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With the widespread application of wireless sensor networks (WSNs), geometric routing has been frequently investigated for its low complexity and high delivery rate. However, most existing geometric routing schemes seldom pay attention to the channel quality of the found route. As multimedia applications are being extensively used in networks, the transmission with higher robustness has become a growing demand. In this article, we propose a channel-controlled geometric STAteless routing (CC-G-STAR) scheme for 3-D wireless networks. The proposed routing scheme inherits the light-weighted merits of the G-STAR protocol and guarantees packet delivery as long as a route exists. Furthermore, multicarrier (MC) transmission such as orthogonal chirp division multiplexing (OCDM) modulation is employed to achieve high data rate and robust communication. The orthogonality deficiency (od) of the channel state information (CSI) is introduced, instructing the routing scheme to find a route with favorable channel conditions. This channel-controlled metric enables low-complexity linear equalizers (LEs) to collect lost diversity at the receiver node. The effectiveness of the proposed scheme was evaluated by simulations. The results show that the CC-G-STAR scheme exhibits a 6-dB advantage in bit error rate (BER) performance while maintaining a 10%–15% higher delivery rate against the routing without channel-controlled factors. Image transmission simulations were conducted and the received images routed by the CC-G-STAR achieved a structural similarity (SSIM) of over 0.9.

The inherent variability in memristor switching behavior has been a challenge to its adoption as a next-generation general-purpose memory. However, the randomness of its switching behavior may be helpful for hardware security applications. Herein, a true random number generator (TRNG) based on a high-speed Ag/amorphous-Si/Pt threshold switching (TS) device was constructed. The device possesses a large on-off ratio of about $10^{{5}}$ and a fast switching speed of about 30 ns. The results show that the delay time of the device decreases as the pulse amplitude or the pulse frequency increases. Using the random delay time as a random source, we built a TRNG circuit and achieved the flipping of “0” and “1” with a fast bit generation rate of 48 kb/s. The random bits generated by our TRNG pass 14 randomness tests of the National Institute of Standards and Technology (NIST) without any processing. This work paves the way for diffusive memristors in hardware security applications in the era of the Internet of Things.

The scale-up of AI models for analyzing large-scale power systems necessitates a thorough understanding of their scaling properties. Existing studies on these properties provide only partial insights, showing predictable decreases in loss function with increased model scales; yet no scaling law for power system AI models has been established, resulting in unpredictable performance. This letter introduces and explores the concept of “emergent abilities” in graph neural networks (GNN) used for analyzing large-scale power systems–a phenomenon where model performance improves dramatically once its scale exceeds a threshold. We further introduce an empirical power-law formula to quantify the relationship between this threshold and the power system size. Our theory precisely predicts the threshold for the emergence of these abilities in large-scale power systems, including both a synthetic 10,000-bus and a real-world 19,402-bus system.

Plasmon decay in an Ag-graphene system induced by a femtosecond pulse is investigated using real-time time-dependent density functional theory. There exists a critical value for plasmon decay dependence on the pulse width. For pulse widths less than the critical value, plasmon remains during the time scale of ∼30 fs after the laser is withdrawn and indicates direct charge transfer from Ag to graphene. While for pulse width greater than the critical value, plasmons rapidly decay into hot electrons involving inverse charge transfer. Electron transition at the critical value of pulse width displays an obvious beat owing to mutual interference between plasmon and single-particle excitation. Femtosecond-pulse width is thus crucially important for controlling plasmon decay in a metal–semiconductor hybrid system.

