System Model Research Articles

Pulmonary epithelial wound healing and the immune system. Mathematical modelling and bifurcation analysis of a bistable system

Respiratory diseases such as asthma, acute respiratory distress syndrome (ARDS), influenza or COVID-19 often directly target the epithelium. Elevated immune levels and a ‘cytokine storm’ are directly associated with defective healing dynamics of lung diseases such as COVID-19 or ARDS. The infected cells leave wounded regions in the epithelium which must be healed for the lung to return to a healthy state and carry out its main function of gas-exchange. Due to the complexity of the various interactions between cells of the lung epithelium and surrounding tissue, it is necessary to develop models that can complement experiments to fully understand the healing dynamics. In this mathematical study we model the mechanism of epithelial regeneration. We assume that healing is exclusively driven by progenitor cell proliferation, induced by a chemical activator such as epithelial growth factor (EGF) and cytokines such as interleukin-22 (IL22). Contrary to previous studies of wound healing, we consider the immune system, specifically the T effector cells TH1, TH17, TH22 and Treg to strongly contribute to the healing process, by producing IL22 or regulating the immune response. We therefore obtain a coupled system of two ordinary differential equations for the epithelial and immune cell densities and two functions for the levels of chemicals that either induce epithelial proliferation or recruit immune cells. These functions link the two cell equations. We find that to allow the epithelium to regenerate to a healthy state, the immune system must not exceed a threshold value at the onset of the healing phase. This immune threshold is supported experimentally but was not explicitly built into our equations. Our assumptions are therefore sufficient to reproduce experimental results concerning the ratio TH17/Treg cells as a threshold to predict higher mortality rates in patients. This immune threshold can be controlled by parameters of the model, specifically the base-level growth factor concentration. This conclusion is based on a mathematical bifurcation analysis and linearization of the model equations. Our results suggest treatment of severe cases of lung injury by reducing or suppressing the immune response, in an individual patient, assessed by their disease parameters such as course of lung injury and immune response levels.

Assessment of horse breeding and husbandry practices in southwest Ethiopia: Its implication to design breeding program

Ethiopian horses are multipurpose horses that have socioeconomic significance for smallholder farmers. However, studies regarding their husbandry practices have received little attention. Thus, the current study was conducted to assess the horse husbandry practices in the southwest Ethiopia. Data were collected through semi-structured questionnaires from a total of 196 randomly selected respondents. The general linear model and frequency procedures of the Statistical Analysis System (SAS 9.0) were used to analyze the quantitative and qualitative data, while indices were calculated using Microsoft Excel 2016. Most of the respondents from the Telo district were illiterate, while the percentage of literate farmers was greater in the Gesha and Masha districts. The within-household flock structure and level of importance were dominated by cattle and sheep, followed by horses. The horses were used for transport, draft power, and breeding purposes. In terms of shelters, the horses in the Telo district stayed in shelters, while the horses in the Masha district stayed in forests during both the dry and wet seasons. However, the majority of the horses in the Gesha district were sheltered during the wet season, while they were left to stay in the forest during the dry season. The respondents provided supplementary feed and water to their horses while the water point was located within a kilometer distance. Government and private veterinary shops were the primary sources of veterinary services. Farmers sold their horses at the local market. Castration was performed to minimize aggressiveness. Body size, conformation, and temperament were used as sire selection criteria. An increasing trend in horse population size due to increased farmer interest was observed. In the studied areas, horses were found to be highly important to the livelihood of the farmers. However, horse husbandry practices were led by a considerably greater number of illiterate farmers. Moreover, horse management activities, including the housing of horses, need more attention. Therefore, successive awareness-raising campaigns, including the introduction of formal schooling, are recommended to improve the horse breeding and husbandry practices. Moreover, breeding programs with the aim of genetic improvement and conservation need to be designed to optimize the sustainable utilization of the horses.

