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Purpose: In order to solve the factors affecting the modeling of seismic shaking table array system, the transfer function of two-array system under three-parameter and multi-parameter control is studied, and the nonlinear characteristics of the system are considered. Methods: In-depth modeling analysis was performed by using MATLAB/Simulink software. Results: The results show that the peak value decreases and the notch increases with the damping ratio increasing, and the interaction effect of G11 and G22 decreases with the damping ratio increasing. In addition, the increase of the mass and natural frequency of the specimen not only leads to the increase of the peak value, but also the decrease of the notch wave, and the interaction effect of G11 and G22 becomes more obvious. Conclusion: These results provide important theoretical support for the optimization of the shaking table array system in order to improve its effectiveness in seismic simulation applications. [ABSTRACT FROM AUTHOR]

The lower hook mechanism has the characteristics of long motion path, high moving speed and high external disturbance, significantly impacting the weaving efficiency of the fishing net weaving machine. The multi-motor-based lower hook mechanism exhibits higher moving speed and lesser cam wear compared with the traditional lower hook mechanism, but it requires a high-precision and high-robust control system. In this study, we established a dynamic model for the multi-motor lower hook mechanism using the Lagrange equation. Based on this model, we developed a control system using linear extended state observer (LESO) and piecewise integral terminal sliding mode controller (PITSMC). By introducing a piecewise integral sliding surface, PISMC solved the slow convergence issue of linear sliding surfaces and the singularity problem of integral terminal sliding surfaces. Simulation results for the multi-motor-based lower hook mechanism demonstrate that the proposed PISMC outperforms the nonsingular terminal sliding mode controller and the conventional integral sliding mode controller in terms of control precision and disturbance rejection capability. [ABSTRACT FROM AUTHOR]

This paper presents an experimentally based study aimed at assessing the viability of employing a commercial energy harvester to develop a self-powered end-stroke and speed sensor for pneumatic cylinders. An energy-harvesting device was integrated into a cylinder end-cap to recover energy from the piston impact at the end of the stroke. The recovered energy powers a radio transmitter that communicates the reach of the end-stroke. This avoids the use of a dedicated end-stroke sensor, reducing the number of components in the system and also saving energy. The experiments aimed to analyze the signal characteristics generated by the module at various activation speeds, assessing whether the impact speed could be distinguished from the signal. Energy output and short-term usage effects were also investigated. The study seeks to further develop and adapt a Simulink model of the system, based on recent studies, and validate it with experimental findings at the tested activation speeds. Following confirmation of the adapted model's validity, the authors propose using genetic algorithms to design an optimized mechanical energy harvester. This approach aims to find the parameters of an energy harvester more suitable for pneumatic cylinder applications that would enable enhanced energy extraction and overall improved performances. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Power integrity is a critical aspect of microcontroller (MCU) system design. The present tendency of increasing current density and operating frequency, along with decreasing operating voltage, significantly diminishes voltage margins. Given the cost efficiency required for MCU systems, this context places important constraints on the design of the power distribution network (PDN), which directly impacts power supply noise. Therefore, characterizing the PDN is necessary. This paper introduces a cost-effective measurement and modeling method to estimate the die-package resonance frequency of the PDN, a major threat to power integrity. The method, applied to two 32-bit MCUs from STMicroelectronics with varying PDN configurations, enables the identification of the die-package resonance frequency. The results lead to the refinement of the die capacitance model for both cases, with a maximum relative error of less than 7%. The final objective is to implement the measurement system in the die in order to adjust the PDN if necessary. [ABSTRACT FROM AUTHOR]

To analyze the impact of interaction size on cooperation level and population average payoff in the public goods game with punishment, by combining Monte Carlo simulation with the characteristics of real complex interaction environments, a dynamic interaction domain model was proposed based on a cooperation threshold. First, a dynamic interaction rule based on the cooperation threshold is designed, taking into account the characteristics of different numbers of cooperators in the game group. Secondly, simulation experiments are conducted to observe the evolution of cooperation level, population payoff, and interaction domain range in the public goods game model after incorporating the interaction domain update rule. Finally, we compare the effects of the traditional and new models on the evolution of cooperation under different parameter values and discuss the principles and effectiveness of the model. Extensive simulation experiments have validated that increasing the cooperation threshold or the variation parameter more efficaciously fosters cooperation and enhances the population's average payoff.This provides a new perspective for the study of public goods game in dynamic interactive environment.

