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Abstract Background Sepsis is a potentially fatal systemic inflammatory response syndrome (SIRS) that threatens millions of lives worldwide. Echinococcus granulosus antigen B (EgAgB) is a protein released by the larvae of the tapeworm. This protein has been shown to play an important role in modulating host immune response. In this study we expressed EgAgB as soluble recombinant protein in E. coli (rEgAgB) and explored its protective effect on sepsis. Methods The sepsis model was established by cecal ligation and puncture (CLP) procedure in BALB/c mice. The therapeutic effect of rEgAgB on sepsis was performed by interperitoneally injecting 5 µg rEgAgB in mice with CLP-induced sepsis and observing the 72 h survival rate after onset of sepsis. The proinflammatory cytokines [tumor necrosis factor (TNF)-α, interleukin (IL)-6] and regulatory cytokines [IL-10, transforming growth factor beta (TGF-β)] were measured in sera, and the histopathological change was observed in livers, kidneys, and lungs of septic mice treated with rEgAgB compared with untreated mice. The effect of rEgAgB on the macrophage polarization was performed in vitro by incubating rEgAgB with peritoneal macrophages. The levels of TLR2 and MyD88 were measured in these tissues to determine the involvement of TLR-2/MyD88 in the sepsis-induced inflammatory signaling pathway. Results In vivo, we observed that treatment with rEgAgB significantly increased the survival rate of mice with CLP-induced sepsis up to 72 h while all mice without treatment died within the same period. The increased survival was associated with reduced pathological damage in key organs such as liver, lung, and kidneys. It was supported by the reduced proinflammatory cytokine levels and increased regulatory cytokine expression in peripheral blood and key organ tissues. Further study identified that treatment with rEgAgB promoted macrophage polarization from classically activated macrophage (M1) to regulatory M2-like macrophage via inhibiting TLR2/MyD88 signal pathway. Conclusions The therapeutic effects of rEgAgB on mice with sepsis was observed in a mice model that was associated with reduced inflammatory responses and increased regulatory responses, possibly through inducing polarization of macrophages from proinflammatory M1 to regulatory M2 phenotype through inhibiting TLR2/MyD88 inflammatory pathway. Graphical Abstract

With the widespread promotion of the concept of green development, China’s green credit policy system has been established, developed, and gradually improved during the past decade. Against the background of the country’s vigorous development of green finance, this finance has had an increasingly important influence on agricultural green total factor productivity (GTFP). In this study, we took 30 provinces (autonomous regions or municipalities directly under central government control) in China as research samples (Hong Kong, Macao, Taiwan, and Tibet were not included due to a lack of data). The time period from 2010 to 2020 was selected as the research period, given that 2010 was the year when the development stage of China’s green finance was first initiated. Through in-depth analysis of the spatial correlations of agricultural GTFP in China and the influences of green finance on agricultural GFTP, we constructed a research framework with multiple dimensions, including green credit, green bonds, green insurance, green investment, and carbon finance. We then systematically studied the influences of green finance on agricultural GTFP. Our results showed that: (1) The development levels of green finance and agricultural GTFP in China were high, but there were not able differences among provinces, with higher agricultural GTFP in northern China and lower agricultural GTFP in central China; (2) green finance had the greatest promoting effect in western China, a weaker promoting effect in central China, and the weakest promoting effect in eastern China; and (3) green finance can indirectly promote improvements in agricultural GTFP by promoting the upgrading of industrial structure, driving technological progress, and optimizing energy consumption structure. Our work not only provides valuable reference data and suggestions for the green and sustainable development of China’s agriculture but also academic support for the development of China’s agricultural economy.

Summary: The manipulation and mechanism of two-dimensional (2D) transition metal dichalcogenides (TMDs) by external electric field are significant to the photoelectric properties. Herein, the 2D MoS2 nanosheets were oxidized to form MoS2-MoO3 local heterojunctions by an electric field, applied in multistable memristors for the proposal of NanoQR code. A modified thermal oxidation model was derived to reveal the mechanism of local electric oxidation on 2D MoS2. From current-voltage curves, the barrier height of the MoS2 device showed an increase of 0.39 eV due to local oxidation after applying voltage for 480 s. Based on density-functional theory, the increase of barrier height was calculated as 0.38 eV between MoS2-MoS2 and MoS2-MoO3 supercells. The 2D MoS2-MoO3 local heterojunctions were further applied as multistable memory storage at the nanoscale. The findings suggest a novel strategy for controlling local electric oxidation on 2D TMDs to manipulate the properties for the application of photoelectric memory nanodevices.

Lithium-ion capacitors （LICs） are novel power energy storage devices， which have the advantages of both lithium-ion batteries and electric double-layer capacitors. However， the slow electrochemical dynamics of the battery-type anode limits the application of LICs. Metal selenides have attracted much attention because of their excellent conductivity， rapid reaction kinetics and high theoretical capacity. In this paper， the classification， structure and properties of metal selenides are systematically introduced， and three energy storage mechanisms of intercalation mechanism， conversion mechanism and alloying mechanism are discussed.Finally， the impacts of structural regulation， carbon material synergy， and bimetallic selenide construction on the electrochemical performance are analyzed. The application of metal selenide materials with structurally stable and high ion/electron transport capability prepared by the three modification methods is introduced in lithium-ion capacitors.

Anisotropic nanopores are essential to regulate materials' anisotropic performance including thermal conductivity, ion blocking and so on. Nevertheless, fabricating anisotropic nanopores remains challenging because the formation mechanism is ambiguous, due to the lack of direct experimental evidence. Here, we reveal a unique strain-induced mechanism for the fabrication of anisotropic nanopores under electron beam irradiation. With the in-situ transmission electron microscopy (TEM) technique, an initial symmetric nanopore, with another nanopore nearby, is found to expand relatively uniformly in the first stage. When the spacing between two nanopores reaches to a critical point, however, the area near to the reference nanopore is stretched drastically by a strong driving force along the spacing direction, resulting in an obvious anisotropic morphology. The surrounding environment, spacing and relative position between two nanopores significantly influence the shape of eventually formed nanopores by controlling the strain's uniformity, action range and direction, respectively. Our work demonstrates a novel mechanism and visual evidence for the preparation of asymmetric nanopores in real time, with the hope of assisting in the controllable fabrication of desired nanostructures.

Highlights The fully-integrated memristor chip for edge learning provides a solid foundation for neural network computation. The fully-integrated memristor chip enables efficient object recognition in noisy backgrounds while minimizing energy consumption. The computing-in-memory chip represents an innovative and interdisciplinary technology that extends beyond multiple research domains.

An accurate panorama depth estimation result is crucial to risk perception in autonomous driving practice. In this paper, an innovative framework is presented to address the challenges of imperfect observation and projection fusion in panorama depth estimation, enabling the accurate capture of distances from surrounding images in driving scenarios. First, the Patch Filling method is proposed to alleviate the imperfect observation of panoramic depth in autonomous driving scenarios, which constructs a panoramic depth map based on the sparse distance data provided by the 3D point cloud. Then, in order to tackle the distortion challenge faced by outdoor panoramic images, a method for image context learning, ViT-Fuse, is proposed and specifically designed for equirectangular panoramic views. The experimental results show that the proposed ViT-Fuse reduces the estimation error by 9.15% on average in driving scenarios compared with the basic method and exhibits more robust and smoother results on the edge details of the depth estimation maps.

