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This article studies the optimal synchronization of linear heterogeneous multiagent systems (MASs) with partial unknown knowledge of the system dynamics. The object is to realize system synchronization as well as minimize the performance index of each agent. A framework of heterogeneous multiagent graphical games is formulated first. In the graphical games, it is proved that the optimal control policy relying on the solution of the Hamilton–Jacobian–Bellmen (HJB) equation is not only in Nash equilibrium, but also the best response to fixed control policies of its neighbors. To solve the optimal control policy and the minimum value of the performance index, a model-based policy iteration (PI) algorithm is proposed. Then, according to the model-based algorithm, a data-based off-policy integral reinforcement learning (IRL) algorithm is put forward to handle the partially unknown system dynamics. Furthermore, a single-critic neural network (NN) structure is used to implement the data-based algorithm. Based on the data collected by the behavior policy of the data-based off-policy algorithm, the gradient descent method is used to train NNs to approach the ideal weights. In addition, it is proved that all the proposed algorithms are convergent, and the weight-tuning law of the single-critic NNs can promote optimal synchronization. Finally, a numerical example is proposed to show the effectiveness of the theoretical analysis.

Recently, the application of finger vein recognition has become popular. Studies have shown finger vein presentation attacks increasingly threaten these recognition devices. As a result, research on finger vein presentation attack detection (fvPAD) methods has received much attention. However, the current fvPAD methods have two limitations. (1) Most terminal devices cannot train fvPAD models independently due to a lack of data. (2) Several research institutes can train fvPAD models; however, these models perform poorly when applied to terminal devices due to inadequate generalisation. Consequently, it is difficult for threatened terminal devices to obtain an effective fvPAD model. To address this problem, the method of federated finger vein presentation attack detection for various clients is proposed, which is the first study that introduces federated learning (FL) to fvPAD. In the proposed method, the differences in data volume and computing power between clients are considered. Traditional FL clients are expanded into two categories: institutional and terminal clients. For institutional clients, an improved triplet training mode with FL is designed to enhance model generalisation. For terminal clients, their inability is solved to obtain effective fvPAD models. Finally, extensive experiments are conducted on three datasets, which demonstrate the superiority of our method. This is the first study that introduced and improved FL to the fvPAD task. The problem of resource‐limited biometric terminals that cannot perform presentation attack detection is solved and the generalisation of the detection model is improved. Numerous experiments have demonstrated the superiority of this method in terms of accuracy and robustness compared to other methods.

To assess the effects of moxibustion on immune reconstitution inflammatory syndrome (IRIS) in patients with acquired immune deficiency syndrome (AIDS) by tracking T-cell subsets over a 48-week prospective cohort study.

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Liquid crystal monomers (LCMs) are emerging organic pollutants due to their potential persistence, toxicity, and bioaccumulation. This study first characterized the levels and compositions of 19 LCMs in organisms in the Pearl River Estuary (PRE), estimated their bioaccumulation and trophic transfer potential, and identified priority contaminants. LCMs were generally accumulated in organisms from sediment, and the LCM concentrations in all organisms ranged from 32.35 to 1367 ng/g lipid weight. The main LCMs in organisms were biphenyls and analogues (BAs) (76.6%), followed by cyanobiphenyls and analogues (CBAs) (15.1%), and the least were fluorinated biphenyls and analogues (FBAs) (11.2%). The most abundant LCM monomers of BAs, FBAs, and CBAs in LCMs in organisms were 1-(4-propylcyclohexyl)-4-vinylcyclohexane (15.1%), 1-ethoxy-2,3-difluoro-4-(4-(4-propylcyclohexyl) cyclohexyl) benzene (EDPBB, 10.1%), and 4′-propoxy-4-biphenylcarbonitrile (5.1%), respectively. The niche studies indicated that the PRE food web was composed of terrestrial-based diet and marine food chains. Most LCMs exhibited biodilution in the terrestrial-based diet and marine food chains, except for EDPBB and 4,4′-bis(4-propylcyclohexyl) biphenyl (BPCHB). The hydrophobicity, position of fluorine substitution of LCMs, and biological habits may be important factors affecting the bioaccumulation and trophic transfer of LCMs. BPCHB, 1-(prop-1-enyl)-4-(4-propylcyclohexyl) cyclohexane, and EDPBB were characterized as priority contaminants. This study first reports the trophic transfer processes and mechanisms of LCMs and the biomonitoring in PRE.

