System Model Research Articles

Extending Non-Perturbative Simulation Techniques for Open-Quantum Systems to Excited-State Proton Transfer and Ultrafast Non-Adiabatic Dynamics.

Excited state proton transfer is an ubiquitous phenomenon in biology and chemistry, spanning from the ultrafast reactions of photobases and acids to light-driven, enzymatic catalysis and photosynthesis. However, the simulation of such dynamics involves multiple challenges, since high-dimensional, out-of-equilibrium vibronic states play a crucial role, while a fully quantum description of the proton's dissipative, real-space dynamics is also required. In this work, we extend the powerful matrix product state approach to open quantum systems (TEDOPA) to study these demanding dynamics, and also more general nonadiabatic processes that can appear in complex photochemistry subject to strong laser driving. As an illustration, we initially consider an open model of a four-level electronic system interacting with hundreds of intramolecular vibrations that drive ultrafast excited state proton transfer, as well as an explicit photonic environment that allows us to directly monitor the resulting dual fluorescence in this system. We then demonstrate how to include a continuous "reaction coordinate" of the proton transfer that allows numerically exact simulations that can be understood, visualized and interpreted in the familiar language of diabatic and adiabatic dynamics on potential surfaces, while also retaining an exact quantum treatment of dissipation and driving effects that could be used to study diverse problems in ultrafast photochemistry.

Enhancing Production in Robot-Enabled Manufacturing Systems: A Dynamic Model and Moving Horizon Control Strategy for Mobile Robot Assignment

Abstract This paper presents a dynamic mathematical model for a robot-enabled manufacturing system, where mobile robots independently manage workstation tasks. Each robot possesses one or multiple skills, enabling collaborative work at workstations. A real-time robot assignment problem is formulated to maximize production of the system, and a novel control strategy is developed to address this problem. Leveraging system properties derived from the model and moving window downtime prediction, the problem of maximizing system production is transformed into a more tractable control problem focused on identifying and achieving a defined ideal clean configurations. The proposed solution significantly outperforms various benchmarks, including a pure reinforcement learning-based strategy, underscoring the importance of system understanding and its crucial role in enhancing flexibility and productivity in manufacturing systems.

Simulation Experiment Research on the Production of Large Box Girders

This paper introduces system simulation technology into large-scale beam field production and uses the simulation software Arena (14.0) to construct a simulation model of the beam field production system considering the randomness of the actual beam field production process operation time. The relationship between the production efficiency of the beam yard and the working time system was studied. In this paper, the improvement in beam-making efficiency in the existing beam field that is achieved by the commonly used reinforcement pre-binding method in the existing beam field is analyzed and calculated, and the improvements in the production efficiency in the ordinary beam field and the intelligent beam field are quantitatively calculated and compared. The results show that (1) when the working time system is increased from 8 h/d to 12 h/d, the average occupancy time of the traditional beam-making pedestal is shortened by 11.5 h when the working time is extended by 1 h per day; (2) with the extension of the working time system, the advantages of the pre-binding method of reinforcement gradually decrease; and (3) the application of intelligent technology not only improves the production efficiency of the beam yard but also makes the beam yard’s production more flexible and more resistant to risks.

Optimization model for mass casualty management system using qos-aware routing protocol and casualty triage prediction