Fluorescent temperature measurement has become a research hotspot due to its characteristics of non-contact, miniaturization, and fast response. However, it remains a key challenge to realize excellent sensitivity and high resolution. Herein, a series of La2MgTiO6:Dy3+,Mn4+ phosphors are developed and the related energy transfer between Dy3+→Mn4+ is discovered. The coupling distance between Mn4+ and adjacent ligands is greatly affected by temperature. However, the energy differences between the emission energy levels and lower energy levels of Dy3+ hinder the multi-phonon relaxation process, which moderates the thermal quenching rate of Dy3+. Therefore, highly sensitive FIR thermometers are built according to the significant discrimination temperature dependence of Dy3+ and Mn4+. The maximum Sa and Sr reach 0.022 K−1(@563 K) and 2.622 %K−1(@544 K). In contrast, a thermometer based on the emission lifetime of Mn4+ is fabricated, which is extremely sensitive to temperature. Interestingly, Dy3+ can effectively promote the quenching of Mn4+, and then improve the sensitivity. The maximum Sr enhances from 1.621 %K−1(@503 K) to 2.305 %K−1 (@480 K). The high sensitivity and resolution of the dual-mode fluorescence thermometry have exceeded most of the current optical thermal measurement materials. Besides, the thermochromic properties presented by the designed phosphors can be combined with PDMS films to qualitatively evaluate the ambient temperature.

Abstract N6‐methyladenosine (m6A) modification orchestrates cancer formation and progression by affecting the tumor microenvironment (TME). For hepatocellular carcinoma (HCC), immune evasion and angiogenesis are characteristic features of its TME. The role of YTH N6‐methyladenosine RNA binding protein 2 (YTHDF2), as an m6A reader, in regulating HCC TME are not fully understood. Herein, it is discovered that trimethylated histone H3 lysine 4 and H3 lysine 27 acetylation modification in the promoter region of YTHDF2 enhanced its expression in HCC, and upregulated YTHDF2 in HCC predicted a worse prognosis. Animal experiments demonstrated that Ythdf2 depletion inhibited spontaneous HCC formation, while its overexpression promoted xenografted HCC progression. Mechanistically, YTHDF2 recognized the m6A modification in the 5′‐untranslational region of ETS variant transcription factor 5 (ETV5) mRNA and recruited eukaryotic translation initiation factor 3 subunit B to facilitate its translation. Elevated ETV5 expression induced the transcription of programmed death ligand‐1 and vascular endothelial growth factor A, thereby promoting HCC immune evasion and angiogenesis. Targeting YTHDF2 via small interference RNA‐containing aptamer/liposomes successfully both inhibited HCC immune evasion and angiogenesis. Together, this findings reveal the potential application of YTHDF2 in HCC prognosis and targeted treatment.

Abstract Background The primary pathophysiological process of sepsis is to stimulate a massive release of inflammatory mediators to trigger systemic inflammatory response syndrome (SIRS), the major cause of multi-organ dysfunction and death. Like other helminths, Echinococcus granulosus induces host immunomodulation. We sought to determine whether E. granulosus cyst fluid (EgCF) displays a therapeutic effect on sepsis-induced inflammation and tissue damage in a mouse model. Methods The anti-inflammatory effects of EgCF were determined by in vitro culture with bone marrow-derived macrophages (BMDMs) and in vivo treatment of BALB/C mice with cecal ligation and puncture (CLP)-induced sepsis. The macrophage phenotypes were determined by flow cytometry, and the levels of cytokines in cell supernatants or in sera of mice were measured (ELISA). The therapeutic effect of EgCF on sepsis was evaluated by observing the survival rates of mice for 72 h after CLP, and the pathological injury to the liver, kidney, and lung was measured under a microscope. The expression of TLR-2/MyD88 in tissues was measured by western blot to determine whether TLR-2/MyD88 is involved in the sepsis-induced inflammatory signaling pathway. Results In vitro culture with BMDMs showed that EgCF promoted macrophage polarization to M2 type and inhibited lipopolysaccharide (LPS)-induced M1 macrophages. EgCF treatment provided significant therapeutic effects on CLP-induced sepsis in mice, with increased survival rates and alleviation of tissue injury. The EgCF conferred therapeutic efficacy was associated with upregulated anti-inflammatory cytokines (IL-10 and TGF-β) and reduced pro-inflammatory cytokines (TNF-α and INF-γ). Treatment with EgCF induced Arg-1-expressed M2, and inhibited iNOS-expressed M1 macrophages. The expression of TLR-2 and MyD88 in EgCF-treated mice was reduced. Conclusions The results demonstrated that EgCF confers a therapeutic effect on sepsis by inhibiting the production of pro-inflammatory cytokines and inducing regulatory cytokines. The anti-inflammatory effect of EgCF is carried out possibly through inducing macrophage polarization from pro-inflammatory M1 to regulatory M2 phenotype to reduce excessive inflammation of sepsis and subsequent multi-organ damage. The role of EgCF in regulating macrophage polarization may be achieved by inhibiting the TLR2/MyD88 signaling pathway. Graphical Abstract

Electro-mechanical brake (EMB) systems have garnered significant attention due to their distributed architecture. However, their signals from the brake pedal to the wheel-end actuators (WEAs) are transmitted electrically, meaning that any fault in EMB systems can severely impair the braking performance of vehicles. Consequently, the functional safety issues of EMB systems are the primary limitation of their widespread adoption. In response, this study first introduced the typical architectures of EMB and evaluated the automotive safety integrity level (ASIL) that must be achieved. Based on this, an EMB system architecture that satisfies functional safety standards was proposed. To accurately analyze the main factors affecting the probabilistic metric for hardware failures (PMHF) of the architecture, the failure rate of WEAs is further discussed. Specifically, a Markov chain was employed to define the operating states of the WEA matrix. The availability of each operating state was assessed based on the fault-tolerant control strategy. Finally, the failure rates of critical EMB parts, particularly the WEA matrix, were calculated. The results indicate that the unavailability of the WEA matrix is 9.244 × 10−3 FIT. Furthermore, the PMHFs of the EMB system for each safety goal are 6.14 FIT, 5.89 FIT, and 6.03 FIT, respectively, satisfying the ASIL-D requirements.