Updates to the wild bird abundance and movement models for the early warning system for avian influenza in the EU

Abstract Highly pathogenic avian influenza (HPAI) viruses pose a significant threat to both poultry and wild bird populations. Migratory wild birds play a key role in the intercontinental spread of avian influenza (AI), introducing the virus into poultry populations. In response to frequent AI outbreaks in Europe, the European Food Safety Authority (EFSA), at the request of the European Commission (EC), produces quarterly and annual epidemiological reports to monitor and analyse AI trends. A key component of this surveillance includes the integration of outbreak data from Member States and contributions from non‐governmental ornithological organisations like the European Bird Census Council (EBCC) and the European Union for Bird Ringing (EURING) together in a predictive spatio‐temporal risk assessment model. Previous data integration and modelling efforts led to the development of an early warning system for predicting HPAI outbreaks accessible through a publicly available online user interface: the Bird Flu Radar. This report presents an improvement of the system by expanding the species coverage and refining the existing base models behind the epidemiological model. Specifically, this report details the exploration to incorporate 12 additional wild bird species into the models, and the changes made to the base models predicting the distribution and movements of wild birds. We demonstrate the improvements respecting the existing base models while at the same time enhancing the effectiveness in predicting HPAI outbreaks and possibly mitigating negative effects in Europe by providing more accurate predictions to different stakeholders.

Multi-Horizon Model Predictive Control for Energy Management in Zero-Emission High-Speed Passenger Vessels

Abstract High-speed passenger vessels (HSPVs) play an important role in coastal regions due to their high energy consumption and emissions. This study develops an optimal energy management system (EMS) for a zero-emission hybrid power system, powered by a fuel cell (FC) and a battery energy storage system (BESS). The global optimal solution for loading the FC and operating the BESS is computed using a linear programming (LP) approach, assuming a priori knowledge of the complete operational profile. Since the global solution is not realizable in an onboard EMS, the main objective of this paper is to develop an algorithm for real-time application. A multi-horizon model predictive control (MH-MPC) algorithm is applied to a model of the power system to solve the optimization problem with a prediction mechanism over the entire route. This approach is benchmarked against the global LP solution to evaluate the effectiveness of the proposed EMS. The results highlight the potential of the MH-MPC approach for achieving a near-optimal EMS in a zero-emission HSPV.

Investigation of a novel combined ab- and ad- sorption based thermal energy storage and upgrade system

This paper presents a novel sorption-based cycle combining energy storage with energy upgrade to store low-grade heat for long periods and transform it on demand to usable energy at elevated temperatures. The system consists of an ammonia-water absorption system coupled to a resorption thermal transformer using manganese chloride and calcium chloride as the working pair with an ammonia absorbate. The absorption and adsorption systems are charged by separating the constituent sorbent-sorbate pairs, and they remain separated during the transition season. The discharge consists of a two-stage sorption process between the two systems. This work presents a transient thermodynamic model and analysis of the two-stage sorption system. For a typical solar thermal energy input of 106 °C during charging, and ambient temperatures of 5 °C and 25 °C in the winter and summer, respectively, the system is capable of discharging at an average elevated temperature of 62 °C with an energy storage efficiency of 27 %. This system is capable of simultaneously producing heat at a lower temperature by varying the mass flow rate through the high-temperature salt. The energy storage density of the combined system under different conditions is between 71 and 185 kJ kg−1. In comparison with other conventional single-stage thermal energy storage systems, this combination of absorption and adsorption-based storage provides a higher coefficient of performance for a comparable temperature output.

Construction of Sports Training Model and Optimization of Health Assessment System Based on CAD

Construction of Sports Training Model and Optimization of Health Assessment System Based on CAD

Improved voltage scanning algorithm based MPPT algorithm for PV systems under partial shading conduction