The spatial constraints imposed by the DNA structure have significant implications for the walking efficiency of three-dimensional DNA walkers. However, accurately quantifying and manipulating steric hindrance remains a challenging task. This study presents a steric hindrance-controlled DNA walker utilizing an enzymatic strand displacement amplification (ESDA) strategy for detecting microRNA-21 (miR-21) with tunable dynamic range and sensitivity. The steric hindrance of the DNA walker was precisely manipulated by varying the length of empty bases from 6.5 Å to 27.4 Å at the end of the track strand and adjusting the volumetric dimensions of the hairpin structure from 9.13 nm3to 26.2 nm3at the terminus of the single-foot DNA walking strand. This method demonstrated a tunable limit of detection for miR-21 ranging from 3.6 aM to 35.6 nM, along with a dynamic range from ∼100-fold to ∼166 000-fold. Impressively, it exhibited successful identification of cancer cells and clinical serum samples with high miR-21 expression. The proposed novel strategy not only enables tunable detection of miRNA through the regulation of steric hindrance but also achieves accurate and quantitative analysis of the steric hindrance effect, promising broader applications in personalized medicine, early disease detection, and drug development.

By transmitting task-related information only, semantic communications yield significant performance gains over conventional communications. However, the lack of mature semantic theory about semantic information quantification and performance evaluation makes it challenging to perform resource allocation for semantic communications, especially when multiple tasks coexist in the network. To cope with this challenge, we propose a quality-of-experience (QoE) based semantic-aware resource allocation method for multi-task networks in this paper. First, semantic entropy is defined to quantify the semantic information for different tasks, and the relationship between semantic entropy and Shannon entropy is analyzed. Then, we develop a novel QoE model to formulate the semantic-aware resource allocation in terms of semantic compression, channel assignment, and transmit power. The compatibility of the formulated problem with conventional communications is further demonstrated. To solve this problem, we decouple it into two subproblems, and solve them by a developed deep Q-network (DQN) based method and a proposed low-complexity matching algorithm, respectively. Finally, simulation results validate the effectiveness and superiority of the proposed method, as well as its compatibility with conventional communications.

The finding of atypical glandular cells (AGC) on Papanicolaou test is becoming more important as the incidence of squamous intraepithelial lesions decreases in recent decades. Therefore, the interpretation and follow-up of patients with AGC are particularly important. The aim of our study was to assess the histologic findings and clinical correlations in patients with AGC identified on Papanicolaou test.

Increasing renewable energy resources introduces uncertainties into utility grids, calling for more frequent use of alternative current optimal power flow (AC-OPF) than before. Conventional AC-OPF solvers are too slow for real-time applications; to speed up the computation, deep neural network (DNN) based solvers are introduced. However, their results typically cannot enforce the security constraints of the utility grid. To overcome this difficulty, FRMNet is proposed in this paper, which combines DNN with feasibility restoration mapping (FRM). The DNN receives the load as input and outputs a partial solution. The following FRM maps the infeasible solution to the feasibility region to satisfy the AC-OPF constraints. The proposed FRMNet has four advantages compared to conventional and DNN-based solvers. First, the feasibility of overall solutions is guaranteed via FRM. Second, FRMNet is a self-supervised training method so that it avoids the laborious preparation of the optimal solutions for a training dataset using conventional solvers. Third, FRM is differentiable so that AC-OPF information encoded in gradients is transferred to DNN, which leads to a DNN with high feasibility rate. Fourth, the overall computation time of FRMNet is fast due to its DNN's high feasibility rate. Comprehensive experiments are conducted on IEEE standard cases. Results show that FRMNet's outcomes satisfy all constraints at a significantly faster average speed than conventional solvers. When FRM participates in training, the performance of DNN is stably improved and achieves better results than other DNN-based models. Even DNN alone achieves a high feasibility rate, a negligible degree of constraint violations and a small optimality gap, compared to the state-of-the-art DNN based solvers.