[Objective] This paper aims to explore dynamic modeling methods for airborne wind energy systems (AWEs) and trajectory tracking control methods for stable trajectories of kite trains and between kite trains when subjected to longitudinal disturbances in high-altitude wind fields. [Method] Taking a 25 MW-level kite-based AWEs as an example, this study investigated the mechanical response characteristics of kite arrays and constructed a multi-rigid body dynamic model of the kite-based system. In a simulated environment replicating high-altitude wind fields subjected to longitudinal disturbances, the study employed kite attitude control to design synchronous control laws, enabling trajectory tracking and ensuring operational safety of the kite-based system.[Result] The results demonstrate that adjusting the effective windward area of the kite can mitigate longitudinal disturbances caused by variations in high-altitude wind fields, leading to the convergence of errors between the actual and desired trajectories of the kite-based system. Moreover, based on the synchronous control laws designed, synchronization among multiple kite- based systems has been achieved, thereby ensuring collision-free and safe operation. [Conclusion] Using kite attitude control as a basis, a synchronous control strategy can be designed for the operation of kite-based AWEs, thereby achieving the objective of safe and efficient operation control in dynamic high-altitude wind environments.

Chemical processes are vital in various industries but are often complex and nonlinear, making accurate modeling essential. Traditional linear approaches struggle with dynamic behaviour and changing conditions. This paper explores the advantages of the new theory of fractional neural networks (FNNs), focusing on applying fractional activation functions for continuous stirred tank reactor (CSTR) modeling. The proposed approach offers promising solutions for real-time modeling of a CSTR. Various numerical analyses demonstrate the robustness of FNNs in handling data reduction, achieving better generalization, and sensitivity to noise, which is crucial for real-world applications. The identification process is more generalized and can enhance adaptability and improve industrial plant management efficiency. This research contributes to the growing field of real-time modeling, highlighting its potential to address the complexities in chemical processes.

This paper presents the operation of a modified speed controller with a standard PI/PID structure that includes the preprocessing of the controller's input signal, focusing on the past behavior of control errors. The modification involves adding a delay line, with the outputs of the individual line segments summed with a weighting method, as detailed in the paper. One of the significant advantages of this method is its use of a standard industrial controller structure, which makes it highly practical and easily implementable in existing systems. By relying on well-established control frameworks, this approach reduces the need for specialized hardware or complex modifications, allowing for smoother integration and lower implementation costs. The delay-based signal shaping shows excellent properties for the electric drive system powered by a hard-switching PWM converter. The set of weighted delays acts as a filter whose parameters are chosen using the quality function to test different configurations for optimal performance. When tested in a speed control system for a Permanent Magnet Synchronous Motor, the modifications improved the control quality index, indicating better performance and efficiency. Importantly, the system allows for reducing or eliminating the gain in the differentiating part of the controller, which decreases motor current chattering and noise. This paper includes an experimental verification of the proposed solution in a laboratory setting under semi-industrial conditions. [ABSTRACT FROM AUTHOR]

Substructuring is a term used to describe the estimation of the dynamics of a coupled system assembly when only the dynamics of each uncoupled component is available. Existing approaches allow for the coupling of physical-to-physical models, physical-to-modal models, modal-to-modal models referred to as Component Mode Synthesis (CMS), and impedance-to-impedance models referred to as Frequency Based Substructuring (FBS). Often times, the component information may not be just modal data for both components or just FRF data for both components so that modal substructuring or FRF substructuring can be performed. In these cases, the component data needs to be converted from either modal data or FRF data to match the data of the other component. A method for directly coupling impedance- and modal-based components has not yet been addressed. A proposed Impedance to Modal Substructuring (IMS) approach addresses this situation by writing the equations in a form that allows the user to directly utilize modal data for one component and FRF data for the other component, offering more flexibility in coupling different component data sets. While intended to be used with experimental data, this approach may also implement analytical components. In this work, an approach was developed to allow for the direct coupling of impedance and modal models without the need for the user to convert component data type. The IMS approach derived in this work was validated using analytical and experimental data with various models. [ABSTRACT FROM AUTHOR]