Currently, most electromechanical brake (EMB) schemes are only suitable for passenger cars, and their maximum clamping force is insufficient to satisfy the braking demands of commercial vehicles. Additionally, previous studies on clamping force control are largely based on an EMB equipped with sensors. Due to constraints in installation space and cost, sensorless EMBs are gradually gaining attention. Furthermore, accurately identifying the contact point between the friction lining and the brake disc is the promise of clamping force control for sensorless EMBs. Hence, a sensorless EMB scheme suitable for commercial vehicles is proposed in this study. Secondly, a dynamics model of the EMB actuator is established. After a comprehensive analysis of the proposed EMB actuator, a clamping force control strategy considering the contact points between the friction lining and the brake disc is proposed. Finally, simulation analyses of the strategy are carried out. The results show that the axial length of the proposed EMB actuator is shortened by 17.6% compared with a mainstream pneumatic disc brake. Furthermore, the proposed method can accurately identify the contact points between the friction lining and the brake disc, and the proposed control strategy enables the EMB actuator to achieve the fast response, accurate tracking, and stable maintenance of the target clamping force.

Given the complex powertrain of fuel cell electric vehicles (FCEVs) and diversified vehicle platooning synergy constraints, a control strategy that simultaneously considers inter-vehicle synergy control and energy economy is one of the key technologies to improve transportation efficiency and release the energy-saving potential of platooning vehicles. In this paper, an energy-oriented hybrid cooperative adaptive cruise control (eHCACC) strategy is proposed for an FCEV platoon, aiming to enhance energy-saving potential while ensuring stable car-following performance. The eHCACC employs a hybrid cooperative control architecture, consisting of a top-level centralized controller (TCC) and bottom-level distributed controllers (BDCs). The TCC integrates an eco-driving CACC (eCACC) strategy based on the minimum principle and random forest, which generates optimal reference velocity datasets by aligning the comprehensive control objectives of the platoon and addressing the car-following performance and economic efficiency of the platoon. Concurrently, to further unleash energy-saving potential, the BDCs utilize the equivalent consumption minimization strategy (ECMS) to determine optimal powertrain control inputs by combining the reference datasets with detailed optimization information and system states of the powertrain components. A series of simulation evaluations highlight the improved car-following stability and energy efficiency of the FCEV platoon.

Highlights A comprehensive summary of the representative promising applications of metal halide perovskite materials, including traditional optoelectronic devices (solar cells, light-emitting diodes, photodetectors, lasers), and cutting-edge technologies in terms of neuromorphic devices (artificial synapses and memristors) and pressure-induced emission. For each application, the fundamentals of the field, the current progress and the remaining challenges are provided, based on the up-to-date works.

BackgroundPrevious retrospective studies have shown a correlation between depression and increased risk of infections, including a moderate rise in sepsis likelihood associated with severe depression and anxiety. To investigate the potential causal links between depression, sepsis, and mortality risks, while considering confounding factors, we employed a Mendelian randomization (MR) approach.MethodsIn this two-sample Mendelian randomization study, we analyzed data from a large-scale genome-wide association study on depression, involving 807,553 European individuals (246,363 cases, 561,190 controls). We extracted SNP associations with sepsis and 28-day mortality from UK Biobank GWAS outcomes. The correlation analysis primarily employed the inverse-variance weighted method, supplemented by sensitivity analyses for heterogeneity and pleiotropy assessment.ResultsOur analysis revealed a potential causal link between depression and an increased risk of sepsis (OR = 1.246, 95% CI: 1.076-1.442, P = 0.003), but no causal association was found with sepsis-induced mortality risk (OR = 1.274, 95% CI: 0.891-1.823, P = 0.184). Sensitivity analyses confirmed the robustness of these findings.ConclusionsWe identified a potential causal association between depression and heightened sepsis risk, while no link was found with sepsis-induced mortality. These findings suggest that effective management of depression could be important in preventing sepsis.

Sepsis is a disease caused by multiple microbial infections resulting in multiple organ failure. Schistosoma japonicum secreted cystatin (Sj-Cys) is a strong immunomodulator that stimulates M2 macrophages and alleviates inflammatory damage caused by sepsis. To determine whether the therapeutic effect of Sj-Cys on sepsis can be conveyed by the exosomes released by Sj-Cys-stimulated macrophages, RAW264.7 macrophages were stimulated with rSj-Cys in vitro, the exosomes were obtained from the cell culture supernatant by ultracentrifugation. Sepsis was induced in BALB/c mice by cecal ligation and puncture (CLP). The septic mice were treated with exosomes derived from Sj-Cys-treated macrophages. The treatment effect of exosomes on sepsis was assessed by examining the survival rate of mice up to 72 hr and measuring serum levels of inflammatory cytokines, liver/kidney damage biomarkers, and observing pathological changes in tissue sections. The tissue levels of M1, M2 macrophage surface markers, and TRL2/MyD88 were measured to explore possible mechanisms. Results. Exosomes derived from Sj-Cys-treated macrophages exhibited significant therapeutic effect on CLP-induced sepsis in mice with prolonged survival rate and less damage of critical organs by downregulating the proinflammatory factors TNF-α and IL-6 and upregulating the anti-inflammatory factor TGF-β. The therapeutic effect of exosomes is associated with macrophage polarization from M1 to M2 in the infected tissues via downregulating TRL2/MyD88 inflammatory pathway. Conclusions. Exosomes derived from Sj-Cys-treated macrophages attenuated sepsis in mice through promoting macrophage polarization from M1 to M2 and reducing inflammatory responses, possibly via downregulating TLR2/MyD88 inflammatory signaling pathway. This offers new approaches for immunotherapy of sepsis.

The escalating environmental concerns and energy crisis caused by internal combustion engines (ICE) have become unacceptable under environmental regulations and the energy crisis. As a promising alternative solution, multi-power source electric vehicles (MPS-EVs) integrate various clean energy systems to enhance the powertrain efficiency. The energy management strategy (EMS) is plays a pivotal role for MPS-EVs to maximize efficiency, fuel economy, and range. Reinforcement Learning (RL) has emerged as an effective methodology for EMS development, attracting continuous attention and research. However, a systematic analysis of the design elements of RL-based EMS is currently lacking. This paper addresses this gap by presenting a comprehensive analysis of current research on RL-based EMS (RL-EMS) and summarizing its design elements. This paper first summarizes the previous applications of RL in EMS from five aspects: algorithm, perception scheme, decision scheme, reward function, and innovative training method. It highlights the contributions of advanced algorithms to training effectiveness, provides a detailed analysis of perception and control schemes, classifies different reward function settings, and elucidates the roles of innovative training methods. Finally, by comparing the development routes of RL and RL-EMS, this paper identifies the gap between advanced RL solutions and existing RL-EMS. Potential development directions are suggested for implementing advanced artificial intelligence (AI) solutions in EMS.

With the ongoing promotion and adoption of electric vehicles, intelligent and connected technologies have been continuously advancing. Electrical control systems implemented in electric vehicles have emerged as a critical research direction. Various drive-by-wire chassis systems, including drive-by-wire driving and braking systems and steer-by-wire systems, are extensively employed in vehicles. Concurrently, unavoidable issues such as conflicting control system objectives and execution system interference emerge, positioning integrated chassis control as an effective solution to these challenges. This paper proposes a model predictive control-based longitudinal dynamics integrated chassis control system for pure electric commercial vehicles equipped with electro–mechanical brake (EMB) systems, centralized drive, and distributed braking. This system integrates acceleration slip regulation (ASR), a braking force distribution system, an anti-lock braking system (ABS), and a direct yaw moment control system (DYC). This paper first analyzes and models the key components of the vehicle. Then, based on model predictive control (MPC), it develops a controller model for integrated stability with double-layer torque distribution. The required driving and braking torque for each wheel are calculated according to the actual and desired motion states of the vehicle and applied to the corresponding actuators. Finally, the effectiveness of this strategy is verified through simulation results from Matlab/Simulink. The simulation shows that the braking deceleration of the braking condition is increased by 32% on average, and the braking distance is reduced by 15%. The driving condition can enter the smooth driving faster, and the time is reduced by 1.5 s~5 s. The lateral stability parameters are also very much improved compared with the uncontrolled vehicles.

