Powerful Control Research Articles

Single threshold adaptive deep brain stimulation in Parkinson's disease depends on parameter selection, movement state and controllability of subthalamic beta activity

BackgroundDeep brain stimulation (DBS) is an invasive treatment option for patients with Parkinson's disease. Recently, adaptive DBS (aDBS) systems have been developed, which adjust stimulation timing and amplitude in real-time. However, it is unknown how changes in parameters, movement states and the controllability of subthalamic beta activity affect aDBS performance. ObjectiveTo characterize how parameter choice, movement state and controllability interactively affect the electrophysiological and behavioral response to single threshold aDBS. MethodsWe recorded subthalamic local field potentials in 12 patients with Parkinson's disease receiving single threshold aDBS in the acute post-operative state. We investigated changes in two aDBS parameters: the onset time and the smoothing of real-time beta power. Electrophysiological patterns and motor performance were assessed while patients were at rest and during a simple motor task. We further studied the impact of controllability on aDBS performance by comparing patients with and without beta power modulation during continuous stimulation. ResultsOur findings reveal that changes in the onset time control the extent of beta power suppression achievable with single threshold adaptive stimulation during rest. Behavioral data indicate that only specific parameter combinations yield a beneficial effect of single threshold aDBS. During movement, action induced beta power suppression reduces the responsivity of the closed loop algorithm. We further demonstrate that controllability of beta power is a prerequisite for effective parameter dependent modulation of subthalamic beta activity. ConclusionOur results highlight the interaction between single threshold aDBS parameter selection, movement state and controllability in driving subthalamic beta activity and motor performance. By this means, we identify directions for the further development of closed-loop DBS algorithms.

Carbon ion irradiation exerts antitumor activity by inducing cGAS-STING activation and immune response in prostate cancer-bearing mice.

As an advanced radiotherapy technique, carbon ion radiotherapy has demonstrated good efficacy and low toxicity for prostate cancer patients, but the radiobiological mechanism of killing tumor cells has not been fully elucidated. This study aims to explore the antitumor effects of carbon ion irradiation (CIR) through investigating the immune response induced by CIR in prostate cancer-bearing mice and the underlying molecular mechanism. We established subcutaneous transplantation tumor models of prostate cancer to evaluate the tumor inhibition effect of CIR. Investigation of immunophenotype alterations were assessed by flow cytometry. Immunofluorescence, western blot, and real-time quantitative PCR was employed to analyze the activation of cGAS-STING pathway. CIR showed more powerful tumor growth control than photon irradiation in immunocompetent syngeneic C57BL/6 mice. CIR exerts antitumor effect by triggering immune response, characterized by increased CD4+ T cells and macrophages in tumor, enhanced CD8+ T cells and T effector memory cells in spleen, improved IFN-γ production of CD8+ tumor-infiltrating lymphocytes, and reduced exhausted T cells in tumor and spleen. Additionally, production of cytoplasmic double-stranded DNA, protein levels of p-TBK1 and p-IRF3 in the cGAS-STING pathway, and gene expression levels of downstream interferon-stimulated genes were significantly increased after CIR in a dose-dependent manner. Treatment of RM1 tumor-bearing mice with the STING inhibitor C-176 impaired the antitumor effect of CIR. The excellent antitumor activity of CIR in immunocompetent prostate cancer-bearing C57BL/6 mice may be attributed to stronger induction of antitumor immune response and higher activation of cGAS-STING pathway.

