System Model Research Articles

A Blockchain-Based Hybrid Model for IoMT-Enabled Intelligent Healthcare System

A Blockchain-Based Hybrid Model for IoMT-Enabled Intelligent Healthcare System

Harmonic State-Space Modeling and System Characteristic Analysis of Grid-Connected Inverter Parallel-Operation System Considering Asynchronous Carriers

Harmonic State-Space Modeling and System Characteristic Analysis of Grid-Connected Inverter Parallel-Operation System Considering Asynchronous Carriers

Proposal for an e-learning system model based on the invocation and semantic discovery of web services

Service-oriented computing (SOC) provides a new framework for designing distributed web applications and software in a flexible, scalable, and cost-effective manner. Its use is widespread to efficiently integrate existing Web services and create high value-added applications. This model, proven in various fields such as e-commerce, also shows significant advantages in the field of e-learning. This approach highlights the discovery and use of Web services listed in specialized directories. In fact, this paper proposes a framework for exploring web services associated with education. This approach is based on the application of a matching algorithm to select the services best suited to the needs of users of the online learning system, as well as the ontology of the e-learning domain and the semantic descriptions of the web services via web ontology language for web services (OWL-S).

Exploring the Factors Controlling the Annual Range of Amazon Precipitation

Abstract The annual range (AR) of precipitation in the Amazon River basin has increased steadily since 1979. This increase may have resulted from natural variability and/or anthropogenic forcing, such as local land-use changes and global warming, which has yet to be explored. In this study, climate model experiments using the Community Earth System Model, version 2 (CESM2), were conducted to examine the relative contributions of sea surface temperatures (SSTs) variability and anthropogenic forcings to the AR changes in the Amazon rainfall. With CESM2, we design several factorial simulations, instead of actual model projection. We found that the North Atlantic SSTs fluctuation dominantly decreases the precipitation AR trend over the Amazon by −85%. In contrast, other factors, including deforestation and carbon dioxide, contributed to the trend changes, ranging from 25% to 35%. The dynamic component, specifically the tendency of vertical motion, made negative contributions, along with the vertical profiles of moist static energy (MSE) tendency. Seasonal-dependent changes in atmospheric stability could be associated with variations in precipitation. It is concluded that surface ocean warming associated with the North Atlantic natural variability and global warming is the key factor in the increased precipitation AR over the Amazon from 1979 to 2014. The continuous local land-use changes may potentially influence the precipitation AR in the future. Significance Statement The annual range (AR) in precipitation, the difference between wet- and dry-season precipitation, has increased from 1979 to 2014 in the Amazon. This increase may have resulted from global warming, deforestation, and sea surface temperature variability in North Atlantic and Pacific. To explore the role of each of these factors in altering the Amazon precipitation AR, five experiments were designed in the climate model (CESM). Among these experiment results, the effect of North Atlantic SSTs was the strongest. In the future, deforestation, global warming, and different ocean temperature states in the North Atlantic and Pacific may become increasingly influential on the changes in precipitation. Further investigation is needed to ascertain how the AR of precipitation in the Amazon will change.

Turbine selection for hydraulic energy recovery in the Yuribia dam aqueduct

The recuperation of hydraulic energy in aqueducts is based on utilizing the energy dissipated by hydraulic devices aimed at regulating the flow or pressure within the system, in addition to fulfilling their primary purpose. This paper presents the methodology for the selection of a turbine coupled to a generator for the recovery of hydraulic energy in the aqueduct of the Yuribia dam situated in the state of Veracruz, Mexico. The results of system modeling, simulation, and calculation of the generated energy are presented.

Integral reinforcement learning-based event-triggered optimal tracking control for modular robot manipulators via non-zero-sum game

Abstract Under an event-triggered mechanism, a non-zero-sum (NZS) game optimal tracking control method for modular robot manipulator (MRM) systems with input constraints is proposed using the adaptive dynamic programming (ADP) method based on integral reinforcement learning (IRL). First, a dynamic model of the MRM system is developed based on joint torque feedback technology, consisting of an n-joint subsystem related to interconnected dynamic coupling (IDC). Second, we design a robust compensation controller to handle the known model term and an optimal compensation controller to deal with the uncertainty term caused by the IDC and friction, respectively. In addition, a nonlinear disturbance observer is established to dispose of the negative effects caused by the uncertain sensor output disturbance. Third, based on differential game theory, we transform the optimal tracking control problem of the MRM system into an n-player NZS game problem. Then, the IRL-based ADP method is adopted, which relaxes the need for system partial unknown dynamic information, and only a critic neural network is used to solve the coupled Hamilton–Jacobi equation, so as to obtain the optimal control policy. Then, using Lyapunov theory, the tracking error of the MRM system is demonstrated to be uniformly ultimately bounded. Finally, the effectiveness and superiority of the proposed algorithm are verified through experiments.