The rise in hydrogen production powered by renewable energy is driving the field toward the adoption of systems comprising multiple alkaline water electrolyzers. These setups present various operational modes: independent operation and multi-electrolyzer parallelization, each with distinct advantages and challenges. This study introduces an innovative configuration that incorporates a mutual lye mixer among electrolyzers, establishing a weakly coupled system that combines the advantages of two modes. This approach enables efficient heat utilization for faster hot-startup and maintains heat conservation post-lye interconnection, while preserving the option for independent operation after decoupling. A specialized thermal exchange model is developed for this topology, according to the dynamics of the lye mixer. The study further details startup procedures and proposes optimized control strategies tailored to this structural design. Waste heat from the caustic fully heats up the multiple electrolyzers connected to the lye mixing system, enabling a rapid hot start to enhance the system's ability to track renewable energy. A control strategy is established to reduce heat loss and increase startup speed, and the optimal valve openings of the diverter valve and the manifold valve are determined. Simulation results indicate a considerable enhancement in operational efficiency, marked by an 18.28% improvement in startup speed and a 6.11% reduction in startup energy consumption in multi-electrolyzer cluster systems, particularly when the systems are synchronized with photovoltaic energy sources. The findings represent a significant stride toward efficient and sustainable hydrogen production, offering a promising path for large-scale integration of renewable energy. [ABSTRACT FROM AUTHOR]

Underwater navigation practice shows that the long baseline survey has the characteristics of coplanar configuration, flat geometry, and large refraction error, which brings challenges to underwater positioning. To address this challenge, this paper proposes a high-precision real-time range-difference location algorithm based on underwater long baseline measurement. Firstly, the system error sources of long baseline positioning are analyzed in detail, the propagation models of different system errors are constructed, and the effects of system error sources on the rangefinder are described. Secondly, the limitations of traditional range iterative location algorithms and geometric analytic location algorithms in long baseline locations are analyzed. Then, using the strategy of converting the long baseline range information into the distance difference information, a high-precision real-time distance difference location algorithm based on long baseline measurement is presented. Finally, the feasibility of the algorithm is analyzed from the perspective of precision analysis. Numerical simulation results show that compared with the two traditional long-baseline positioning algorithms, the proposed algorithm has higher positioning accuracy and potential application value in the field of underwater real-time positioning. [ABSTRACT FROM AUTHOR]

Quantum computers accelerate many algorithms based on the superposition principle of quantum mechanics. The Grover algorithm provides significant performance to malicious users attacking symmetric key systems. Since the performance of attacks using quantum computers depends on the efficiency of the quantum circuit of the encryption algorithms, research research on the implementation of quantum circuits is essential. This paper presents a new framework to construct quantum circuits of substitution boxes (S-boxes) using system modeling. We model the quantum circuits of S-boxes using two layers: Toffoli and linear layers. We generate vector spaces based on the values of qubits used in the linear layers and apply them to find quantum circuits. The framework finds the circuit by matching elements of vector spaces generated from the input and output of a given S-box, using the forward search or the meet-in-the-middle strategy. We developed a tool to apply this framework to 4-bit S-boxes. While the 4-bit S-box quantum circuit construction tool LIGHTER-R only finds circuits that can be implemented with four qubits, the proposed tool achieves the circuits with five qubits. The proposed tool can find quantum circuits of 4-bit odd permutations based on the controlled NOT, NOT, and Toffoli gates, whereas LIGHTER-R is unable to perform this task in the same environment. We expect this technique to become a critical step toward optimizing S-box quantum circuits. [ABSTRACT FROM AUTHOR]

With the increasingly severe airspace congestion problem, the aviation industry is facing huge challenges, including the surge in pressure on air traffic controllers, significant increase in flight delays, and frequent flight conflicts. Efficient conflict detection and resolution technology is the primary task to ensure flight safety, which is not only particularly important for complex and high-density airspace environments, but also has significance for maintaining flight order, preventing aircraft collisions, alleviating air traffic pressure, and ensuring air traffic safety. This paper proposes a flight separation assurance model based on reinforcement learning (RL) technology, aiming to address the shortcomings of existing conflict resolution models in terms of state and action dimensions that cannot meet the needs of real-world control scenarios. Based on the air traffic environment model, agents are allowed to learn separation assurance policies through simulation interaction. Simulation experiments show that the model can search for optimal policies, which can provide assistance for air traffic controllers in decision-making. [ABSTRACT FROM AUTHOR]

