Dynamic Management System Research Articles

Photothermal regulated multi-perceptive poly(ionic liquids) hydrogel sensor for bioelectronics

Hydrogels with excellent flexibility, water retention, and biocompatibility are natural carriers for drug delivery. However, the poor drug compatibility seriously limits the practical application of traditional PNIPAM-based hydrogel. Herein, a photothermal regulated smart hydrogel was synthesized by introducing poly(ionic liquids) chain into the binary polymer chain. The temperature-induced hydrophilic-hydrophobic switchable poly(ionic liquids) chain endows the smart hydrogel with editable transparency, enabling intuitive visualization of drug loading and releasing capacity. The smart hydrogel could be used as NIR/temperature-controlled drug carrier to achieve efficient and visualized release (40.8 % drug release rate in 30 min). Besides, the smart hydrogel sensor showed excellent strain sensing sensitivity (gauge factor = 3.73), short response time (116 ms), high durability (1800 s at 20 % strain), and impressive temperature sensing sensitivity (TCR = -1.252 %/°C). Based on the excellent sensing performance and editable drug-delivery ability, the smart hydrogel was assembled into a dynamic wound management system to realize personalized wound treatment and wireless state diagnosis. The wireless sensor early warning system could realize wireless monitoring and state warning of joints (fingers, ankles, and elbows), exhibiting guiding significance for rehabilitation treatment. Moreover, the high-resolution sensor array composed of 5 × 5 smart hydrogels could accurately identify the strain, temperature and NIR light in three-dimensional space and direction. In short, the smart hydrogel provides an innovative solution to personalized wound diagnosis and assisted rehabilitation.

Smart contract token-based privacy-preserving access control system for industrial Internet of Things

Due to mobile Internet technology's rapid popularization, the Industrial Internet of Things (IIoT) can be seen everywhere in our daily lives. While IIoT brings us much convenience, a series of security and scalability issues related to permission operations rise to the surface during device communications. Hence, at present, a reliable and dynamic access control management system for IIoT is in urgent need. Up till now, numerous access control architectures have been proposed for IIoT. However, owing to centralized models and heterogeneous devices, security and scalability requirements still cannot be met. In this paper, we offer a smart contract token-based solution for decentralized access control in IIoT systems. Specifically, there are three smart contracts in our system, including the Token Issue Contract (TIC), User Register Contract (URC), and Manage Contract (MC). These three contracts collaboratively supervise and manage various events in IIoT environments. We also utilize the lightweight and post-quantum encryption algorithm-Nth-degree Truncated Polynomial Ring Units (NTRU) to preserve user privacy during the registration process. Subsequently, to evaluate our proposed architecture's performance, we build a prototype platform that connects to the local blockchain. Finally, experiment results show that our scheme has achieved secure and dynamic access control for the IIoT system compared with related research.

Optimized Dynamic Monitoring and Quality Management System for Post-Harvest Matsutake of Different Preservation Packaging in Cold Chain.

The quality of Tibetan matsutake drops during cold chain transportation. To extend the shelf life and improve the market value, this study analyzed the matsutake logistics process, and optimized the dynamic monitoring and quality management systems for post-harvest matsutake with different preservation packaging in the cold chain. This system monitored the micro-environmental parameters of the cold chain in real time, and it identified the best preservation method by analyzing the quality change characteristics of the matsutake with different preservation packaging. It was concluded that the matsutake were best preserved under the conditions of modified atmosphere packaging. The data analysis on the collected data verified the performance of the system. Relevant personnel were invited to participate in the system performance analysis and offer optimization suggestions to improve the applicability of the established monitoring system. The optimized model could provide a more effective theoretical reference for the dynamic monitoring and quality management of the system.

Hybridization of battery and ultracapacitor for electric vehicle application with dynamic energy management and non-linear state feedback controller