Maximum Power Point Tracking (MPPT) algorithms are crucial for maximizing power extraction from photovoltaic (PV) systems. Traditional MPPT methods often exhibit suboptimal performance under partial shading conditions. Hence, advanced MPPT algorithms have been developed to enhance efficiency in such scenarios. The voltage scanning-based MPPT algorithm is notable for its superior performance under partial shading, characterized by high tracking speed and efficiency. This study introduces a novel enhancement to this method—namely, a voltage skipping algorithm designed to further improve tracking speed. Unlike conventional scanning techniques, this approach dynamically calculates skipping voltages during operation, eliminating the need for prior knowledge of PV panel characteristics. Performance evaluation was conducted using a MATLAB/Simulink model of a PV system comprising 4 series panels and a boost converter, subjected to simulations under 5 distinct partial shading scenarios. Comparative analyses with established optimization algorithms such as particle swarm optimization, cuckoo search algorithm, and grey wolf optimization highlight the proposed method's effectiveness in terms of tracking speed and maximum power output. The proposed algorithm has worked with high efficiency of 99.28 %, 99.61 %, 99.13 %, 99.16 % and 99.58 % in 5 different scenarios, respectively. By using the proposed method, a significant superiority has been achieved over other methods, with tracking speeds of 0.26s, 0.22s, 0.2s, 0.22s and 0.26s, respectively.

Development of optimal energy management strategy for proton exchange membrane fuel cell-battery hybrid system for drone propulsion

Drones powered by fuel cells have been developed to ensure long flight distances. Due to the low dynamic responsiveness of proton exchange membrane fuel cell (PEMFC) systems, they must be integrated with batteries to fully meet the power demand profiles of drones. In this study, a dynamic model of a PEMFC-battery hybrid system for drone propulsion is developed in MATLAB-Simulink® to establish the optimal energy management strategy (EMS) for ensuring efficient and safe operation. The proposed PEMFC system comprises two modules of an open-cathode PEMFC stack, which are connected in parallel. To estimate the dynamic behavior and performance of PEMFC, one-dimensional dynamic model of PEMFC considering two-phase transport through the gas diffusion layer is developed and simulated. A zero-dimensional dynamic model of the battery was also developed based on an equivalent circuit model. The resulting PEMFC-battery hybrid system model was simulated to determine the power required for the propulsion of the drone according to a flight scenario comprising five phases: takeoff hover, climb, cruise, descent, and landing hover. Three different EMSs are developed and simulated to define the optimal one for the PEMFC-battery hybrid system by comparing the total hydrogen consumption and the final SOC of the battery As a result, the EMS that equally splits the required power between two PEMFCs exhibits the lowest hydrogen consumption during the flight simulation. This study helps provide the optimal system design and control scheme of drones powered by fuel cells.

Integrating Values to Improve the Relevance of Climate‐Risk Research

AbstractClimate risks are growing. Research is increasingly important to inform the design of risk‐management strategies. Assessing such strategies necessarily brings values into research. But the values assumed within research (often only implicitly) may not align with those of stakeholders and decision makers. These misalignments are often invisible to researchers and can severely limit research relevance or lead to inappropriate policy advice. Aligning strategy assessments with stakeholders' values requires a holistic approach to research design that is oriented around those values from the start. Integrating values into research in this way requires collaboration with stakeholders, integration across disciplines, and attention to all aspects of research design. Here we describe and demonstrate a qualitative conceptual tool called a values‐informed mental model (ViMM) to support such values‐centered research design. ViMMs map stakeholders' values onto a conceptual model of a study system to visualize the intersection of those values with coupled natural‐human system dynamics. Through this mapping, ViMMs integrate inputs from diverse collaborators to support the design of research that assesses risk‐management strategies in light of stakeholders' values. We define a visual language for ViMMs, describe accompanying practices and workflows, and present an illustrative application to the case of flood‐risk management in a small community along the Susquehanna river in the Northeast United States.