Crude oil prices crashed and dropped into negative territory at the onset of the COVID-19 pandemic. This extreme event triggered a series of great-magnitude aftershocks. We seek to investigate the cascading dynamics and the characteristics of the series immediately following the oil market crash. Utilizing a robust method named the Omori law, we quantify the correlations of these events. This research presents empirical regularity concerning the number of times that the absolute value of the percentage change in the oil index exceeds a given threshold value. During the COVID-19 crisis, the West Texas Intermediate (WTI) oil prices exhibit greater volatility compared to the Brent oil prices, with higher relaxation values at all threshold levels. This indicates that larger aftershocks decay more rapidly, and the period of turbulence for the WTI is shorter than that of Brent and the stock market indices. We also demonstrate that the power law's exponent value increases with the threshold value's magnitude. By proposing this alternative method of modeling extreme events, we add to the current body of literature, and the findings demonstrate its practical use for decision-making authorities—particularly financial traders who model high-volatility products like derivatives. [ABSTRACT FROM AUTHOR]

The emergence of data-intensive applications, such as Deep Neural Networks (DNN), exacerbates the well-known memory bottleneck in computer systems and demands early attention in the design flow. Electronic System-Level (ESL) design using SystemC Transaction Level Modeling (TLM) enables effective performance estimation, design space exploration (DSE), and gradual refinement. However, memory contention is often only detectable after detailed TLM-2.0 approximately-timed or cycle-accurate RTL models are developed. A memory bottleneck detected at such a late stage can severely limit the available design choices or even require costly redesign. In this work, we propose a novel TLM-2.0 loosely-timed contention-aware (LT-CA) modeling style that offers high-speed simulation close to traditional loosely-timed (LT) models, yet shows the same accuracy for memory contention as low-level approximately-timed (AT) models. Thus, our proposed LT-CA modeling breaks the speed/accuracy tradeoff between regular LT and AT models and offers fast and accurate observation and visualization of memory contention. Our extensible SystemC model generator automatically produces desired TLM-1 and TLM-2.0 models from a DNN architecture description for design space exploration focusing on memory contention. We demonstrate our approach with a real-world industry-strength DNN application, GoogLeNet. The experimental results show that the proposed LT-CA modeling is 46× faster in simulation than equivalent AT models with an average error of less than 1% in simulated time. Early detection of memory contentions also suggests that local memories close to computing cores can eliminate memory contention in such applications. [ABSTRACT FROM AUTHOR]

This article considers new approaches to studying wave phenomena in objects and the processes of cyberphysical system devices in relation to their indirect attributes. The concept of indirect indicators is defined from the standpoint of studying the information state of systems. Examples of using indirect attributes in the theory of systems are disclosed. The classification of indirect attributes and models of their study is proposed for solving problems of information security of systems by controlling their indirect attributes. [ABSTRACT FROM AUTHOR]

Predicting river water quality in the Special Region of Yogyakarta (DIY) is crucial. In this research, we modeled a river water quality prediction system using the artificial neural network (ANN) backpropagation method. Backpropagation is one of the developments of the multilayer perceptron (MLP) network, which can reduce the level of prediction error by adjusting the weights based on the difference in output and the desired target. Water quality parameters included biochemical oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), dissolved oxygen (DO), total phosphate, fecal coliforms, and total coliforms. The research object was the upstream, downstream, and middle parts of the Oya River. The data source was secondary data from the DIY Environment and Forestry Service. Data were in the form of time series data for 2013–2023. Descriptive data results showed that the water quality of the Oya River in 2020–2023 was better than in previous years. However, increasing community and industrial activities can reduce water quality. This was concluded based on the prediction results of the ANN backpropagation method with a hidden layer number of 4. The prediction results for period 3 in 2023 and period 1 in 2024 are that 1) the concentrations of BOD, fecal coli, and total coli will increase and exceed quality standards, 2) COD and TSS concentrations will increase but will still be below quality standards, 3) DO and total phosphate concentrations will remain constant and still on the threshold of quality standards. The possibility of several water quality parameters increasing above the quality standards remains, so the potential for contamination of the Oya River is still high. Therefore, early prevention of river water pollution is necessary.