Real-time MPC with Control Barrier Functions for Autonomous Driving using Safety Enhanced Collocation

The autonomous driving industry is continuously dealing with safety-critical scenarios, and nonlinear model predictive control (NMPC) is a powerful control strategy for handling such situations. However, standard safety constraints are not scalable and require a long NMPC horizon. Moreover, the adoption of NMPC in the automotive industry is limited by the heavy computation of numerical optimization routines. To address those issues, this paper presents a real-time capable NMPC for automated driving in urban environments, using control barrier functions (CBFs). Furthermore, the designed NMPC is based on a novel collocation transcription approach, named RESAFE/COL, that allows to reduce the number of optimization variables while still guaranteeing the continuous time (nonlinear) inequality constraints satisfaction, through regional convex hull approximation. RESAFE/COL is proven to be 5 times faster than multiple shooting and more tractable for embedded hardware without a decrease in the performance, nor accuracy and safety of the numerical solution. We validate our NMPC-CBF with RESAFE/COL on digital twins of the vehicle and the urban environment and show the safe controller's ability to improve crash avoidance by 91%. Supplementary visual material can be found at https://youtu.be/_EnbfYwljp4.

Design of infinite horizon LQR controller for discrete delay systems in satellite orbit control: A predictive controller and reduction method approach

In the realm of satellite orbit control, powerful controller design plays a pivotal role in minimizing fuel consumption and ensuring orbit stability. This article introduces an advanced approach to the design of a Linear Quadratic Regulator (LQR) controller with an infinite horizon, tailored for discrete delay systems. The proposed methodology integrates predictive control with a reduction method, aiming for optimality while addressing performance and system constraints. Formulating the control problem as a quadratic program, the predictive control method generates a sequence of control inputs using a reducing horizon strategy. Stability analysis, employing Lyapunov-Krasovsky functions and linear matrix inequalities, yields delay-independent conditions for exponential convergence. A numerical example showcases the controller's effectiveness in maintaining orbit and reducing fuel consumption, underlining its capacity to achieve control objectives despite uncertainties and time delays. This research contributes to robust control strategies in satellite orbit systems, enhancing control performance and operational efficiency.

Performance enhancement in wavefront shaping of multiply scattered light: a review.

In nonballistic regime, optical scattering impedes high-resolution imaging through/inside complex media, such as milky liquid, fog, multimode fiber, and biological tissues, where confocal and multiphoton modalities fail. The significant tissue inhomogeneity-induced distortions need to be overcome and a technique referred as optical wavefront shaping (WFS), first proposed in 2007, has been becoming a promising solution, allowing for flexible and powerful light control. Understanding the principle and development of WFS may inspire exciting innovations for effective optical manipulation, imaging, stimulation, and therapy at depths in tissue or tissue-like complex media. We aim to provide insights about what limits the WFS towards biomedical applications, and how recent efforts advance the performance of WFS among different trade-offs. By differentiating the two implementation directions in the field, i.e., precompensation WFS and optical phase conjugation (OPC), improvement strategies are summarized and discussed. For biomedical applications, improving the speed of WFS is most essential in both directions, and a system-compatible wavefront modulator driven by fast apparatus is desired. In addition to that, algorithm efficiency and adaptability to perturbations/noise is of concern in precompensation WFS, while for OPC significant improvements rely heavily on integrating physical mechanisms and delicate system design for faster response and higher energy gain. Substantial improvements in WFS implementations, from the aspects of physics, engineering, and computing, have inspired many novel and exciting optical applications that used to be optically inaccessible. It is envisioned that continuous efforts in the field can further advance WFS towards biomedical applications and guide our vision into deep biological tissues.

NONLINEAR STABILITY CONTROL OF INVERTED PENDULUM ON A CART USING LQR-BASED T-S FUZZY CONTROL

The inverted pendulum on a cart is a challenging and widely studied control problem in the fields of control systems and robotics. To address the need for more effective control strategies, this paper presents a novel approach that combines the strengths of two powerful control techniques: Linear-Quadratic Regulator (LQR) and Takagi-Sugeno (T-S) fuzzy control. LQR control is well-established for stabilizing linear systems, but it faces limitations with nonlinear systems like the inverted pendulum. On the other hand, T-S fuzzy control excels in handling nonlinearities by approximating the system's behavior with local linear models. Our proposed approach leverages T-S fuzzy systems to approximate complex nonlinearities, while using LQR control for each local linear subsystem. The combined method's efficacy is substantiated by simulation results, specifically considering criteria such as stability under disturbance conditions, variations in the initial angle of the pendulum, and a comparative analysis between stability-only scenarios and those involving both swing-up and stability control.

