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The time-modulated active medium with linear independent frequency conversion method has been demonstrated to enable wave orientation and reconstruction. However, due to the symmetric scattering field, this technique requires intricate microcircuit designs. To overcome this limitation, this paper proposes a tunable piezoelectric metasurface based on acoustic black holes (ABHs) to redirect flexural wave reflections. The system can convert an incident flexural wave into a reflected wave of any direction and frequency. This is accomplished through the linear time modulation of the sensing signal, which breaks the constraints of Snell's law inherent in traditional designs and is insensitive to the incident amplitude. The coupling of the ABH damping system with a linear independent frequency conversion mechanism allows for the conversion of an incident flexural wave into a reflected wave in any direction and frequency while also eliminating the influence of second harmonic reflection on the wave field and simplifying the time modulation circuit. In addition, this paper demonstrates arbitrary angle reflection, focusing, beam splitting, and frequency conversion of the incident wave. By improving the flexibility of elastic wave manipulation, this paper introduces a new approach for active control of elastic waves and provides a design method that can be employed in a variety of applications ranging from vibration protection of engineering structures to vibration sensing and evaluation. [ABSTRACT FROM AUTHOR]

In this work, we report on the development and application of a simple reflectivity technique for real-time monitoring of the fabrication of porous-silicon multilayers. The technique allows for high-resolution quantification of the effective optical thickness of the layers during the fabrication process, enabling calibration of attack rates and detection of changes in porosity. Our experiments revealed that the porosity of the layers increases during the attack, indicating incomplete passivation of the pores. By studying thin porous-silicon layers in the absence of applied current, we developed a three-layer model to understand passive chemical etching. Furthermore, our study allowed discrimination between the two mechanisms responsible for the reduction of effective optical thickness with time, with changes in porosity being the dominant one. The technique has the potential to enable real-time control of multilayer fabrication, offering flexibility in controlling environmental conditions and silicon wafer conduction properties. Our results contribute to the understanding of multilayer fabrication and reflectivity-based process monitoring. [ABSTRACT FROM AUTHOR]

Atomic layer plasma technologies require localizing ions' impact within nanometers up to an atomic layer. The possible way to achieve this is the decrease in the ion energy up to surface binding energy. At such low ion kinetic energies, the impact of different plasma effects, causing the surface modification, can be of the same order as kinetic ones. In this work, we studied the sputtering of amorphous silicon films by Ar+, Kr+, and Xe+ ions at energies of 20–200 eV under the low-pressure inductively coupled plasma discharge in pure argon, krypton, and xenon, respectively, at a plasma density of 1–1.5 × 1010 cm−3. Under the plasma conditions, a high asymmetry of discharge allowed to form ion flux energy distribution functions with narrow energy peak (5 ± 2 eV full width at half maximum). Real time in situ control over the ion composition and flux as well as the sputtering rate (the ratio of the film thickness change to the sputtering time) provided accurate determination of the sputtering yields Y (E i). It is shown that at ion energy above ∼70 eV, the "classical" kinetic sputtering mechanism prevails. In this case, Y (E i) grows rather rapidly with ion energy, increasing with the decrease in the ion mass: the closer the ion mass to the target atom mass, the higher the Y (E i). Below 70 eV, the growth of Y (E i) strongly slows down, with Y (20 eV) being still high (>10−3), indicating the impact of plasma. The obtained trends of Y (E i) are discussed in light of surface modification studied by atomic force microscopy and angular x-ray photoelectronic spectroscopy. [ABSTRACT FROM AUTHOR]

The automotive industry has transitioned from internal combustion engine (ICE) vehicles to electric vehicles (EVs) in recent years. Also essential to the development of the next generation are the enhancement of engine performance and production of lightweight vehicle bodies. Through this method, it is possible to increase vehicle output. However, as the vehicle's excitation strength increases, structural noise and vibrations caused by the engine increase. Intelligent structure-based active mounting systems are widely used in the automotive industry to reduce vibration and noise. It can continuously control the dynamic characteristics of the mount system, enabling real-time control of active mounting systems. When determining the position of the active mounting system, it must be installed in the most efficient position possible. Therefore, the purpose of this study is to determine the optimal placement criteria for an active mounting system consisting of a piezoelectric actuator and a rubber grommet. The lumped parameter model is used to model the plate structure with two hybrid mounting systems based on the source–path–receiver structure. In addition, the forces of two active mounts are calculated using static and dynamic approaches. When the calculated force on the hybrid mount system is relatively low, the location with the greatest vibration reduction can be determined, and this location can be regarded as the optimal location for the active mounting system. In addition, a feasibility test is conducted to validate the proposed criteria and validate the static and dynamic results. The findings indicate that the optimal location for the active engine mounting system can be determined. [ABSTRACT FROM AUTHOR]

The interaction force control problem for the object manipulated by the dual-arm manipulator with the unknown environment is addressed in this paper. An adaptive dual-loop impedance control scheme is designed to meet the three control targets, including the trajectory tracking control of the object, the internal force control of the object with the two arms, and the interaction force control of the object with the unknown environment. Firstly, an adaptive variable impedance control method is proposed in the outer loop to copy with the unknown stiffness and position of the environment. Secondly, an impedance control scheme based on the internal force is designed in the inner loop to plan the internal force and the modified position supplied with the outer-loop control. Thirdly, to realize the reference trajectory supplied with the inner-loop control, the virtual decomposition control (VDC) method is incorporated, which is a recursive adaptive control progress, so that the unknown dynamics of the manipulators is acceptable and the computation complexity is also very low to satisfy the real-time control requirement. The stability verifications of the inner-loop impedance control and the VDC method are given based on the Lyapunov method, and the stability analysis of the outer-loop impedance control is obtained using the Routh criterion. Finally, a plane dual-arm robotic manipulator with three degrees of freedom is used to verify the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

During the epoch of sustainable development, leveraging cellular systems for production of diverse chemicals via fermentation has garnered attention. Industrial fermentation, extending beyond strain efficiency and optimal conditions, necessitates a profound understanding of microorganism growth characteristics. Specific growth rate (SGR) is designated as a key variable due to its influence on cellular physiology, product synthesis rates and end-product quality. Despite its significance, the lack of real-time measurements and robust control systems hampers SGR control strategy implementation. The narrative in this contribution delves into the challenges associated with the SGR control and presents perspectives on various control strategies, integration of soft-sensors for real-time measurement and control of SGR. The discussion highlights practical and simple SGR control schemes, suggesting their seamless integration into industrial fermenters. Recommendations provided aim to propose new algorithms accommodating mechanistic and data-driven modelling for enhanced progress in industrial fermentation in the context of sustainable bioprocessing. [ABSTRACT FROM AUTHOR]