Abstract Purpose Unexpected events, whether man-made or natural, cause significant distress and disorder. The number and magnitude of disasters and catastrophes have been rising globally in recent decades, according to historical data. Continued real-time monitoring of mass casualty along with the arrangement of needed medical resources to handle the mass casualty is required to reduce the mortality and morbidity rates. Methods Electronic tag-based casualty monitoring and machine learning-based Decision Support Systems are emerging as a more effective and proactive solution that provides continuous remote monitoring of patients. A novel framework based on Body-to-Body Network, Prediction model, and Genetic Algorithm-based medical resource optimization is proposed for the continuous monitoring of the mass casualty and medical resource allocation at the incident. The aim of this work is to give priority to the handling of critical casualties. Firstly, a Quality of Service and load-sensitive routing protocol for transmitting mass casualties' physiological parameters across a wireless network is proposed, with the critical casualty being emphasized. Secondly, the clinical seriousness degree of the mass casualty is predicted using Backpropagation Artificial Neural Network. Finally, an optimization model using a Genetic algorithm and queuing theory is proposed to find the required optimal number of medical resources to handle critical and non-critical casualties separately. Also, the proposed optimization model considers the predicted clinical level transition rates of the mass casualty. Results The performance and accuracy of the proposed framework are evaluated using the MIMIC-II dataset. The outcome demonstrates that the framework emphasizes critical casualty management. Furthermore, the framework allocates an adequate number of servers by incorporating the proposed routing protocol in comparison to the AODV protocol. Conclusion The inclusion of a prediction model in the framework aids in allocating an adequate number of servers by considering the predicted clinical deteriorating transition rates of casualties at a mass casualty incident. In terms of the estimated length of the casualty at the incident, the results suggest that incorporating a medical resource optimization model outperforms the non-optimal option.

Dynamic modeling and robust control of a multi-cable payload transfer system for offshore cranes subjected to random waves and wind loads

The payload often experiences unpredictable motion due to the combined effects of random waves and wind loads, significantly impacting the safety and efficiency of offshore operations. To expand the effective working window for offshore transfer operations, this study introduces a multi-cable payload transfer system (MPTS). Using a 45-ton offshore crane mounted on a multipurpose vessel from COSCO Shipping Group as a prototype, this study establishes a dynamic model of MPTS, considering the effects of vessel motion, crane actions, and wind loads. The payload remains in an unstable state due to external disturbances. Additionally, variations in payload specifications and mass lead to considerable uncertainties in the dynamic parameters of MPTS. To address these uncertainties, a second-order linear observer is developed. Subsequently, an improved computed torque controller is proposed, and the boundedness of the tracking errors is proven using the Lyapunov theory. The transfer process is divided into three distinct phases: lifting, slewing, and lowering. The feasibility of the transfer scheme is verified through high-fidelity simulations and prototype experiments. This research provides a solid theoretical foundation for the subsequent engineering application of MPTS.

Dynamic model-based intelligent fault diagnosis method for fault detection in a rod fastening rotor

A complete fault sample database is of great significance for the intelligent fault diagnosis method of rod fastening rotor. However, the lack of fault samples makes the fault diagnosis results unbelievable. To solve this issue, the dynamic model-based intelligent fault diagnosis method is established for a rod fastening rotor, and the fault sample database is enriched by numerical simulations. First, the lumped parameter model of the rod fastening rotor system is constructed and further updated using Euclidean Distance between measurement and numerical simulation of the intact system. Second, mathematical models of various fault types are incorporate into the updated model to obtain numerical simulation fault samples. Thirdly, the utilization of numerical simulation fault samples is severed as training data to the artificial intelligence (AI) models and the unknown measurement test samples will be finally classified. In this paper, Support Vector Machine, Random Forest, Bayesian Network and Decision Tree are selected as the typical AI models. Subsequently, the feasibility of classification is validated by the test bench of the rod fastening rotor system, and the problem of insufficient fault samples can be solved.

Punishment or reward? Environmental information disclosure in Chinese listed companies based on evolutionary game systems

AbstractIn February 2022, the Chinese government implemented a mandatory environmental information disclosure policy for publicly traded corporations. To assess the factors influencing environmental information disclosure, we developed a tripartite evolutionary game system model based on strategic interactions between the government, enterprises and the public. Afterwards, we conducted a simulation to examine the systemic interaction equilibrium among the parties involved. Additionally, we assessed critical factor changes in decision‐making for three stakeholders: initial strategy selection, related policy subsidies, regulatory penalties and public incentives. Incorporating Gaussian white noise into the evolutionary game model enhances the realism of simulations, allowing for a more accurate representation of how random disturbances affect decision‐making among the government, the public and enterprises. The study reveals that the active participation of the public in information disclosure activities has a certain degree of substitution effect on the government's choice to regulate high‐quality information disclosure by enterprises actively. The penalty mechanism is more effective than the subsidy mechanism. The public's decision‐making process is susceptible to the direct economic benefits that enterprises can gain from disclosing low‐quality environmental information.