Quadrotor Position and Attitude Tracking Using Advanced Second-Order Sliding Mode Control for Disturbance

Although sliding mode control (SMC) provides powerful control performance, it exhibits chattering phenomena that can lead to operational issues in quadrotor systems when certain thresholds are reached. To address this limitation, this study introduces second-order sliding mode control (SOSMC), which significantly reduces chattering. Furthermore, a modified version of the SOSMC, called advanced second-order sliding mode control (ASOSMC), is proposed by incorporating an additional term to enhance its control capabilities. The ASOSMC exhibits a stability higher than that of traditional SOSMC during quadrotor flight. We established the stability of the ASOSMC system using a more powerful strict Lyapunov stability analysis instead of the conventional Lyapunov stability employed in SMC. This paper presents the design and performance evaluation of the ASOSMC via simulation and compares it with those of conventional SMC (CSMC) and SOSMC. The simulation results confirm that the ASOSMC offers superior control performance in quadrotor systems.

Learning-based model predictive energy management for fuel cell hybrid electric bus with health-aware control

Advanced energy management strategy (EMS) can ensure healthy, stable, and efficient operation of the on-board energy systems. Model Predictive Control (MPC) and Deep Reinforcement Learning (DRL) are two powerful control methods that have been extensively researched in the field of vehicle energy management. However, there are some problems with both approaches. On the one hand, MPC is difficult to cope with the complex systems and the excessive computational load caused by the non-linear solving over long prediction horizon, on the other hand, DRL lacks adaptability to different driving conditions and is poorly interpretable. Therefore, this paper innovatively proposes a learning-based model predictive (L-MPC) EMS for fuel cell hybrid electric bus (FCHEB) with health-aware control. This method effectively merges the advantages of control theory and machine learning. Specifically, firstly, the precise aging models for vehicular energy systems are established and incorporated into the optimization framework along with hydrogen consumption related metrics. Secondly, the principles of the learning-based MPC algorithm are thoroughly elucidated. In addition, to ensure driving details under future conditions, a speed predictor based on a double-layer Bi-directional Long Short-Term Memory (BiLSTM) is proposed at the strategy supervision layer. Finally, the superiority of the proposed strategy in prolonging the lifespan of the energy systems and reducing overall vehicle operating cost is verified by comprehensive comparisons with state-of-the-art MPC and DRL methods under real-world collected driving condition.

Direct active and reactive powers control of double-powered asynchronous generators in multi-rotor wind power systems using modified synergetic control

In the future, renewable energy sources will be of great importance in overcoming the problems of power generation using traditional sources, as their use contributes to protecting the environment from pollution and helps to raise human development. Therefore, it is necessary to search for a generation system that has high characteristics to raise the quality of energy and reduce the total cost of producing, transmitting, and consuming energy. This work proposes a new nonlinear direct reactive and active power control (DRAPC) for grid-connected double-powered asynchronous generators (DPAGs) in multi-rotor wind power (MRWP) systems. The designed DRAPC employs a modified synergetic control theory (MSCT) to regulate the DFPG-MRWP stator power. With out involving any synchronous coordinate transformations, the DRAPC-MSCT reduces the steady-state error (SSE), ripples and response time of stator powers while directly determining the required rotor control voltage. This technique fundamentally improves the transient performance compared to the DRAPC-SCT. Constant switching frequency is achieved as well by using pulse width modulation, which eases the designs of the power converters and AC harmonic filters.Through the use of three test cases (tracking test, robustness test, and variable-speed test using the Maximum Power Point Tracking), the potential of the DRAPC-MSCT to reduce system instability and uncertainty is evaluated. This comprises the ability of the controller to track DPAG powers references and improve performance during variations in system parameters and wind speed.Simulated results on a 1.5 MW grid-connected DPAG-MRWP are presented and compared with those of the DRAPC-SCT. The DRAPC-MSCT improves the reactive and active power ripples by 73.33% and 84.50%, respectively. Besides, improves the SSE of the reactive and active power by 51.61% and 69%, respectively compared to the DRAPC-SCT. The simulation results demonstrate the high performance and robustness of the DRAPC-MSCT for parametric variations of DPG-MRWP compared to the DRAPC-SCT.

