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Stability of a standalone hybrid power system (HPS) in a smart grid is always a challenging task. Further, the operational stability of the power system depends on the associated communication infrastructure. Therefore, it is always crucial to pick up a controller that can assure system's stability along with performance, despite disturbances like (load and input wind variations) with communication delays. Present study focuses on reactive power management and voltage stability issues of an isolated HPS. The stability aspects of HPS are improved through reactive power compensation, by custom power devices like static var compensator. The control aspects of SVC as well as the whole hybrid system are taken care by H∞ linear matrix inequalities approach. Further, H-infinity control, Lyapunov stability along with linear matrix inequalities techniques estimate the delay boundary of controllers. The iterative performance of the proportional–integral–derivative controllers, and robust H∞ damping controller of the HPS, are designed through LMI approach. Later experimental study of the HPS is done, with a prototype model in dSPACE real-time control environment. In this case, dSPACE 1104 is added for data acquisition and control. Adaptability and robustness of the proposed controllers are verified under fluctuating loads and uncertain wind power input.

Since the advent of digital coding metamaterials, a new paradigm is unfolded to sample, compute and program electromagnetic waves in real time with one physical configuration. However, one inconvenient truth is that actively tunable building blocks such as diodes, varactors, and biased lines must be individually controlled by a computer‐assisted field programmable gate array and physically connected by electrical wires to the power suppliers. This issue becomes more formidable when more elements are needed for more advanced and multitasked metadevices and metasystems. Here, a remote‐mode metasurface is proposed and realized that is addressed and tuned by illuminating light. By tuning the intensity of light‐emitting diode light, a digital coding metasurface composed of such light‐addressable elements enables dynamically reconfigurable radiation beams in a control‐circuitry‐free way. Experimental demonstration is validated at microwave frequencies. The proposed dynamical remote‐tuning metasurface paves a way for constructing unprecedented digital metasurfaces in a noncontact remote fashion. A remote‐mode metasurface addressed and tuned by the illuminating light is proposed and realized. By tuning the intensity of illuminating light, a digital coding metasurface composed of such light‐addressable elements enables dynamically reconfigurable radiation beams in a control‐circuitry‐free way. The proposed dynamical remote‐tuning metasurface paves a way for constructing unprecedented digital metasurfaces in a noncontact remote fashion.

Traffic congestion is a major problem in today’s society, and the intersection, as an important hub of urban traffic, is one of the most common places to produce traffic congestion. To alleviate the phenomenon of congestion at urban traffic intersections and relieve the traffic pressure at intersections, this paper takes the traffic flow at intersections as the research object and adopts the swarm intelligent algorithm to establish an optimization model of intersection traffic signal timing, which takes the average delay time of vehicles, the average number of stops of vehicles and the traffic capacity as the evaluation indexes. This model adjusts the intersection traffic signal timing intelligence according to the real-time traffic flow and carries out simulation experiments with MATLAB. Compared with the traditional timing schemes, the average delay time of vehicles is reduced by 10.25%, the average number of stops of vehicles is reduced by 24.55%, and the total traffic capacity of the intersection is increased by 3.56%, which verifies that the scheme proposed in this paper is effective in relieving traffic congestion.

Traditional methods for optimizing light source emissions rely on a time‐consuming trial‐and‐error approach. While in situ optimization of light source gain media emission during growth is ideal, it has yet to be realized. In this work, in situ reflection high‐energy electron diffraction (RHEED) is integrated with machine learning (ML) to correlate the surface reconstruction with the photoluminescence (PL) of InAs/GaAs quantum dots (QDs), which serve as the active region of lasers. A lightweight ResNet‐GLAM model is employed for the real‐time processing of RHEED data as input, enabling effective identification of optical performance. This approach guides the dynamic optimization of growth parameters, allowing real‐time feedback control to adjust the QDs emission for lasers. InAs QDs on GaAs substrates are successfully optimized, with a 3.2‐fold increase in PL intensity and a reduction in full width at half maximum (FWHM) from 36.69 to 28.17 meV. Automated, in situ self‐optimized lasers with 5‐layer InAs QDs achieved electrically pumped continuous‐wave operation at 1240 nm with a low threshold current of 150 A cm− 2 at room temperature, an excellent performance comparable to samples grown through traditional manual multi‐parameter optimization methods. These results mark a significant step toward intelligent, low‐cost, and reproductive light emitters production. This work presents a machine learning‐driven approach integrated with in situ high‐energy electron diffraction (RHEED) monitoring to automate the optimization of quantum dot (QD) growth for lasers. A 3.2‐fold increase in photoluminescence intensity is achieved alongside a narrow spectral linewidth. The QD lasers fabricated demonstrate efficient lasing at 1240 nm with low threshold currents, highlighting a scalable alternative to manual optimization.