Examining Tropical Convection Features at Storm‐Resolving Scales Over the Maritime Continent Region

AbstractGlobal Storm Resolving Models (GSRMs) provide a way to understand weather and climate events across scales for better‐informed climate impacts. In this work, we apply the recently developed and validated CAM (Community Atmosphere Model)—MPAS (Model for Prediction Across Scales) modeling framework, based on the open‐source Community Earth System Model (CESM2), to examine the tropical convection features at the storm resolving scale over the Maritime Continent region at 3 km horizontal spacing. We target two global numerical experiments during the winter season of 2018 for comparison with observation in the region. We focus on the investigation of the representations of the convective systems, precipitation statistics, and tropical cyclone behaviors. We found that regional‐refined experiments show more accurate precipitation distributions, diurnal cycles, and better agreement with observations for tropical cyclone features in terms of intensity and strength statistics. We expect the exploration of this work will further advance the development and use of the storm‐resolving model in precipitation predictions across scales.

The Global Atmospheric Energy Cycle in TaiESM1: Present and Future

AbstractThe Lorenz Energy Cycle (LEC) in the Taiwan Earth System Model Version 1 (TaiESM1) historical simulation is calculated and compared with ERA5 to evaluate the model performance from the thermodynamic aspect. The future change of LEC is accessed by comparing the SSP5‐8.5 and historical simulations in TaiESM1. TaiESM1 reasonably simulates the global mean, seasonal cycle, and spatial patterns of the energy reservoirs with larger values in the mean energy components and smaller in the eddy energy components. The energy cycle in TaiESM1 is about 35%–45% stronger than ERA5, except from December to February. The impact of global warming on the LEC is different at the vertical levels. The influence of meridional temperature gradient change is the dominant factor in the intensity of the energy cycle, and the change in static stability only contributes to the lower troposphere. Lifting the tropopause in the tropics increases the meridional temperature gradient and produces more zonal mean potential energy (PM) in the upper troposphere. PM is the primary driver of the LEC and leads to a more active energy cycle in the upper troposphere. As the tropical tropospheric depth increases and the mid‐latitude eddy activities become more active, more (less) energy is stored in the upper (lower) troposphere, and the energy conversion processes tend to become stronger (weaker) in the upper (lower) troposphere.

Coal Phase-Out and Carbon Tax Analysis with Long-Term Planning Models: A Case Study for the Chilean Electric Power System

Large CO2 emissions constitute a significant problem today due to their effect on climate change, and the need to design appropriate energy policies to mitigate their consequences and reduce emissions requires a detailed analysis of one of the main sources of such emissions: the electricity system. Thus, this paper presents a study on the effects of energy policies on decarbonization by comparing the detailed phase-out of coal-fired power plants across a range of cases with the implementation of a carbon tax to meet Nationally Determined Contributions (NDCs). The case study focuses on the Chilean electricity system, using a long-term generation and transmission expansion planning model (GTEP) that incorporates a wide range of generation technologies. The study examines the long-term effects of these policies, including costs, investments, and CO2 emissions, as well as their impact on consumer prices reflected in the marginal costs of the system. The transmission system modeling covers various regions of Chile and significant projections for renewable energy sources. It evaluates three economic scenarios based on generation technology costs, fuel prices, and electricity demand under four different closure schemes and fourteen different carbon tax levels. The results indicate that implementing a carbon tax can be more cost-effective for the system than the implementation of a phase-out schedule for coal plants, taking the form of reduced CO2 emission and overall system costs, with an optimal carbon tax value of 37 USD/tCO2. Additionally, the study reveals significant effects on consumer prices, showing that a carbon tax as an energy policy leads to lower prices compared to a phase-out scheme.