In modern manufacturing processes, ensuring the precision of 3D profiles of products is crucial. Nonetheless, achieving this accuracy is challenging due to the complex interactions between process inputs and the data structure of the 3D profile data. Our solution, a 3D profile-based control framework, addresses this challenge by actively adapting and controlling the manufacturing process to enhance 3D shape accuracy. 3D profile scans represent the ultimate measure of desired part quality. Therefore, utilizing them as the system responses for control purposes yields the most direct and effective feedback. We leverage recent advancements from Koopman operator theory to create an effective model-based control strategy. Initially, we estimate the process model by exploring the relationship between 3D profiles and heterogeneous process inputs. Then, we formulate an online model predictive control law. Challenges include dealing with unstructured, high-dimensional 3D point cloud data, capturing spatial and temporal structures, and integrating heterogeneous, high-dimensional process input data into the control model. To overcome these challenges, we introduce RETROFIT, a solution designed for the real-time control of time-dependent 3D point cloud profiles. Unlike traditional models, RETROFIT is not bound by linear assumptions and can handle unstructured 3D point cloud data directly. We demonstrate its effectiveness through a wire arc additive manufacturing case study, highlighting its potential to enhance 3D profile accuracy in manufacturing processes. [ABSTRACT FROM AUTHOR]

Modified atmosphere storage containers with gas-permeable membranes are effective in reducing post-harvest losses, mainly at low temperatures but become ineffective in controlling O 2 and CO 2 at temperatures above 3 °C. This study aimed to develop a method to control O 2 in a storage box. This method uses a diffusion tube, that blocks air from entering the box but allows for air exchange when an air blower is activated. The blower ON frequency (BOF) was modelled as a function of temperature, considering the type and amount of produce, blower properties, tube dimension, and the O 2 setpoint. The effects of temperature and gas composition on respiration rate kinetics were analysed with Michaelis-Menten and Arrhenius equations. The model was used to program the microcontroller to control the blower. Validation was performed using a 190 L box containing 25 kg of broccoli. The developed model maintained the desired O 2 concentration under fluctuating temperatures. The BOF values ranged from 25.6 to 549.2 s h−1, for temperature fluctuations between 4 and 23 °C. The time required to reach the 3% O 2 in the box differed depending on the blower's state. With the blower turned ON from the beginning, it took 24.9 h. However, when the blower was initially turned OFF, it took 11.1 h. Despite temperature changes, the system maintained O 2 at 3.8 ± 0.29% and CO 2 at 14.4 ± 0.66%. The system is promising for commercial use and best suited for CO 2 tolerant produce because it requires a separate mathematical model and control mechanism. • Simple method to control gas concentrations under fluctuating temperature. • Real-time O 2 control in storage based on temperature sensor and predictive model. • Mathematical model guides real-time air-blower activation for maintaining O 2. • Suitable for CO 2 -tolerant produce or when combined with CO 2 removal tools. [ABSTRACT FROM AUTHOR]

In recent years, path-tracking controllers for autonomous passenger vehicles and Control Allocation (CA) methods for handling and stability control have both received extensive discussion in the literature. However, the integration of the path-tracking control with CA methods for autonomous racing vehicles has not attracted much attention. In this study, we design an integrated path-tracking and CA method for a prototype autonomous racing electric vehicle with a particular focus on the maximising the turning speed in tight cornering. The proposed control strategy has a hierarchical structure to improve the computational efficiency: the high-level path-tracking Model Predictive Control (MPC) based on a rigid body model is designed to determine the virtual control forces according to the desired path and desired maximum velocity profile, while the low-level CA method uses a Quadratically Constrained Quadratic Programming (QCQP) formulation to distribute the individual control actuator according to the desired virtual control values. The proposed controller is validated in a high-fidelity simulation vehicle model with the computational time of the optimisation controller presented to demonstrate the real-time control performance. [ABSTRACT FROM AUTHOR]

It is difficult to achieve low-order harmonic tracking control without static difference when PI control is used in unified power quality conditioner (UPQC). What is more, the traditional PI parameter trial method has the problems such as low accuracy and anti-interference to meet the needs of real time control. Aiming at this issue, this paper proposes a UPQC harmonic control method based on the beetle antennae search (BAS) algorithm to optimize the parameters of quasi-proportional integral resonance (QPIR) controller. The harmonic current is detected by the i p – i q method based on the instantaneous power theory, and the detected signal is input to the QPIR controller. The controller parameters are optimized by the BAS algorithm to improve the dynamic response performance and control accuracy of the UPQC. Then, the MATLAB/Simulink software platform is used to build UPQC simulation models for PV and wind microgrids. Finally, the simulation comparison is verified between the optimized QPIR controller strategy based on by BAS method and PI control strategy based on neural network algorithm, respectively. The results show that using QPIR controller optimized through the beetle antennae search (BAS) method is more accurate and resistant to interference. So, the proposed UPQC control strategy has innovation and feasibility, and it can be applied in the practical engineering. [ABSTRACT FROM AUTHOR]

Quality is an indispensable requirement in medical laboratories, where controls are performed at different phases of the laboratory processes. In the following, we consider the internal quality control activities during the analytical phase of medical laboratories for verifying the test results prior to their release. To monitor the measurement process for errors in the test results, one approach that is considerably attracting the health professionals is patient‐based real‐time quality control. In this respect, real‐time patient test results are monitored, commonly by implementing the Moving Average (MA) control chart with truncation limits. A usual practice is the use of truncation limits to exclude abnormal patient test results from the MA calculations. However, an open issue in the literature is how to determine the width of the truncation limits and the window size in the MA calculations. Furthermore, the distributions of the test results are often nonnormal and difficult to model by personnel having insufficient training in statistics. Consequently, robustness of the performance of the MA control chart to non‐normal distributions of test results is another open topic in the literature. In this study, a detailed robustness analysis is performed by considering various distributions having different shapes. Performance of several MA control chart designs constructed by altering the truncation limits and window sizes are investigated. Based on our simulation results with selected distributions, we conclude that the truncation limits should not be used and a window size of 20 is recommended. Under such a setting, the MA control chart performs similar to its intended performance in terms of false alarms and in detecting systematic errors, hence being robust to non‐normality of the test results. [ABSTRACT FROM AUTHOR]

Massive smart water conservancy object (WCO) need to be connected for real-time monitoring and control, which produces massive data. Unfortunately, heterogeneous data structures and semantics lead to low interoperability between WCO and management systems. To address this challenge, we propose a novel interoperability structure for a smart water conservancy system based on the Internet of Things (IoT), and the key design includes a smart WCO terminal, interoperability network, special interoperability protocol, WCO information model, and cloud platform. Universal terminal and network are the base of interoperability hardware, and special interoperability protocol and information model for interconnection of WCO are designed for smart water conservancy management system. WCO can be connected to a water conservancy Big Data processing cloud platform for interoperability applications. The application results demonstrate that our proposed WCO's interoperability structure has obvious advantages than the general IoT at WCO interoperability. The interoperability protocol is reliable, the information model can ease interoperability and security, and the semantic dictionary is very rich and covers all semantic services of WCO. [ABSTRACT FROM AUTHOR]