As the fourth generation advanced reactor, the Brayton cycle system of small helium-xenon cooled reactor has the advantages of light weight, compact structure, simple system, fast start or stop, and is very suitable for the energy supply of remote areas and islands. Nuclear power plant is radioactive and highly hazardous after accidents, so it is very important to study its safety characteristics. In addition, helium-xenon cooled reactor is an advanced future reactor type, and its accident safety characteristics are not yet clear. Therefore, it is necessary to carry out the safety characteristics research of Brayton cycle system of small helium-xenon cooled reactor, especially under the condition of unprotected control. Firstly, based on Modelica language, through the modular dynamic modeling of key equipment, the accident safety characteristics analysis program of helium-xenon cooled reactor Brayton cycle system was developed. Secondly, the steady-state rated condition of the system was simulated, and the reliability of the accident safety characteristics analysis program of the system was preliminarily verified by comparing the error between the simulation value and the design value of the steady-state rated condition of the system, in which the maximum relative error is 2.31%. Finally, the transient conditions of various system accidents were simulated, and the dynamic response characteristics of transient conditions such as step introduction positive reactivity accident, step introduction negative reactivity accident, coolant incorrect charging accident and coolant incorrect discharging accident were analyzed. The results show that the simulation results of the system analysis program are reasonable. The reactor system has a certain self-stability and self-adjustment ability for step introduction positive reactivity accident and coolant incorrect charging accident under the condition of constant load and unprotected control. Under the conditions of step introduction positive reactivity accident and coolant incorrect charging accident, after the rotational speed increases by 21.6% and 12% respectively, it can be stabilized in another steady-state without protection and control. However, under the conditions of constant load and unprotected control, the reactor system can not be stabilized under another steady-state condition through self-regulation for the step introduction negative reactivity accident and the coolant incorrect discharge accident. Under the conditions of step introduction negative reactivity accident and coolant incorrect discharge accident, the corresponding rotational speed drop rate at 800 s is 85 rpm/s and 10 rpm/s respectively. It is necessary to control the rotational speed drop rate of the rotor through some protection and control measures to ensure the safety of reactor system. The relevant results provide a theoretical basis for the safety analysis of Brayton cycle system of helium-xenon cooled reactor.

Chronic kidney disease (CKD) is one of the most important causes of chronic pediatric morbidity and mortality and places an important burden on the medical system. Current diagnosis and progression monitoring techniques have numerous sensitivity and specificity limitations. New biomarkers for monitoring CKD progression have been assessed. Neutrophil gelatinase-associated lipocalin (NGAL) has had some promising results in adults, but in pediatric patients, due to the small number of patients included in the studies, cutoff values are not agreed upon. The small sample size also makes the statistical approach limited. The aim of our study was to develop a fuzzy logic approach to assess the probability of pediatric CKD progression using both NGAL (urinary and plasmatic) and routine blood test parameters (creatinine and erythrocyte sedimentation rate) as input data. In our study, we describe in detail how to configure a fuzzy model that can simulate the correlations between the input variables ESR, NGAL-P, NGAL-U, creatinine, and the output variable Prob regarding the prognosis of the patient's evolution. The results of the simulations on the model, i.e., the correlations between the input and output variables (3D graphic presentations) are explained in detail. We propose this model as a tool for physicians which will allow them to improve diagnosis, follow-up, and interventional decisions relative to the CKD stage. We believe this innovative approach can be a great tool for the clinician and validates the feasibility of using a fuzzy logic approach in interpreting NGAL biomarker results for CKD progression. [ABSTRACT FROM AUTHOR]

This study focused on the effect of glass structures of modern architecture on the indoor thermal environment during summer. In particular, this study examined how solar radiation significantly altered people's thermal sensations. Laboratory tests on convection–radiation air conditioning systems were conducted, encompassing 12 different scenarios, including diverse indoor open areas, terminal forms, and levels of solar radiation. These tests aimed to explore the physiological and psychological responses of the human body to solar radiation penetrating through windows into the inner room. During the experiments, the participants' subjective thermal sensations and thermal comfort were recorded, along with continuous monitoring of their physiological and environmental parameters. Results showed that solar radiation significantly increased local skin temperature, with a maximum rise of 2.15 °C. Operative temperature is a reliable indicator of human skin temperature and thermal sensation vote (TSV). This study established two models that could predict the skin temperature of individuals indoors through operative temperature under conditions without or with solar radiation, and identified sensitive ranges of operative temperature for both models, to be specific, 26.32 °C to 28.43 °C and 28.51 °C to 34.11 °C, respectively. Furthermore, this study established the relationship between skin temperature and TSV under conditions with and without solar radiation. The results indicate that solar radiation enhances the human body's adaptability to indoor environmental parameters; a convection–radiation system (FC+RF) could be used to optimize indoor thermal control under solar radiation, achieving more stable environmental temperatures and improved indoor comfort. [ABSTRACT FROM AUTHOR]