Research on position tracking control of electro-hydraulic actuator system combining feedforward inverse model controller and exponential functional link network adaptive controller

The electro-hydraulic actuator system plays a crucial role in indoor high-frequency simulation experiments. Many components of the system exhibit unavoidable nonlinear characteristics, which reduce its dynamic position response capability. Additionally, most algorithms are unsuitable for practical high-frequency conditions due to either local instability or high computational complexity. To improve position tracking accuracy under high-frequency control, a novel combined control strategy is proposed, integrating an appropriate feedforward inverse model controller (FFIMC) with an exponential functional link network adaptive controller (EFLNAC). Initially, the system has reached a stable state under the action of the PID controller. Subsequently, the FFIMC estimates both the model and the inverse model of the PID system by applying the AIC technique developed through the DCT-VSSNLMS adaptive algorithm, which extends the system’s bandwidth. Following this, the EFLNAC, as the outermost loop, acquires the inverse model of the FFIMC system and updates internal parameters in real-time, thereby enhancing the system’s resistance to nonlinear disturbances. Finally, the combined controller is validated through simulations and experiments under various working conditions. The results show a significant increase in the PID system’s bandwidth and demonstrate that the tracking error of the signal is maintained within 0.079 mm. Therefore, the combined controller proves to be an effective solution for high-precision, high-frequency displacement tracking, offering the advantages of low computational complexity, high accuracy, and strong effectiveness.

Optimal Propagation Model for DVB-T2 System in Urban Area

The large-scale implementation of analog switch-off for television broadcasting in Indonesia has led to blank spots in several regions. To address this issue, an optimal propagation model is needed. Proper selection and analysis of the channel model can enhance transmitter coverage, increase coverage percentage, improve energy efficiency, and boost field strength due to optimal transmit power. Previous studies have explored various DVB-T2 propagation models, notably the ITU-R P.1812-4 and Longley-Rice models, which are sophisticated and consider various environmental parameters, making them suitable for diverse broadcasting conditions. This research introduces a novel approach by specifically focusing on the urban context of Semarang City, Indonesia, to reduce blank spots by applying the ITU-R P.1812-4 and Longley-Rice propagation models. This study uniquely compares the two models to determine the most effective one for this urban area. Results indicate that the ITU-R P.1812-4 model provides a higher field strength value than the Longley-Rice model, with average field strengths of 108.3425 dBμV/m and 108.2325 dBμV/m, respectively. The difference in average field strength of 0.11 dBμV/m, despite having the same free space loss value of 100.9025 dB, indicates that one model has a slightly stronger signal. This stronger signal can improve coverage by reaching further distances and penetrating obstacles better. Additionally, a stronger signal means less power is needed to maintain the same coverage area, thus improving energy efficiency. This research not only offers empirical data specific to Semarang City but also provides insights that can guide future digital broadcasting optimizations in similar urban environments.

Modulating nonreciprocal transmission in levitated magnomechanical systems

In this paper, we propose a model of a dual cavity magnomechanical system with two levitated yttrium iron garnet spheres to investigate nonreciprocal transmission of a microwave field. We use an external Coulomb force to bias the steady-state position of the sphere levitated in each microwave cavity, thereby establishing an independent and controllable effective couplings between the cavity modes and the magnon modes. This can break the symmetry of the system and serve as the basis for nonreciprocal transmission in this system. We demonstrated how to achieve the system nonreciprocity with an extremely high isolation ratio and flexible controllability by appropriately selecting the suspended positions of the levitated spheres, which are related to the external bias forces. We also analyze in detail the influence of the cavity detunings and the driving power on the bias-force-induced nonreciprocity. Our study provides an effective approach to manipulating flexibly nonreciprocal transmission of a microwave field and may have potential implications for the development of future nonreciprocal transmission devices.