Under the envisioned smart grid paradigm, there is an increasing demand for a fast, accurate, and efficient power flow solution for distribution system operation and control. Various solution techniques have been proposed, each with its own unique formulation, solution methodology, advantages, and drawbacks. Motivated by challenges associated with the integration of renewable distributed energy resources and electric vehicles into distribution systems and further by the speed and convergence limitations of existing tools, this paper presents a novel graph‐based power flow solution for smart grid's real‐time operation and control, named Flow‐AugmentationPF algorithm. The proposed method formulates a power flow problem as a network‐flow problem and solves it by using a maximum‐flow algorithm, inspired by the push‐relabel max‐flow technique. The performance of the proposed algorithm is tested and validated using several benchmark networks of different sizes, topologies, and parameters and compared against the most commonly used solution techniques and commercial software packages, namely PSS/E and PSCAD. The proposed formulation is simple, accurate, fast, yet computationally efficient, as it is based on matrix‐vector multiplication, and is also scalable, considering the formulation works as a graph‐based method, which, inherently, allows for parallel computation for added computational speed. [ABSTRACT FROM AUTHOR]

Upper limb robotic (myoelectric) prostheses are technologically advanced, but challenging to use. In response, substantial research is being done to develop person-specific prosthesis controllers that can predict a user's intended movements. Most studies that test and compare new controllers rely on simple assessment measures such as task scores (e.g., number of objects moved across a barrier) or duration-based measures (e.g., overall task completion time). These assessment measures, however, fail to capture valuable details about: the quality of device arm movements; whether these movements match users' intentions; the timing of specific wrist and hand control functions; and users' opinions regarding overall device reliability and controller training requirements. In this work, we present a comprehensive and novel suite of myoelectric prosthesis control evaluation metrics that better facilitates analysis of device movement details—spanning measures of task performance, control characteristics, and user experience. As a case example of their use and research viability, we applied these metrics in real-time control experimentation. Here, eight participants without upper limb impairment compared device control offered by a deep learning-based controller (recurrent convolutional neural network-based classification with transfer learning, or RCNN-TL) to that of a commonly used controller (linear discriminant analysis, or LDA). The participants wore a simulated prosthesis and performed complex functional tasks across multiple limb positions. Analysis resulting from our suite of metrics identified 16 instances of a user-facing problem known as the "limb position effect". We determined that RCNN-TL performed the same as or significantly better than LDA in four such problem instances. We also confirmed that transfer learning can minimize user training burden. Overall, this study contributes a multifaceted new suite of control evaluation metrics, along with a guide to their application, for use in research and testing of myoelectric controllers today, and potentially for use in broader rehabilitation technologies of the future. [ABSTRACT FROM AUTHOR]

We discuss the chosen concepts, detailed design, implementation and calibration of the 85-electrode adaptive optics (AO) system of the Swedish 1-meter Solar Telescope (SST), which was installed in 2013. The AO system is unusual in that it uses a combination of a monomorph mirror with a Shack-Hartmann (SH) wavefront sensor (WFS) and a second high-resolution SH microlens array to aid the characterization, calibration, and modal control of the deformable mirror. An Intel PC workstation performs the heavy image processing associated with cross-correlations and real-time control at a 2 kHz update rate with very low latency. The computer and software continue the successful implementation since 1995 of earlier generations of correlation tracker and AO systems at SST and its predecessor, the 50-cm Swedish Vacuum Solar Telescope, by relying entirely on work-station technology and an extremely efficient algorithm for implementing cross-correlations with the large field of view of the WFS. We describe critical aspects of the design, calibrations, software, and functioning of the AO system. The exceptionally high performance is testified through the highest Strehl ratio (inferred from the measured granulation contrast) of existing meter-class solar telescopes, as demonstrated here at wavelengths shorter than 400 nm and discussed in more detail in a previous separate publication We expect that some aspects of this AO system may also be of interest outside the solar community. [ABSTRACT FROM AUTHOR]

Despite its rich history of success in controlling powered prostheses and emerging commercial interests in ubiquitous computing, myoelectric control continues to suffer from a lack of robustness. In particular, EMG-based systems often degrade over prolonged use resulting in tedious recalibration sessions, user frustration, and device abandonment. Unsupervised adaptation is one proposed solution that updates a model's parameters over time based on its own predictions during real-time use to maintain robustness without requiring additional user input or dedicated recalibration. However, these strategies can actually accelerate performance deterioration when they begin to classify (and thus adapt) incorrectly, defeating their own purpose. To overcome these limitations, we propose a novel adaptive learning strategy, Context-Informed Incremental Learning (CIIL), that leverages in situ context to better inform the prediction of pseudo-labels. In this work, we evaluate these CIIL strategies in an online target acquisition task for two use cases: (1) when there is a lack of training data and (2) when a drastic and enduring alteration in the input space has occurred. A total of 32 participants were evaluated across the two experiments. The results show that the CIIL strategies significantly outperform the current state-of-the-art unsupervised high-confidence adaptation and outperform models trained with the conventional screen-guided training approach, even after a 45-degree electrode shift (p < 0.05). Consequently, CIIL has substantial implications for the future of myoelectric control, potentially reducing the training burden while bolstering model robustness, and leading to improved real-time control. [ABSTRACT FROM AUTHOR]

Multidimensional microdriving stage is one of the key components to realize precision driving and high-precision positioning. To meet nanometer displacement and positioning in the fields of micro-/nano-machining and precision testing, a new six-degree-of-freedom microdriving stage (6-DOF-MDS) of multilayer spatially distributed piezoelectric ceramic actuators (PZTs) is proposed and designed. The interior of the 6-DOF-MDS is a hollow design. The flexure hinge is used as the transmission mechanism, and the series–parallel hybrid driving of the corresponding PZTs achieves the microtranslation in the X, Y, and Z directions and the microrotation around the three axes of the microdriving stage, forming a microdisplacement mechanism with high rigidity and simple structure, which can realize the microfeed of 6-DOF. The force–displacement theory and lug boss structure optimization of the 6-DOF-MDS are analyzed, while the strength checking and natural frequency of the 6-DOF-MDS are also simulated by the finite element method. In addition, the real-time motion control system of the 6-DOF-MDS is designed based on Advanced RISC Machines. Through a series of verification experiments, the stroke and resolution results of the 6-DOF-MDS are obtained, where the displacements in the X, Y, and Z directions are 20.72, 20.02, and 37.60 μm, respectively. The resolution is better than 0.68 nm. The rotation angles around X, Y, and Z are 38.96″, 33.80″, and 27.87″, respectively, with an angular resolution of 0.063″. Relevant coupling experiments were also performed in this paper; in the full stroke linear running of X-axis, the maximum coupling displacements of the Y- and Z-axes are 1.04 and 0.17 μm, respectively, with the corresponding coupling rates of ∼5.0% and 0.8%. The maximum coupling angles for the X-, Y-, and Z-axes are 0.33″, 0.14″, and 2.30″, respectively. Considering the coupling of the 6-DOF-MDS, decoupling measures and specific mathematical models have also been proposed. The proposed multidimensional microdriving stage achieves subnanometer resolution and can be used for the precise positioning and attitude control of precision instruments at the nano-/subnanometer level. [ABSTRACT FROM AUTHOR]