This paper focuses on the nonlinear system modeling based on using a modified Hammerstein system model. The proposed Hammerstein structure is composed of a bilinear neural network (BNN) and a recursive digital system in the cascaded form. The former is taken to be the nonlinear function part of the Hammerstein model, and the latter is used as the linear dynamic subsystem. The BNN is then constructed by the bilinear digital system and the recurrent neural network, which already possesses a satisfactory modeling capacity. To update all of adjustable parameters within the proposed Hammerstein model, a popular and powerful evolutionary computation called the differential evolution (DE) is utilized so that the model output can be closely to the actual nonlinear system output. Finally, a simulated nonlinear chemical process system, continuously stirred tank reactor (CSTR), is illustrated with the modeling phase and testing phase. Some experiment results as compared with another method from the subject literature are provided to demonstrate the feasibility of the proposed method and its good modeling. [ABSTRACT FROM AUTHOR]

Copyright of Atomic Energy Science & Technology is the property of Editorial Board of Atomic Energy Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Soft robots can adaptively interact with unstructured environments. However, nonlinear soft material properties challenge modeling and control. Learning‐based controllers that leverage efficient mechanical models are promising for solving complex interaction tasks. This article develops a closed‐loop pose/force controller for a dexterous soft manipulator enabling dynamic pushing tasks using deep reinforcement learning. Force tests investigate the mechanical properties of a soft robot module, resulting in orthogonal forces of 9−13$9 - 13$ N. Then, the policy is trained in simulation leveraging a dynamic Cosserat rod model of the soft robot. Domain randomization mitigate the sim‐to‐real gap while careful reward engineering induced pose and force control even without explicit force inputs. Despite the approximate simulation, the sim‐to‐real transfer achieved an average reaching distance of 34±14$34 \pm 14$ mm (8.1%L±3.4%L$ L \pm L$), an average orientation error of 0.40±0.29$0.40 \pm 0.29$ rad (23°±17°$\left(23\right)^{\circ} \pm \left(17\right)^{\circ}$) and applied pushing forces up to 3$3$ N. Such performance is reasonable for the intended assistive tasks of the manipulator. The experiments uncovered that the soft robot interacting with the environment exhibited torsional and counter‐balancing movements. Although not explicitly enforced, they emerged from the mechanical intelligence of the manipulator. The results demonstrate the potential of soft robotic manipulation via reinforcement learning. [ABSTRACT FROM AUTHOR]

Using the behind-meter data, this study applied a comparison and optimization-based framework to evaluate the energy flexibility and resilience of distributed energy resources within existing houses during cold wave event. Comparative analysis demonstrates the effectiveness of high envelope insulation level in improving energy resilience, identifies impacts of distributed energy resources on variations of household electricity demand. Specifically, a 14.6% reduction in the median value of the normalized load of building group with low U-values, implementations of cogeneration system effectively suppressed variations of electricity load. Dynamic energy performances of on-site generators are evaluated based on high resolution data, energy flexibility of domestic hot water and thermostatically controlled loads were investigated through built demand response model. Results reveal that electrifying hot water demand offers additional power flexibility, the integration of fuel cell cogeneration system has proven to be an efficient energy resource, enabling on-site generation of both electricity and hot water, substantially reducing grid import. The extreme cold event resulted in significant spikes in space heating power consumption. The optimization results demonstrate that reducing the indoor setpoint temperature effectively decreases daily power consumption by approximately 5.0% per degree Celsius. These findings help acquire better understanding of interconnections between energy efficiency and resilience of residential energy-efficient measures. [ABSTRACT FROM AUTHOR]

A critical enabler for Model‐Based Systems Engineering (MBSE) and Digital Engineering (DE) is the generation of coherent and consistent views of a system‐of‐interest based on information within a system model. In practice, system model development is facilitated through domain‐specific profiles, style guides, reference models, and low‐fidelity meta‐models to create coherent and consistent system information. Each of these approaches are useful but are insufficient for robust and automated verification of system models to an ideal. Furthermore, the expression of domain‐specific concepts and semantics relies on the proliferation of non‐standard, domain‐specific profiles as standard system modeling languages like the Systems Modeling Language (SysML) are general purpose. This paper proposes a novel approach to creating precise, machine‐interpretable metamodels implemented as a lightweight Unified Modeling Language (UML) profile. The profile includes numerous features that allow model architects to quickly specify context and domain‐specific modeling constructs without creating non‐standard stereotypes to apply domain‐specific meaning and usage rules. Three kinds of constraints can be inferred based on the relationships between meta‐model elements: type, multiplicity, and default value. Applications of well‐formed metamodels include a shared understanding of the intended model format and structure, as well as the one‐time programmatic generation of an encompassing suite of validation rules to evaluate a system model against the inferred constraints, thus ensuring consistency. Additional applications include programmatic generation of model analysis metrics, system models from metamodels, metamodels from reference models and element finding queries, and the ability to update a system model based upon the updated metamodel automatically. Use of the approach results in reduced time in system model development and analysis and ensures coherency and consistency of information thus increasing stakeholder use and confidence in the system model. [ABSTRACT FROM AUTHOR]