SDGs-centric approach to strategic planning of community development

The research of the community growth and the formation of strategies for their development in the context of an approach focused on achieving sus­tainable development goals have been conduc­ted. The problems of the sustainable community deve­lopment and their crucial understanding of achieving the Sustainable Development Goals (SDGs) at all levels of management have been considered. The research is based on the hypothesis that taking into account the features of the Central Development Center approach to the strategic planning of the community development in Ukraine will lead to the improvement of sustainable development indicators at the current stage of the community growth and the sustainable development of Ukraine. Such research methods were used, as: assessment of qualitative, quantitative and social indicators, as well as methods of positional analysis, focus groups, diagnostic interviews, ex­pert assessments, measurement and comparison, system analysis and modeling. The basic concepts and components of sustainable development were analyzed. It has been proposed to define the features of the Central Development Center-cen­tered approach to the strategic planning of com­munity development. The role of intersectoral cooperation between local authorities, business and civil society in the context of achieving the SDGs was considered in detail. The article focuses on the integration of environmental, social and economic aspects into strategic planning, and ex­plores the problems and challenges faced by communities in implementing these approaches. The importance of building the institutional capa­city of communities for the effective imple­men­tation of the SDGs and their adaptation to the changing conditions of the post-war period was sepa­rately emphasized. Recommendations were given for improving the methods of monitoring and evaluating the SDGs achievement at the local level. It is substantiated that only in the presence of a high-quality strategy, joint efforts and mutual understanding of all stakeholders it is possible to achieve the goal of "rebuilding better than it was". It was emphasized that the reconstruction of the country should be carried out by joint efforts, on the basis of transparency, innovation and strategic vision. The leadership role of communities in the Ukrainian post-war reconstruction was defined, the significance of state regional policy and the Road Map adoption of decentralization, as well as the consideration of the need to achieve the sustainable development goals in communities, were revealed. The ranking of the sustainable deve­lop­ment goals for the communities of Vinnytsia was carried out

Invariant Extended Kalman Filtering for Pedestrian Deep-Inertial Odometry

Abstract. Indoor localization for pedestrians, which relies solely on inertial odometry, has been a topic of great interest. Its significance lies in its ability to provide positioning solutions independently, without the need for external data. Although traditional strap-down inertial navigation shows rapid drift, the introduction of pedestrian dead reckoning (PDR), and artificial intelligence (AI) has enhanced the applicability of inertial odometry for indoor localization. However, inertial odometry continues to be affected by drift, inherent to the nature of dead reckoning. This implies that even a slight error at a given moment can lead to a significant decrease in accuracy after continuous integration operations. In this paper, we propose a novel approach aimed at enhancing the positioning accuracy of inertial odometry. Firstly, we derive a learning-based forward speed using inertial measurements from a smartphone. Unlike mainstream methods where the learned speed is directly used to determine the position, we use the forward speed combined with non-holonomic constraint (NHC) as a measurement to update the state predicted within a strap-down inertial navigation framework. Secondly, we employ an invariant extended Kalman filter (IEKF)-based state estimation to facilitate fusion to cope with the nonlinearity arising from the system and measurement model. Experimental tests are carried out in different scenarios using an iPhone 12, and traditional methods, including PDR, robust neural inertial navigation (RONIN), and the EKF-based method, are compared. The results suggest that the method we propose surpasses these traditional methods in performance.

A hybrid of RSM, ANFIS, and machine learning algorithm approach for ultrasound-assisted extraction of bioactive compounds from Semecarpus anacardium Linn

ABSTRACT Ultrasound-assisted solvent extraction (UASE) was appslied to extract phytoconstituents from Semecarpus anacardium L. The effective extraction parameters were optimized using the Response Surface Methodology (RSM) with Central Composite Face Centered design (CCFC), and the optimization parameters were validated through Adaptive Neuro-Fuzzy Inference System (ANFIS) and Machine Learning (ML) algorithm models. The four independent parameters, i.e. ethanol concentration (X 1: 62–72% v/v), temperature (X 2: 38–48°C), ultrasonic-exposure time (X 3: 12–24 min), and particle size (X 4: 2–5 mm) were optimized. The maximum yield of phytocompounds was achieved at X 1 = 67%, X 2 = 43°C, X 3 = 18 min, and X 4 = 3.5 mm. The optimized yield of total phenolic, flavonoid, and antioxidant activity was considered when selecting process parameters. In this scenario, the optimized yields of total polyphenolic content (y1 = 619.25 mg gallic acid equivalent (GAE)/g), total flavonoid content (y2 = 151.24 mg Quercetin equivalents (QE)/g), free radical scavenging abilities (%DPPH*sc (y3 = 66.41%), and %ABTS*sc (y4 = 56.94%). The LC-MS peaks identify the presence of semecarpetin, butein, and amentoflavone, whereas GS-MS peaks represent seventeen gaseous components. Furthermore, the bioactive-rich optimized extract was nontoxic and supported the growth of macrophage- and epithelial cells in vitro. Conclusively, optimizing the UASE parameters enhanced the yield of bioactive compounds of S. anacardium L.

