Switching States Research Articles

Mix and Match Tuning of the Conformational and Multistate Redox Switching Properties of an Overcrowded Alkene.

Overcrowded alkenes have received considerable attention as versatile structural motifs in a range of optical switches and light-driven unidirectional motors. In contrast, their actuation by electrochemical stimuli remains underexplored, even though this alternative energy input may be preferred in various applications and enables additional control over molecular switching states and properties. While symmetric bistricyclic overcrowded enes (BAEs) containing two identical halves based on either thioxanthene (TX) or acridine (Acr) motifs are known to be reversible conformational redox switches, their redox potentials are generally too high or low, respectively, thereby preventing wider applications. Herein, we demonstrate that the "mixed" TX-Acr switch possesses redox properties that lie between those of its parent symmetric analogs, enabling interconversion between three stable redox and conformational states at mild potentials. This includes the neutral anti-folded, the dicationic orthogonal, and a unique twisted monoradical cation state, the latter of which is only accessible in the case of the mixed TX-Acr switch and in a pathway-dependent manner. Consequently, with this multistate redox switch, a myriad of molecular properties, including geometry, polarity, absorbance, and fluorescence, can be modulated with high fidelity and reversibility between three distinct stable states. More generally, this study highlights the versatility of the "mix and match" approach in rationally designing redox switches with specific (redox) properties, which in turn is expected to enable a myriad of applications ranging from molecular logic and memory to actuators and energy storage systems.

A - 22 Effects of Gender on the Relationship between Coping Styles and Executive Functioning in Alcohol Use Disorders

Abstract Objective Executive functioning (EF) and coping styles are important factors likely to influence risk and resilience to relapse following Alcohol Use Disorder (AUD) treatment. This study aims to evaluate EF and coping as independent predictors of relapse and the interplay between these two constructs by treatment outcome group and gender differences. Methods 23 females and 59 males in AUD residential treatment programs completed self-report questionnaires, a neuropsychological battery, and were followed for six months to determine treatment outcome. A logistic regression analysis was performed to evaluate coping (Brief COPE Inventory) and EF (Delis-Kaplan Executive Function System, Color-Word Interference Test, CWIT; inhibition and switching conditions) variables as predictors of relapse status. Significant predictors were evaluated by group status and gender to understand the association between coping and EF using Pearson correlation within each group. Results Gender (B = 8.6, p = 0.032), two coping (self-distraction (B = 2.3, p = 0.051) and humor (B = 1.9, p = 0.051)) and two EF (CWIT inhibition (B = 0.5, p = 0.007) and CWIT switching (B = 1.9, p = 0.006)) variables resulted in 85.4% overall classification accuracy. Correlations between these coping and EF, when evaluated by treatment group and gender, revealed significant gender-specific associations only among the relapse groups: self-distraction and CWIT inhibition (r = 0.379) in females and humor and CWIT inhibition (r = 0.242) in males. Conclusion Overall, EF and coping styles have independent predictive value for determining relapse risk in individuals with AUD. Poor EF skills may inhibit an individual’s ability to engage in appropriate coping during times of emotional distress. Understanding gender differences in relation to these dynamic relationships is essential for creating supportive and effective psychosocial interventions.

Constructing A Theoretical Model to Bridge Neural Transition with a State Switch in Bipolar Disorder.

Constructing A Theoretical Model to Bridge Neural Transition with a State Switch in Bipolar Disorder.

New Approaches in Finite Control Set Model Predictive Control for Grid-Connected Photovoltaic Inverters: State of the Art

Grid-connected PV inverters require sophisticated control procedures for smooth integration with the modern electrical grid. The ability of FCS-MPC to manage the discrete character of power electronic devices is highly acknowledged, since it enables direct manipulation of switching states without requiring modulation techniques. This review discusses the latest approaches in FCS-MPC methods for PV-based grid-connected inverter systems. It also classifies these methods according to control objectives, such as active and reactive power control, harmonic suppression, and voltage regulation. The application of FCS-MPC particularly emphasizing its benefits, including quick response times, resistance to changes in parameters, and the capacity to manage restrictions and nonlinearities in the system without the requirement for modulators, has been investigated in this review. Recent developments in robust and adaptive MPC strategies, which enhance system performance despite distorted grid settings and parametric uncertainties, are emphasized. This analysis classifies FCS-MPC techniques based on their control goals, optimal parameters and cost function, this paper also identifies drawbacks in these existing control methods and provide recommendation for future research in FCS-MPC for grid-connected PV-inverter systems.