The crucial role of zwitterionic phosphatidylcholines (PC) within mucus gel is essential for maintaining intestinal homeostasis, while the underlying mechanism remains incompletely understood. Herein, we compared the dynamic interfacial adsorption behavior of saturated dipalmitoylphosphatidylcholine (DPPC) and unsaturated dioleoylphosphatidylcholine (DOPC) to intestinal mucin and their impact on the intestinal mucus barrier function. Results of quartz crystal microbalance with dissipation showed that the highly surface-hydrated DPPC vesicles exhibited significantly faster and more extensive adsorption to purified intestinal mucin than the slightly surface-hydrated DOPC vesicles. Utilizing an intestinal Caco-2/HT29-MTX coculture model, we observed that DPPC vesicles adsorbed much more to the mucus gel compared to DOPC vesicles. Additionally, DPPC vesicle adsorption displayed increased wetting, and converse for DOPC vesicles. Interestingly, both of them exhibited nearly the same protective effects against cell injury induced by peptic-tryptic digests of gliadin (PTG). The partial mechanism involved the binding of PTG to DPPC and DOPC within the mucus gel, thereby restricting PTG contact with the underlying epithelial cells. These findings shed light on the intricate interfacial dynamics of PC adsorption to mucin and their implications for maintaining the integrity of the intestinal mucus barrier.

To unveil the charge storage mechanisms and interface properties of electrode materials is very challenging for Na-ion storage. In this work, we report that the novel layered perovskite Bi2TiO4F2@reduced graphene oxides (BTOF@rGO) serves as a promising anode for Na-ion storage in an ether-based electrolyte, which exhibits much better electrochemical performance than in an ester-based electrolyte. Interestingly, BTOF@rGO possesses a prominent specific capacity of 458.3–102?mAh?g-1/0.02–1?A?g-1and a high initial coulombic efficiency (ICE) of 70.3%. Cross-sectional morphology and depth profile surface chemistry indicate not only a denser reactive interfacial layer but also a superior solid electrolyte interface film containing a higher proportion of inorganic components, which accelerates Na+migration and is an essential factor for the improvement of ICE and other electrochemical properties. Electrochemical tests and ex situ measurements demonstrate the triple hybridization Na-ion storage mechanism of conversion, alloying, and intercalation for BTOF@rGO in the ether-based electrolyte. Furthermore, the Na-ion batteries assembled with the BTOF@rGO anode and the commercial Na3V2(PO4)2F3@C cathode exhibit remarkable energy densities and power densities. Overall, the work shows deep insights on developing advanced electrode materials for efficient Na-ion storage. Bi2TiO4F2@reduced graphene oxide (BTOF@rGO) can form a superior interface layer and solid electrolyte interface (SEI) composition in ether-based electrolytes than ester-based electrolytes, which promotes charge transfer and Na-ion diffusion, thus improving initial coulombic efficiency (ICE) and kinetics, and it demonstrates conversion–alloying–intercalation triple hybridization mechanism in the ether-based electrolyte and a promising application for sodium-ion batteries (SIBs).

In this study, magnetic granular activated carbon (MGAC) system based on persulfate (PS) for organic pollutant removal and its regeneration performance were comprehensively evaluated. Iron was successfully loaded onto the granular activated carbon (GAC) in the form of Fe3O4through XRD patterns and SEM images. GAC and MGAC could activate PS for degrading tetracycline (TC). Compared with GAC, the adsorption and degradation of TC was significantly increased by 28% using MGAC. The optimized removal conditions were obtained: MGAC dosage of 1.5 g/L, PS concentration of 10 mM, and pH of 4.6. TC could be removed in a relatively wide pH from 3~9 using MGAC/PS system. The degradation mechanism was mainly ascribed to SO4•-, O2•-, and •OH radicals, according to the results of radical scavengers’ studies. Moreover, MGAC had good recycling and reusability through peroxymonosulfate regeneration. Finally, a fixed bed reactor using MGAC was carried out to simulate practical application. This work not only provided insight into the antibiotics degradation using MGAC but also laid a foundation for the practical application of this composite.