In the period 2015-2017, the City of Cape Town, South Africa, faced the possibility of taps running dry due to a prolonged drought. To mitigate the impacts of water scarcity, many households installed rainwater tanks to harvest water to use for non-potable purposes such as toilet flushing and washing. The installation of the rainwater tanks was mainly arbitrary, in response to perceived impact of water scarcity rather than a systematic needs assessment. This study was thus undertaken to determine the available opportunity to optimise the use of these rainwater tanks using real-time control (RTC) techniques. Many studies have demonstrated the potential of rainwater harvesting (RWH) systems to supplement potable water supply and minimize stormwater flows to downstream drainage networks. RTC technology can be used to enhance the performance of RWH systems in achieving these two objectives, by receiving a rainfall forecast and initiating pre-storm release in real time. In this study, RTC was applied on the RWH system at the New Engineering Building, University of Cape Town (UCT) to enhance water supply and increase rainwater retention period. The performance with RTC was compared with the conventional management of the RWH system. It was determined that RWH with RTC technology was generally superior in simultaneously achieving water supply and rainwater retention benefits compared to the conventional management approach. RTC provides an active operation which optimizes the performance of the system across varying conditions but requires an assiduous management process designed to meet set objectives. It was concluded that the active release mechanism employing RTC exhibited great potential; the system opens up the possibility of delivering a more robust and reliable system due to its ability to provide failure detection and centralised control. The system can readily be adapted to variation of local climatic conditions in the short and long term.

Control theory underpins the stabilization of dynamic systems, including cardiac tissue, where disruptions in electrical conduction cause arrhythmias. Current treatments either act rapidly but without precision or deliver targeted interventions that cannot adapt in real time. We present an integrated platform combining optical voltage mapping (OVM), machine learning (ML), and optogenetics for autonomous, real‐time detection and correction of cardiac rhythm disorders in vitro. OVM provides high‐resolution membrane potential visualization; the ML module identifies arrhythmic events and drives microLED‐based light patterns restoring normal conduction; and optogenetics enables light‐based modulation of excitable cells. This integration of electrical, optical, and bioelectrical domains through a unified computational control layer enables adaptive, closed‐loop rhythm stabilization, a significant advance in real‐time electrophysiological interventions. Because inference and actuation run in real time on modest hardware, the same control loop could be embedded into miniaturized devices or microcontrollers, accelerating the transition from in‐vitro to in‐vivo automated rhythm management. We are able to stop dangerous heart‐rhythm spirals before they fully form. Within about 100 ms, it pinpoints the spiral's tiny central tip (≈0.9 mm) using light‐based sensing and machine learning, then shines targeted light to shut it down. This fast, precise, closed‐loop approach detects, targets, and terminates arrhythmias in real time.

Severe disturbances in a power network can cause the system frequency to exceed the safe operating range. As the last defensive line for system emergency control, under frequency load shedding (UFLS) is an important method for preventing a wide range of frequency excursions. This paper proposes a hierarchical UFLS scheme of “centralized real-time decision-making and decentralized real-time control” for inter-connected systems. The centralized decision-layer of the scheme takes into account the importance of the load based on the equivalent transformation of kinetic energy (KE) and potential energy (PE) in the transient energy function (TEF), while the load PE is used to determine the load shedding amount (LSA) allocation in different loads after faults in real-time. At the same time, the influence of inertia loss is considered in the calculation of unbalanced power, and the decentralized control center is used to implement the one-stage UFLS process to compensate for the unbalanced power. Simulations are carried out on the modified New England 10-generator 39-bus system and 197-bus system in China to verify the performance of the proposed scheme. The results show that, compared with other LSA allocation indicators, the proposed allocation indicators can achieve better f nadir and t d . At the same time, compared with other multi-stage UFLS schemes, the proposed scheme can obtain the maximum f nadir with a smaller LSA in scenarios with high renewable energy sources (RES) penetration.

Real-time multisensory social interactions (RMSIs) between people are at the center of the metaverse, a new computer-mediated environment consisting of virtual “worlds” in which people act and communicate with each other in real-time via avatars. This research investigates whether RMSIs in the metaverse, when accessed through virtual-reality headsets, can generate more value for interactants in terms of interaction outcomes (interaction performance, evaluation, and emotional responses) than those on the two-dimensional (2D) internet (e.g., Zoom meetings). We combine theoretical logic with extensive field-experimental probes (which support the value-creation potential of the virtual-reality metaverse, but contradict its general superiority) to develop and refine a framework of how RMSIs in the metaverse versus on the 2D internet affect interaction outcomes through interactants’ intermediate conditions. The refined framework serves as foundation for a research roadmap on RMSIs in the metaverse, in which we highlight the critical roles of specific mediating and moderating forces along with interactional formats for future investigations of the metaverse and also name key business areas and societal challenges that deserve scholarly attention.