Predicting river water quality in the Special Region of Yogyakarta (DIY) is crucial. In this research, we modeled a river water quality prediction system using the artificial neural network (ANN) backpropagation method. Backpropagation is one of the developments of the multilayer perceptron (MLP) network, which can reduce the level of prediction error by adjusting the weights based on the difference in output and the desired target. Water quality parameters included biochemical oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), dissolved oxygen (DO), total phosphate, fecal coliforms, and total coliforms. The research object was the upstream, downstream, and middle parts of the Oya River. The data source was secondary data from the DIY Environment and Forestry Service. Data were in the form of time series data for 2013-2023. Descriptive data results showed that the water quality of the Oya River in 2020-2023 was better than in previous years. However, increasing community and industrial activities can reduce water quality. This was concluded based on the prediction results of the ANN backpropagation method with a hidden layer number of 4. The prediction results for period 3 in 2023 and period 1 in 2024 are that 1) the concentrations of BOD, fecal coli, and total coli will increase and exceed quality standards, 2) COD and TSS concentrations will increase but will still be below quality standards, 3) DO and total phosphate concentrations will remain constant and still on the threshold of quality standards. The possibility of several water quality parameters increasing above the quality standards remains, so the potential for contamination of the Oya River is still high. Therefore, early prevention of river water pollution is necessary. [ABSTRACT FROM AUTHOR]

In the field of molecular nonequilibrium transports, physical mechanisms of multiple reaction dynamics of these systems are the core of deep understanding complex reactions and transport mechanisms. In order to explore related mechanisms, establishing multiple systems coupled with tremendous exit dynamics and studying their exit dynamics properties are quite vital. Beyond previous researches, new stochastic transport processes are emphasized here. Multiple new exit dynamic systems are established, which are motivated by the multiplicity of paths and products of real biochemical processes in organisms. In order to ensure research universality, core system modeling factors are fully considered. Countable parallel orbits, uniform connection with external sources, countable parallel orbits as subsystems in middle lattices and influences of all lattices on transport trajectories on dynamic properties are analyzed. Dynamic properties of different particles located in orbits are explored by deeply studying average exit time and time scale. Quantitative spatiotemporal impacts are extensively studied. The rationality of average exit time as a time scale in the universal exit dynamic system is proved. Main findings and fruitful results can not only serve as theoretical bases for broadening reaction path modeling, but also be helpful to support understanding nonequilibrium transport mechanisms, especially stochastic biochemical processes. [ABSTRACT FROM AUTHOR]

This paper presents a rehabilitation exoskeleton featuring a link-type design, driven by pneumatic muscles (PMs), with the driving torques transmitted to the leg orthoses via multiple linkages. The inherent complexity of this link-type exoskeleton presents significant challenges in both modeling and control. To address these challenges, we construct constrained kinematics and dynamics through geometric analysis, and introduce a robust prescribed performance controller with a virtual elastic element (PPC-VE) to enhance the safety of passive gait training. The controller has the capability to directly adjust the intensity of chattering in control inputs and ensure system transient/steady states performance by introducing a non-zero proxy mass and an error transformation. Theoretical analysis indicated the uniform ultimate boundedness of the states, with varying behaviors observed in the controller as the proxy mass changed. Simulation and experimental results demonstrated the validity of the dynamic model, and the proposed controller effectively enhanced system safety by addressing output constraints and reducing abrupt variations in control inputs. [ABSTRACT FROM AUTHOR]