Construction of Practical Teaching Mode of Law Course Based on Multi-Mode and Low-Resource Language Learning

China's legal profession is a new profession emerging in the process of China's rule of law. Its development and refinement are increasing in accordance with the rapid social, economic and political development. However, as a lawyer, people need to learn real kung fu. For complex and changeable legal practice problems, people should use the simplest and most effective way to solve them. Therefore, it is necessary to study the curriculum of law specialty to achieve its purpose. It is also necessary to reform the traditional education methods, introduce modern scientific and technological means, and improve their effectiveness, so as to construct a practical teaching mode of Professional Courses in Law (PCL). For practical legal courses, it can be conducted through multi-mode low-resource language learning method. This paper aimed to study how to study the ability of students to obtain information, process information and analyze and process information based on intelligent multi-image feature fusion. Based on the investigation of teaching objectives and teachers' teaching requirements, and the analysis and arrangement of the investigation results, a multi-task learning system model based on image intelligent features was established. In this experiment, 494 valid questionnaires were used to analyze the current situation of PCL practical teaching. Among them, 10.32% and 65.59% of the students were very satisfied with the results of PCL practical teaching, and 18.02% and 59.31% of the students liked PCL practical teaching very much. In the survey on the influencing factors of PCL practical teaching activities, 61.34% of the students thought that the students did not know enough and were not enthusiastic enough. On the whole, the students are satisfied with the practical teaching arrangement of PCL, but from the actual situation, the effect of practical teaching is not very good, and the expected goal has not been achieved.

Proactive enforcement of provisions and obligations

We present an approach to the proactive enforcement of provisions and obligations, suitable for building policy enforcement mechanisms that both prevent and cause system actions. Our approach encompasses abstract requirements for proactive policy enforcement, a system model describing how enforcement mechanisms interact with and control target systems, and concrete policy languages and associated enforcement mechanisms. As examples of policy languages, we consider finite automata and timed dynamic condition response (DCR) graphs. We use finite automata to illustrate the basic principles and DCR graphs to show how these principles can be adapted to a practical, real-time policy language. In both cases, we show how to algorithmically determine whether a given policy is enforceable and, when this is the case, construct an associated enforcement mechanism. Our approach improves upon existing formalisms in two ways: (1) we exploit the target system’s existing functionality to avert policy violations proactively, rather than compensate for them reactively; and (2) rather than requiring the manual specification of remedial actions in the policy, we deduce required actions directly from the policy.

On the behavior of auxetic inserts: a numerical analysis to derive design guidelines

Auxetics are a class of materials and metamaterials with a negative Poisson’s ratio (ν) and have gained tremendous popularity over the last three decades. Many studies have focused on characterizing designs that allow obtaining a negative ν. However, some open issues remain concerning understanding the auxetic behavior in operational conditions. Studies have been centered on analyzing the response of specific auxetic topologies instead of treating auxeticity as a property to be analyzed in a well-defined structural context. This study aims to contribute to the investigation of auxetic materials with a structural application, focusing on maximizing performance. The field of application of auxetics for designing inserts was selected and a model of a nail-cavity system was created to determine the effects of different design choices on the system behavior by exploring relationships between selected parameters and the auxetic insert behavior. The exploration combines finite element modeling analyses with their surrogate models generated by supervised learning algorithms. This approach allows for exploring the system’s behavior in detail, thus demonstrating the potential effectiveness of auxetics when used for such applications. A list of design guidelines is elaborated to support the exploitation of auxetics in nail-cavity systems.

Modeling and analysis of a novel 6-DOF magnetic levitation system with experimental verification

To improve the uniformity of magnetic flux and increase the stroke of the magnetic levitation system, this paper designs a novel 6-DOF magnetic levitation system and proposes an improved novel Halbach array. First, a repulsive passive magnetic bearing is utilized to provide levitation force, leading to a noticeable reduction in levitation power consumption. Then, a magnetic circuit model for the magnetic levitation system is established, and the improved novel Halbach array is optimized based on this model. Next, the stable levitation model and measurement model for the system are established, and the magnetic field distribution and magnetic bearing stiffness are analyzed. Results indicated an increase of 0.1[Formula: see text]T in the maximum magnetic flux density, accompanied by an improvement in the uniformity of magnetic flux. Additionally, there is an enhancement of 10% in magnetic bearing stiffness. Finally, a prototype of the system is constructed, and the stable levitation simulation and experiment of the actuator are conducted with this system. The maximum levitation absolute displacement error obtained by simulation is about 655[Formula: see text]nm, and the experimental results exhibited a maximum levitation absolute displacement error of approximately 750 nm for the system, confirming the effectiveness of the proposed modeling method.