During the braking process of electric vehicles, both the regenerative braking system (RBS) and anti-lock braking system (ABS) modulate the hydraulic braking force, leading to control conflict that impacts the effectiveness and real-time capability of coordinated control. Aiming to enhance the coordinated control effectiveness of RBS and ABS within the electro-hydraulic composite braking system, this paper proposes a coordinated control strategy based on explicit model predictive control (eMPC-CCS). Initially, a comprehensive braking control framework is established, combining offline adaptive control law generation, online optimized control law application, and state compensation to effectively coordinate braking force through the electro-hydraulic system. During offline processing, eMPC generates a real-time-oriented state feedback control law based on real-world micro trip segments, improving the adaptiveness of the braking strategy across different driving conditions. In the online implementation, the developed three-dimensional eMPC control laws, corresponding to current driving conditions, are invoked, thereby enhancing the potential for real-time braking strategy implementation. Moreover, the state error compensator is integrated into eMPC-CCS, yielding a state gain matrix that optimizes the vehicle braking status and ensures robustness across diverse braking conditions. Lastly, simulation evaluation and hardware-in-the-loop (HIL) testing manifest that the proposed eMPC-CCS effectively coordinates the regenerative and hydraulic braking systems, outperforming other CCSs in terms of braking energy recovery and real-time capability.

This paper presents an enhanced ground vehicle localization method designed to address the challenges associated with state estimation for autonomous vehicles operating in diverse environments. The focus is specifically on the precise localization of position and orientation in both local and global coordinate systems. The proposed approach integrates local estimates generated by existing visual–inertial odometry (VIO) methods into global position information obtained from the Global Navigation Satellite System (GNSS). This integration is achieved through optimizing fusion in a pose graph, ensuring precise local estimation and drift-free global position estimation. Considering the inherent complexities in autonomous driving scenarios, such as the potential failures of a visual–inertial navigation system (VINS) and restrictions on GNSS signals in urban canyons, leading to disruptions in localization outcomes, we introduce an adaptive fusion mechanism. This mechanism allows seamless switching between three modes: utilizing only VINS, using only GNSS, and normal fusion. The effectiveness of the proposed algorithm is demonstrated through rigorous testing in the Carla simulation environment and challenging UrbanNav scenarios. The evaluation includes both qualitative and quantitative analyses, revealing that the method exhibits robustness and accuracy.

With the rapid development of the intelligent driving technology, achieving accurate path planning for unmanned vehicles has become increasingly crucial. However, path planning algorithms face challenges when dealing with complex and ever-changing road conditions. In this paper, aiming at improving the accuracy and robustness of the generated path, a global programming algorithm based on optimization is proposed, while maintaining the efficiency of the traditional A* algorithm. Firstly, turning penalty function and obstacle raster coefficient are integrated into the search cost function to increase the adaptability and directionality of the search path to the map. Secondly, an efficient search strategy is proposed to solve the problem that trajectories will pass through sparse obstacles while reducing spatial complexity. Thirdly, a redundant node elimination strategy based on discrete smoothing optimization effectively reduces the total length of control points and paths, and greatly reduces the difficulty of subsequent trajectory optimization. Finally, the simulation results, based on real map rasterization, highlight the advanced performance of the path planning and the comparison among the baselines and the proposed strategy showcases that the optimized A* algorithm significantly enhances the security and rationality of the planned path. Notably, it reduces the number of traversed nodes by 84%, the total turning angle by 39%, and shortens the overall path length to a certain extent.

Advancements in electric vehicle technology have promoted the development trend of smart and low-carbon environmental protection. The design and optimization of electric vehicle braking systems faces multiple challenges, including the reasonable allocation and control of braking torque to improve energy economy and braking performance. In this paper, a multi-source braking force system and its control strategy are proposed with the aim of enhancing braking strength, safety, and energy economy during the braking process. Firstly, an ENMPC (explicit nonlinear model predictive control)-based braking force control strategy is proposed to replace the traditional ABS strategy in order to improve braking strength and safety while providing a foundation for the participation of the drive motor in ABS (anti-lock braking system) regulation. Secondly, a grey wolf algorithm is used to rationally allocate mechanical and electrical braking forces, with power consumption as the fitness function, to obtain the optimal allocation method and provide potential for EMB (electro–mechanical brake) optimization. Finally, simulation tests verify that the proposed method can improve braking strength, safety, and energy economy for different road conditions, and compared to other methods, it shows good performance.

Summary: The application of wearable intelligent systems toward human-computer interaction has received widespread attention. It is still desirable to conveniently promote health and monitor sports skills for disabled people. Here, a wireless intelligent sensing system (WISS) has been developed, which includes two ports of wearable flexible triboelectric nanogenerator (WF-TENG) sensing and an upper computer digital signal receiving intelligent processing. The WF-TENG sensing port is connected by the WF-TENG sensor and flexible printed circuit (FPC). Due to its flexibility, the WF-TENG sensing port can be freely adhered on the surface of human skin. The WISS can be applied to entertainment reaction training based on human-computer interaction, and to the technical judgment and analysis on wheelchair curling sport. This work provides new application opportunities for wearable devices in the fields of sports skills monitoring, sports assistive devices and health promotion for disabled people.

Optical thermometry has the distinct superiority of non-invasive, non-contact, and high spatial resolution. However, the simultaneous improvement of temperature sensitivity and resolution remains challenging. Herein, the tunable emission colors can be realized via energy transfer in Bi3+-Sm3+ co-doped La3Ta0.8Sb0.2O7, which further applied for fluorescence temperature sensing and imaging. With the partial substitution of Sb5+ with Ta5+, the emission peak of La3SbO7:0.04Bi3+ shiftes from 530 nm to 460 nm, together with a 2.1-fold enhancement of emission intensity. The emission shifted from bright blue to purple under 365 nm excitation based on the energy transfer. The energy transfer efficiency from Bi3+ to Sm3+ can reach 41.6 % via dipole–dipole interaction. The La3Ta0.8Sb0.2O7:(0.04Bi3+,0.005Sm3+) phosphors exhibit maximum relative sensitivity (Sr) of 1.32 %K−1 (@498 K) and absolute sensitivity (Sa) of 0.059 K−1 (@417 K), which have excellent temperature resolution and repeatability. Thus, the energy transfer in co-coped phosphors can simultaneously improve the temperature sensitivity and resolution for fluorescence temperature sensing. Besides, the phosphor/PDMS films can exhibit obvious thermochromic imaging.