Reliably straining suspended van der Waals heterostructures

2D materials provide a rapidly expanding platform for the observation of novel physical phenomena and for the realization of cutting-edge optoelectronic devices. In addition to their peculiar individual characteristics, 2D materials can be stacked into complex van der Waals heterostructures, greatly expanding their potential. Moreover, thanks to their excellent stretchability, strain can be used as a powerful control knob to tune or boost many of their properties. Here, we present a novel method to reliably and repeatedly apply a high uniaxial tensile strain to suspended van der Waals heterostructures. The reported device is engineered starting from a silicon-on-insulator substrate, allowing for the realization of suspended silicon beams that can amplify the applied strain. The strain module functionality is demonstrated using single- and double-layer graphene layers stacked with a multilayered hexagonal boron nitride flake. The heterostructures can be uniaxially strained, respectively, up to ∼1.2% and ∼1.8%.

Operation optimization of multi-boiler district heating systems using artificial intelligence-based model predictive control: Field demonstrations

District energy systems through fourth and fifth generations have shown great promises to help integrate renewable energy sources at large scale and to decarbonize the built environment. However, older generations using boilers represent a large proportion of systems currently in operation, and significantly contribute to greenhouse gas (GHG) emissions. Moreover, operational data has become increasingly available for these older generation systems and represents a suitable opportunity for Artificial Intelligence (AI)-based modelling and advanced controls. In this context, model predictive control (MPC) has appeared as a powerful control solution; however, field implementation remains relatively scarce. This paper aims to develop an AI-based MPC strategy for multi-boiler district heating systems. It relies on heating load forecasting machine learning models and data-driven boiler performance curves to optimize boiler thermal outputs. This strategy was implemented in two Canadian demonstration sites and showed GHG emissions reductions of 1.3 % and 2.8 %. Although relatively modest, statistical analysis confirmed the realization of these savings. In absolute terms, the strategy helped avoid 45 t and 77 t CO2eq emissions and save $10,268 and $19,975 CAD during the testing period of 2–3 months. The model and strategy developed in this work could be easily scalable to similar district heating systems.

An extensive critique on expert system control in solar photovoltaic dominated microgrids

AbstractSolar and wind power have recently become a potential option in power systems and act significantly to meet load penetration demands. The present growth of such renewable energy sources has shown an exponential increase. The high penetration of such system helps a grid effectively meet its load in an irregular demand but also creates some disturbances in the grid due to frequent additions and detachments of load or source. The way by which the renewable energy sources usually work in the on‐grid mode is to be attached to and cut down from the grids without creating disturbances in a stable grid. Another important requirement is effective load management with fewer transmission losses. This article presents a detailed review of a microgrid and enumerates the possible methods for the analysis of the system, feature extraction, control methods, and options for machine learning. This paper examines the factors affecting the operations in a power system, their nature, interdependability, and controllability. It also inspects the various machine learning algorithms, their feasibility, and possible applications in power systems. The major contribution of the paper is the elucidation of expert system control methods for the performance improvement of solar PV assisted DC microgrids. The major objective of the paper is to provide an overview on various algorithms intended for the microgrid systems pertaining to its accuracy, precision, classification, prediction and forecasting.