Quantum thermal machines can generate steady-state entanglement by harvesting spontaneous interactions with local environments. However, using minimal resources and control, the entanglement is typically weak. Here, we study entanglement generation in a two-qubit quantum thermal machine in the presence of a continuous feedback protocol. Each qubit is measured continuously and the outcomes are used for real-time feedback to control the local system-environment interactions. We show that there exists an ideal operation regime where the quality of entanglement is significantly improved, to the extent that it can violate standard Bell inequalities and uphold quantum teleportation. In agreement with (Khandelwal et al 2020 New J. Phys. 22 073039), we also find, for ideal operation, that the heat current across the system is proportional to the entanglement concurrence. Finally, we investigate the robustness of entanglement production when the machine operates away from the ideal conditions. [ABSTRACT FROM AUTHOR]

Traditional methods for assessing the cleanliness of liquid metal are characterized by prolonged detection times, delays, and susceptibility to variations in sampling conditions. To address these limitations, an online cleanliness-analyzing system grounded in the method of the electrical sensing zone has been developed. This system facilitates real-time, in situ, and quantitative analysis of inclusion size and amount in liquid metal. Comprising pneumatic, embedded, and host computer modules, the system supports the continuous, online evaluation of metal cleanliness across various metallurgical processes in high-temperature environments. Tests conducted with gallium liquid at 90 °C and aluminum melt at 800 °C have validated the system's ability to precisely and quantitatively detect inclusions in molten metal in real time. The detection procedure is stable and reliable, offering immediate data feedback that effectively captures fluctuations in inclusion amount, thereby meeting the metallurgical industry's demand for real-time analyzing and control of inclusion cleanliness in liquid metal. Additionally, the system was used to analyze inclusion size distribution during the hot-dip galvanizing process. At a zinc melt temperature of 500 °C, it achieved a detection limit of 21 μm, simultaneously providing real-time data on the size and amount distribution of inclusions. This represents a novel strategy for the online monitoring and quality control of zinc slag throughout the hot-dip galvanizing process. [ABSTRACT FROM AUTHOR]

With the widespread use of new equipment such as distributed photovoltaics, distributed energy storage, electric vehicles, and distributed wind power, the control of low-voltage distribution networks (LVDNs) has become increasingly complex. Acquiring the most recent topological structure is essential for conducting accurate analysis and real-time control of LVDNs. The signal injection-based topology identification algorithm is favored for its speed and efficiency. This research introduces an innovative topology identification algorithm based on signal injection, specifically designed to address the challenges of incomplete and inaccurate identifications caused by the missing data in feature signal records (FSRs). Based on the correlations among FSRs at various devices, the algorithm introduces a dual-axis completion strategy—both vertical and horizontal—to effectively address missing data. Subsequently, an inclusion detection process is devised to process the completed FSRs, culminating in an accurate topology of LVDNs. Based on the study of actual LVDN data, the results indicate that the proposed algorithm markedly enhances the completeness and accuracy of topology identification. This advancement offers a robust solution tailored to accommodate the dynamic and swiftly changing topological configurations of LVDNs. [ABSTRACT FROM AUTHOR]

Controlled de-orbiting is crucial for a low earth orbiting (LEO) satellite or capsule intending to land in a desired location and to prevent damage to people and property on the ground caused by debris. Drag modulation is one possible mechanism to control de-orbiting, exploiting atmospheric drag variation to reduce the necessary orbital energy from the initial conditions to the re-entry interface. In this context, this paper proposes a novel targeted de-orbiting Artificial Neural Network (ANN) and drag-based guidance algorithm for a LEO artificial satellite. It relies on the combination of empirical and analytical relations with Deep Neural Networks (DNNs) to estimate the main parameters of the optimal control law in real time. The training set is composed of several optimal control solutions generated with a previously developed optimal control algorithm, exploiting the formulation in equinoctial modified orbital parameters to manage the large time scale of the problem and the computational cost. An innovative procedure for the training set generation led to the achievement of general results with a limited number of samples. The successful outcome of the guidance algorithm on over 1000 cases demonstrates its robustness and generalization of results. To conclude, a Linear Quadratic Regulator (LQR) feedback control is applied to one case to deal with a more realistic and uncertain density model. • Analytical optimal guidance for a satellite de-orbiting from Low Earth Orbit. • A novel approach based on Deep Neural Networks. • Prediction of the key parameters of the optimal control law in real time. • A Monte Carlo analysis showed the algorithm's robustness and generalization. • Successful feedback control thanks to a Linear Quadratic Regulator (LQR). [ABSTRACT FROM AUTHOR]

Conventionally, neutral beam injection (NBI) in tokamaks is controlled via engineering parameters such as injection voltage and power. Recently, the high-fidelity real-time NBI code RABBIT has been coupled to the discharge control system of ASDEX Upgrade. It allows to calculate the NBI fast-ion distribution and hence the properties of NBI in real-time, making it possible to control them directly. We successfully demonstrate control of driven current, ion heating and stored fast-ion energy by modifying the injected beam power. A combined ECRH and NBI controller is also successfully tested, which is able to adjust the heating mix between ECRH and NBI to match a certain desired ion heating fraction at given total power. Further experiments have been carried out towards control of the ion heat flux (i.e. ion heating plus collisional heat transfer between ions and electrons). They show good initial success, but also leave room for future improvements as the controller runs into instabilities at too high requests. [ABSTRACT FROM AUTHOR]

As an emerging technology, virtual coupling improves the efficiency and flexibility of metro services by forming multiple trains (units) as a virtually coupled train set (VCTS) without mechanical couplers. However, to realize the desired VCTS operation in practical metro services, a significant gap to be filled is that the implicit and nonlinear safety constraints are hard to be addressed in real‐time control. Thus, this paper proposes a hierarchical control approach with a two‐layer framework. In the upper layer, a trajectory planning method is designed, which addresses the safety constraints and prescribes a reference trajectory for each unit. In the lower layer, a model predictive control approach is constructed, by which each unit accurately tracks its reference trajectory in real time. Afterward, the proposed hierarchical control approach is employed to realize VCTS inter‐station operation in field tests. The average tracking error of speed is around 8 cm/s and the stopping error is less than 12 cm. The following distance between two units in VCTS is reduced to less than 105 m at the speed of 80 km/h, which is a breakthrough in the development of VCTS. [ABSTRACT FROM AUTHOR]