Fixed-time active fault-tolerant control for dynamical systems with intermittent faults and unknown disturbances

In this article, the problem of fixed-time active fault-tolerant control is investigated for dynamical linear systems with intermittent faults and unknown disturbances. Unlike traditional active fault-tolerant control, fixed-time control is taken into account in this article since intermittent faults appear and disappear within a certain period of time. The entire active fault-tolerant control framework is composed of fault detection, fault isolation, fault and state estimation as well as the reconfigurable controller. Using the homogeneity-based observers, states and faults are well estimated and a fault diagnosis scheme is proposed for the sake of detecting and isolating intermittent faults in a fixed time. The fault-tolerant controller, which provides global practical fixed-time stability of the closed-loop system, has two switching states corresponding to the appearance and disappearance of intermittent faults. As a consequence, intermittent faults are compensated via the designed active fault-tolerant control method and the system reaches practical stability with the entire convergence time bounded in a fixed time. Finally, two examples are exploited to demonstrate the effectiveness of theoretical results.

Diffusive Memristor with CuS Nanoparticles Embedded in Polymeric Film as Artificial Nociceptor.

The threshold behavior and the ion diffusion dynamics in diffusive volatile memristors have a very uncanny resemblance to the transduction process of biological nociceptors. Hence, the diffusive memristors are considered the most suited for making artificial nociceptive systems. To facilitate their widespread adoption, it is imperative to develop polymeric or organic-inorganic hybrid material-based diffusive memristors that are economical, biocompatible, and easily processable. In this study, we present a cluster-type polymeric diffusive memristor where copper is used as the active top electrode. The switching medium comprises copper(II) sulfide (CuS) nanoparticles embedded in poly(ethylene oxide) (PEO). The devices show electrochemical metalization (ECM)-type and bidirectional diffusive volatile memory with high nonlinearity (104) and turn-on slope (5.6 mV/dec). They reliably remain diffusive volatile with up to 10 wt % CuS in PEO and for a wide range of compliance (10-6 to 10-2 A) without transitioning to the bipolar nonvolatile type. The low reduction potential of CuS and optimal segmental dynamics of PEO work synergistically to ensure stable and reproducible diffusive memory. The CuS nanoparticles act as bipolar electrodes, undergoing local oxidation and reduction under the influence of the bias. The switching of resistance states in the CuS-PEO memristors is attributed to the formation of cluster-type filaments between CuS nanoparticles within the PEO matrix supported by the participation of copper ions from the top Cu electrode. The observation of low filament temperature and the independence of on-state resistance with respect to the device area and temperature further corroborate the cluster-type filament in CuS-PEO memristors. Using a 5 wt % CuS-based device, an artificial nociceptor is realized, which successfully mimics most of the nociceptive plasticities such as threshold, relaxation, no adaptation, and sensitization.

Music listening for improvement of sleep in post-acute rehabilitation of adults with acquired brain injury: A feasibility study

ABSTRACT Introduction Acquired brain injury (ABI) often leads to sleep disturbances impacting recovery. Previous research has shown that listening to music at bedtime can have a beneficial effect on sleep quality in various populations, but the impact of bedtime music on sleep problems related to ABI has not been studied. This feasibility study investigates the acceptability and effectiveness of music listening for sleep improvement in ABI patients. Methods In a within-subjects crossover design with repeated measures for 28 days, ABI patients with sleep disturbance were allocated to a control or music condition, switching conditions after 14 days. Acceptability was assessed with questionnaires on compliance and attrition. The effect was evaluated with the Pittsburgh Sleep Quality Index (PSQI) at baseline, day 14 and 28, daily sleep ratings, and actigraphy. Results The intervention was well-liked, and participants listened to the music most of the intervention nights. A descriptive statistical comparison of outcome data on PSQI and daily sleep ratings regarding how well participants slept indicated that participants (n = 4) experienced better sleep with the music intervention. The objective sleep measures reflected a slightly longer time to fall asleep with the music intervention. Discussion The results suggest good acceptability and a potential beneficial effect of the intervention. Music listening may be a safe and effective tool, and a full RCT is recommended to evaluate the efficacy of music listening for sleep improvement in ABI patients. https://www.clinicaltrials.gov, Identification number: NCT05620069.