Identification of heat exchanger process through Hammerstein and polynomial models

Process industries extensively use heat exchangers in their operations, playing a crucial role in various sectors by facilitating efficient heat transfer, conserving energy, and reducing operational costs. This paper focuses on identifying and validating the system model, with the primary task of designing the controller involving the determination of the mathematical representation of the system. The First Order Plus Dead Time (FODT) model, derived from basic principles, is used to represent the system. To analyze the system’s behavior and construct a suitable controller, model development is essential, achieved by constructing principles models using energy balance equations to identify the heat exchanger process. Data collected from the model are utilized in the identification process, with the temperature at the outlet of the hot air blower being the controlled variable in this investigation. The research aims to determine the mathematical model based on time versus temperature data acquired from the Heat Exchanger. Various system identification methods, such as Hammerstein Wiener (HW), Auto Regressive with Exogenous Input (ARX), Box-Jenkins (BJ), Output-Error (OE), and Auto Regressive Moving Average with Exogenous Input (ARMAX) models, are implemented for the heat exchanger. The models obtained undergo validation, and the best-fit model closest to the physical system is considered for controller design. After conducting the analysis, it was found that the Output-Error (OE) model outperforms other models in terms of achieving the best fit.

Multi-objective optimal power flow with wind–solar–tidal systems including UPFC using Adaptive Improved Flower Pollination Algorithm(AIFPA)

ABSTRACT Among the most significant non-linear challenges for power network design and smooth functioning of current modern updated power system networks is the optimum power flow (OPF) problem. Importance of the electrical system modeling has recently come to light due to the incremental use of energy from renewable sources in power systems networks. The goal is to use wind, solar and tidal sources to recreate the issue of OPF. In this work, Weibull, Lognormal, and also Gumbel probability distribution functions were applied to simulate the uncertainties of wind, photovoltaic, and tidal energy system. Additionally, by adding test scenarios of unpredictable wind, solar and tidal energy systems involving minimization of the cost function, loss of active power, voltage deviation, and increase stability of voltage. In accordance with the chosen thermal producing units, the solutions were evaluated using different locations using IEEE 30-bus systems of testing that incorporate renewable energy sources. The proposed power system planning problem was solved by multi-objective function where unified power flow controller are utilized as flexible AC transmission systems controllers via recently introduced optimization algorithms and the simulation outcomes of the aforementioned technique have been compared with Multi Objective Adaptive Guided Differential Evolution algorithms. The adaptive improved flower pollination algorithm (AIFPA) is a strong and reliable algorithm that is presented in this work. The AIFPA can efficiently deal with many kinds of high-complexity objective regions in multi-objective optimization situations. Utilizing an IEEE 30-bus test system, the suggested approaches’ performance was examined and evaluated for a range of objective functions. The simulation results obtained from the proposed algorithm were effective in finding the optimal solution compared to the results of the meta-heuristic algorithm and were reported in the literature.

Modeling of Li-ion Battery Management System for Unmanned Aerial Vehicles

Nowadays, electricity usage in aircraft has increased due to the increasing gas emission problem. This increase has created the need to store electrical energy and accelerated the trend towards batteries. Since energy storage in batteries occurs as a result of chemical reactions, problems may occur that will damage the battery group or the entire system. These problems are caused by high current, voltage and temperature, which affect the reaction rate during battery charging/discharging. A Battery Management System (BMS) is needed to prevent problems and to use the required electrical energy safely. In this study, it is aimed to meet the energy needs of the system in a controlled manner by disabling only the damaged cell in case of problems that may occur in the cells in the battery. For this purpose, a model of an Unmanned Aerial Vehicle (UAV) system was created by adding a BMS block to each cell to control the battery cells. The BYS model was realized based on the cell temperature, the SoC value of the cell and the output voltage values of the cell. The UAV system was modeled using MATLAB/Simulink software. Thanks to the proposed BMS, in case of a problem that may occur in any cell in the battery, that cell is disabled and the required energy is met through the remaining cells. It is observed from the results obtained that when the cell parameters become normal, it continues to feed the system again.