In conventional knowledge, ferroelectric solid solutions were formed between members belonging to the same crystal structure family. Since both tungsten bronze and perovskite structures are constructed by connecting the corner-sharing oxygen octahedra, it offers a possibility for formatting an unusual solid solution between these two families. Herein, (1 – x)Sr0.6Ba0.4Nb2O6-xBaTiO3, (1 – x)SBN-xBT, solid solutions were synthesized and the solution mechanism was resolved from a structure viewpoint. With increasing BT content, the solid solution persists of tetragonal tungsten bronze structure, but the lattice parameter a(= b) decreases whereas cincreases, resulting in the significant reduction of grains anisotropy. The ferroelectric–relaxor phase transition temperature shows a monotonic increase as xincreases. However, the ferroelectricity evolution is not monotonous as a function of BT content because of the competitive effects of Ba and Ti on the property. As a result, the x= 0.10 ceramic shows the strongest ferroelectricity and a remarkable electrocaloric effect of 1.4 K near room temperature. This work challenges the traditional view of solid solution formation and provides an alternative way to modulate the structure and properties of ferroelectrics.

The surface of three-dimensional materials provides an ideal and versatile platform to explore quantum-confined physics. Here, we systematically investigate the electronic structure of Na-intercalated CrTe2, a van der Waals antiferromagnet, using angle-resolved photoemission spectroscopy and ab initiocalculations. The measured band structure deviates from the calculation of bulk NaCrTe2but agrees with that of ferromagnetic monolayer CrTe2. Consistently, we observe unexpected exchange splitting of the band dispersions, persisting well above the Néel temperature of bulk NaCrTe2. We argue that NaCrTe2features a quantum-confined 2D ferromagnetic state in the topmost surface layer due to strong ferromagnetic correlation in the CrTe2layer. Moreover, the exchange splitting and the critical temperature can be controlled by surface doping of alkali-metal atoms, suggesting the feasibility of tuning the surface ferromagnetism. Our work not only presents a simple platform for exploring tunable 2D ferromagnetism but also provides important insights into the quantum-confined low-dimensional magnetic states.

Two-dimensional (2D) flume experiments are useful in investigating the performances of floating breakwaters (FBs), including hydrodynamic performances, motion responses, and mooring forces. Designing a reasonable gap between the flume wall and the FBs is a critical step in 2D flume tests. However, research on the effect of the gap on the accuracy of 2D FB experimental results is scarce. To address this issue, a numerical wave tank is developed using CFD to estimate the wave-FB interaction of a moored dual-cylindrical FB, and the results are compared to experimental data from a previously published work. There is good agreement between them, indicating that the numerical model is sufficiently accurate. The numerical model is then applied to explore the effect of gap diffraction on the performance of FBs in 2D experiments. It was discovered that the nondimensional gap length LGap/WPoolshould be smaller than 7.5% to ensure that the relative error of the transmission coefficient is smaller than 3%. The influence of the gap is also related to the entering wave properties, such as the wave height and period.

The pursuit of discovering new high-temperature superconductors that diverge from the copper-based model1–3has profound implications for explaining mechanisms behind superconductivity and may also enable new applications4–8. Here our investigation shows that the application of pressure effectively suppresses the spin–charge order in trilayer nickelate La4Ni3O10−δsingle crystals, leading to the emergence of superconductivity with a maximum critical temperature (Tc) of around 30 K at 69.0 GPa. The d.c. susceptibility measurements confirm a substantial diamagnetic response below Tc, indicating the presence of bulk superconductivity with a volume fraction exceeding 80%. In the normal state, we observe a strange metal behaviour, characterized by a linear temperature-dependent resistance extending up to 300 K. Furthermore, the layer-dependent superconductivity observed hints at a unique interlayer coupling mechanism specific to nickelates, setting them apart from cuprates in this regard. Our findings provide crucial insights into the fundamental mechanisms underpinning superconductivity, while also introducing a new material platform to explore the intricate interplay between the spin–charge order, flat band structures, interlayer coupling, strange metal behaviour and high-temperature superconductivity.