A new structural reconfiguration for multilevel inverters with fault tolerance capability

Keeping electrical systems running is critical in industries especially in those that rely on static converters, for example in the case of inverters, continuous and safe of operation must be ensured even if one of the inverter’s static switch modules fails. This paper presents a method for fault detection in static converters. The study presents a 3-phase 3-level NPC (Neutral Point Clamped) inverter with IGBTs as static switches where open-circuit or closed-circuit faults are considered. In addition, leg failure and structural reconfiguration are also considered in this fault-tolerant inverter by adding a fourth 3-level leg.

A dopamine-dependent mechanism for Reward-induced modification of Fear memories.

A positive mental state has been shown to modulate fear-related emotions associated with the recall of fear memories. These, and other observations suggest the presence of central brain mechanisms for affective states to interact. The neurotransmitter dopamine is important for both Reward- and fear-related processes, but it is unclear whether dopamine contributes to such affective interactions. Here, we show that precisely timed Reward-induced activation of dopamine neurons in mice potently modifies fear memories and enhances their extinction. This Reward-based switch in fear states is associated with changes in dopamine release and dopamine-dependent regulation of fear encoding in the central amygdala (CeA). These data provide a central mechanism for Reward-induced modification of fear states that have broad implications for treating generalized fear disorders.

Electrical Sensing of Molecular Spin State Switching in a Spin Crossover Complex Using an Organic Field‐Effect Transistor

AbstractAn organic semiconductor – spin crossover polymer composite heterostructure is fabricated, and it is integrated into an organic field‐effect transistor (OFET) with the aim to achieve electrical sensing of molecular spin state switching events. The OFETs display ≈50–70% increase in drain‐source current intensity when going from the low spin (LS) to the high spin (HS) state. This phenomenon is reversible without apparent fatigue and the application of a gate voltage significantly enhances the sensing sensitivity. Capacitance measurements and finite element calculations allow identifying mechanical stress, induced by the spin state switching, at the origin of the transistor response. These results open up appealing perspectives for the integration of spin crossover molecules into technological applications, such as soft robotics .

Predictive control method with conduction mode detection to suppress input current distortion of three‐level power factor correction

AbstractA novel predictive control method with conduction mode detection is proposed to suppress input current distortion of three‐level power factor correction (PFC) converters. In PFC converter, the input current is mostly distorted in continuous conduction mode (CCM) with conventional control methods. The proposed predictive control method comprises of a predictive CCM algorithm, a predictive discontinuous conduction mode (DCM) algorithm, and a conduction mode detection technique. By analysing four switching states of PFC converter and based on the inductor current ripple, predictive control duty cycles in CCM and DCM are derived, and the optimal duty cycle is determined by forecasting the next cycle current. According to the duty cycle characteristics, the conduction mode is detected without additional detection algorithms and circuits, achieving the input current tracking the input voltage. Finally, the effectiveness and correctness of the proposed predictive control method are validated through simulation and experiment.

Base Station Energy Saving based on Imitation Learning in 5G Network

Abstract With the rapid development of communication technology, the large-scale deployment of base stations (BSs) has led to an increase in power consumption. To reduce power consumption, energy saving technologies for BSs have emerged, which are in line with the concept of green communications and can save operators’ costs. In this paper, our goal is to minimize the total power consumption of the base station by dynamically controlling the switching status of the base station. This article first proposes a dynamic base station switching framework based on deep reinforcement learning (DRL), which optimizes the power consumption of switching BSs. Then, considering the dynamic nature of the environment, this article proposes a dynamic BS switching algorithm that introduces the idea of imitation learning (IL) to update the policy by learning the expert’s action. The simulation results show that this scheme can effectively reduce power consumption.