Soluble solid content (SSC) and firmness are significant indexes to evaluate the quality of wolfberry. This study employed hyperspectral imaging (HSI) technology for the rapid detection and visualization of the distribution of SSC and firmness in mature wolfberries. The hyperspectral images of Ningqi 1 and Ningqi 7 were collected in the range of 400–1000 nm. The image segmentation method was used to determine the region of interest (ROI) of the wolfberry samples and extract the mean spectra, and the performance of the four preprocessing techniques was evaluated based on the partial least squares (PLSR) model, which concluded that the standard normal variable transformation (SNV) and multiple scattering correction (MSC) preprocessing methods were able to achieve the optimal results. Principal component analysis (PCA), successive projection algorithm (SPA), competitive adaptive reweighted sampling method (CARS) and their combination were used to select the characteristic wavelength, with CARS-SPA being more accurate. PLSR, support vector machine regression (SVR) and backpropagation genetic algorithm (BPNN-GA) models were used to predict the soluble solid content and firmness of wolfberry by full wavelength and characteristic wavelength, respectively. The optimal model for SSC and firmness of Ningqi 1 was identified as MSC-CARS-SPA-BPNN-GA, with Rp2of 0.949 and 0.913, RMSEP of 0.365 and 0.524, and RPD of 4.104 and 3.422, respectively. For Ningqi 7, the optimal model was SNV-CARS-SPA-BPNN-GA, with Rp2of 0.936 and 0.880, RMSEP of 0.364 and 0.537, and RPD of 3.860 and 2.706, respectively. Finally, these optimal models were utilized to visualize the distribution of SSC and firmness in the ROI. The findings underscore the rapid and precise nature of hyperspectral imaging in detecting the SSC and firmness of wolfberry, thereby establishing a technological and theoretical foundation for expedited wolfberry quality assessment.

This article investigates the adaptive optimal output-feedback consensus tracking problem for nonlinear multiagent systems (MASs). Although adaptive optimal output-feedback control schemes for nonlinear systems have been developed recently, most results do not consider the two-way interaction between the state observer and its associated subsystem. To address this issue, we formulate the state-observer and the subsystem as a two-player Stackelberg game framework, where the state-observer acts as the follower-player and the subsystem acts as the leader-player. Such a framework helps us to reveal the two-way interaction between the subobserver and the subsystem. Based on this, we design the optimal auxiliary input of the state-observer and the optimal subsystem controller. We implement the optimal policy pair using integral reinforcement learning (IRL) and adaptive critic learning, which provides a critic-only structure. We prove that the Stackelberg-Nash equilibrium is reached and that the closed-loop signals are ultimately uniformly bounded (UUB). We demonstrate the effectiveness of the proposed scheme using a numerical simulation example.

Spondyloarthritis (SpA) is a prevalent genetic disorder that significantly impairs mobility, particularly in the spine, sacroiliac, and peripheral joints. Recent evidence highlights early involvement of the sternoclavicular joint in SpA, which may serve as an initial indicator. Diagnosis often relies on CT and MRI, neglecting ultrasound's potential in identifying SpA‐related sternoclavicular arthritis. This review focuses on the joint's anatomy, exploring ultrasound's diagnostic and therapeutic role in SpA‐related sternoclavicular arthritis, aiming to provide insights for future ultrasound applications in SpA management.

The power industry's carbon emissions stand out as a primary contributor to the overall carbon dioxide emissions within the energy system under the context of energy Internet. Thus, reducing emissions in the power sector has become crucial for achieving carbon neutrality. However, challenges from the electricity–carbon nexus have surfaced in effectively coordinating and integrating the carbon market with the electricity market. This paper initiates the exploration of such nexus by analysing the current status of major carbon emission trading markets on a global scale. Subsequently, it delves into a comprehensive examination of the coupling between the electricity and carbon markets across three levels: market participants, operational models and market mechanisms. Four key issues are then identified in the electricity–carbon nexus: challenges in decision-making for market participants, discrepancies in operational timelines, the intricate design of market coupling mechanisms and the spillover effects of market risks. To tackle the above challenges in the electricity–carbon nexus, this paper takes a deep dive into two different models in understanding the nexus including econometric/statistical models and optimisation models, serving as the foundation for understanding the intricacies of electricity–carbon market coupling. This paper concludes with a detailed exploration for future roadmap and research prospects in the electricity–carbon market nexus.