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Augmented reality smart glasses, in general, have highest requirements as compared with smart phones and watches. Its display must be of light weight (a few grams), tremendous brightness (millions of nits), low power consumption (hundreds of milliwatts), and extremely small volume (a small fraction of cubic centimeter) to allow fashionable smart glass designs that would be socially and aesthetically favored by consumers. We have been focusing on micrometer‐scale native semiconductor MicroLED integration, to create miniaturized panels and projectors, which can be seamlessly integrated into the frames of the smart glasses. Among many technical aspects of microLED displays, two key parameters will be emphasized here. First, a microLED panel must be bright and efficient at the same time, especially for red microLED panel with small pixel pitch, such as 2.5 µm. Second, a microLED technology must enable reasonable reliability at a high energy density of, such as 10 watts per square centimeter. Vertically stacked native semiconductors monolithic integration, quantum dots conversion, perovskites, etc., will be discussed, in a context of facing challenges in materials and device physics and fulfilling the two requirements. In addition, an image quality correction method on the waveguide‐microLED display module will be proposed.

This paper presents a novel finite-and-quantized output feedback asymptotic tracking control method for a general class of continuous-time linear time-invariant systems. First, we construct a finite quantizer with time-varying thresholds and design a pole placement control law that exclusively utilizes the finite-and-quantized output signal and an external reference signal. Then, we establish the boundedness of all closed-loop signals and prove the asymptotic convergence of the output tracking error to zero. The proposed method combines the advantages of classical pole placement control technique and finite quantization feedback technique. It not only reduces the requirement for feedback information compared with existing tracking control methods but also effectively handles unstable poles and zeros in controlled systems, thereby achieving asymptotic output tracking. Finally, we provide a representative example to validate the effectiveness and new features of our proposed method.

AlN crystal growth on SiC seeds is an alternative approach for obtaining wafer-sized AlN crystals and has been demonstrated in experiments. Various characteristics of AlN crystals grown on SiC seeds were reported by different researchers, leading to complications in designing a reliable growth process. Here, we used comprehensive and comparative methods to reveal key factors affecting the characteristics of the AlN crystal. These include the initial SiC seed morphology evolved at the beginning of AlN crystal growth; in short, two typical morphologies were formed in the heterointerface between the SiC seed and AlN. One is AlN grown on a flat heterointerface, and the other is the AlN crystal developed from SiC pyramid islands formed due to the decomposition of the SiC seed under an AlN atmosphere. Such dissimilar interfaces result in differences in aspects such as the incorporated Si impurity content, strain, and uniformity of the grown AlN crystal. Significantly, a near-linear decrease in both the Si content (0.005%/μm) and the strain (represented by a Raman peak blue shift of 0.0084 cm–1/μm) with increasing AlN crystal thickness is found and well verified in a 1.1 mm-thick AlN crystal, which shows a narrow 112 arcsec full width at half-maximum in its (0002) X-ray rocking curve. Our results suggest that the AlN crystal grown on a flat SiC seed possesses more uniform physical properties, which are suitable for the growth of wafer-sized AlN crystals. The characteristics of AlN crystals grown on SiC seeds with/without voids at the heterointerface are studied comparatively, including a quantitative variation of Si doping contents, shift rates of the Raman peak position, and an fwhm of (0002), with increasing AlN crystal thickness. These results are important to understand the various phenomena observed in AlN crystals grown on SiC seeds.