Switched Auto-Regressive Neural Control (S-ANC) for Energy Management of Hybrid Microgrids

Switched model predictive control (S-MPC) and recurrent neural networks with long short-term memory (RNN-LSTM) are powerful control methods that have been extensively studied for the energy management of microgrids (MGs). These methods ease constraint satisfaction, computational demands, adaptability, and comprehensibility, but typically one method is chosen over the other. The S-MPC method dynamically selects optimal models and control strategies based on the system’s operating mode and performance objectives. On the other hand, integration of auto-regressive (AR) control with these powerful control methods improves the prediction accuracy and the adaptability of the system conditions. This paper compares the two control approaches and proposes a novel algorithm called switched auto-regressive neural control (S-ANC) that combines their respective strengths. Using a control formulation equivalent to S-MPC and the same controller model for learning, the results indicate that pure RNN-LSTM cannot provide constraint satisfaction. The novel S-ANC algorithm can satisfy constraints and deliver comparable performance to MPC, while enabling continuous learning. The results indicate that S-MPC optimization increases power flows within the MG, resulting in efficient utilization of energy resources. By merging the AR and LSTM, the model’s computational time decreased by nearly 47.2%. In addition, this study evaluated our predictive model’s accuracy: (i) the R-squared error was 0.951, indicating a strong predictive ability, and (ii) mean absolute error (MAE) and mean square error (MSE) values of 0.571 indicate accurate predictions, with minimal deviations from the actual values.

A strong secure path planning/following system based on type-3 fuzzy control, multi-switching chaotic systems, and random switching topology

This paper studies the synchronization and control of chaotic systems while proposing a novel chaotic-based path-tracking application for mobile robots (MRs) to ensure their safety and security. In security-based applications that use MRs, such as patrol MRs, the path of the MRs must be complex enough to prevent easy prediction. Multiple chaotic systems with a chaotic switching mechanism are introduced for secure path planning. The main challenges are that the dynamics of MRs are entirely unknown. The modeled dynamics of the MRs are unreliable in practice due to a broad range of uncertainties related to the parameters, operating conditions, environmental impacts, time delays, unmodeled frictions, noisy sensors, and faulty actuators. Also, the chaotic switching of reference signals between chaotic signals imposes a high dynamic perturbation. The main novelties are as follows: (1) a strong secure path is introduced for MRs. (2) A powerful fractional-order predictive controller using type-3 (T3) fuzzy-logic systems (FLSs) is developed. (3) The estimation and prediction errors of T3-FLSs are compensated by a designed parallel compensator. (4) T3-FLSs are tuned online, such that stability is ensured, and prediction accuracy is guaranteed. (5) The suggested scheme is implemented on a real-world MR, and the results demonstrate the feasibility and accuracy of the proposed method. Also, in several simulations, the efficacy of the introduced controller is examined.

Evaluation of hosting capacity of the power grid for electric vehicles – A case study in a Swedish residential area

The deployment of electric vehicles (EVs) is growing significantly in recent years. The increasing EV charging loads pose great stress on power grids in Sweden, as many existing power grids are not designed to host such large shares of new electric loads. Hence, studies investigating the impact of EV charging are needed. This study conducts a case study based on an existing Swedish residential power grid using real-life EV charging data to estimate the local grid hosting capacity (HC) for EVs. A combined time-series and stochastic HC assessment method is used with voltage deviation, cable loading and transformer loading as the performance indices. Uncertainty in EV charging locations and individual charging behaviour have been considered via Monte Carlo simulations. The power grid HC is analysed and compared under three charging strategies and four EV penetration levels. Study results show that a charging strategy based on low electricity prices gave lower HC due to simultaneous EV loads compared to the other two strategies: charging directly after plugging in the EV and an even charging load through the plug-in session. This implies the need for coordinated charging controls of EV fleets or diversified power tariffs to balance power on a large scale.