• Proposed a dynamic energy management system for hybrid sources in an electric vehicle. • Buck-boost H-bridge converters are introduced for bidirectional power flow control. • System non-linearities are addressed using a nonlinear state feedback controller. • The proposed system accurately delivers instantaneous vehicle speed and torque. This paper describes a methodology to control a multi-source battery-capacitor hybrid EV incorporating a dynamic power splitting strategy. Considering the requirement for precise speed control characteristics of the EV and the non-linear behavior of traction motor with system disturbances, a non-linear state feedback controller (NLSFC) with a disturbance observer is suggested as the speed control technique for the EV traction motor. To interface a parallel configured multi-source system, a non-inverted buck-boost H-bridge is proposed as the source side converter for effective recovery of the braking energy. Detailed control design for traction motor and converter is also presented for efficient control of EV. Besides, an adaptive energy management strategy considering power profile and dynamic source characteristics is incorporated into the overall control system. The simulation results from various case studies indicate that the proposed system can follow the required vehicle speed and torque despite the sudden change in EV load torque. Furthermore, the comparative analysis between NLSFC and PI controller shows improvement with steady-state error reduction from 0.43 to 0.0014 and peak overshoot drop from 12.07% to 3.51% during acceleration of the system when proposed NLSFC is adopted. The proposed solutions will be implemented in a prototype EV system in the future.

Dynamic power management based on model predictive control for hybrid-energy-storage-based grid-connected microgrids

In this study, an efficient and reliable dynamic power management system (PMS) is proposed for microgrids (μGs) based on hybrid energy storage systems. Owing tothe differences in the response times of the different components (i.e., the battery, supercapacitor, and fuel cell) of the μG, efficiently allocating the power between the different devices is a challenging task for system designers. To address this issue and optimize the operation of the μG, a dynamic PMS is necessary. The contribution of this study is the application of model predictive control (MPC) for operating and controlling such μGs. The MPC method is used to manage power electronic devices such as DC-DC converters (for the battery, fuel cell, and supercapacitor) and grid-connected inverters. After the proposed PMS is successfully implemented in simulations, itsreal-time performance is validatedunder different operating scenarios that could occur in actual situations. The proposed PMS is found to be effective in controlling the control and operation of the μG.It can regulate the DC bus voltage during transient power imbalances, control the fuel cell current slope as per the requirement, and smoothen the operation between isolated and grid-connected modes.

Designing and implementing a data model for describing environmental monitoring and research sites

With new technological advancements and increasing demands of open data in environmental sciences, the requirements for data are increasing in a variety of ways. Having machine and human readable documentation about environmental research and monitoring sites available online is one of them. The Dynamic Ecological Information Management System – Site and Dataset Registry (DEIMS-SDR, https://www.deims.org/) is a research and monitoring site registry that allows the description of in-situ observation or experimental sites, generating persistent, unique and resolvable identifiers for each site. The aim of DEIMS-SDR is to collect site information in a catalogue describing a wide range of sites across the globe, providing information including each site's location, ecosystems, facilities, measured parameters and research themes and enabling that standardised information to be openly available. This article describes the outcomes of the revision of its data model, the conceptual considerations behind it and how it is implemented. These conceptual considerations also encompass the definition of what we call the “onion model of site data interoperability” – a fundamental concept for the design of site data models against the backdrop of data interoperability. Furthermore, we illustrate the capabilities of the revised data model and provide an overview of common data formats for the description of sites, current initiatives driving the harmonisation of descriptions and the outlook of future developments.

Controlling pedestrian flows with moving walkways

Moving walkways are pedestrian dedicated hardware which generally decrease pedestrian travel time. We propose the utilization of these devices to dynamically control pedestrian flows in order to improve pedestrian dynamics. Three variations of a control strategy which use moving walkways are proposed in the context of a dynamic pedestrian management system. In this paper, we discuss how a control strategy based upon either historical data, real-time data or predicted data can improve pedestrian dynamics. After analysing whether moving walkways as a control strategy are beneficial for pedestrians, focus is given towards the advantages of the predictive algorithm over the reactive (real-time) algorithm. While the reactive algorithm exploits real-time measurements, the predictive approach exploits the Adaptive Large Neighbourhood Search (ALNS) optimization algorithm to seek the optimal configuration of the moving walkways over the short term future. Furthermore, we discuss the advantage of using multi-objective optimization to explicitly quantify the trade-off between improving pedestrian travel time or congestion. The simulation results show that the predictive algorithm is generally fairer across different population groups and is better at preventing high congestion. Furthermore, the predictive algorithm can significantly decrease the travel time compared to the reactive and reference scenarios. The results from the prediction approach emphasize the trade-off which must be made between travel time and density. Finally, these results confirm the need for control strategies which are tailored to pedestrian flow dynamics.