Background: Patient-based real-time quality control (PBRTQC) can be a valuable tool in clinical laboratories due to its cost-effectiveness and constant monitoring. More focus is placed on discovering and improving algorithms that compliment conventional internal control techniques. The practical implementation of PBRTQC with a biochemical instrument comparison is lacking. We aim to evaluate PBRTQC's efficacy and practicality by comparing low-density lipoprotein cholesterol (LDL-C) test results to ensure consistent real-time monitoring across biochemical instrumentations in clinical laboratories. Method: From 1 September 2021 to 30 August 2022, the First Affiliated Hospital of Xi'an Jiaotong University collected data from 158,259 both healthy and diseased patients, including 84,187 male and 74,072 female patients, and examined their LDL-C results. This dataset encompassed a group comprising 50,556 individuals undergoing health examinations, a group comprising 42,472 inpatients (IP), and a group comprising 75,490 outpatients (OP) for the PBRTQC intelligent monitoring platform to conduct daily tests, parameter configuration, program development, real-time execution, and performance validation of the patients' data. Moreover 40 patients' LDL-C levels were assessed using two biochemical analyzers, designated as the reference and comparator instruments. A total of 160 LDL-C results were obtained from 40 both healthy and diseased patients, including 14 OP, 16 IP, and 10 health examination attendees, who were selected to represent LDL-C levels broadly. Two biochemical instruments measured LDL-C measurements from the same individuals to investigate consistency and reproducibility across patient statuses and settings. We employed exponentially weighted moving average (EWMA) and moving median (MM) methods to calculate inter-instrument bias and ensure analytical accuracy. Inter-instrument bias for LDL-C measurements was determined by analyzing fresh serum samples, different concentrations of quality control (QC), and commercialized calibrators, employing both EWMA and MM within two assay systems. The assessment of inter-instrumental bias with five different methods adhered to the external quality assessment standards of the Clinical Laboratory Center of the Health Planning Commission, which mandates a bias within ±15.0%. Result: We calculated inter-instrument comparison bias with each of the five methods based on patient big data. The comparison of fresh serum samples, different concentrations of QC, commercialized calibrators, and EWMA were all in the permissive range, except for MM. MM showed that the bias between two biochemical instruments in the concentration ranges of 1.5 mmoL/L–6.2 mmoL/L exceeded the permissible range. This was mainly due to the small number of specimens, affected by variations among individual patients, leading to increased false alarms and reduced effectiveness in monitoring the consistency of the inter-instrumental results. Moreover, the inter-comparison bias derived from EWMA was less than 3.01%, meeting the 15% range assessment criteria. The bias result for MM was lower than 24.66%, which was much higher than EWMA. Thus, EWMA is better than MM for monitoring inter-instrument comparability. PBRTQC can complement the use of inter-comparison bias between biochemical analyzers. EWMA has comparable inter-instrument comparability monitoring efficacy. Conclusions: The utilization of AI-based PBRTQC enables the automated real-time comparison of test results across different biochemical instruments, leading to a reduction in laboratory operating costs, enhanced work efficiency, and improved QC. This advanced technology facilitates seamless data integration and analysis, ultimately contributing to a more streamlined and efficient laboratory workflow in the biomedical field. [ABSTRACT FROM AUTHOR]

Motion recognition (MR)‐based somatosensory interaction technology, which interprets user movements as input instructions, presents a natural approach for promoting human‐computer interaction, a critical element for advancing metaverse applications. Herein, this work introduces a non‐intrusive muscle‐sensing wearable device, that in conjunction with machine learning, enables motion‐control‐based somatosensory interaction with metaverse avatars. To facilitate MR, the proposed device simultaneously detects muscle mechanical activities, including dynamic muscle shape changes and vibrational mechanomyogram signals, utilizing a flexible 16‐channel pressure sensor array (weighing ≈0.38 g). Leveraging the rich information from multiple channels, a recognition accuracy of ≈96.06% is achieved by classifying ten lower‐limb motions executed by ten human subjects. In addition, this work demonstrates the practical application of muscle‐sensing‐based somatosensory interaction, using the proposed wearable device, for enabling the real‐time control of avatars in a virtual space. This study provides an alternative approach to traditional rigid inertial measurement units and electromyography‐based methods for achieving accurate human motion capture, which can further broaden the applications of motion‐interactive wearable devices for the coming metaverse age. [ABSTRACT FROM AUTHOR]

LoRaWAN has become the technology of choice for increasing Internet of Things applications owing to its long range and low power consumption characteristics. However, in the uplink confirmed messaging cases, the entire retransmission could take several seconds, so it cannot be used in scenarios that require rapid confirmed messaging, such as emergency alerting and real-time controlling applications. Nevertheless, there has been limited work targeting this issue. This study presents a novel LoRaWAN gateway using edge computing to expedite the confirmed messaging process by generating the acknowledgment (ACK) locally, so that the confirmed messaging time can be significantly reduced. Additionally, the resource utilization of the network server can also be decreased due to the use of edge computing. We verified the effectiveness of our solution through extensive simulations and experiments. The confirmed messaging time between the end nodes and the gateway averaged 43 ms for a maximum of 2 retransmissions. With the adoption of edge computing on the gateway, the network server's central processing unit (CPU), memory, and bandwidth peak utilization decrease from 53.51 to 39.46, 73.88 to 72.11%, and 4422.68 kbps to 3271.27 kbps, respectively. In addition, the network server's system load decreases from 2.15 to 1.69, while the gateway cost is reduced by almost $ 38 compared to the benchmark products. [ABSTRACT FROM AUTHOR]

In this paper, we propose a broadband tunable acoustic matching layer (BTAML) comprising an array of piezoelectric elements with non-uniform gradient shunt circuits (NGSCs). The effective impedance of the BTAML can be controlled in real time by regulating the parameters of NGSCs. The theoretical results demonstrate that BTAML is capable of adjusting impedance from 1.5 to 20 MRayl and has a broad bandwidth compared with the traditional matching layer. Furthermore, we experimentally verified the acoustic transmission property of the BTAML, and good agreement was achieved with numerical simulations. The approach can significantly promote research on tunable acoustic matching and offer effective impedance matching layers with a broad bandwidth in industrial applications. [ABSTRACT FROM AUTHOR]

Hydrogen masters have high short-term stability, using them as master clocks can generate high-precision local standard time in a short period of time. However, the hydrogen maser has a frequency drift phenomenon, which leads to its poor long-term stability, thus affecting the accuracy of the local time. The rotation of the millisecond pulsar is highly stable. With the help of its high long-term stability, it can regularly control the frequency of the hydrogen atomic clock, thus control the real-time signal. The stability of four millisecond pulsars in the second data set released by the International Pulsar Timing Array (IPTA) is analyzed. At the same time, the frequency stability of a hydrogen maser of the National Time Service Center of the Chinese Academy of Sciences (NTSC) is analyzed by Hadamard variance. Finally, a strategy for steering the frequency of hydrogen master using pulsars is given. [ABSTRACT FROM AUTHOR]