In this study, a full brake-by-wire system with a dual-drive motor as a redundant power source and a planetary roller screw (PRSM) as a motion conversion mechanism is proposed for automated driving and wire-controlled chassis. Its parameters are matched, and the prototype is trial-produced. Aiming at the proposed full brake-by-wire system, a clamping force estimation control strategy is proposed that comprehensively considers electric caliper dynamics, stiffness, and friction models. In particular, considering the influence of motor temperature on the output torque, a temperature estimation model based on the proximal policy optimization-reinforcement learning (PPO-RL) algorithm was proposed, which was proven to effectively predict the motor temperature with an average error of only 1.06%. Finally, a hardware-in-the-loop test was performed, and the experimental results verified the effectiveness and superiority of the proposed system and control strategy, with a fast response, good clamping force continuous-following effect, and high clamping force estimation accuracy.

Free lane change (FLC) is an important research direction of intelligent driving vehicles. In this paper, a lane change decision model based on deep learning is established with ego and adjacent lane risk and properties of environmental vehicles. A long short-term memory neural network that can be associated with time series characteristics is used to model the lane change process based on analysis of factors affecting the lane change decision. The result of decision-making model training shows that the recognition accuracy of lane change and lane keeping decision reaches more than 92%. Then, a human-like FLC trajectory is planned based on polynomial curve, a quintic polynomial trajectory cluster based on preview distance is generated according to driving conditions, and the optimal FLC trajectory is selected by optimizing an objective function. The model predictive control method is used to dynamically control the vehicle to follow the trajectory. Finally, the lane change decision, trajectory planning, and tracking control model are built in simulation environment, and the control of vehicle dynamics model verifies the integrity and feasibility of free lane change function.

Summary: Severe weather conditions pose a significant challenge for computer vision algorithms in autonomous driving applications, particularly regarding robustness. Image rain-removal algorithms have emerged as a potential solution by leveraging the power of neural networks to restore rain-free backgrounds in images. However, existing research overlooks the vulnerability concerns in neural networks, which exposes a potential threat to the intelligent perception of autonomous vehicles in rainy conditions. This paper proposes a universal rain-removal attack (URA) that exploits the vulnerability of image rain-removal algorithms. By generating a non-additive spatial perturbation, URA significantly diminishes scene restoration similarity and image quality. The imperceptible and generic perturbation employed by URA makes it a crucial tool for vulnerability detection in image rain-removal algorithms and a potential real-world AI attack method. Experimental results demonstrate that URA can reduce scene repair capability by 39.5% and image generation quality by 26.4%, effectively targeting state-of-the-art rain-removal algorithms.

The traditional von Neumann computing architecture has relatively-low information processing speed and high power consumption, making it difficult to meet the computing needs of artificial intelligence (AI). Neuromorphic computing systems, with massively parallel computing capability and low power consumption, have been considered as an ideal option for data storage and AI computing in the future. Memristor, as the fourth basic electronic component besides resistance, capacitance and inductance, is one of the most competitive candidates for neuromorphic computing systems benefiting from the simple structure, continuously adjustable conductivity state, ultra-low power consumption, high switching speed and compatibility with existing CMOS technology. The memristors with applying MXene-based hybrids have attracted significant attention in recent years. Here, we introduce the latest progress in the synthesis of MXene-based hybrids and summarize their potential applications in memristor devices and neuromorphological intelligence. We explore the development trend of memristors constructed by combining MXenes with other functional materials and emphatically discuss the potential mechanism of MXenes-based memristor devices. Finally, the future prospects and directions of MXene-based memristors are briefly described.

Abstract Background MicroRNAs (miRNAs) play crucial roles in the development of hepatocellular carcinoma (HCC). Hsa-microRNA-27b-3p (hsa-miR-27b) is involved in the formation and progression of various cancers, but its role and clinical value in HCC remain unclear. Methods The expression of hsa-miR-27b in HCC was examined by quantitative real-time PCR (qRT-PCR) and in situ hybridization (ISH) assays of clinical samples. Cell Counting Kit-8 assays (CCK-8), 5-ethynyl-2′-deoxyuridine (EdU) incorporation assays, Transwell assays, filamentous actin (F-actin) staining and western blot analyses were used to determine the effects of hsa-miR-27b on HCC cells in vitro. Subcutaneous xenograft and lung metastatic animal experiments were conducted to verify the role of hsa-miR-27b in HCC in vivo. In silico prediction, qRT-PCR, western blot, anti-Argonaute 2 (AGO2) RNA immunoprecipitation (RIP) and dual luciferase reporter assays were applied to identify the target genes of hsa-miR-27b. To detect the impacts of hsa-miR-27b on nuclear factor kappa B (NF-кB) signalling cascades mediated by transforming growth factor-activated kinase-binding protein 3 (TAB3), we performed qRT-PCR, western blot assays, immunofluorescence staining, immunohistochemistry (IHC) and dual-luciferase reporter assays. Recombinant oncolytic adenovirus (OncoAd) overexpressing hsa-miR-27b was constructed to detect their therapeutic value in HCC. Results The expression of hsa-miR-27b was lower in HCC than in adjacent non-tumourous tissues (ANTs), and the reduced expression of hsa-miR-27b was associated with worse outcomes in patients with HCC. Hsa-miR-27b significantly inhibited the proliferation, migration, invasion, subcutaneous tumour growth and lung metastasis of HCC cells. The suppression of hsa-miR-27b promoted the nuclear translocation of NF-κB by upregulating TAB3 expression. TAB3 was highly expressed in HCC compared with ANTs and was negatively correlated with the expression of hsa-miR-27b. The impaired cell proliferation, migration and invasion by hsa-miR-27b overexpression were recovered by ectopic expression of TAB3. Recombinant OncoAd with overexpression of hsa-miR-27b induced anti-tumour activity compared with that induced by negative control (NC) OncoAd in vivo and in vitro. Conclusions By targeting TAB3, hsa-miR-27b acted as a tumour suppressor by inactivating the NF-кB pathway in HCC in vitro and in vivo, indicating its therapeutic value against HCC. Graphical Abstract

The major constituents of sand are silica and silicates. The facile and low-cost purification technology of silica from natural sand is of magnificent importance to the industrial applications of silicon-based materials. Herein, we report a green, low-cost, and universal method to purify silica from natural sand. Sand from deserts, rivers, and seas is selected as the representative of natural sand. The initial purity of silica is 52.1 wt% for desert sand, 39.3 wt% for river sand, and 35.8 wt% for sea sand. High-purity silica has been successfully separated and purified from natural sand via ball milling, reacting with 30 bar CO2 and hydrochloric acid. The purity of silica derived from natural sand reaches >96 wt%. In this work, the mechanism for the purification of silica from natural sand is discussed. This study provides a new method to separate high-purity silica from natural sand without the generation of toxic and harmful substances.