Animal experiments are important in trauma-related studies because they simulate in vivo effects. Rodents are a good choice for preparing trauma models; however, contractile healing in rodents results in a healing pattern that differs considerably from that in humans. Therefore, this study developed a new rodent model that avoids contractile healing of the skin around the wound using an anticontraction ring, and the skin in the wound’s center remains intact and acts as a source for epithelialized diffusion healing. Cell proliferation, migration, revascularization, and collagen secretion did not differ between the novel and conventional full-skin defect trauma models. However, the healing rate at various stages significantly differed between the 2 groups owing to differences in the healing patterns. And without effective treatment, the experimental group cannot heal. The stabilities of the novel and conventional methods were good regardless of operator or batch. In summary, this new animal trauma model provides a stable experimental environment similar to that in humans, which may promote trauma-related research.

Large eddy simulations generally are used to predict 3D wind field characteristics in complex mountainous areas. Certain simulation boundary conditions, such as the height and length of the computational domain or the characteristics of inflow turbulence, can significantly impact the quality of predictions. In this study, we examined these boundary conditions within the context of the mountainous terrain around a long-span cable-stayed bridge using a wind tunnel experiment. Various sizes of computational domains and turbulent incoming wind velocities were used in large eddy simulations. The results show that when the height of the computational domain is five times greater than the height of the terrain model, there is minimal influence from the top wall on the wind field characteristics in this complex mountainous area. Expanding the length of the wake region of the computational domain has negligible effects on the wind fields. Turbulence in the inlet boundary reduces the length of the wake region on a leeward hill with a low slope, but has less impact on the mean wind velocity of steep hills.

Engineering of a new type of plant base editor for simultaneous adenine transition and transversion within the editing window will greatly expand the scope and potential of base editing in directed evolution and crop improvement. Here, we isolated a rice endogenous hypoxanthine excision protein, N-methylpurine DNA glycosylase (OsMPG), and engineered two plant A-to-K (K = G or T) base editors, rAKBE01 and rAKBE02, for simultaneous adenine transition and transversion base editing in rice by fusing OsMPG or its mutant mOsMPG to a plant adenine transition base editor, ABE8e. We further coupled either OsMPG or mOsMPG with a transactivation factor VP64 to generate rAKBE03 and rAKBE04, respectively. Testing these four rAKBEs, at five endogenous loci in rice protoplasts, indicated that rAKBE03 and rAKBE04 enabled higher levels of A-to-G base transitions when compared to ABE8e and ABE8e-VP64. Furthermore, whereas rAKBE01 only enabled A-to-C/T editing at one endogenous locus, in comparison with rAKBE02 and rAKBE03, rAKBE04 could significantly improve the A-to-C/T base transversion efficiencies by up to 6.57- and 1.75-fold in the rice protoplasts, respectively. Moreover, although no stable lines with A-to-C transversion were induced by rAKBE01 and rAKBE04, rAKBE04 could enable simultaneous A-to-G and A-to-T transition and transversion base editing, at all the five target loci, with the efficiencies of A-to-G transition and A-to-T transversion editing ranging from 70.97 to 92.31% and 1.67 to 4.84% in rice stable lines, respectively. Together, these rAKBEs enable different portfolios of editing products and, thus, now expands the potential of base editing in diverse application scenario for crop improvement.

Tendinopathy leads to low-grade tissue inflammation and chronic damage, which progresses due to pathological imbalance in angiogenesis. Reducing early pathological vascularization may be a new approach in helping to regenerate tendon tissue. Conventional stem cell therapy and tissue engineering scaffolds have not been highly effective at treating tendinopathy. In this study, tissue engineered stem cells (TSCs) generated using human umbilical cord mesenchymal stem cells (hUC-MSCs) were combined with microcarrier scaffolds to limit excessive vascularization in tendinopathy. By preventing VEGF receptor activation through their paracrine function, TSCs reduced in vitroangiogenesis and the proliferation of vascular endothelial cells. TSCs also decreased the inflammatory expression of tenocytes while promoting their anabolic and tenogenic characteristics. Furthermore, local injection of TSCs into rats with collagenase-induced tendinopathy substantially reduced early inflammation and vascularization. Mechanistically, transcriptome sequencing revealed that TSCs could reduce the progression of pathological angiogenesis in tendon tissue, attributed to Rap1-mediated vascular inhibition. TSCs may serve as a novel and practical approach for suppressing tendon vascularization, and provide a promising therapeutic agent for early-stage clinical tendinopathy.