Global sensitivity analysis with multifidelity Monte Carlo and polynomial chaos expansion for vascular haemodynamics.

Computational models of the cardiovascular system are increasingly used for the diagnosis, treatment, and prevention of cardiovascular disease. Before being used for translational applications, the predictive abilities of these models need to be thoroughly demonstrated through verification, validation, and uncertainty quantification. When results depend on multiple uncertain inputs, sensitivity analysis is typically the first step required to separate relevant from unimportant inputs, and is key to determine an initial reduction on the problem dimensionality that will significantly affect the cost of all downstream analysis tasks. For computationally expensive models with numerous uncertain inputs, sample-based sensitivity analysis may become impractical due to the substantial number of model evaluations it typically necessitates. To overcome this limitation, we consider recently proposed Multifidelity Monte Carlo estimators for Sobol' sensitivity indices, and demonstrate their applicability to an idealized model of the common carotid artery. Variance reduction is achieved combining a small number of three-dimensional fluid-structure interaction simulations with affordable one- and zero-dimensional reduced-order models. These multifidelity Monte Carlo estimators are compared with traditional Monte Carlo and polynomial chaos expansion estimates. Specifically, we show consistent sensitivity ranks for both bi- (1D/0D) and tri-fidelity (3D/1D/0D) estimators, and superior variance reduction compared to traditional single-fidelity Monte Carlo estimators for the same computational budget. As the computational burden of Monte Carlo estimators for Sobol' indices is significantly affected by the problem dimensionality, polynomial chaos expansion is found to have lower computational cost for idealized models with smooth stochastic response.

Rotor Speed Prediction Model of Multi-Rotor Unmanned Aerial Spraying System and Its Matching with the Overall Load

During continuous spraying operations, the liquid in the pesticide tank gradually decreases, and the flight speed changes as the route is altered. To maintain stable flight, the rotor speed of a multi-rotor unmanned aerial spraying system (UASS) constantly adjusts. To explore the variation law of rotor speed in a multi-rotor UASS under objective operation attributes, based on indoor and outdoor experimental data, this paper constructs a mathematical model of the relationship between rotor speed and thrust. The model fitting parameter (R2) is equal to 0.9996. Through the neural network, the rotor speed prediction model is constructed with the real-time flight speed and the payload of the pesticide tank as the input. The overall correlation coefficient (R2) of the model training set is 0.728, and the correlation coefficients (R2) of the verification set and the test set are 0.719 and 0.726, respectively. Finally, the rotor speed is matched with the load of the whole UASS through thrust conversion. It is known that the single-axis load capacity under full-load state only reaches about 50% of its maximum load capacity, and the load increase is more than 75.83% compared with the no-load state. This study provides a theoretical and methodological reference for accurately predicting the performance characterization results of a power system during actual operation and investigating the dynamic feedback mechanism of a UASS during continuous operation.

A comprehensive dynamic model and configuration control of a free-floating soft manipulator-spacecraft system

Space robots are inseparable components of many space missions. However, capabilities of space robots with rigid manipulators are restricted in confronting with unknown and confined environments and situations require high safety. To overcome these defects, the potential of bio-inspired soft robots for space applications has recently been addressed in the literature. Dynamic modeling and control of soft manipulators are challenging due to their infinite degrees of freedom. In this paper, a general comprehensive three-dimensional dynamic modeling framework is developed for a floating soft manipulator-spacecraft system employing the Euler-Lagrange method. This framework derives the coupled equations of motion adapting the generalized Jacobian approach. Consequently, the opportunity of exploiting model-based control methods will be provided. In addition, the proposed model avoids numerical singularities once the bending angles are near zero. Furthermore, the generalized Jacobian matrix is defined for any arbitrary point of the soft manipulator essential for the contact dynamics. Eventually, to verify the proposed dynamic modeling procedure, the dynamic response of a floating soft manipulator-spacecraft system released from an initial configuration is investigated. Then, a sliding mode controller is designed for the floating soft manipulator to track the desired shape and compared to the proportional-derivative control scheme. The obtained results follow the physical principles and fulfill the control objective.