Summary: Memristors are promising information storage devices for commercial applications because of their long endurance and low power consumption. Particularly, perovskite memristors have revealed excellent resistive switching (RS) properties owing to the fast ion migration and solution fabrication process. Here, an n-i-p type double perovskite memristor with “ITO/SnO2/Cs2AgBiBr6/NiOx/Ag” architecture was developed and demonstrated to reveal three resistance states because of the p-n junction electric field coupled with ion migration. The devices exhibited reliable filamentary with an on/off ratio exceeding 50. The RS characteristics remained unchanged after 1000 s read and 300 switching cycles. The synaptic functions were examined through long-term depression and potentiation measurements. Significantly, the device still worked after one year to reveal long-term stability because of the all-inorganic layers. This work indicates a novel idea for designing a multistate memristor by utilizing the p-n junction unidirectional conductivity during the forward and reverse scanning.

Abstract As the most common and abundant RNA modification in eukaryotic cells, N6-methyladenosine (m6A) modification plays an important role in different stages of tumor. m6A can participate in the regulation of tumor immune escape, so as to enhance the monitoring of tumor by the immune system and reduce tumorgenesis. m6A can also affect the tumor progression by regulating the immune cell responses to tumor in tumor microenvironment. In addition, immunotherapy has become the most popular method for the treatment of cancer, in which targets such as immune checkpoints are also closely associated with m6A. This review discusses the roles of N6-methyladenosine modification in tumor immune regulation, their regulatory mechanism, and the prospect of immunotherapy. Graphical Abstract

The complexity inherent in navigating intricate traffic environments poses substantial hurdles for intelligent driving technology. The continual progress in mapping and sensor technologies has equipped vehicles with the capability to intricately perceive their exact position and the intricate interplay among surrounding traffic elements. Building upon this foundation, this paper introduces a deep reinforcement learning method to solve the decision-making and trajectory planning problem of intelligent vehicles. The method employs a deep learning framework for feature extraction, utilizing a grid map generated from a blend of static environmental markers such as road centerlines and lane demarcations, in addition to dynamic environmental cues including vehicle positions across varied lanes, all harmonized within the Frenet coordinate system. The grid map serves as the input for the state space, and the input for the action space comprises a vector encompassing lane change timing, velocity, and vertical displacement at the lane change endpoint. To optimize the action strategy, a reinforcement learning approach is employed. The feasibility, stability, and efficiency of the proposed method are substantiated via experiments conducted in the CARLA simulator across diverse driving scenarios, and the proposed method can increase the average success rate of lane change by 6.8% and 13.1% compared with the traditional planning control algorithm and the simple reinforcement learning method.

Abstract YT521-B homology (YTH) domain-containing proteins (YTHDF1-3, YTHDC1-2) are the most crucial part of N6-methyladenosine (m6A) readers and play a regulatory role in almost all stages of methylated RNA metabolism and the progression of various cancers. Since m6A is identified as an essential post-transcriptional type, YTH domain-containing proteins have played a key role in the m6A sites of RNA. Hence, it is of great significance to study the interaction between YTH family proteins and m6A-modified RNA metabolism and tumor. In this review, their basic structure and physical functions in RNA transcription, splicing, exporting, stability, and degradation as well as protein translation are introduced. Then we discussed the expression regulation of YTH domain-containing proteins in cancers. Furthermore, we introduced the role of the YTH family in cancer biology and systematically demonstrated their functions in various aspects of tumorigenesis and development. To provide a more institute understanding of the role of YTH family proteins in cancers, we summarized their functions and specific mechanisms in various cancer types and presented their involvement in cancer-related signaling pathways.

Abstract An ongoing outbreak of severe respiratory illness and pneumonia caused by the severe acute respiratory coronavirus 2 (SARS-CoV-2) commenced in December 2019, and the disease was named as coronavirus disease 2019 (COVID-19). Soon after, scientists identified the characteristics of SARS-CoV-2, including its genome sequence and protein structure. The clinical manifestations of COVID-19 have now been established; and nucleic acid amplification is used for the direct determination of the virus, whereas immunoassays can determine the antibodies against SARS-CoV-2. Clinical trials of several antiviral drugs are ongoing. However, there is still no specific drugs to treat COVID-19. Traditional Chinese medicine (TCM) was used in the treatment of COVID-19 during the early stages of the outbreak in China. Some ancient TCM prescriptions, which were efficacious in the treatment of severe acute respiratory syndrome (SARS) in 2002–03 and the influenza pandemic (H1N1) of 2009, have been improved by experienced TCM practitioners for the treatment of COVID-19 based on their clinical symptoms. These developed new prescriptions include Lianhua Qingwen capsules/granules, Jinhua Qinggan granules and XueBiJing injection, among others. In this review, we have summarized the presenting features of SARS-CoV-2, the clinical characteristics of COVID-19, and the progress in the treatment of COVID-19 using TCMs.

Perovskite solar cells (Pero-SCs) exhibited a bright future for the next generation of photovoltaic technology because of their high power conversion efficiency (PCE), low cost, and simple solution process. The certified laboratory-scale PCE has reached 25.7% referred to small scale (2) of Pero-SCs. However, with the increase of the area to module scale, the PCE drops dramatically mainly due to the inadequate regulation of growing large-area perovskite films. Therefore, there is a dire need to produce high-quality perovskite films for large-area photovoltaic modules. Herein, we summarize the recent advances in perovskite photovoltaic modules (PPMs) with particular attention paid to the coating methods, as well as the growth regulation of the high-quality and large-area perovskite films. Furthermore, this study encompasses future development directions and prospects for PPMs.

Under the trend of vehicle intelligentization, many electrical control functions and control methods have been proposed to improve vehicle comfort and safety, among which the Adaptive Cruise Control (ACC) system is a typical example. However, the tracking performance, comfort and control robustness of the ACC system need more attention under uncertain environments and changing motion states. Therefore, this paper proposes a hierarchical control strategy, including a dynamic normal wheel load observer, a Fuzzy Model Predictive Controller and an integral-separate PID executive layer controller. Firstly, a deep learning-based dynamic normal wheel load observer is added to the perception layer of the conventional ACC system and the observer output is used as a prerequisite for brake torque allocation. Secondly, a Fuzzy Model Predictive Control (fuzzy-MPC) method is adopted in the ACC system controller design, which establishes performance indicators, including tracking performance and comfort, as objective functions, dynamically adjusts their weights and determines constraint conditions based on safety indicators to adapt to continuously changing driving scenarios. Finally, the executive controller adopts the integral-separate PID method to follow the vehicle’s longitudinal motion commands, thus improving the system’s response speed and execution accuracy. A rule-based ABS control method was also developed to further improve the driving safety of vehicles under different road conditions. The proposed strategy has been simulated and validated in different typical driving scenarios and the results show that the proposed method provides better tracking accuracy and stability than traditional techniques.