FRI419 Progesterone Regulation Of The GnRH Pulse Generator

Abstract Disclosure: E.G. Wall: None. A group of kisspeptin neurons located in the arcuate nucleus (ARN) of the hypothalamus represent the GnRH pulse generator and their activity is thought to be controlled primarily by the feedback actions of estradiol and progesterone (P4). In a healthy menstruating woman, following the mid-cycle ovulation, P4 levels dramatically increase and exert a negative feedback action onto the hypothalamus to slow pulsatile LH secretion. However, the exact mechanism by which P4 negatively feeds back to the hypothalamus remains unknown. The ARN kisspeptin neurons display synchronized episodes (SEs) of activity approximately every hour for most of the cycle but these dramatically reduce in number following the LH surge. As these ARN kisspeptin neurons express the progesterone receptor (PR) it is hypothesized that they may be the site at which P4 negative feedback occurs. Given the lack of detailed information in the mouse, we first assessed the profile of circulating P4 levels in mice at 8-hr intervals throughout the estrous cycle. Terminal blood samples were taken from individual mice along the 4 stages of the estrous cycle at 3-time points 8 hrs apart and the gonadal hormones were analyzed using LC-MS. The concentration of P4 was found to peak around the time of the LH surge at 6 pm on proestrus (21.94 ± 3.78 ng/ml) and was at its lowest at diestrus 10 am (0.42 ± 0.11 ng/ml). We next assessed the impact of circulating P4 on the activity of the ARN kisspeptin pulse generator using GCaMP fibre photometry in freely behaving mice. A prior study found that IP administration of 8 mg/kg P4 resulted in circulating P4 concentrations reaching 34 ng/ml (Wong et al., J Pharm Pharmacol 64, 2012). Hence, in this study, diestrus mice equipped for monitoring the activity of the kisspeptin pulse generator were injected with either 8 or 4 mg/kg P4 at 10 am and SEs monitored for 24 hrs. Both doses resulted in a significantly decreased frequency of SEs compared to the vehicle (p<0.05), which persisted for 6 hrs. To next examine whether P4 may be acting directly on the ARN kisspeptin neurons to slow pulse generation, P4 (25 ng/ml) was infused directly into the ARN while the activity of the pulse generator was monitored. Again, this resulted in a decreased frequency of SEs compared to the vehicle with the effects lasting approximately 5 hrs (p<0.05).These results indicate that P4 exerts powerful inhibitory control over the activity of the kisspeptin pulse generator and that these effects may be direct. Elucidating the mechanisms of P4 negative feedback may provide a platform for the development of new therapeutic strategies in the infertility clinic. Presentation: Friday, June 16, 2023

A High Robust Optimal Nonlinear Control with MPPT Speed for Wind Energy Conversion System (WECS) Based on Doubly Fed Induction Generator (DFIG)

As part of efforts to improve wind energy production using a doubly fed induction generator (DFIG), this paper presents modeling with power maximization through control of speed (MPPT) and control of stator active and reactive power for doubly fed induction generator. The quality of the generated energy and the performance of wind energy conversion systems (WECS) based on DFIG are affected by uncertainties and external disturbances. Therefore, the system requires high-performance control under the nonlinearity influence and the applied external disturbances. This work provides a new contribution and approach that combines nonlinear control (Backstepping) and optimal control (LQR). Backstepping control has been applied to give a good performance using nonlinear strategy and stability condition, but its main drawback is that it is relatively robust for sudden changes in wind speed or any external disturbances as well as it is sensitive against the uncertainties, which may lead to a chattering and an instability as well as overshoot or undershoot. To overcome the problems mentioned above the optimal control (LQR) has been applied to address this drawback in nonlinear control. Furthermore, it, the hybrid approach of Backstepping and LQR control resulting a robust control under the hardest scenarios during the wind system operating as well as provides a good performance in terms of chattering, stability and other performance characteristics which help to produce a high-quality energy. Overall, integrating LQR control with Backstepping can lead to a more powerful, flexible, and efficient control system with improved performance and reduced computational complexity.