The adaptive fuzzy control method has been widely used in the control of nonlinear systems. The system state is required to stay in a compact set for the effectiveness of the fuzzy logic system approximation. Therefore, many control schemes integrating adaptive fuzzy control and full-state constraint control techniques have been proposed for nonlinear systems. In this brief, a novel adaptive fuzzy controller is developed for a class of nonlinear planar systems. The system state is required to satisfy the specific constraints which are first proposed in this brief. It is strictly proved that the control scheme can make the system state asymptotically converge to the origin (not merely the neighborhood of the origin) and guarantee that the specified state constraints are not violated. In addition, the restriction on the rated constraint boundaries of different state components is explicitly given, which guarantees that the feasibility condition is naturally satisfied. Finally, the developed control scheme is applied to a specified PMSM system to show its effectiveness.

Soy protein is widely known to have serum triglyceride (TG) and cholesterol-lowering effects associated with a reduced risk of cardiovascular disease. Recent studies highlighted that the extension region (ER) domain of soy 7S globulin (β-conglycinin) is a key component responsible for the serum TG-lowering effect via modulation of bile acid (BA) homeostasis. Here, we studied the sequestration of BAs by ER peptides during intestinal digestion in vitroand assessed the anti-inflammatory effects of ER peptides using Caco-2/HT29-MTX/RAW264.7 triple-cell cocultures as an intestine cell model. Results show that ER peptides, which share characteristics of intrinsically disordered regions (IDRs), are capable of forming peptide condensates and exhibit the capability to sequester BA-containing colloidal structures during intestinal digestion in vitro. Moreover, BAs enhance the penetration of peptide condensates within the mucus layer, enabling ER peptides to mitigate lipopolysaccharide (LPS)-induced gut inflammation. These results provide a possible explanation for the molecular mechanisms underlying the modulation of BA homeostasis by soybean proteins.

Electrical arc machining (EAM) is an efficient process for machining difficult-to-cut materials. However, limited research has been conducted on sloped surface machining within this context, constraining the further application for complex components. This study conducts bevel machining experiments, pointing out that the surface quality becomes unsatisfactory with the increasing bevel angle. The discharge condition is counted and analyzed, while the flow field and the removed particle movement of the discharge gap are simulated, demonstrating the primary factor contributing to the degradation of surface quality, namely the loss of flushing. This weakens both the plasma control effect and debris evacuation, leading to the poor discharge condition. To address this issue, the magnetic field is implemented in blasting erosion arc machining (BEAM). The application of a magnetic field effectively regulates the arc plasma, enhances debris expulsion, and significantly improves the discharge conditions, resulting in a smoother and more uniform sloped surface with a reduced recast layer thickness. This approach provides the possibility of applying BEAM to complex parts made of difficult-to-cut materials in aerospace and military industries.

In this article, the fault-tolerant leader-following consensus problem is investigated for a class of multiagent systems subject to random intermittent actuator faults. The process of actuator fault occurrence for each agent is described by a Markov chain. By the backstepping procedure with virtual controllers designed based on sampled states, a novel fault-tolerant periodic sampled-data consensus protocol scheme is developed to ensure that all followers can track the trajectory of a leader. Different from existing works, in the proposed design framework, both zero and time-variant effectiveness of the actuators are considered. Using the graph theory, probability theory, and Lyapunov function method, the sufficient conditions on the fault-tolerant leader-following consensus are derived. Finally, two numerical examples are provided to demonstrate the effectiveness of the proposed scheme.