Abstract Neuromorphic computing simulates the operation of biological brain function for information processing and can potentially solve the bottleneck of the von Neumann architecture. This computing is realized based on memristive hardware neural networks in which synaptic devices that mimic biological synapses of the brain are the primary units. Mimicking synaptic functions with these devices is critical in neuromorphic systems. In the last decade, electrical and optical signals have been incorporated into the synaptic devices and promoted the simulation of various synaptic functions. In this review, these devices are discussed by categorizing them into electrically stimulated, optically stimulated, and photoelectric synergetic synaptic devices based on stimulation of electrical and optical signals. The working mechanisms of the devices are analyzed in detail. This is followed by a discussion of the progress in mimicking synaptic functions. In addition, existing application scenarios of various synaptic devices are outlined. Furthermore, the performances and future development of the synaptic devices that could be significant for building efficient neuromorphic systems are prospected.

This paper introduces an energy management strategy design method for a plug-in hybrid electric vehicle based on the data-driven approach and online signal analysis. It includes two parts, mode division strategy design, and power distribution strategy design. Using the random forest in data mining technology to analyze optimization results of dynamic programming can quickly extract key information and establish optimal and understandable mode division strategy with high precision and stability directly. Besides, integrating the classic “Engine optimal operating curve control” strategy with wavelet transform and Markov prediction, which not only enhances the adaptability of the strategy to different driving conditions but also improves the fuel economy by reducing the impact of transient power on the engine operation. At the same time, to improve the prediction accuracy of the algorithm without increasing the computational complexity, this paper adds a prediction result correction function to the first-order Markov prediction model to reduce the impact of slow update of the probability matrix on prediction accuracy. The simulation results show the average prediction error of the improved Markov prediction model is reduced by 5.3% and the new energy management strategy designed reduces fuel consumption by 8.28% at the cost of a small increase in electricity consumption.

Hepatocellular carcinoma (HCC) is the subtype of liver cancer with the highest incidence, which is a heterogeneous malignancy with increasing incidence rate and high mortality. For ethical reasons, it is essential to validate medical clinical trials for HCC in animal models before further consideration on humans. Therefore, appropriate models for the study of the pathogenesis of the disease and related treatment methods are necessary. For tumor research, mouse models are the most commonly used and effective in vivo model, which is closer to the real-life environment, and the repeated experiments performed on it are closer to the real situation. Several mouse models of HCC have been developed with different mouse strains, cell lines, tumor sites, and tumor formation methods. In this review, we mainly introduce some mouse HCC models, including induced model, gene-edited model, HCC transplantation model, and other mouse HCC models, and discuss how to choose the appropriate model according to the purpose of the experiments.

Abstract Organic cation and halide anion defects are omnipresent in the perovskite films, which will destroy perovskite electronic structure and downgrade the properties of devices. Defect passivation in halide perovskites is crucial to the application of solar cells. Herein, tiny amounts of trivalent rhodium ion incorporation can help the nucleation of perovskite grain and passivate the defects in the grain boundaries, which can improve efficiency and stability of perovskite solar cells. Through first-principle calculations, rhodium ion incorporation into the perovskite structure can induce ordered arrangement and tune bandgap. In experiment, rhodium ion incorporation with perovskite can contribute to preparing larger crystalline and uniform film, reducing trap-state density and enlarging charge carrier lifetime. After optimizing the content of 1% rhodium, the devices achieved an efficiency up to 20.71% without obvious hysteresis, from 19.09% of that pristine perovskite. In addition, the unencapsulated solar cells maintain 92% of its initial efficiency after 500 h in dry air. This work highlights the advantages of trivalent rhodium ion incorporation in the characteristics of perovskite solar cells, which will promote the future industrial application.

Abstract Background Myocardial dysfunction is one of the most common complications of multiple organ failure in septic shock and significantly increases mortality in patients with sepsis. Although many studies having confirmed that helminth-derived proteins have strong immunomodulatory functions and could treat inflammatory diseases, there is no report on the therapeutic effect of Schistosoma japonicum-produced cystatin (Sj-Cys) on sepsis-induced cardiac dysfunction. Methods A model of sepsis-induced myocardial injury was established by cecal ligation and puncture (CLP) in mice. Upon CLP operation, each mouse was intraperitoneally treated with 10 µg of recombinant Sj-Cys (rSj-Cys). Twelve hours after CLP, the systolic and diastolic functions of the left ventricular were examined by echocardiography. The levels of myoglobin (Mb), cardiac troponin I (cTnI), N-terminal pro-Brain Natriuretic peptide (NT-proBNP) in sera, and the activity of myeloperoxidase (MPO) in cardiac tissues were examined as biomarkers for heart injury. The heart tissue was collected for checking pathological changes, macrophages and pro-inflammatory cytokine levels. To address the signaling pathway involved in the anti-inflammatory effects of rSj-Cys, myeloid differentiation factor 88 (MyD88) was determined in heart tissue of mice with sepsis and LPS-stimulated H9C2 cardiomyocytes. In addition, the therapeutic effects of rSj-Cys on LPS-induced cardiomyocyte apoptosis were also detected. The levels of M1 biomarker iNOS and M2 biomarker Arg-1 were detected in heart tissue. The pro-inflammatory cytokines TNF-α and IL-6, and regulatory cytokines IL-10 and TGF-β were measured in sera and their mRNA levels in heart tissue of rSj-Cys-treated mice. Results After rSj-Cys treatment, the sepsis-induced heart malfunction was largely improved. The inflammation and injury of heart tissue were significantly alleviated, characterized as significantly decreased infiltration of inflammatory cells in cardiac tissues and fiber swelling, reduced levels of Mb, cTnI and NT-proBNP in sera, and MPO activity in heart tissue. The therapeutic efficacy of rSj-Cys is associated with downregulated pro-inflammatory cytokines (TNF-α and IL-6) and upregulated regulatory inflammatory cytokines (IL-10 and TGF-β), possibly through inhibiting the LPS-MyD88 signal pathway. Conclusions RSj-Cys significantly reduced sepsis-induced cardiomyopathy and could be considered as a potential therapeutic agent for the prevention and treatment of sepsis associated cardiac dysfunction.

This work presents the control strategies for the configuration involving an open-end winding induction motor (OW-IM) fed by dual two-level inverters, which can realize the controllable power sharing between two isolated power sources without using the DC/DC converter. Based on the mathematical model of the OW-IM and the principles of the voltage vector distribution, two rotor flux calculation algorithms, namely the motor loss minimization (MLM) algorithm and the maximum power sharing capability (MPSC) algorithm are proposed and compared, with their selection strategy to select the suitable algorithm depending on the operating condition. The MLM algorithm has the optimal efficiency performance, while the MPSC algorithm can maximize the power sharing capability of the dual inverter while holding the widest operation range of the motor. Based on the dual space vector pulse-width modulation (SVPWM) control scheme, the golden section (GS) search method is introduced to allocate the stator voltage vector to the dual inverter for the power sharing demand, with a flexible iteration number to obtain a stable and efficient computational performance, which fully utilizes the power sharing capability via the voltage vector distribution. Simulation results prove the validity and the advantages of the proposed strategy. This system also lays a foundation for energy management function of the dual-power electric vehicles.

