently, a checklist was developed based on the weight of each item, carrying out on-site inspections. A clear variation in scores depending on the management status was confirmed.