Cloud cover leads to great loss of spatial details in wide-swath multispectral images, and thus significantly affects their application value. Wide-swath images are huge in size and are usually cropped into patches before thin cloud removal. In addition, wide-swath images contain a rich variety of types and shapes of thin clouds, with each patch containing different clouds. However, most of the existing methods and datasets were primarily designed for natural image dehazing. These datasets had limited types and shapes of clouds. When applied to remote sensing image thin cloud removal task, these methods are unable to remove various types of clouds and lead to severe cross-patch color difference. To address this problem, a DecloudNet based on cross-patch consistency supervision was proposed. First, a multiscale cloud perception block (MCPB) with multisize convolutional kernels was proposed to enhance the network’s capability to extract clouds feature of different sizes. Second, a cross-patch consistency supervision was designed to reduce the network’s inconsistent cloud removal strength in different patches and remove cross-patch color difference when processing wide-swath images. Finally, a thin cloud simulation method based on Perlin noise, domain warping, and atmospheric scattering model was proposed to construct a high-quality declouding dataset containing clouds of multiple sizes and shapes, which can improve the performance of DecloudNet on different kinds of thin clouds. The DecloudNet and compared methods were tested for simulated thin cloud removal performance on images from QuickBird (QB), GaoFen-2 (GF2), and WorldView-2 (WV2) satellites, and for real thin cloud removal performance on wide-swath images from GaoFen-1 Wide Field of View (GF-1 WFV), GaoFen-1 (GF-1), and Earth-Observing-1 (EO-1) satellites. The experimental results demonstrated that DecloudNet outperformed the existing state-of-the-art (SOTA) methods. DecloudNet and cross-patch consistency supervision made it possible to perform thin cloud removal on wide-swath images of large size on most GPUs without worrying about graphics memory limitation. The source code and dataset are available at https://github.com/N1rv4n4/DecloudNetthe link.

In this article, the stator cooling system of an axial-flux permanent-magnet (PM) motor with an integer-slot distributed winding (ISDW) for high-speed, high-power electric vehicle traction applications is discussed. The aim of this research is to quantitatively figure out the power/power density boundaries of this type of axial field PM motor with the adoption of the proposed oil-immersed cooling system consisting of multicooling channels. First, the structural–cooling design considerations are provided. Then, a comparative study of the stator thermal performance of the high-power axial field PM motor with various cooling methods is conducted with three-dimensional computational fluid dynamics analysis. The oil circuits inside the stator are further optimized to achieve a better cooling effect, with a focus on improving the oil circuit layout configuration. Finally, a full-scale oil-immersed cooling prototype motor with 120 kW output peak power and 15 000 r/min is manufactured and tested. The long-time stable operation at high speed verifies the reliability of the stator structural design. The thermal performance is verified with measured results of end-winding temperature rise under continuous conditions.

We present in this paper a novel denoising training method to speed up DETR (DEtection TRansformer) training and offer a deepened understanding of the slow convergence issue of DETR-like methods. We show that the slow convergence results from the instability of bipartite graph matching which causes inconsistent optimization goals in early training stages. To address this issue, except for the Hungarian loss, our method additionally feeds GT bounding boxes with noises into the Transformer decoder and trains the model to reconstruct the original boxes, which effectively reduces the bipartite graph matching difficulty and leads to faster convergence. Our method is universal and can be easily plugged into any DETR-like method by adding dozens of lines of code to achieve a remarkable improvement. As a result, our DN-DETR results in a remarkable improvement ($+1.9$+1.9AP) under the same setting and achieves 46.0 AP and 49.5 AP trained for 12 and 50 epochs with the ResNet-50 backbone. Compared with the baseline under the same setting, DN-DETR achieves comparable performance with 50% training epochs. We also demonstrate the effectiveness of denoising training in CNN-based detectors (Faster R-CNN), segmentation models (Mask2Former, Mask DINO), and more DETR-based models (DETR, Anchor DETR, Deformable DETR).