A Novel Method for Damping State Switching Based on Machine Learning of a Strapdown Inertial Navigation System

A Novel Method for Damping State Switching Based on Machine Learning of a Strapdown Inertial Navigation System

Optimal Reconfiguration of Bipolar DC Networks Using Differential Evolution

The search for more efficient power grids has led to the concept of microgrids, based on the integration of new-generation technologies and energy storage systems. These devices inherently operate in DC, making DC microgrids a potential solution for improving power system operation. In particular, bipolar DC microgrids offer more flexibility due to their two voltage levels. However, more complex tools, such as optimal power flow (OPF) analysis, are required to analyze these systems. In line with these requirements, this paper proposes an OPF for bipolar DC microgrid reconfiguration aimed at minimizing power losses, considering dispersed generation (DG) and asymmetrical loads. This is a mixed-integer nonlinear optimization problem in which integer variables are associated with the switch statuses, and continuous variables are associated with the nodal voltages in each pole. The problem is formulated based on current injections and is solved by a hybridization of the differential evolution algorithm (to handle the integer variables) and the interior point method-based OPF (to minimize power losses). The results show a reduction in power losses of approximately 48.22% (33-bus microgrid without DG), 2.87% (33-bus microgrid with DG), 50.90% (69-bus microgrid without DG), and 50.50% (69-bus microgrid with DG) compared to the base case.

Finite control set model predictive current control for three phase grid connected inverter with common mode voltage suppression

This research introduces an advanced finite control set model predictive current control (FCS-MPCC) specifically tailored for three-phase grid-connected inverters, with a primary focus on the suppression of common mode voltage (CMV). CMV is known for causing a range of issues, including leakage currents, electromagnetic interference (EMI), and accelerated system degradation. The proposed control strategy employs a system model that predicts the inverter’s future states, enabling the selection of optimal switching states from a finite set to achieve dual objectives: precise current control and effective CMV reduction, a meticulously designed cost function evaluates the potential switching states, balancing the accuracy of current tracking against the necessity to minimize CMV. The approach is grounded in a comprehensive mathematical model that captures the dynamics of CMV within the system, and it utilizes an optimization process that functions in real-time to determine the most suitable control action at each interval, Experimental validations of the proposed FCS-MPCC scheme have demonstrated its effectiveness in significantly improving the performance and durability of three-phase grid-connected inverters, Experimental validations of the proposed (MPC with CMV) scheme have demonstrated its effectiveness in significantly improving the performance and durability of three-phase grid-connected inverters. The proposed method achieved substantial reductions in CMV, notable improvements in current tracking accuracy, and extended system lifespan compared to conventional control methods.

Soft‐switching boundary and piecewise control of LLC converter with hybrid modulation

AbstractThe voltage gain range of LLC converter can be effectively broadened by using the hybrid modulation of phase‐shift modulation (PSM) and pulse frequency modulation (PFM). However, the harmonic of resonant tank increases dramatically in PSM mode, and the first harmonic approximation is no longer suitable for solving the voltage gain. Moreover, the conditions for soft switching in PSM mode are not clear. Meanwhile, the small‐signal models of the converter in different modulation modes are quite different, and it is difficult for a fixed controller to ensure a good dynamic performance of the converter in both modulation modes. To address above issues, a voltage gain modeling method of PSM mode LLC converter is proposed. By establishing the function between input current and resonant current, the voltage gain model of the converter is obtained according to the power conservation. Moreover, considering the parasitic capacitance charge and resonant current commutation time, the condition for soft switching is derived. Furthermore, a control strategy of LLC converter in hybrid modulation is proposed. The piecewise proportional integral (PI) controller is adopted, and the parameters of the controller are dynamically adjusted with the modulation mode, so that the converter has excellent dynamic performance in both modes. And the hysteresis control is used to ensure the stability of mode switching. An experimental prototype with 200–340 V input and 28 V/1 kW output was built based on gallium nitride (GaN) devices. The peak efficiency was 97.82%, and the power density was 65.76 w/in3.

Event‐triggered adaptive neural network‐based optimal control of strictly feedback switched nonlinear systems with state constraints

AbstractIn this article, we present an innovative approach for controlling nonlinear switched systems (NSSs) with strict feedback utilizing adaptive neural networks (ANNs). Our methodology encompasses several facets, addressing key challenges inherent to these systems. To commence, we tackle the constrained nature of NSSs with strict feedback by designing a barrier Lyapunov function. This function ensures that all states within the switched systems remain within prescribed constraints. Additionally, we harness neural networks (NNs) to approximate the unknown nonlinear functions inherent to the system. Furthermore, we deploy an ANN state observer to estimate unmeasurable states. Our approach then proceeds to develop a cost function for the subsystem. Building upon this, we apply the Hamiltonian–Jacobi–Bellman (HJB) solution in conjunction with observer and behavior critic architectures, all rooted in backstepping control (BC) principles. This integration yields both a virtual optimal controller and a real optimal controller. Furthermore, we introduce a novel ANN event‐triggered control (ETC) strategy tailored explicitly for strictly feedback systems. This strategy proves highly effective in reducing the utilization of communication resources and eliminating the occurrence of Zeno behavior. Our analysis provides formal proof that all states within the closed‐loop system exhibit half‐leaf consistency and are ultimately bounded, regardless of arbitrary switching conditions. Finally, we substantiate the efficacy and viability of our control scheme through comprehensive numerical simulations.