Optimization and Simulation of a Dynamic Management System for Building Construction Based on Low-Power Wireless Sensor Networks

In this paper, we use a low-power wireless sensor network to conduct in-depth research and analysis on the optimization simulation of the dynamic management system of building construction and study the specific application of BIM technology in the information management of building construction sites. By analysing the classical delustering routing and combining the advantages in the delustering routing protocols, we propose a two-level Mesh wireless sensor network delustering routing protocol in terms of cluster head uniformity, energy consumption and balance, working mode, and handling of isolated nodes. The most important feature of this protocol compared to other delustering protocols is that it retains the design of flat network topology and designs three routing methods with the corresponding functions of wireless sensor networks to achieve better network performance. The supported personnel location technology and construction measures and environmental monitoring technology are introduced, the applicability of the technology in the building construction process is compared and analysed, the topology of construction measures and environmental monitoring technology is designed, and finally, the targeted development of the collection scheme for personnel, scaffolding, formwork, pits, cranes, construction hoists, and physical environment is completed. Firstly, it analyses the development status of BIM-based building construction site information management technology and application and discusses the basis of BIM technology implementation and the advantages of BIM technology application in information management. Then, this paper constructs the framework of a BIM-based building construction site information management system from three dimensions: functional, logical, and physical. To ensure the realizability and operability of the system, this paper discusses the operation mechanism of the BIM-based building construction site information management system, studies the key technology of system operation, designs the basic operation process of each application system of the system, and elaborates the organizational structure of the building construction site and the functional division of the relevant personnel.

A Blockchain and Metaheuristic-Enabled Distributed Architecture for Smart Agricultural Analysis and Ledger Preservation Solution: A Collaborative Approach

Distributed forecasting of agriculture commodity prices has an attractive research perspective that delivers active breakthrough analysis of the rapid fluctuations in pricing forecasts for participating stakeholders without being manually dispatched lists. The increased use of an efficient forecasting mechanism for the agriculture information management of generated records and processing creates emerging challenges and limitations. These include new government mandates and regulations, the price of land for expansion, forecasting the growing demand for commodities, fluctuations in the global financial market, food security, and bio-based fuels. Building and deploying distributed dynamic scheduling, management, and monitoring systems of agricultural activities for commodity price forecasting and supply chains require a significant secure and efficient approach. Thus, this paper discusses a collaborative approach where two different folds are demonstrated to cover distinct aspects with different objectives. A metaheuristic-enabled genetic algorithm is designed to receive day-to-day agricultural production details and process and analyze forecast pricing from the records by scheduling, managing, and monitoring them in real-time. The blockchain hyperledger sawtooth distributed modular technology provides a secure communication channel between stakeholders, a private network, protects the forecasting ledger, adds and updates commodity prices, and preserves agricultural information and node transactions in the immutable ledger (IPFS). To accomplish this, we design, develop, and deploy two distinct smart contracts to register the system’s actual stakeholders and allow for the addition of node transactions and exchanges. The second smart contract updates the forecasting commodity pricing ledger and distributes it to participating stakeholders while preserving detailed addresses in storage. The simulation results of the proposed collaborative approach deliver an efficient E-agriculture commodity price forecast with an accuracy of 95.3%. It also maintains ledger transparency, integrity, provenance, availability, and secure operational control and access of agricultural activities.

Adaptive learning in university students’ opinions: Cross-border research

Contemporary education is often based on using e-learning courses, which have become a popular means of delivering didactic material to students. Among the main advantages mentioned is the potential possibility of creating individual ways of learning and teaching. The purpose of this article is to provide a description of the various approaches to adaptive learning, to present the comparative research results of a Polish-Ukrainian study, as well as to highlight the options offered by LMS Moodle (Modular object-oriented dynamic learning environment Learning Management System) for the implementation of adaptive learning and the possibility of taking into account the expectation of students regardless of the country. During the study, an online survey was administered to 59 students at the University of Silesia in Katowice (US), Poland and 121 students at the Borys Grinchenko Kyiv University (BGKU), Ukraine between March–June 2020. All of the students were studying online due to the pandemic caused by COVID-19. The classes were conducted in synchronous and in asynchronous modes using e-learning courses in the Moodle system, as well as MS Teams for Polish students and Google Suite for Ukrainian students. At the same time, the majority of the surveyed students declared that they lack personalization, both in terms of materials and the learning process, which was limited in terms of fulfilment and they would like to have a choice of the level of study.