Background It is critical for laboratories to conduct multianalyzer comparisons as a part of daily routine work to strengthen the quality management of test systems. Here, we explored the application of patient‐based real‐time quality controls (PBRTQCs) on comparative assays to monitor the consistency among clinical laboratories. Methods The present study included 11 commonly tested analytes that were detected using three analyzers. PBRTQC procedures were set up with exponentially weighted moving average (EWMA) algorithms and evaluated using the AI‐MA artificial intelligence platform. Comparative assays were carried out on serum samples, and patient data were collected. Patients were divided into total patient (TP), inpatient (IP), and outpatient (OP) groups. Results Optimal PBRTQC protocols were evaluated and selected with appropriate truncation limits and smoothing factors. Generally, similar comparative assay performance was achieved using both the EWMA and median methods. Good consistency between the results from patients' data and serum samples was obtained, and unacceptable bias was detected for alkaline phosphatase (ALP) and gamma‐glutamyl transferase (GGT) when using analyzer C. Categorizing patients' data and applying specific groups for comparative assays could significantly improve the performance of PBRTQCs. When monitoring the inter‐ and intraanalyzer stability on a daily basis, EWMA was superior in detecting very small quality‐related changes with lower false‐positive alarms. Conclusions We found that PBRTQCs have the potential to efficiently assess multianalyzer comparability. Laboratories should be aware of population variations concerning both analytes and analyzers to build more suitable PBRTQC protocols. Optimal PBRTQC procedures demonstrated its effective role in monitoring test comparability and stability in laboratories. The performance of PBRTQC in comparative assays could be better improved by categorizing patients and considering sample sizes especially for analytes with wide biological variations. The present study pioneers to take PBRTQC in practical and we believe that it makes great sense to improve the management of healthcare qualities by expanding the application of PBRTQC on daily analyzer comparisons in clinical laboratories.

Background Patient‐based real‐time quality control (PBRTQC) has garnered increasing attention, yet false positive alerts are common in practical applications. In patients undergoing dialysis, serum potassium (K+) levels exhibit large fluctuations before and after dialysis, often leading to false positive quality control alerts in routine PBRTQC applications. We aimed to reduce false positive alerts in PBRTQC applications by distinguishing between the test results of dialysis and non‐dialysis patients and constructing separate PBRTQC models. Methods We collected K+ test results from 362,077 patients at our center from September 2023 to September 2024. The data were divided into dialysis, physical examination, and non‐dialysis groups, with data from September 2023 to February 2024 comprising the training set. We constructed PBRTQC models for dialysis patients (n = 3217), those undergoing physical examination (n = 7339), and non‐dialysis patients (n = 153,565) using four statistical methods: moving median, moving average, weighted moving average, and exponentially weighted moving average. We validated the three models using data from the dialysis group (validation set 1) from March to September 2024 and the non‐dialysis group (validation set 2) from March to April 2024. By comparing false positive rates, the average number of patient results affected prior to error detection or median number of patient results affected prior to error detection, and the average probability of error detection in the three models, we evaluated whether the pre‐classified PBRTQC model can reduce the false positive rate of K+. Results Statistical analysis revealed significant differences among the dialysis, physical examination, and non‐dialysis groups (p < 0.001). Based on the minimum sum of the false positive rate, false negative rate, and average number of patient results affected prior to error detection, the models for the dialysis and non‐dialysis groups used the exponentially weighted moving average; the MM method was used in the physical examination group. Validation set 1 showed false positive rates of 69.257% for the physical examination group, 1.143% for the dialysis group, and 35.675% for the non‐dialysis group. According to the total allowable error (TEA), the median number of patient results affected prior to error detection in the dialysis group (1/2TEA, positive: 307.30, negative: 795.20) was higher than that in the physical examination group (1/2TEA, positive: 10.57, negative: 4.67) and non‐dialysis group (1/2TEA, positive: 24.57, negative: 29.57). The average probability of error detection in the dialysis group (1/2TEA, positive: 2.83%, negative: 0.67%) was lower than that in the physical examination group (1/2TEA, positive: 41.47%, negative: 45.11%) and non‐dialysis group (1/2TEA, positive: 16.00%, negative: 18.00%). In validation sets 2 and 3, the false positive rate for the non‐dialysis group and physical examination group was 1.906% and 2.83%, respectively. This indicates that pre‐classifying dialysis specimens can significantly reduce the occurrence of false positives. Additionally, K+ results in the non‐dialysis group exhibited notable seasonal variations. Conclusions Establishing PBRTQC models through pre‐classification of dialysis patients can significantly lower the false positive rate of K+, enhancing the accuracy of real‐time monitoring for laboratory testing systems. Establishing patient‐based real‐time quality control models through pre‐classification of dialysis patients can significantly lower the false positive rate of K+, enhancing the accuracy of real‐time monitoring for laboratory testing systems.