PSAO: An enhanced Aquila Optimizer with particle swarm mechanism for engineering design and UAV path planning problems

Metaheuristic algorithms have become increasingly significant in solving complex optimization problems. To address the limitations of the original Aquila Optimizer (AO), such as insufficient local exploitation ability, low optimization precision, and slow convergence rate, an enhanced Aquila Optimizer (PSAO) for global optimization has been proposed. PSAO uses a better ergodic good point set to initialize the Aquila population and modifies the search method by employing the golden sine operator and the mechanism of self-learning and social learning based on particle swarm, followed by designing a nonlinear balance factor γ as the switching condition of the algorithm. The simulation experiments on benchmark functions and CEC2017 functions have verified that the PSAO has better global optimization ability and stronger robustness compared with other intelligent algorithms. Meanwhile, the contribution of each component that belongs to PSAO has been validated by ablation experiments for the CEC2022 test functions. To further illustrate the practical application potential of PSAO, PSAO is successfully applied to four typical engineering design problems, three of which reach the best fitness values. In addition, utilizing PSAO to solve the UAV trajectory planning problem by considering the objectives of trajectory length, altitude and corner of the flight process, the total flight cost is reduced by 60.22 %, 27.94 %, and 22.41 % compared with AO, PSO and Gold-SA, respectively, which proves its applicability and superiority in solving the real optimization problems.

Capacitor voltage balancing, capacitance monitoring, and fast fault detection in a nested neutral point clamped (NNPC) converter with the reduced number of sensors

In multilevel converters (MCs), system components such as capacitors and semiconductor switches are very susceptible to damage. Furthermore, utilizing voltage sensors to balance capacitor voltages increases the system's cost and complexity while decreasing reliability. This paper presents methods for voltage balancing of capacitors, capacitance monitoring and open-circuit fault detection in nested neutral point-clamped (NNPC) converter with a reduced number of voltage and current sensors. In the proposed method, converter capacitors' voltage and current sensors are eliminated, and only the output sensors are employed to control the converter. In order to balance the voltage of capacitors, their voltages are estimated in three cases without using individual sensors. Comparing the capacitors' measured and estimated voltage change ratios is used to assess their health in the monitoring method. Thus, the capacitors' status can be checked by considering the system's health indicator during each period. Furthermore, the open-circuit fault detection method can identify defective switches within one processing cycle by comparing the converter's measured and standard output voltages in all switching states. The proposed methods have been evaluated in an experimental study of the system, and the results confirmed the effectiveness of the methods based on the new system configuration.© 2017 Elsevier Inc. All rights reserved.

Tunable Stochastic State Switching in 2D MoS2 Nanomechanical Resonators with Nonlinear Mode Coupling and Internal Resonance.

Coupled nanomechanical resonators have unveiled fascinating physical phenomena, including phonon-cavity coupling, coupled energy decay pathway, avoided crossing, and internal resonance. Despite these discoveries, the mechanisms and control techniques of nonlinear mode coupling phenomena with internal resonances require further exploration. Here, we report on the observation of stochastic switching between the two resonance states with coupled 1:1 internal resonance, for resonant two-dimensional (2D) molybdenum disulfide (MoS2) nanoelectromechanical systems (NEMS), which is directly driven to the critical coupling regime without parametric pumping. We further demonstrate that the probability of state switching is linearly tunable from ∼0% to ∼100% by varying the driving voltage. Furthermore, we gradually increase the white noise amplitude and show that the probability of obtaining the higher-energy state decreases, and the stochastic switching phenomenon eventually disappears. The results provide insights into the dynamics of coupled NEMS resonators and open up new possibilities for sensing and stochastic computing.