Design of Web-Based Inventory Accounting Information System at PT XYZ

PT XYZ is a company engaged in the retail trade of large and small clothing, and it is located in the city of Bandung. As a trading company, PT XYZ needs to utilize information technology in managing warehouse inventory, which is no longer manual management, namely recording in paper media, which will be susceptible to data discrepancies. The web application here is interpreted as an inventory application through a display that includes goods to be stored in the warehouse and product transactions that come in and out. The research method used here is the interview and observation method. Software creation is carried out with a waterfall model consisting of the following stages: system planning analysis, system analysis, design, implementation, and maintenance. In the object of system modeling with visual data flow diagrams and flowcharts. The results of this study produce web-based software using PHP as a programming language with MySQL as a database and visual code for its program editor. Through this system, it is expected to facilitate work and can be used at any time. This inventory application can be a benchmark for managing incoming and outgoing goods. Another benefit is that this web-based application aims to control the stock of goods at any time.

Model-driven development for functional correctness of avionics systems: a verification framework for SysML specifications

AbstractCurrently, the most widespread software quality assurance methods in the avionics domain are semi-automated reviews and testing. However, their effort grows disproportionately to the size of the system under development. Also, these methods cannot achieve exhaustive coverage due to the complexity of today’s avionics systems and their potentially infinite set of combinations of possible inputs and system states. Furthermore, the later software issues are detected in the development process, the more expensive it is to fix them. To overcome these issues, a model-driven verification approach for modeling and analyzing avionics systems in early phases of the development is presented. To this end, semantics is given to SysML v2 models by a mapping to a theorem prover encoding. The development of a dedicated SysML v2 profile supporting event-driven data flow specifications, the encoding of corresponding structures in the theorem prover Isabelle, and a generator creating theorems from SysML v2 models are presented. The approach is evaluated by formally proving a representative liveness property of a hierarchical system model from the avionics domain. Since liveness properties can be negated only by infinite data sequences and thus cannot be covered exhaustively by testing, this case study demonstrates the added value for meeting typical safety requirements in the avionics domain. The results can be transferred from avionics to other domains, as well.

Microstructure and Mechanical Properties of a Weld Seam from Magnetron High-Current CO2 Welding

External magnetic field (EMF)-assisted high-current CO2 welding is beneficial for improving the large spatter and poor performance of the welding heat-affected zone for mild steels under high-current welding specifications. In this paper, the droplet transfer behaviors were determined using a high-speed camera on a self-developed magnetically controlled CO2 welding system. Based on these welding specifications, a three-dimensional, transient, multi-energy field coupling welding system model to investigate the mechanism of the droplet and molten pool in EMF-assisted welding was developed. The microstructure and mechanical properties of the welded joint were systematically studied. The results show that the Lorentz force applied by the EMF to twist the droplet decreases the accumulated energy in the short-circuited liquid bridge and changes the liquid metal flow condition, both of which reduce the spatter by 7% but increase the welded joint hardness by 10% and tensile strength by 8%.

Dynamic performance enhancement of DFIG wind turbine using an active disturbance rejection control -based sliding mode control

The present paper proposes a hybrid control law that combines the active disturbance rejection control (ADRC) and the sliding mode control (SMC) to improve the decoupled control of the active and reactive power stator applied to a doubly-fed induction generator (DFIG) integrated into a wind energy conversion system. The stator is directly connected to the electric grid, while the rotor is connected to the electric grid via a back-to-back converter. The Lyapunov theory proves the stability of nonlinear control. The performance of the proposed strategy is compared with that of traditional ADRC and proportional-integral control (PI). The simulation results in MATLAB/SIMULINK environment using a 1.5MW wind system model show the proposed approach. The proposed algorithm shows an excellent dynamic performance that reduces the time response, overshoot, and steady-state error compared to the conventional controllers.Considering parametric variation, applying a novel controller results in fewer oscillations in stator powers, outperforming the PI regulator and classic ADRC.