Power electronic systems are rapidly evolving; thus, effective prototyping methods are required to test control algorithms and assess system performance prior to hardware implementation. This research suggests a Model-In-The-Loop (MIL), Software-In-The-Loop (SIL), and Processor-In-The-Loop (PIL) methodologies-based complete prototyping strategy for flyback converters using the TI F28069M Launchpad kit. These techniques can be combined to evaluate control strategies accurately and quickly, speeding up design cycles and enhancing system reliability. The proposed prototyping platform is presented in this work, along with a thorough explanation of each prototyping stage and its associated advantages. The effectiveness of the suggested approach for 50W rated power flyback converter in terms of quick algorithm development, system simulation, real-time control implementation and controller design accuracy is analyzed and shown by experimental results. The results show that the performance of the designed controller for the flyback converter is almost the same as in MIL and SIL implementation in terms of the overshoot and settling time in the reference voltage tracking. On the other hand, in the PIL implementation, the overshoot performance of the controller deviates by 1.18% more than in SIL and MIL implementation. These also confirm that the flyback control system's performance is reliable and effective during all phases of development. [ABSTRACT FROM AUTHOR]

In order to meet the increasing demand for high‐speed joining processes, laser beam micro welding is used to reproducibly weld metallic joining partners such as steel. Long weld seams or high cycle speeds, however, remain a challenge to achieve a constant welding depth throughout the entire welding process without major fluctuations. This paper shows the development and evaluation of a weld penetration depth control system based on interferometric measurement of the depth of the vapor capillary. High path speeds and small weld depths are challenging for real‐time depth control. Therefore, the offline data of the interferometric measurements are used to implement an iterative learning control of the weld penetration depth. The quality of the control is verified by welding with and without spatial power modulation on 1.4301 steel while following a linear and sinusoidal trajectory. [ABSTRACT FROM AUTHOR]

This study illustrates at‐line application of hyperspectral imaging in the visible range for quality control of large‐scale offset printing. In particular, the measurement stability of a competitive device is assessed and compared to traditional handheld and desktop spectrophotometers. The performance of the commercially available instruments was assessed based on collected spectra and their corresponding L*, a*, and b* values. The printing process was described by hyperspectral images (in visible range) of selected regions from template color fields acquired at 17 sampling occasions. Spectra constituting hyperspectral images were visualized and evaluated in the space of significant principal components obtained from the principal component analysis. Furthermore, confidence ellipses were constructed for each set of spectra characterizing a specific moment of the printing process. Comparing their mutual locations, shapes, orientations, and sizes enabled effective visualization of process variability and was more comprehensive regarding the classic approach based on information provided by desktop and handheld spectrometers. This study demonstrates the effectiveness of hyperspectral imaging for real‐time quality control in large‐scale offset printing, in particular by studying sampling distributions. By assessing measurement stability and comparing competitive devices with traditional spectrophotometers, it reveals the advantages of hyperspectral imaging in measurement speed, spatial dimension inclusion, and process variability visualization. Chemometric analysis aids in effective color assessment, while local segmentation can offer insights into color perception, crucial for process monitoring and detection of various sources of variation. [ABSTRACT FROM AUTHOR]

The accurate online detection of laser welding penetration depth has been a critical problem to which the industry has paid the most attention. Aiming at the laser welding process of TC4 titanium alloy, a multi-sensor monitoring system that obtained the keyhole/molten pool images and laser-induced plasma spectrum was built. The influences of laser power on the keyhole/molten pool morphologies and plasma thermo-mechanical characteristics were investigated. The results showed that there were significant correlations among the variations of the keyhole–molten pool, plasma spectrum, and penetration depth. The image features and spectral features were extracted by image processing and dimension-reduction methods, respectively. Moreover, several penetration depth prediction models based on single-sensor features and multi-sensor features were established. The mean square error of the neural network model built by multi-sensor features was 0.0162, which was smaller than that of the model built by single-sensor features. The established high-precision model provided a theoretical basis for real-time feedback control of the penetration depth in the laser welding process. [ABSTRACT FROM AUTHOR]

Current real-time direction judgment systems are inaccurate and insensitive, as well as limited by the sampling rate of analog-to-digital converters. To address this problem, we propose a dynamic real-time direction judgment system based on an integral dual-frequency laser interferometer and field-programmable gate array technology. The optoelectronic signals resulting from the introduction of a phase subdivision method based on the amplitude resolution of the laser interferometer when measuring displacement are analyzed. The proposed system integrates the optoelectronic signals to increase the accuracy of its direction judgments and ensures these direction judgments are made in real time by dynamically controlling the integration time. Several experiments were conducted to verify the performance of the proposed system. The results show that, compared with current real-time direction judgment systems, the proposed system makes accurate judgements during low-speed motions and can update directions within 0.125 cycles of the phase difference change at different speeds. Moreover, a sweep frequency experiment confirmed the system's ability to effectively judge dynamic directions. The proposed system is capable of accurate and real-time directional judgment during low-speed movements of a table in motion. [ABSTRACT FROM AUTHOR]

A vehicle system motion decoupling control method was proposed to address challenges in controlling articulated heavy vehicles (AHVs). The method, based on differential geometry theory, focused on distributed electric drive AHVs. Its objective was to separate the highly nonlinear and strongly coupled dynamics system into two relatively independent subsystems: longitudinal and lateral motions. Additionally, a robust controller was designed to improve the vehicle's resistance to external disturbances like side winds. Simulation tests using a TruckSim model of a distributed electric drive AHV show significant improvements compared to vehicles without decoupling control. The rearward amplification (RA) is reduced by 4.5%, the longitudinal velocity deviation by 67.5%, and the yaw rate deviation by 69.7%. The vehicle also demonstrates enhanced stability when subjected to strong breeze disturbances. To validate the control performance in real-time systems, the hardware-in-the-loop tests were conducted, which confirms the effectiveness of the proposed control approach in practical applications. [ABSTRACT FROM AUTHOR]

Optimization of the spatial distribution of green infrastructures (GIs) was performed for a combined sewer system located in the Province of Quebec, Canada, using a simulation-optimization tool with the aim of reducing seasonal combined sewer overflows (CSOs). The performance of four CSOs control alternatives involving the individual and integrated implementation of GIs with storage tanks and real time control (RTC) of the sewer was evaluated for a nine-year simulation period of historical rainfall data and for 20%-increased rainfall data (representative of potential climate change impact). The integration of GIs with RTC of the sewer (with or without storage tanks) lowered the total CSO volume by 95% to 99% under historical rainfall data and by 93% to 96% under increased rainfall intensities when compared to the reference scenario. Adapting GI's number and location for optimal CSO control rather than according to space availability criteria reduced CSO frequency but had only a slight impact on CSO volume reduction. [ABSTRACT FROM AUTHOR]