Construction and design of dynamic community management system for the elderly

In view of that the current information management and service efficiency of most pension institutions is low and can not meet the needs of the elderly for professional care, combined with the convenience of the current application of the Internet, a dynamic community management system for the elderly was designed. This system is designed for managers and members in the community, and it is composed of five modules: membership management, housing management, check-in management, entertainment venue management and user management. In the realization of multiple sub-functions, the combination of file operation and structure array makes the structure clear and complete. At the same time, the realization of sub-functions directly applies the array function, reducing the number of lines of code inside the function, making the function more prominent and easier to identify.

Development of Mobile Robot Management System cooperated with Indoor Dynamic Map Management System

Development of Mobile Robot Management System cooperated with Indoor Dynamic Map Management System

Design and development of industrial IoT-based system for behavior profiling of non-linear dynamic production systems based on energy flow theory

In this paper, a solution effective energy consumption monitoring of fast-response energy systems in industrial environments was proposed, designed, and developed. Moreover, in this research, production systems are characterized as non-linear dynamic systems, with the hypothesis that the identification and introduction of non-linear members (variables) can have a significant impact on improving system performance by providing clear insight and realistic representation of system behavior due to a series of non-linear activities that stimulate the system state changes, which can be spotted through the manner and intensity of energy use in the observed system. The research is oriented towards achieving favorable conditions to deploy dynamic energy management systems by means of the IoT and big data, as highly prominent concepts of Industry 4.0 technologies into scientifically-driven industrial practice. The motivation behind this is driven by the transition that this highly digital modern age brought upon us, in which energy management systems could be treated as a continual, dynamic process instead of remaining characterized as static with periodical system audits. In addition, a segmented system architecture of the proposed solution was described in detail, while initial experimental results justified the given hypothesis. The generated results indicated that the process of energy consumption quantification, not only ensures reliable, accurate, and real-time information but opens the door towards system behavior profiling, predictive maintenance, event forensics, data-driven prognostics, etc. Lastly, the points of future investigations were indicated as well.

Analysis of the Traffic Parameters on a Section in the City of the National Road during Several Years of Operation

The study carried out an analysis of the vehicle traffic parameters on a national road in 2011-2016. The variability and uncertainty of results were evaluated. An analysis of traffic data recorded on the city's entry and exit lanes was carried out. The variations in traffic volume are of interest e.g. in dynamic traffic management systems and navigation services, examining the benefits of flexible work time and places and assessing the environmental effects of traffic congestion. Research has shown that the assumption that lanes perform equally is not always true. Traffic volume models should be periodically calibrated taking into account the shape of the daily profile, which may, for example, allow public transport timetables to be more responsive to the needs of travelers.

TeMoto: A Software Framework for Adaptive and Dependable Robotic Autonomy With Dynamic Resource Management

For widespread deployment of robots in challenging environments (fire fighting, search and rescue, planetary exploration, etc), the software of the robot must allow for reliability and adaptability. For many existing systems, an unexpected change in mission specification, component failures, or energy conservation requires downtime for adaption (redesign of mission logic, switching sensor data processing pipeline, etc.). This is because software and hardware components for robotic applications are commonly chosen or designed based on task requirements and integrated either directly in the source code or via system configuration scripts, such as ROS launch files, leading to a fixed monolithic design. As the necessity and extent of adaptive behaviors is not always known prior to deployment, the structure of a robot’s software needs to support it by design. In this paper, we propose TeMoto, a novel architecture for adaptive autonomous robots, and a ROS-based framework of openly available software tools that implement the TeMoto architecture. TeMoto is a developer tool which combines dynamic (run-time) task and resource management, encourages modular and scalable system design and is task and platform agnostic - TeMoto provides the foundation for an adaptive robotic system. The feasibility of the TeMoto framework is qualitatively assessed via experiments on single and multi-robot setups spanning common scenarios (teleoperation, autonomous surveillance, cargo delivery, etc.). TeMoto-based systems exhibit increased fault tolerance and dynamic reconfigurability of software and hardware resources, as well as up to a 47% reduction in power consumption compared to the non TeMoto-enabled reference setup.