Investigation of a novel multigeneration system driven by an ammonia-methane Brayton cycle with partial production and utilization of ammonia, methane, and hydrogen: Energy and carbon footprint assessments

In this paper, to address gaps in the development of low-carbon energy systems, an innovative concept of an ammonia-methane Brayton cycle is proposed. This system is interconnected with various subsystems, including the Kalina cycle, high-temperature steam electrolysis, thermoelectric generators, and ammonia and methane synthesis reactors. The primary aim of this system was to reduce dependence on external resources by establishing local sources for water and fuel, thus reducing the environmental impact and enhancing energy sustainability. A thermodynamic model of an energy system was developed via EES codes which leads to a thoughtful perception into the interactions of the proposed system parameters. Also, comprehensive investigations involved the examination of different energy system scenarios. Modeling outputs of the proposed multigeneration system discovered excellent thermal efficiency and power production, with figures of 56.5 % and 17 MW, respectively, while the Brayton system showed the results of 23.7 % and 3.8 MW. This study also highlighted the environmental benefits of the multigeneration system by demonstrating a carbon dioxide emission of 0.058 kg/kWh, representing its significant positive impact on environmental sustainability. Also, the produced ammonia and methane within the system was 0.37 % and 9 %, of fuel in combustion chamber, respectively. Also, the rest of demand fuel was provided from the external resources.

On-line recognition of mixture control chart patterns using hybrid CNN and LSTM for auto-correlated processes

Statistical process control (SPC), designed to detect and identify process disturbances, is an effective quality control method for ensuring process stability. Owing to the growing automation of the industry, the manufacturing process can be autocorrelated. Engineer process control (EPC) is typically used to address autocorrelation. However, process disturbances can be offset by feedback compensation, making control chart patterns (CCPs) difficult to identify. Most studies on control chart pattern recognition (CCPR) techniques are based on a single abnormal control chart pattern (CCP). However, a mixture of CCPs can occur during real-world manufacturing processes. With the development of intelligent manufacturing systems, early detection of abnormal concurrent CCPs has become an important issue. In this study, a hybrid model combining a convolutional neural network (CNN) and long short-term memory (LSTM) in an online detection system was used to recognize the concurrent CCPR problem in SPC-EPC processes. The results showed that the average accuracy of the deep learning CNN-LSTM method was 99.83%, which was significantly better than that of the machine learning method. In addition, the running time of the CNN-LSTM model was shortened. In a comparison of online monitoring between the deep learning method and the machine learning method, the CNN-LSTM model for an online monitoring system identified abnormal concurrent patterns faster. Therefore, the proposed CNN-LSTM online monitoring scheme can be applied confidently and successfully to identify mixture CCPs in an SPC-EPC process.

A brain-wide solute transport model of the glymphatic system.

Brain waste is largely cleared via diffusion and advection in cerebrospinal fluid (CSF). CSF flows through a pathway referred to as the glymphatic system, which is also being targeted for delivering drugs to the brain. Despite the importance of solute transport, no brain-wide models for predicting clearance and delivery through perivascular pathways and adjacent parenchyma existed. We devised such a model by upgrading an existing model of CSF flow in the mouse brain to additionally solve advection-diffusion equations, thereby estimating solute transport. We simulated steady-state transport of 3 kDa dextran injected proximal to the perivascular space (PVS) of the middle cerebral artery, mimicking in vivo experiments. We performed a sensitivity analysis of 11 biological properties of PVSs and brain parenchyma by repeatedly simulating solute transport with varying parameter values. Parameter combinations that led to a large total pressure gradient, poor CSF perfusion or a steep solute gradient were deemed unrealistic. Solute concentrations in parenchyma were most sensitive to changes in pial PVS size, as this parameter linearly affects volume flow rates. We also found that realistic transport requires both highly permeable penetrating PVSs and high-resistance parenchyma. This study highlights the potential of brain-wide models to provide insights into solute transport processes.

Ground test modeling of cylindrical aero-engine casing under high-temperature and high-pressure aerodynamic load

The aero-engine casing is subjected to high-temperature and high-pressure aerodynamic loads during operation. It is essential to perform the ground strength tests for the casing. To optimize the ground strength test and to improve the accuracy of temperature and pressure loading during the test, a mathematical model of high-temperature and high-pressure aerodynamic loading system for simulating the ground strength test of typical aero-engine casing is established to study the actual temperature and pressure loads on casing during ground test. The temperature and pressure results calculated by the mathematical model under typical loads matched well with the experimental measurements. The verification results show that under the test conditions, the thermal inertia of the heater, the radiation heating of the gas in the test chamber by the heater, and the heat transfer of gas mixing in the pressure process can be ignored. The sensitivity analysis of the parameters affecting the accuracy of pressure and temperature loading is carried out. We show that the pressure can be controlled quantitatively by adjusting the inlet and exhaust pipe parameters, and accurate temperature control can be achieved by designing reasonable heater power and selecting appropriate heating control parameters.