Objective. Digital breast tomosynthesis (DBT) has significantly improved the diagnosis of breast cancer due to its high sensitivity and specificity in detecting breast lesions compared to two-dimensional mammography. However, one of the primary challenges in DBT is the image blur resulting from x-ray source motion, particularly in DBT systems with a source in continuous-motion mode. This motion-induced blur can degrade the spatial resolution of DBT images, potentially affecting the visibility of subtle lesions such as microcalcifications. Approach. We addressed this issue by deriving an analytical in-plane source blur kernel for DBT images based on imaging geometry and proposing a post-processing image deblurring method with a generative diffusion model as an image prior. Main results. We showed that the source blur could be approximated by a shift-invariant kernel over the DBT slice at a given height above the detector, and we validated the accuracy of our blur kernel modeling through simulation. We also demonstrated the ability of the diffusion model to generate realistic DBT images. The proposed deblurring method successfully enhanced spatial resolution when applied to DBT images reconstructed with detector blur and correlated noise modeling. Significance. Our study demonstrated the advantages of modeling the imaging system components such as source motion blur for improving DBT image quality. [ABSTRACT FROM AUTHOR]

In the active magnetic bearing (AMB)-rotor system, a displacement sensor is one of the indispensable components. It detects the displacement of the rotor in real time and feeds back to the control system to realize the stable suspension of the rotor. However, due to electromagnetic interference, unstable power supply, poor grounding, and uneven rotor surface, the signal runout of the displacement sensor can often occur, resulting in rotor vibration. Given the above problems, this paper establishes the control model of the AMB-rotor system, lists three typical sensor runout signals, then analyzes the influence of different types of sensor runout on the system, and discusses the control methods under different sensor runout conditions. The research results have a specific guiding significance for vibration control of the maglev rotor under sensor runout influence. [ABSTRACT FROM AUTHOR]

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The global energy demand is increasing consistently, paralleled by rising greenhouse gas emissions. The worldwide drive to mitigate CO2 emissions in power generation has prompted countries to prioritize low carbon emission technologies such as renewable energy along with performance improvements. However, despite the appeal of solar power in sun-rich regions, the intermittent nature of this source poses an impediment to the electricity grid. Following renewables, pricey and high-demand natural fuel power is anticipated to serve a complementary role in addressing these contemporary issues. This study proposes integrating the sCO2 Brayton cycle with concentrated solar power as a novel resolution to reduce the reliance on fossil fuels while focusing on the economic and environmental concerns and maintaining a reliable power supply to meet the pressing need. The power cycle deviates from conventional cycles by incorporating both renewable and non-renewable energy sources to reduce reliance on the latter, addressing concerns related to fuel depletion. This study offers a thorough analysis of the energy and exergy aspects within the integrated system, integrating parametric optimization across various operational conditions. The findings reveals that the parametric optimization has enhanced the system's efficiency to approximately 55.45 %, leading to in a maximal power output of approximately 56.65 MW with a 10-15 % reduction in fuel consumption, depending on the available DNI across various regions. In addition, the research evaluates the dynamic performance of the integrated cycle under various seasonal conditions, highlighting the summer season being emerged as the most favorable period with 14.58 % fuel reduction. Overall, the results of this study suggest a promising economic trajectory by lessening the environmental consequences, while ensuring reliable and efficient power generation that might be a feasible option in arid regions with limited water and fossil fuel resources.

In traditional digital twin communication system testing, we can apply test cases as completely as possible in order to ensure the correctness of the system implementation, and even then, there is no guarantee that the digital twin communication system implementation is completely correct. Formal verification is currently recognized as a method to ensure the correctness of software system for communication in digital twins because it uses rigorous mathematical methods to verify the correctness of systems for communication in digital twins and can effectively help system designers determine whether the system is designed and implemented correctly. In this paper, we use the interactive theorem proving tool Isabelle/HOL to construct the formal model of the X86 architecture, and to model the related assembly instructions. The verification result shows that the system states obtained after the operations of relevant assembly instructions is consistent with the expected states, indicating that the system meets the design expectations.

In the context of precision horticulture, decision support tools play a significant role in providing fruit growers with insights into orchard conditions, facilitating informed decisions regarding orchard management practices. This study presents the development of an autonomous yield estimation system designed to provide decision support to small commercial orchards. Autonomous yield estimation is based on the application of UAVs and AI. AI is used to identify and quantify fruitlets and fruits in photographs collected by UAV. In this article, we present our prototype of an autonomous yield estimation system. The adapted “4+1” architecture was applied to design a system with a holistic approach analyzing software, hardware, and ecosystem requirements. Six datasets are presented, which contain the images of fruitlets and fruits of apples, pears, and cherries. Three CNN models were trained: YOLOv8m, YOLOv9m, and YOLOv10m. The experiment showed that the most accurate was YOLOv9m, which achieved mean accuracies of 0.896 mAP@50 and 0.510 mAP@50:95 for all datasets.