Simultaneous Analysis of Ezetimibe and Rosuvastatin by Q absorption Ratio Method

A Novel UV spectrophotometric method has been developed for the simultaneous estimation of ezetimibe and rosuvastatin based on Q absorption ratio method. The wavelengths of 234.8nm (lsosbestic point) and 242nm (λmax of rosuvastatin) were chosen as analytical wavelengths. The linearity was established in concentration range 2-40µg/mL for ezetimibe and rosuvastatin with correlation coefficient greater than 0.990. Analytical method validation was performed by following ICH guidelines. The % assay for ezetimibe and rosuvastatin in tablet formulation complied with acceptance criteria. The developed method thus can be used as a powerful quality control tool for simultaneous analysis of the drugs in bulk and in formulation by UV spectroscopy.

Carbon Ion Radiotherapy Exerts Anti-Tumor Activity by Inducing cGAS-STING Activation and Immune Response in Prostate Cancer-Bearing Mice

The aim of this study was to assess the influence on immune cell infiltration and T cell effector function to explore the immune response evoked by carbon ion radiotherapy (CiRT) in prostate cancer-bearing mice, and explored the mechanisms underlying CiRT-induced anti-tumor efficacy, involved in cGAS-STING signaling pathway. C57BL/6 and BALB/c nude mouse tumor models were used to evaluate the efficacy of CiRT on tumor growth. Activation of cGAS-STING signaling pathway was performed by immunofluorescence analysis of cytoplasmic double-stranded DNA, western blot analysis of key factors involved in cGAS-STING pathway, and qRT-PCR analysis of the key downstream molecules like CCL5, CXCL10 and IFNβ1. Investigation of alterations of immunophenotypes including the quantification, memory status, exhaustion marker expression, and effector function were assessed by flow cytometry. CiRT showed more powerful tumor growth control of immunocompetent syngeneic C57BL/6 mice than photon radiotherapy did at biological equivalent dose of 5Gy. CiRT induces cytoplasmic DNA and cGAS-STING activation, and is functionally responsible for the observed tumor growth suppression. CiRT exerts anti-tumor effect by triggering immune response, characterized by increased infiltration of CD4+ T cells and macrophages in tumor, enhanced frequencies of CD8+ T cells and CD8+ T effector memory cells in spleen, improved interferon (IFN)-γ production ability of CD8+ tumor-infiltrating lymphocyte cells, and reduced expression of exhausted T cells in tumor and spleen. Our findings indicate that CIRT exerts excellent anti-tumor activity, which may be attributed to the induction of cGAS-STING activation and immune response, manifested by increased immune cell infiltration, improved T cell effector function and enhanced immune memory.

Power system resilience against climatic faults: An optimized self-healing approach using conservative voltage reduction

The resilience of power systems is critical in mitigating faults caused by the impending effects of climate change. Typical operational methods, such as network reconfiguration, may be insufficient to mitigate faults in the event of a contingency. On the other hand, reducing system demand may help increase the number of restored loads. As a result, this paper proposes a novel self-healing optimization approach based on a power grid concept known as conservative voltage reduction (CVR), which results in system demand reduction. The proposed optimization model is formulated as a mixed integer non-linear (MINLP) problem to fulfill an objective of minimizing unserved loads within the system. Voltage, thermal capacity, system radiality, and several more constraints have been taken into consideration while formulating the proposed model to handle both grid-connected and isolated modes of operation. Both dispatchable and non-dispatchable distributed generators (DGs) are taken into consideration. Dispatchable DGs are assumed to be capable of switching back and forth between constant power (PQ) and droop controls for the purpose of grid following and grid forming in grid-connected and isolated modes of operation, respectively. The proposed optimization approach is coded and solved using the General Algebraic Modeling System (GAMS) software. The IEEE 69-bus power distribution test system is utilized to test the validity and superiority of the proposed model. The results show that the proposed model effectively increases the system resilience by reducing the unserved power/energy within the system following a contingency.