A Multi-Phase Mission Success Evaluation Approach for Maritime Autonomous Surface Ships considering Equipment Performance Degradation and System Composition Changes

With the development of Maritime Autonomous Surface Ships (MASSs), the mission success assessment problem cannot be ignored because of potential security issues of MASSs. This paper addresses the issue of performance degradation at different stages, the problem of state dependence in multi-stage missions of MASSs, and the correlation problem between missions of stages. Based on the coupling of the reliability model of each single-stage mission system, a multi-stage mission success evaluation method for MASSs is proposed. By leveraging the ability of the conditional probability tables in Bayesian networks to express the complex relationships between the nodes, the dynamic Bayesian network model of stages is constructed based on the fault tree. Based on the Markov process, the problem of state dependence on shared equipment between stages is solved. Considering the complex relationship between multi-stage missions, the virtual node is introduced, and the multi-state Bayesian network is combined to realize the coupling of the reliability evaluation results of each single-stage mission. It is applied to the multi-stage mission success evaluation of the MASS to obtain the success probability of MASSs and the key equipment of each stage. The results show that evaluation results are more suitable for engineering practice.

Steam Pressure Control of Marine Boiler Based on AFSA-PSO-LSSVM

Abstract To solve the problem that the main steam pressure fluctuates greatly when the load of the combustion control system of the marine main boiler changes suddenly, present a combined inverse control system of Support vector machine and PID to control the steam pressure intelligently. The inverse model of the nonlinear system identified by LSSVM is used as the feedforward controller and the PID controller is used as the feedback controller. The hybrid intelligent algorithm of artificial fish swarm Algorithm and particle swarm optimization is used to optimize the parameters of the control system. The AFSA algorithm is used to search the initial values of the parameters and the PSO algorithm is locally updated. Finally, the mechanism of the steam pressure control system of the marine boiler is modeled and simulated, and the algorithm models of AFSA-PSO-LSSVM-PID, AFSA-LSSVM-PID, PSO-LSSVM-PID and LSSVM-PID are established. The experimental results show that the AFSA-PSO-LSSVM-PID model has a good dynamic, stability, anti-interference ability and robustness.

A novel heat load prediction model of district heating system based on hybrid whale optimization algorithm (WOA) and CNN-LSTM with attention mechanism

Machine learning models, particularly long short-term memory (LSTM) networks, have been extensively employed for heat load prediction in district heating systems (DHS). Nevertheless, the over-reliance on default hyperparameter settings in most methods hinders further enhancement of prediction accuracy. A novel load prediction model is presented, which integrates the whale optimization algorithm (WOA) to refine the hyperparameters of an LSTM model bolstered by an attention mechanism (ATT) and convolutional neural network (CNN). Three hybrid models (WOA-CNN-ATT-LSTM, PSO-CNN-ATT-LSTM and GA-CNN-ATT-LSTM) are constructed by comparing WOA with particle swarm optimization (PSO) and genetic algorithm (GA). The proposed hybrid models are evaluated against traditional LSTM models using an 1100-hour dataset from a real DHS. The outcomes reveal that the WOA-CNN-ATT-LSTM model surpasses both the PSO-CNN-ATT-LSTM and GA-CNN-ATT-LSTM models in heat load prediction accuracy, achieving improvements of 1.9% and 3.2% respectively, and attaining the highest prediction accuracy (R2=0.9962, MSE=0.0001, MAE=0.0082). Moreover, the WOA-CNN-ATT-LSTM model demonstrates superior performance across various time scales (half-day, one-day, three-days, and one-week), highlighting its robustness in heat load prediction. This novel model adaptively adjusts its hyperparameters to identify the optimal configuration, thereby significantly augmenting the overall predictive capabilities of the model.