In this work, we designed a Mn-based hexacyanoferrate with partial substitution of MnIIIby FeII. The presence of FeIIat the low-spin position in Na2Mn1/6Fe5/6Fe(CN)6plays an important role in inhibiting the Jahn–Teller effect and reducing the dissolution of Mn2+, contributing to the robustness of the crystal structure. As a result, Na2Mn1/6Fe5/6Fe(CN)6can be qualified as an electrode material for aqueous nickel-ion batteries. A series of electrochemical evaluations show that it can achieve a charge capacity of 76.1 mAh g–1at 100 mA g–1, as well as an excellent cycling performance with 69.1% capacity retention upon 500 cycles. Besides, the reversible insertion/extraction mechanism of Ni2+in Na2Mn1/6Fe5/6Fe(CN)6is investigated by in situ/ex situ methods, demonstrating its stability and reversibility for electrochemical energy storage. In summary, Fe2+substitution enhances the structural integrity and electrochemical stability of Na2Mn1/6Fe5/6Fe(CN)6as a host material for nickel-ion storage.

Photocatalytic technology is one of the promising technologies for wastewater treatment. Herein, zinc oxide/multi-walled carbon nanotubes (ZnO/CNTs) photocatalyst was successfully prepared by hydrothermal method with combining in-situ synthesis technology. The micro-morphology, crystalline structure, surface chemical elements, and optical properties were characterized by SEM, TEM, XRD, FTIR, UV-Vis, and DRS technologies. The ZnO/CNTs photo-catalyst exhibited enhancement photo activity for degradation of organic pollutants under simulated light irradiation. Specifically, the photo-catalytic activity of the ZnO/CNTs catalysts improved with the rise of CNTs content in the composites. Investigation on the photo-degradation mechanism verified that the presence of CNTs in the catalyst not only optimized the band structure of ZnO semiconductor but also contributed to the transfer of photo-generated electrons and reducing the recombination of electron-hole pairs due to its excellent conductivity. Moreover, the active radical groups such as superoxide radical (O-2), hole (h+), and hydroxyl radical (·OH) played the dominated role for the pollutants degradation under the simulated sunlight irradiation. In addition, ZCT20 catalysts and light irradiation had synergistic effects on antibacterial activity, whose antibacterial rates against E. coli and S. aureus were up to 99.96% and 99.94%, respectively. Investigation on antibacterial mechanisms revealed that the existence of ROS and the continuous release of Zn2+ played an important role for improving the antibacterial activity of the ZCT20 catalyst under the simulated sunlight irradiation.

ABSTRACTHyperGraph Neural Network (HGNN) has recently emerged as a promising approach for hyperspectral image classification (HSIC), reconciling state-of-the-art performance with powerful representation capabilities. However, existing HGNN-based methods have limited ability for hypergraph structure exploitation, leading to imperfect classification results. In this paper, we propose a framework called the residual enhanced hypergraph Neural Network (ResHGNN) to discover the potential structural features in hyperspectral image (HSI) data during deep neural networks. Specifically, ResHGNN first generates hyperedges from spatial-spectral features to construct a hypergraph representing fused spatial-spectral feature relationships in HSI. Then, the higher-order relationship among fused modal features is optimized by a residual enhanced hypergraph convolution learning process, to circumvent the HGNN-related over-smoothing issue. Experiments over three popular hyperspectral datasets show that the proposed classification method yields better performance than other models on the visual and numerical comparison.