In this paper, a recursive d -step-ahead predictive control scheme based on multi-dimensional Taylor network (MTN) is proposed for the real-time tracking control of multiple-input multiple-output (MIMO) nonlinear systems with input time-delay. The MTN predictive model is designed using a recursive approach to compensate the influence of time-delay, and an extended Kalman filter (EKF) is applied as its learning algorithm. An MTN controller is developed based on a proportional–integral–derivative (PID) controller where the closed-loop errors between the reference input and the system output are set as the MTN controller's inputs. Then, a back propagation (BP) algorithm, designed to update its weights according to errors caused by system uncertainty, is used as a learning algorithm for the MTN controller. Meanwhile, the convergence of the MTN predictive model and the stability of the closed-loop system are evaluated. Two numerical examples and a practical example – continuous stirred tank reactor (CSTR) process are presented to verify the superiority of the proposed scheme. The experimental results and the computational complexity analysis show that the proposed scheme is effective, promising its desirable robustness, anti-disturbance, tracking and real-time performance. [ABSTRACT FROM AUTHOR]

Motivation Many organisms' survival and behavior hinge on their responses to environmental signals. While research on bacteria-directed therapeutic agents has increased, systematic exploration of real-time modulation of bacterial motility remains limited. Current studies often focus on permanent motility changes through genetic alterations, restricting the ability to modulate bacterial motility dynamically on a large scale. To address this gap, we propose a novel real-time control framework for systematically modulating bacterial motility dynamics. Results We introduce MotGen, a deep learning approach leveraging Generative Adversarial Networks to analyze swimming performance statistics of motile bacteria based on live cell imaging data. By tracking objects and optimizing cell trajectory mapping under environmentally altered conditions, we trained MotGen on a comprehensive statistical dataset derived from real image data. Our experimental results demonstrate MotGen's ability to capture motility dynamics from real bacterial populations with low mean absolute error in both simulated and real datasets. MotGen allows us to approach optimal swimming conditions for desired motility statistics in real-time. MotGen's potential extends to practical biomedical applications, including immune response prediction, by providing imputation of bacterial motility patterns based on external environmental conditions. Our short-term, in-situ interventions for controlling motility behavior offer a promising foundation for the development of bacteria-based biomedical applications. Availability and implementation MotGen is presented as a combination of Matlab image analysis code and a machine learning workflow in Python. Codes are available at https://github.com/bgmseo/MotGen , for cell tracking and implementation of trained models to generate bacterial motility statistics. [ABSTRACT FROM AUTHOR]

Bioretention is a type of stormwater best management practice that can reduce stormwater runoff volume, and with appropriate design, simultaneously reduce nitrogen loads. To meet the water quality potential of bioretention systems, improved simulation models of the transport and transformations of nitrogen are needed. In this work, two versions of a three-nitrogen-pool (3P) model (3P-1 and 3P-m) are applied to simulate observed nitrogen transport through a field bioretention system. The 3P models provide predictions of net export of dissolved organic carbon, dissolved organic nitrogen, and inorganic nitrogen species. The 3P-m model has promising results: (1) relative to the Storm Water Management Model (SWMM), it reduced the prediction error of percent removal of total dissolved nitrogen by up to 10.6% and reduces the scaled root-mean square error of total dissolved nitrogen loads by up to 53.0%; (2) it successfully captured the correlation between impactful environmental factors and event mean concentrations of total dissolved nitrogen and nitrate nitrogen in underdrain effluent; (3) its predictions on denitrification showed good alignment with observed isotope results; and (4) it demonstrated higher stability in simulated time series among calibration results than 3P-1. The 3P-m is expected to assist the design of bioretention system and valve control strategies by providing more accurate predictions on nitrogen removal under the impacts of soil moisture content and temperature. Future research and collaboration opportunities are also proposed in this article. [ABSTRACT FROM AUTHOR]

• Developed mandatory lane change models to various intelligent connected vehicles environments employing game theory. • Model demonstrated exceptional predictive accuracy for both lane changes and waiting events. • Predicted lane change positions varied significantly based on different intelligent connected vehicles environments. • Discrepancies in lane change duration were notably influenced by diverse game strategies. In the environment of intelligent connected vehicles, drivers are capable of making wiser and safer decisions. However, the interaction between drivers and vehicle systems has undergone changes in the intelligent connected vehicles environment, leading to a decrease in the applicability of existing microscopic driving models, such as the mandatory lane change model, which requires reevaluation or improvement. Therefore, to investigate the influence of different intelligent connected vehicles environments on the microscopic mandatory lane-changing model, this study developed three interaction systems to characterize different intelligent connected vehicles environments: the baseline, warning group, and guidance group. The Baseline provides basic information, the warning group adds icons of preceding vehicles and real-time headway information, while the guidance group further includes speed and voice guidance functions. The baseline describes the traditional environment, while the other two groups describe the intelligent connected vehicles environment. Using a self-developed intelligent connected vehicle testing platform, we conducted driving simulation experiments with 43 participants at the interchange merging area of a highway. This study, grounded in game theory, establishes function models for participants, strategies, and payoff functions in the mandatory lane-changing process. Utilizing data from driving simulation experiments, the parameters of the dual-layered planning model are calibrated. Evaluation of the constructed model is conducted through confusion matrices and lane-changing spatiotemporal characteristic indicators. The results demonstrate satisfactory predictive performance of the baseline group model, warning group model, and guidance group model across different intelligent connected vehicles environments. Specifically, compared to existing literature, the baseline group model exhibits improvements of 7 % and 2 % respectively in overall lane-changing detection accuracy by drivers. The warning group model shows improvements of 2.9 % and 1.7 %, while the guidance group model exhibits improvements of 5.1 % and 4.3 %. Additionally, the baseline group model reduces the mean absolute error in predicting different game strategies by 16.7 % and 5.6 % respectively compared to existing literature. Concerning lane-changing position prediction, the warning and guidance group models demonstrate minimal errors, whereas the baseline group model exhibits good consistency in predicting lane-changing duration. Furthermore, both the warning and guidance group models show some delay in predicting lane-changing duration. While intelligent connected vehicles environments significantly influence the prediction of lane-changing positions, they do not significantly affect the prediction of lane-changing duration. However, game strategies significantly impact the prediction of lane-changing duration but do not significantly affect the prediction of lane-changing positions. The study findings offer valuable insights into micro lane-changing behaviors of drivers in intelligent connected vehicles environments, bearing crucial significance for the in-depth investigation of real-time control and guidance strategies for vehicles in merge areas of highways under intelligent connected vehicles conditions. [ABSTRACT FROM AUTHOR]

Autonomous control systems can enhance the economic viability of complex systems such as nuclear microreactors. Advanced forms of these control systems benefit from techniques for high-resolution field data reconstruction from sensor data. Basis projection methods present a useful set of methodologies for nonparametric reconstruction of these field data. In this work, high fidelity multiphysics simulations are used to generate microreactor temperature distributions that are then used to evaluate the viability of a basis projection method which employs a generalized set of basis functions, or dictionaries. Additionally, metrics for evaluating the accuracy of the temperature distribution are presented that are motivated both from the fields of traditional signal processing and statistics. The performance of a method based on matching pursuits is compared to a method that uses the sequential ordering of basis functions. The matching pursuits-based method gives improved performance for highly sparse representations of temperature distributions. Additionally, accuracy is analyzed for multiple temperature distributions which represent different potential reactor operation configurations. Overall, the results from this work should be considered as an upper bound on the performance of basis projection methods which employ generalized basis functions for real time reactor control. • Autonomous systems enhance nuclear microreactor economic viability. • Basis projection methods aid in nonparametric field data reconstruction. • Matching pursuits-based method outperforms sequential basis function method. • Theoretical foundation provided for evaluating alternative bases. • Generalized bases evaluated for nuclear reactor field reconstruction. [ABSTRACT FROM AUTHOR]