Suppression of synchronous current of active magnetic bearings considering radial displacement inconsistency

Suppressing rotor synchronous current is vital for achieving precise control in active magnetic bearing (AMB) rotor systems. The displacement amplitude inconsistency in orthogonal channels presents new challenges for synchronous current suppression. This paper proposes an improved complex least mean square (CLMS) algorithm with error compensation to effectively mitigate rotor unbalanced vibration. Firstly, a dynamic model of the AMB-rotor system with an unbalanced mass is established, and the displacement amplitude inconsistency within the orthogonal channels of the radial magnetic bearings is analyzed. Subsequently, the rotor displacement signal from the orthogonal channels is converted into a complex plane signal. By calculating the negative sequence weight component, the displacement signal is reconstructed, and an error compensation channel based on the CLMS is introduced to enable real-time tracking and compensation of displacement amplitude inconsistencies in the orthogonal channels. Moreover, system stability across the full speed range is ensured through the application of the phase compensation angle method. Finally, experimental validation on a magnetic levitation molecular pump prototype confirms the effectiveness of the proposed algorithm.

Structural properties and mechanical behavior of three-dimensional cylindrical particle-like systems under in situ loading

To investigate the mechanical behavior of granular systems and their impact on density uniformity during compression, a three-dimensional micro-CT in situ loading experiment was conducted on a cylindrical granular system. The granular system was subjected to tomographic scanning and reconstruction, and the variation patterns of structural parameters such as volume fraction, porosity, and intrinsic density during the in situ loading process were analyzed. Internal pores within the granular system were extracted to obtain changes in pore area at each layer, allowing for an analysis of the evolving patterns of layered pore areas across different regions of the granular system during loading. This characterization elucidated the dynamic evolution process of compaction within the granular system. A contact network model for the granular system was established using particle contact area as a representation of inter-particle contact forces, revealing inherent connections between particle contact networks and macroscopic mechanical behaviors within the granular system.

Sampled-Data H∞ Dynamic Output-Feedback Control for NCSs With Successive Packet Losses and Stochastic Sampling.

The problem of sampled-data H∞ dynamic output-feedback control for networked control systems with successive packet losses (SPLs) and stochastic sampling is investigated in this article. The aim of using sampled-data control techniques is to alleviate network congestion. SPLs that occur in the sensor-to-controller (S-C) and controller-to-actuator (C-A) channels are modeled using a packet loss model. Additionally, it is assumed that stochastic sampling follows a Bernoulli distribution. A model is established to capture the stochastic characteristics of both the SPL model and stochastic sampling. This model is crucial as it allows us to determine the probability distribution of the sampling interval between successive update instants, which is essential for stability analysis. An exponential mean-square stability condition for the constructed equivalent discrete-time stochastic system, which also guarantees the prescribed H∞ performance, is established by incorporating probability theory. The desired controller is designed using a step-by-step synthesis approach, which may offer lower design conservatism compared to some existing methods. Finally, our designed approach using a networked F-404 engine system model is validated and its merits relative to existing results are discussed. The proposed method is finally validated by employing a networked model of the F-404 engine system. Furthermore, the advantages of our method are presented in comparison to previous results.

Dynamic modelling and a model-based control strategy of joint servo system for power transmission line inspection robot

Abstract The time-varying load inertia is one of the important dynamic characteristics of the power transmission line inspection robots (PTLIRs) in joint space, which can heavily affect the stability of the robot in the inspection process. In this paper, with considering the time-varying characteristics of load inertia, a method for modelling and control of the flexible joint servo system of the PTLIR is studied. First, the control system of the PTLIR is established based on the two inertia system. In order to calculate the load inertia, a dynamic model of the inspection robot is established. Then, the model-based notch filter is designed to improve dynamic performance of the robot, and suppress the vibration amplitude change which caused by the time-varying load inertia. Finally, the comparison of the control performance between the traditional control strategy and the model-based control strategy with notch filter are compared. The simulation results show that the paper can provide an effective tool for modelling and improving the stability of the PTLIR.