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As an efficient way to integrate multiple distributed energy resources (DERs) and the user side, a microgrid is mainly faced with the problems of small-scale volatility, uncertainty, intermittency and demand-side uncertainty of DERs. The traditional microgrid has a single form and cannot meet the flexible energy dispatch between the complex demand side and the microgrid. In response to this problem, the overall environment of wind power, thermostatically controlled loads (TCLs), energy storage systems (ESSs), price-responsive loads and the main grid is proposed. Secondly, the centralized control of the microgrid operation is convenient for the control of the reactive power and voltage of the distributed power supply and the adjustment of the grid frequency. However, there is a problem in that the flexible loads aggregate and generate peaks during the electricity price valley. The existing research takes into account the power constraints of the microgrid and fails to ensure a sufficient supply of electric energy for a single flexible load. This paper considers the response priority of each unit component of TCLs and ESSs on the basis of the overall environment operation of the microgrid so as to ensure the power supply of the flexible load of the microgrid and save the power input cost to the greatest extent. Finally, the simulation optimization of the environment can be expressed as a Markov decision process (MDP) process. It combines two stages of offline and online operations in the training process. The addition of multiple threads with the lack of historical data learning leads to low learning efficiency. The asynchronous advantage actor–critic (Memory A3C, M-A3C) with the experience replay pool memory library is added to solve the data correlation and nonstatic distribution problems during training. The multithreaded working feature of M-A3C can efficiently learn the resource priority allocation on the demand side of the microgrid and improve the flexible scheduling of the demand side of the microgrid, which greatly reduces the input cost. Comparison of the researched cost optimization results with the results obtained with the proximal policy optimization (PPO) algorithm reveals that the proposed algorithm has better performance in terms of convergence and optimization economics.

The stimulator of interferon genes (STING) is a critical protein in the activation of the immune system in response to DNA. It can participate the inflammatory response process by modulating the inflammation-preferred translation program through the STING-PKR-like endoplasmic reticulum kinase (PERK)-eIF2α pathway or by inducing the secretion of type I interferons (IFNs) and a variety of proinflammatory factors through the recruitment of TANK-binding kinase 1 (TBK1) and interferon regulatory factor 3 (IRF3) or the regulation of the nuclear factor kappa-B (NF-κB) pathway. Based on the structure, location, function, genotype, and regulatory mechanism of STING, this review summarizes the potential value of STING inhibitors in the prevention and treatment of infectious diseases, psoriasis, systemic lupus erythematosus, non-alcoholic fatty liver disease, and other inflammatory and autoimmune diseases.

Cell-mediated drug-delivery systems have received considerable attention for their enhanced therapeutic specificity and efficacy in cancer treatment. Neutrophils (NEs), the most abundant type of immune cells, are known to penetrate inflamed brain tumours. Here we show that NEs carrying liposomes that contain paclitaxel (PTX) can penetrate the brain and suppress the recurrence of glioma in mice whose tumour has been resected surgically. Inflammatory factors released after tumour resection guide the movement of the NEs into the inflamed brain. The highly concentrated inflammatory signals in the brain trigger the release of liposomal PTX from the NEs, which allows delivery of PTX into the remaining invading tumour cells. We show that this NE-mediated delivery of drugs efficiently slows the recurrent growth of tumours, with significantly improved survival rates, but does not completely inhibit the regrowth of tumours. Neutrophils carrying drug-containing liposomes can suppress recurrence of brain tumours after surgical removal of the tumour.

Cytosolic DNA activates cGAS (cytosolic DNA sensor cyclic AMP-GMP synthase)-STING (stimulator of interferon genes) signaling, which triggers interferon and inflammatory responses that help defend against microbial infection and cancer. However, aberrant cytosolic self-DNA in Aicardi–Goutière’s syndrome and constituently active gain-of-function mutations in STING in STING-associated vasculopathy with onset in infancy (SAVI) patients lead to excessive type I interferons and proinflammatory cytokines, which cause difficult-to-treat and sometimes fatal autoimmune disease. Here, in silico docking identified a potent STING antagonist SN-011 that binds with higher affinity to the cyclic dinucleotide (CDN)-binding pocket of STING than endogenous 2′3′-cGAMP. SN-011 locks STING in an open inactive conformation, which inhibits interferon and inflammatory cytokine induction activated by 2′3′-cGAMP, herpes simplex virus type 1 infection, Trex1 deficiency, overexpression of cGAS-STING, or SAVI STING mutants. In Trex1 −/− mice, SN-011 was well tolerated, strongly inhibited hallmarks of inflammation and autoimmunity disease, and prevented death. Thus, a specific STING inhibitor that binds to the STING CDN-binding pocket is a promising lead compound for STING-driven disease.

Ice accumulation on heating, ventilation, air conditioning, and refrigeration (HVACR) structures presents significant operational challenges. These challenges include reduced efficiency, increased energy consumption, and potential damage to equipment. Traditional de-icing methods, such as chemical treatments, mechanical scraping, or heating-based techniques, are often labor-intensive, costly, and environmentally harmful. This study uniquely investigates ultrasonic de-icing as an energy-efficient alternative for HVACR applications, focusing on the specific structural geometries found in these systems. A comprehensive numerical simulation framework was developed using finite element analysis to explore ultrasonic wave propagation across four distinct HVACR structures. Key parameters such as ultrasonic frequency, power levels, and the number and placement of actuators were examined for their impact on ice detachment efficiency. Results from simulations on a plate structure reveal that ultrasonic excitation can propagate effectively across large areas (at least 150 × 150 mm), enhancing the de-icing coverage. Lower frequency (e.g., 30 to 45 kHz) excitation results in greater displacement, improving de-icing performance, while increased actuator numbers with the same total power input also enhance effectiveness. Two actuators seem sufficient for the de-icing of a 300 × 300 mm plate. For tube-and-fin structures, specific high-power ultrasonic frequencies selectively excite the fin plates, demonstrating efficient ice removal when actuated on the tube. However, optimal performance requires careful design of actuator placement and vibration modes to accommodate the irregular shapes of these structures.