Detecting subsequent targets behind obstacles has always been a major challenge for humans. Based on the characteristic that low-frequency through-wall radar signals can penetrate obstacles and reflect on the target surface, we propose a real-time multiperson 3-D pose estimation method based on multiple-input multiple-output (MIMO) through-wall radar. Our proposed approach addresses the problem of being unable to recover human poses in real time when the human body is completely obstructed by an obstacle. This method follows a cross-modal supervision learning pipeline, first using a synchronized camera and a self-designed through-wall radar to obtain paired RGB images and through-wall radar images. Then, we use a monocular camera-based 3-D pose estimation method as a teacher network to extract 3-D poses from RGB images as a supervision signal. Finally, we design an end-to-end radar pose estimation network based on a transformer (ERPENT) as a student network, which learns pose features from two different temporal radar signals and directly predicts the final 3-D human pose. Unlike existing radar-based multiperson pose estimation methods, the proposed method in this article does not require an additional radar target reflection region detection process, thus greatly shortening the necessary pose estimation time. In practical applications, the 3-D human pose can be predicted by simply collecting through-wall radar images and inputting them into ERPENT. The experiment shows that under the condition of a 28-cm compact brick wall scene, the average positioning accuracy of human keypoints is 40.15 mm. The time cost of reconstructing a frame of the 3-D pose is 0.082 s.

Long-term respiratory monitoring plays an extremely important role in the diagnosis of respiratory system-related diseases. It can detect diseases temporally and improve the effectiveness of treatment, which is crucial for home health monitoring. With the rapid development of radar chip technology, noncontact respiratory monitoring based on commercial millimeter-wave radar sensors has received increasing attention. However, traditional signal processing methods are difficult to extract fine respiratory waveforms from radar signals mixed with human body movements. Fortunately, deep learning provides a solution, as we can use its powerful learning ability to learn a mapping from radar signals to real respiratory waveforms. With this method, we can directly observe fine-grained respiratory waveforms and further improve the accuracy of respiratory rate detection. Therefore, we propose MM-FGRM, a respiratory monitoring system based on a commercial 77 GHz multiple input multiple output (MIMO) radar. The core of this system is a deep learning-based network called IQ-Transformer with a self-attention mechanism that aims at capturing the latent respiratory-related features from I/Q components of radar signals directly in each human body region of interest (ROI) and recovering the respiratory waveforms. We collected 12 h of data from eight subjects and conducted experiments. The experimental results show that MM-FGRM can accurately recover respiratory waveforms and provide accurate respiratory rates. In addition, we perform testing on data from two other users who do not participate in the training, and the results verify that MM-FGRM has a strong generalization ability. Our results demonstrate the feasibility of further development of home respiratory monitoring products.

The silicon charge detector (SCD) is a subdetector of the high-energy cosmic radiation detection (HERD) payload. The dynamic range of the silicon microstrip detector can be extended by the capacitive coupling effect, which is related to the interstrip capacitance and the coupling capacitance. A detector prototype with several sets of parameters was designed and tested in the ion beams at the Conseil Européen pour la Recherche Nucléaire (CERN) superproton synchrotron (SPS). The capacitive coupling fractions with readout strip and floating strip incidences were studied using the beam test data and SPICE simulation.

Multilevel converters have attracted more and more attention for their advantages over the two-level converters in breaking voltage limitation and multichannel wireless power transfer (WPT), but their switching loss is unbearable due to the lack of an effective soft-switching modulation method. To fill this gap, this article proposes and implements a segmented-vector pulse frequency modulation (SVPFM) of a half-bridge three-level inverter for WPT systems. Moreover, the sigma-delta (Σ-Δ) modulator for the SVPFM-WPT system is proposed, and the modulation range is expanded to [0, 1]. It can achieve wide-range zero-voltage switching (ZVS) without auxiliary circuits and inverter modification, and it suppresses the power losses, further improving the system efficiency of a three-level inverter-based WPT system. In the meantime, a switching state-based capacitor voltage balancing method is proposed to control the half-bridge three-level inverter for the WPT system. It can balance the capacitor voltage of the flying capacitor at the reference value with a small voltage ripple. Software simulation and hardware experimentation are both given to verify the feasibility of the proposed Σ-Δ SVPFM of the three-level inverter for WPT systems. The system efficiency can reach 95.2% with the wide-range ZVS operation.