СОЗДАНИЕ ОПТИМИЗИРОВАННОЙ ДИНАМИЧЕСКОЙ СИСТЕМЫ УПРАВЛЕНИЯ ПЕРЕВОЗКАМИ

This article defines the main tasks of the management of the transport system. The main criteria for the quality of road transport management have been identified. The generalized control scheme for the transportation of goods and passengers is analyzed. On its basis, three types of a dynamic transit management system have been compiled, which in turn differ in the presence of initial information. An analysis of the main problems that arise during the development of these systems is made and methods for their solution are proposed. In the course of the research, a conclusion was formed that, based on the rational operation of vehicles, will increase the level of efficiency of transportation of goods and passengers.

Medical Arrangement Strategies for Infertility Female Patients during COVID-19 Mini-Outbreak.

Over the past two years, COVID-19 pandemic is an unprecedented health emergency. All countries have taken their own measures to mitigate the spread of the virus in the first and subsequent mini-outbreaks of infection. In view of the current situation of small outbreaks of COVID-19, guidelines on epidemic prevention should be developed specifically for reproductive medical centers. It is necessary to establish a dynamic patient assessment and management system to identify patients who need priority fertility treatment during epidemic control. Female Patients were assigned as grade A and required hospitalization in the inpatient ward after egg retrieval. Patients who underwent controlled ovarian stimulation were classified as grade B, and they can choose to be hospitalizedat home according to their own convenience. Patients undergoing frozen embryo transfer (FET) cycle or planned downregulation with gonadotropinreleasing hormone agonists were defined as grade C, who could continue the assisted reproductive technology (ART) treatment cycle with negative COVID-19 nucleic acid test and there was no fever or respiratory symptoms. This brief comment summarizes the working procedure of the reproductive medical center in the first hospital of Lanzhou University in China to minimize the probability of hospital infection and ensure the safe conduct of assisted reproductive technology therapy.

IoT based monitoring of air quality and traffic using regression analysis

Dynamic traffic management (DTM) systems are used to reduce the negative externalities of traffic congestion, such as air pollution in urban areas. They require traffic and environmental monitoring infrastructures. In this paper we present a prototype of a low-cost Internet of Things (IoT) system for monitoring traffic flow and the Air Quality Index (AQI). The computation of the traffic flows is based on processing video in the compressed domain. Only using motion vectors as input, traffic flow is computed in real-time over an embedded architecture. An estimation of the AQI is supported by machine learning regression techniques, using different feature data obtained from the IoT device. These automatic learning techniques overcome the need for complex calibration and other limitations of embedded devices in making the needed measurements of the pollutant gases for the computation of the actual AQI. The experimentation with the data obtained from different cities representing different scenarios with a variety of climate and traffic conditions, allows validating the proposed architecture. As regressors, Linear Regression (LR), Gaussian Process Regression (GPR) and Random Forest (RF) are compared using the performance metrics R2, MSE, MAE and MRE resulting in a relevant improvement of the AQI estimations of our proposal.

Power Grid Planning and Dynamic Intelligent Management System Construction Based on Operation Simulation

The operation simulation system of the power grid is a complex and huge dynamic model, and its establishment and management are of great significance in power enterprises. The intelligence of the power grid is to manage the power system through computer technology, so that it can provide users with high-quality, convenient, safe, reliable and high-efficiency services. To this end, this article is based on operating simulation experiments to construct a power grid planning and dynamic intelligent management system. The purpose is to provide people with a fast and accurate power management system to facilitate life. This article mainly constructs the system through investigation method, experimental analysis method, data analysis method and so on. Experimental research shows that the accuracy of data obtained by using information technology in power grid planning and intelligent management system construction is relatively high, reaching 93.2%.

A Smart Contract-Based Dynamic Consent Management System for Personal Data Usage under GDPR.

A massive amount of sensitive personal data is being collected and used by scientists, businesses, and governments. This has led to unprecedented threats to privacy rights and the security of personal data. There are few solutions that empower individuals to provide systematic consent agreements on distinct personal information and control who can collect, access, and use their data for specific purposes and periods. Individuals should be able to delegate consent rights, access consent-related information, and withdraw their given consent at any time. We propose a smart-contract-based dynamic consent management system, backed by blockchain technology, targeting personal data usage under the general data protection regulation. Our user-centric dynamic consent management system allows users to control their personal data collection and consent to its usage throughout the data lifecycle. Transaction history and logs are recorded in a blockchain that provides trusted tamper-proof data provenance, accountability, and traceability. A prototype of our system was designed and implemented to demonstrate its feasibility. The acceptability and reliability of the system were assessed by experimental testing and validation processes. We also analyzed the security and privacy of the system and evaluated its performance.