Using a combined approach of molecular dynamics simulation and density functional theory, we develop a phosphorene nanopore to realize the tunable H2 sieving from mixtures with CH4 or CO2 via introducing the in-plane tensile strain. Our results show that 0%–10% strains exerted on the phosphorene membrane ensures a fast permeation of H2 while completely prohibiting the passage of CH4, demonstrating high efficiency and selectivity. Thanks to the outstanding mechanical flexibility of phosphorene, the strain tension can be utilized to easily control the pore size by which the permeance speed of H2 can be controlled in real time. However, all strained pores allow the passage of CO2, indicating a weaker strain regulation for H2/CO2 sieving by the phosphorene pore. Density functional theory calculations further confirm that the transport of H2 is energetically more favorable than CH4 and CO2 to traverse all phosphorene pores. Our findings exploit a flexible phosphorene membrane for real-time tunable H2/CH4 separation by controlling the in-plane strain. [ABSTRACT FROM AUTHOR]

Printing metallic nanoscale features remains challenging, but particularly important for making electronic devices. In existing techniques, complex operation, and material parameters significantly limit printing resolution, materials development, and real‐time control. Alternatively, aerosols and ions are synergized to print nanoscale metals, but the ions turn out to be instable, thereby making the printing lose the ability in controlling feature sizes. Herein electrically biased parallel planes are utilized to configure electric fields for programmable and parallel printing of aerosol nanoparticles into high‐purity (>98.5 wt.%) nanoscale features in arrays over large areas (≈mm2). By applying different potentials to three horizontal parallel planes, with the middle plane containing periodic circular perforations, the fields are configured into repeating "funnels" with sizes controllable at both ends. Charged nanoparticles are then subjected to a gas flow orthogonal to the field assembling them to 3D nanoarchitectures. The "funnels" size is predictable and used to control the feature sizes. The printed nanopillars are modulated from 67 to 743 nm in diameters with aspect ratios up to 25 and show excellent mechanical and electrical properties. The programmable and parallel nanoprinting of metallic nanoscale features offers an enabling technology for accelerating the development of next‐generation high‐frequency ultra‐large scale integrated electronic devices. [ABSTRACT FROM AUTHOR]

There are many fields where it is of interest to measure the elastic moduli of tiny fragile fibers, such as filamentous bacteria, actin filaments, DNA, carbon nanotubes, and functional microfibers. The elastic modulus is typically deduced from a sophisticated tensile test under a microscope, but the throughput is low and limited by the timeconsuming and skill-intensive sample loading/unloading. Here, we demonstrate a simple microfluidic method enabling the high-throughput measurement of the elastic moduli of microfibers by rope coiling using a localized compression, where sample loading/unloading are not needed between consecutive measurements. The rope coiling phenomenon occurs spontaneously when a microfiber flows from a small channel into a wide channel. The elastic modulus is determined by measuring either the buckling length or the coiling radius. The throughput of this method, currently 3,300 fibers per hour, is a thousand times higher than that of a tensile tester. We demonstrate the feasibility of the method by testing a nonuniform fiber with axially varying elastic modulus. We also demonstrate its capability for in situ inline measurement in a microfluidic production line. We envisage that high-throughput measurements may facilitate potential applications such as screening or sorting by mechanical properties and real-time control during production of microfibers. [ABSTRACT FROM AUTHOR]

This paper is a review of the results on the real-time optimal control problem for linear systems obtained by the Minsk school by mathematical methods of optimal control. We consider optimal control problems for dynamical objects, their deterministic mathematical models and perfect measurements of states, objects with disturbances and imperfect measurements of the observed input and output signals, the problem of optimal decentralized control of groups of interconnected dynamical objects, and application of real-time optimal control problems and the control principle to solving stabilization problems. [ABSTRACT FROM AUTHOR]

In order to achieve an automatic monitoring system for the operation status of motor equipment, the author proposes a research and application based on the thermal energy recovery and automatic monitoring system of motor equipment. The author first analyzes the operating characteristics of the motor and collects four basic parameters of the motor, namely voltage, current, speed, and temperature, in order to determine whether the motor is in normal working condition. Secondly, the system combines electrical parameter detection and acquisition, wireless network technology, industrial field bus technology, and computer user software to achieve remote real-time monitoring, control, and management of the motor's remote travel status. This enables the entire system to achieve intelligent, networked, and systematic management, solving the technical problem of real-time control and system management that cannot be achieved in motor systems distributed in multiple locations. Finally, the monitoring results indicate that after installation and debugging, the data is displayed on the upper computer. This effect is the data collection of the motor during low speed operation, which is reflected in real-time on the control panel. The actual parameters of the motor were compared with the test parameters of this system, and the test basically achieved the desired effect, with accurate detection data. [ABSTRACT FROM AUTHOR]

This study presents a data-driven assisted real-time optimization model which is an innovative approach to address the challenges posed by integrating Submerged Arc Furnace (SAF) systems with renewable energy sources, specifically photovoltaic (PV) and wind power, with modern intelligent energy terminals. Specifically, the proposed method is divided into two stages. The first stage is related to data-driven prediction for addressing local time-varying renewable energy and electricity market prices with predicted information, and the second stage uses an optimization model for real-time SAF dispatch. Connections between intelligent energy terminals, demand-side devices, and load management systems are established to enhance local renewable resource utilization. Additionally, mathematical formulations of the operating resistance in SAF are explored, and deep neuron networks are employed and modified for dynamic uncertainty prediction. The proposed approach is validated through a case study involving an intelligent energy terminal with a 12.5 MVA SAF system and 12 MW capacity renewable generators in an electricity market with fluctuating prices. The findings of this research underscore the efficacy of the proposed optimization model in reducing operational costs and enhancing the utilization of localized renewable energy generation. By integrating four distinct dissatisfaction coefficients into the optimization framework, we demonstrate the model's adaptability and efficiency. The application of the optimization strategy delineated herein results in the SAF system's profitability oscillating between $111 and $416 across various time intervals, contingent upon the coefficient settings. Remarkably, an aggregate daily loss recovery amounting to $1,906.84 can be realized during the optimization period. Such outcomes not only signify considerable economic advantages but also contribute to grid stability and the diminution of renewable energy curtailment, thereby underscoring the dual benefits of economic efficiency and sustainability in energy management practices. [ABSTRACT FROM AUTHOR]