Sequential service restoration (SSR) is essential to restore critical loads after the distribution system outage through gradually forming microgrids (MGs). However, high penetration of renewables induces low-inertia issues, especially in small-scale MGs, which could impede a large-size load restoration for ensuring the MG dynamic stability. To expedite the restoration process, we propose the interconnection of MGs as part of SSR. A developed SSR model is proposed to generate restoration sequence and time of MG interconnection operation. An auxiliary variable is introduced to indicate the system topology before and after MG interconnection, and then the maximum permissive step load in each MG is quantified. By this means, isolated MGs are formed gradually, and adjacent MGs can be reconnected to improve the primary response when picking up a large load. Moreover, to address uncertainties of renewables, the stochastic approach is used. And an improved progressive hedging algorithm (PHA) is utilized to solve the stochastic model. The original model is decomposed by bundles of scenarios, and penalty factors are adaptively updated to accelerate the PHA convergence. Simulation results show the effectiveness of the proposed SSR method in improving the restoration efficiency of distribution systems and reducing computational burdens.

The cardio-cerebrovascular disease is a significant disease that can directly threaten life and health. Vascular intervention surgery is currently the mainstream treatment method. This kind of surgery requires doctors to be exposed to radiation for a long, with extensive body damage. In response, domestic and foreign researchers began to develop endovascular intervention surgery robots. Although the robot has specific additional capabilities, it is still in the research and development stage. It must continuously improve performance, reliability, and functionality before it can be used in interventional surgery. Therefore, to further reduce doctors’ learning and training costs, optimize the operation steps in surgery, and improve delivery accuracy, we developed a reciprocating delivery device-based endovascular intervention robot with multimanipulators collaboration. The third-party type test confirmed that the robot’s feasibility and overall performance meet the surgery’s needs (average linear accuracy is less than 0.1 mm). In vivo experiments have demonstrated the efficacy, safety, and reduction of radiation exposure to doctors (nearly 98.68%).

Microgrids (MGs) provide a promising solution to ensure the constant power supply under extreme conditions due to their islanding ability. However, unintentional islanding caused by emergencies may trigger significant a power imbalance in MGs, leading to unsuccessful MG formation in the event that static or transient operational constraints are violated. To address this challenge, we propose a proactive distribution system scheduling method to continue serving critical loads in emergencies by forming reliable MGs. The power flow on the lines is restricted prior to interruption to mitigate the power imbalance in MGs when equipment failures occur. Both static and transient constraints are incorporated into the proposed model to ensure the adequacy of MGs in supplying demands and transient security during the islanding transition. Note that the inclusion of nonlinear transient constraints makes it difficult to solve the model directly. Thus, a two-level simulation-assisted solution approach is presented, where simulation level sends the maximum allowable power imbalance in the MGs to optimization level to determine optimal scheduling schemes. By interfacing with a transient simulation of MGs, the frequency dynamics are exactly incorporated into the constraints. Numerical results validate the effectiveness of the proposed method in secure islanding transition and MG survivability improvement.

The pseudokinase mixed lineage kinase domain-like (MLKL) is an essential component of the activation of the necroptotic pathway. Emerging evidence suggests that MLKL plays a key role in liver disease. However, how MLKL contributes to hepatocarcinogenesis has not been fully elucidated. Herein, we report that MLKL is upregulated in a diethylnitrosamine (DEN)-induced murine HCC model and is associated with human hepatocellular carcinomas. Hepatocyte-specific MLKL knockout suppresses the progression of hepatocarcinogenesis. Conversely, MLKL overexpression aggravates the initiation and progression of DEN-induced HCC. Mechanistic study reveals that deletion of MLKL significantly increases the activation of autophagy, thereby protecting against hepatocarcinogenesis. MLKL directly interacts with AMPKα1 and inhibits its activity independent of its necroptotic function. Mechanistically, MLKL serves as a bridging molecule between AMPKα1 and protein phosphatase 1B (PPM1B), thus enhancing the dephosphorylation of AMPKα1. Consistently, MLKL expression correlates negatively with AMPKα1 phosphorylation in HCC patients. Taken together, our findings highlight MLKL as a novel AMPK gatekeeper that plays key roles in inhibiting autophagy and driving hepatocarcinogenesis, suggesting that the MLKL-AMPKα1 axis is a potential therapeutic target for HCC.