Delamanintions and reinforcement corrosion are two common problems in concrete bridge decks. No single nondestructive testing method (NDT) is able to provide comprehensive characterization of these defects. In this work, two NDT methods, acoustic scanning and Ground Penetrating Radar (GPR), were used to image a straight concrete bridge deck and a curved intersection ramp bridge. An acoustic scanning system has been developed for rapid delamination mapping. The system consists of metal-ball excitation sources, air-coupled sensors, and a GPS positioning system. The acoustic scanning results are presented as a two-dimensional image that is based on the energy map in the frequency range of 0.5–5 kHz. The GPR scanning results are expressed as the GPR signal attenuation map to characterize concrete deterioration and reinforcement corrosion. Signal processing algorithms for both methods are discussed. Delamination maps from the acoustic scanning are compared with deterioration maps from the GPR scanning on both bridges. The results demonstrate that combining the acoustic and GPR scanning results will provide a complementary and comprehensive evaluation of concrete bridge decks.

Unsorted retired batteries with varied cathode materials hinder the adoption of direct recycling due to their cathode-specific nature. The surge in retired batteries necessitates precise sorting for effective direct recycling, but challenges arise from varying operational histories, diverse manufacturers, and data privacy concerns of recycling collaborators (data owners). Here we show, from a unique dataset of 130 lithium-ion batteries spanning 5 cathode materials and 7 manufacturers, a federated machine learning approach can classify these retired batteries without relying on past operational data, safeguarding the data privacy of recycling collaborators. By utilizing the features extracted from the end-of-life charge-discharge cycle, our model exhibits 1% and 3% cathode sorting errors under homogeneous and heterogeneous battery recycling settings respectively, attributed to our innovative Wasserstein-distance voting strategy. Economically, the proposed method underscores the value of precise battery sorting for a prosperous and sustainable recycling industry. This study heralds a new paradigm of using privacy-sensitive data from diverse sources, facilitating collaborative and privacy-respecting decision-making for distributed systems. Unsorted retired batteries pose recycling challenges due to diverse cathodes. Here, the authors propose a privacy-preserving machine learning system that enables accurate sorting with minimal data, important for a sustainable battery recycling industry.

Producing hydrogen by water electrolysis has attracted significant attention as a potential renewable energy solution. In this work, a catalyst with reduced graphene oxide (rGO) loaded on IrO2/TiO2 (called rGO/IrO2/TiO2) was designed for the catalytic oxygen evolution reaction (OER). The catalyst was synthesized by coating graphene oxide onto a pretreated IrO2/TiO2 precursor, followed by thermal treatment at 450 °C to achieve reduction and the adhesion of graphene to the substrate. The graphene support retained its intact sp2 carbon framework with minor oxygen-containing functional groups, which enhanced electrical conductivity and hydrophilicity. Benefiting from the synergistic effect of an rGO, IrO2, and TiO2 matrix, the rGO/IrO2/TiO2 catalyst only needed overpotentials of 240 mV and 320 mV to reach 10 mA cm−2 and 100 mA cm−2 in the OER, along with excellent stability over 50 h. Its morphology and crystalline structure were characterized by SEM and XRD spectroscopy, and its electrochemical performance was tested by LSV analysis, EIS impedance spectrum, and double-layer capacitance (Cdl) measurements. This work introduces an innovative and eco-friendly strategy for constructing a high-performance, functionalized Ir-based catalyst.

There is growing consensus that gas-fired generators will play a crucial role during the transition to net-zero energy systems, both as an alternative to coal-fired generators and as a flexibility service provider for power systems. However, malfunctions of gas networks have caused several large-scale power blackouts. The transition from coal and oil to gas fuels significantly increases the interdependence between gas networks and electric power systems, raising the risks of more frequent and widespread power blackouts due to the malfunction of gas networks. In a coupled gas–electricity system, the identification and transmission of gas network malfunction information, followed by the redispatch of electric power generation, occur notably faster than the propagation and escalation of the malfunction itself, e.g., significantly diminished pressure. On this basis, we propose a gas-electric early warning system that can reduce the negative impacts of gas network malfunctions on the power system. A proactive control strategy of the power system is also formulated based on the early warning indicators. The effectiveness of this method is demonstrated via case studies of a real coupled gas–electricity system in China. Gas-fired generators play crucial role in the transition to net-zero energy systems as an alternative to coal-fired. But malfunctions of gas networks can cause large-scale power blackouts. Here, authors apply the early warning concept to the coupled gas-electric system towards enhanced power supply security.

Magnetic resonance is essential in revealing the structure and dynamics of biomolecules. However, measuring the magnetic resonance spectrum of single biomolecules has remained an elusive goal. We demonstrate the detection of the electron spin resonance signal from a single spin-labeled protein under ambient conditions. As a sensor, we use a single nitrogen vacancy center in bulk diamond in close proximity to the protein. We measure the orientation of the spin label at the protein and detect the impact of protein motion on the spin label dynamics. In addition, we coherently drive the spin at the protein, which is a prerequisite for studies involving polarization of nuclear spins of the protein or detailed structure analysis of the protein itself.