Key barriers to the provision and utilization of maternal health services in low-and lower-middle-income countries; a scoping review

BackgroundThe preservation and promotion of maternal health (MH) emerge as vital global health objectives. Despite the considerable emphasis on MH, there are still serious challenges to equitable access to MH services in many countries. This review aimed to determine key barriers to the provision and utilization of MH services in low- and lower-middle-income countries (LLMICs).MethodsIn this scoping review, we comprehensively searched four online databases from January 2000 to September 2022. In this study, the approach proposed by Arksey and O’Malley was used to perform the review. Consequently, 117 studies were selected for final analysis. To determine eligibility, three criteria of scoping reviews (population, concept, and context) were assessed alongside the fulfillment of the STROBE and CASP checklist criteria. To synthesize and analyze the extracted data we used the qualitative content analysis method.ResultsThe main challenges in the utilization of MH services in LLMICs are explained under four main themes including, knowledge barriers, barriers related to beliefs, attitudes and preferences, access barriers, and barriers related to family structure and power. Furthermore, the main barriers to the provision of MH services in these countries have been categorized into three main themes including, resource, equipment, and capital constraints, human resource barriers, and process defects in the provision of services.ConclusionsThe evidence from this study suggests that many of the barriers to the provision and utilization of MH services in LLMICs are interrelated. Therefore, in the first step, it is necessary to prioritize these factors by determining their relative importance according to the specific conditions of each country. Consequently, comprehensive policies should be developed using system modeling approaches.

Microbial competition for phosphorus limits the CO2 response of a mature forest

The capacity for terrestrial ecosystems to sequester additional carbon (C) with rising CO2 concentrations depends on soil nutrient availability1,2. Previous evidence suggested that mature forests growing on phosphorus (P)-deprived soils had limited capacity to sequester extra biomass under elevated CO2 (refs. 3–6), but uncertainty about ecosystem P cycling and its CO2 response represents a crucial bottleneck for mechanistic prediction of the land C sink under climate change7. Here, by compiling the first comprehensive P budget for a P-limited mature forest exposed to elevated CO2, we show a high likelihood that P captured by soil microorganisms constrains ecosystem P recycling and availability for plant uptake. Trees used P efficiently, but microbial pre-emption of mineralized soil P seemed to limit the capacity of trees for increased P uptake and assimilation under elevated CO2 and, therefore, their capacity to sequester extra C. Plant strategies to stimulate microbial P cycling and plant P uptake, such as increasing rhizosphere C release to soil, will probably be necessary for P-limited forests to increase C capture into new biomass. Our results identify the key mechanisms by which P availability limits CO2 fertilization of tree growth and will guide the development of Earth system models to predict future long-term C storage.

Path Loss Models for 5G Communications System in Corridors Environment

This paper presents the propagation path loss channel models, developed from real-field measurement in Universiti Teknologi Malaysia (UTM) Kuala Lumpur, Malaysia. The purpose of the study is to characterize the channel at 28 GHz for 5G communications system in line-of-sight corridors environment. Measurement campaigns were conducted to measure the wireless signal of received signal strength at three different construction of straight corridors, narrow, wide, and open corridors. The large-scale path loss models are developed using the closed-in reference distance and floating–intercept modeling approaches. Besides contributing path loss models at 28 GHz for different corridors dimension, the result found in this work discovered the breakpoint distance of radio propagation is seen varies differently at those corridors.

Comprehensive analysis of the impacts of climate and land use changes on flood damage (Case study: AqQala, Iran)

ABSTRACT Population growth, community development, and waste generation impact climate, land use, water resources, and urban heat island effect. This research assesses the impacts of climate and land use changes on flood damage for different return periods (RPs) in AqQala area, Iran. Climate change anticipation is conducted using the CMCC-ESM2 model under shared socioeconomic pathways (SSPs) 126, 370, and 585 of the Intergovernmental Panel on Climate Change (IPCC) sixth assessment report (Coupled Model Intercomparison Project Phase 6 – CMIP6). The LARS-WG model is used to downscale climatic information, and land use mapping is processed through Landsat satellite images in ENVI 5.3. The Markov chain method is implemented for 2050 and 2080 via TerrSet. Hydrographs and inundation maps are generated by an Autodesk Storm and Sanitary Analysis (SSA) and a Hydrologic Engineering Center's River Analysis System (HEC-RAS) models. Results show an increase in average annual precipitation (up to 46%) and temperatures (up to 3.39 °C) under different SSP scenarios until 2080. The result indicates that land use changes are more significant than climate change. Peak flood discharge and damages could escalate by 38 and 29%, respectively, in the worst-case scenario. Eco-friendly design and implementation are crucial for improving the situation.