The Cooperative Adaptive Cruise Control (CACC) is considered to be an effective method to improve traffic flow. However, the comfortability and economy need to be paid more attention to besides stability and safety. In this paper, a CACC strategy is proposed for an electric vehicle platoon to improve the economy, following performance, safety, and comfortability characteristics, based on the model predictive control (MPC) with simulated annealing-particle swarm optimization (SA-PSO) algorithm. Firstly, the braking force distribution strategy of electric vehicles is designed to improve the efficiency of regenerative braking. Secondly, based on the variable vehicle spacing with fixed time headway, a vehicle platoon following strategy is established to meet the following performance and safety. Thirdly, an MPC controller is used to control the states of the vehicles in the platoon to satisfy the performance. A multi-objective function of the MPC controller is established, including the economy, following performance, comfortability, and safety of the vehicles in the platoon. The SA-PSO algorithm effectively solves the problem of the discrete variables in the objective function. Simulations are conducted to validate the sufficient conditions of the economy, following performance, comfortability, and safety. Simulation results demonstrate that the economic efficiency of the CACC strategy with the economic index is 16.5% higher than that of the existing ACC strategy. Meanwhile, the other characteristics can also meet the control requirement.

Oxidative stress (OS) refers to a state of imbalance between oxidation and antioxidation. OS is considered to be an important factor leading to aging and a range of diseases. The eyes are highly oxygen-consuming organs. Due to its continuous exposure to ultraviolet light, the eye is particularly vulnerable to the impact of OS, leading to eye diseases such as corneal disease, cataracts, glaucoma, etc. The N6-methyladenosine (m6A) modification is the most investigated RNA post-transcriptional modification and participates in a variety of cellular biological processes. In this study, we review the role of m6A modification in oxidative stress-induced eye diseases and some therapeutic methods to provide a relatively overall understanding of m6A modification in oxidative stress-related eye diseases.

The vehicle longitudinal control algorithm is the core function of the adaptive cruise control system, whose main task is to convert vehicle acceleration and deceleration requirements into vehicle driving and braking commands so that the vehicle can quickly and accurately track the desired acceleration. Traditional longitudinal control algorithms rely on accurate vehicle dynamic modeling or complex controller parameter calibrations. To overcome those difficulties, a longitudinal control algorithm based on RBF-PID is proposed in this paper. The algorithm uses the RBFNN (radial basis function neural network), which can simply and quickly approximate any complex nonlinear system, to identify the Jacobian information of the vehicle and perform parameter tuning for PID control and achieve vehicle longitudinal control with self-tuning capability. Finally, the algorithm of this paper is verified by the joint simulation of Matlab/Simulink and Carsim. The results show that this algorithm has a better response rate and anti-jamming capability than the traditional PID control and can achieve accurate and rapid tracking of the desired acceleration.

The mechanical coupling of multiple powertrain components makes the energy management of 4-wheel-drive (4WD) plug-in fuel cell electric vehicles (PFCEVs) relatively complex. Optimizing energy management strategies (EMSs) for this complex system is essential, aiming at improving the vehicle economy and the adaptability of operating conditions. Accordingly, a novel adaptive equivalent consumption minimization strategy (A-ECMS) based on the dragonfly algorithm (DA) is proposed to achieve coordinated control of the powertrain components, front and rear motors, as well as the fuel cell system and the battery. To begin with, the equivalent consumption minimization strategy (ECMS) with extraordinary instantaneous optimization ability is used to distribute the vehicle demand power into the front and rear motor power, considering the different motor characteristics. Subsequently, under the proposed novel hierarchical energy management framework, the well-designed A-ECMS based on DA empowers PFCEVs with significant energy-saving advantages and adaptability to operating conditions, which are achieved by precise power distribution considering the operating characteristics of the fuel cell system and battery. These provide state-of-the-art energy-saving abilities for the multi-degree-of-freedom systems of PFCEVs. Lastly, a series of detailed evaluations are performed through simulations to validate the improved performance of A-ECMS. The corresponding results highlight the optimal control performance in the energy-saving performance of A-ECMS.

Atherosclerosis is a chronic inflammation of the arterial vessel wall driven by lipid metabolism disorders. Although helminthic infection and their derivatives have been identified to attenuate the chronic inflammatory diseases, the immunomodulatory effect of recombinant Schistosoma japonicum cystatin (rSj-Cys) on metabolic diseases and atherosclerosis has not been reported. In this study, we investigated the therapeutic efficacy of rSj-Cys on atherosclerotic renal damage and explored the related immunological mechanism. The results demonstrated that treatment with rSj-Cys significantly reduced body weight gain, hyperlipidemia, and atherosclerosis induced by the high-fat diet in apoE–/– mice. The treatment of rSj-Cys also significantly improved kidney functions through promoting macrophage polarization from M1 to M2, therefore inhibiting M1 macrophage–induced inflammation. The possible mechanism underlying the regulatory effect of rSj-Cys on reducing atherosclerosis and atherosclerotic renal damage is that rSj-Cys stimulates regulatory T cell and M2 macrophage polarization that produce regulatory cytokines, such as interleukin 10 and transforming growth factor β. The therapeutic effect of rSj-Cys on atherosclerotic renal damage is possibly through inhibiting the activation of TLR2/Myd88 signaling pathway. The results in this study provide evidence for the first time that Schistosoma-derived cystatin could be developed as a therapeutic agent to treat lipid metabolism disorder and atherosclerosis that threats million lives around the world.

Energy management strategies are vitally important to give full play to the energy-saving of the four-wheel drive electric vehicle (4WD EV). The cooperative output of multi-power components is involved in the process of driving and braking energy recovery of 4WD EV. This paper proposes a novel energy management strategy of dual equivalent consumption minimization strategy (D-ECMS) to improve the economy of the vehicle. According to the different driving and braking states of the vehicle, D-ECMS can realize the proportional control of the energy cooperative output among the multi-power components. Under the premise of satisfying the dynamic performance of the vehicle, the operating points of the power components are distributed more in the high-efficiency range, and the economy and driving range of the vehicle are optimized. In order to achieve the effectiveness of D-ECMS, MATLAB/Simulink is used to realize the simulation of the vehicle. Compared with the rule-based strategy, the economy of D-ECMS increased by 4.35%.

An energy management strategy is a key technology used to exploit the energy-saving potential of a plug-in hybrid electric vehicle. This paper proposes the environmental perceiver-based equivalent consumption minimization strategy (EP-ECMS) for parallel plug-in hybrid vehicles. In this method, the traffic characteristic information obtained from the intelligent traffic system is used to guide the adjustment of the equivalence factor, improving the environmental adaptiveness of the equivalent consumption minimization strategy (ECMS). Two main works have been completed. First, a high-accuracy environmental perceiver was developed based on a graph convolutional network (GCN) and attention mechanism to complete the traffic state recognition of all graph regions based on historical information. Moreover, it provides the grade of the corresponding region where the vehicle is located (for the ECMS). Secondly, in the offline process, the search for the optimal equivalent factor is completed by using the Harris hawk optimization algorithm based on the representative working conditions under various grades. Based on the identified traffic grades in the online process, the optimized equivalence factor tables are checked for energy management control. The simulation results show that the improved EP-ECMS can achieve 7.25% energy consumption optimization compared with the traditional ECMS.