As for the deep tissue infections of chronic osteomyelitis, antibiotics are hard to deliver into the infected bone tissue, which makes it difficult to be cured completely in clinic. Microwave has strong penetration, and the medium can produce a good bactericidal effect through the microwave thermal effect (MTE). Here, a new microwave sensitizer (Fe3O4@APNs) was prepared and evaluated. Black phosphorus nanosheets modified with phytic acid dodecasodium (APNs) were fabricated by a liquid-phase exfoliation method that exhibited good water oxygen stability. A complex with Fe3O4compound and APNs (Fe3O4@APNs) was formed by an ultrasonic mixing process, which showed excellent MTE (quickly increased to 53.5 °C in 5 min at 2.45 GHz, 10 W/cm2) via dielectric versus magnetic loss (reflect loss value of −5.94 dB at 2.45 GHz). The Fe3O4@APNs microwave sensitizer developed in this study has an outstanding in vitro antibacterial effect and might show promise for the treatment of chronic osteomyelitis enabled by local tissue heating via the MTE.

In order to realize the flexible capture of noncooperative targets, a variable-stiffness planning method considering both the overall configuration and cable tension for a hyper-redundant manipulator is proposed in this article. First, combining its kinematics and statics, an analytical stiffness model of the hyper-redundant manipulator is developed to evaluate its stiffness. The overall configuration and cable tension of the hyper-redundant manipulator are both identified as main factors that affect the stiffness. Then, a concept of configuration-stiffness mesh and tension-stiffness mesh is proposed to characterize the stiffness trend which can be optimized by active control points. On one hand, the gradient projection method is used to change the overall configuration of the hyper-redundant manipulator to optimize its stiffness. On the other hand, the cascade proportional integral differential control law is used to change the cable tension of the hyper-redundant manipulator to optimize its stiffness. Finally, simulations and experiments are carried out to validate the analytical stiffness model and variable-stiffness planning method considering both the overall configuration and cable tension for hyper-redundant manipulators.

The cable-driven redundant manipulator (CDRM) has a light slender and highly dexterous body, which is especially suitable for fine manipulation in confined spaces or unstructured environments. However, for dexterous manipulation and physical interaction with its surrounding environment, the force/torque sensor is indispensable, which will significantly increase the mass, dimension, and cost of the whole CDRM system. In this article, a force-sensing algorithm and compliance control framework for CDRM without a six-axis force/torque sensor are proposed. First, we design a modular cable-driven manipulator with two-level internal sensors, i.e., joint encoders and cable tension sensors. The multispace kinetic model is derived to establish the mapping between motor, cable, joint, and end-effector states. At the same time, we build a recursive dynamics model that takes the cables’ tension, cable–hole friction, links’ gravity, and end-effector forces into account. Then, the indirect force-sensing algorithm of the end-effector is proposed by combining the kinematic and dynamic equations and internal sensor information. Furthermore, a compliance controller based on indirect force sensing is designed. Finally, typical experiments are carried out based on the CDRM. Experiment results indicate that the force-sensing accuracy exceeds 95%, whereas the compliance controller demonstrates outstanding compliant behavior in human–robot interaction tasks.

• Cu-substituted nickel hexacyanoferrate is prepared by a coprecipitation route. • The electrode potential of nickel hexacyanoferrate can be adjusted by Cu doping. • Cu doping affects positively the electrochemical property of Ni 2 Fe(CN) 6. • The capacity retention of Cu 0.4 Ni 1.6 Fe(CN) 6 is 97.54% at 0.3 C after 120 cycles. In this work, a variety of Cu x Ni 2– x Fe(CN) 6 (x = 0, 0.4, 0.8, 1.2, 1.6, 2) cathodes for ammonium ion batteries are prepared and their electrochemical performances are investigated. During the introduction of copper in nickel hexacyanoferrate, the electrochemical performance varies without changing the structure of nickel hexacyanoferrate. The increase of Cu content in nickel hexacyanoferrate leads to the enhancement of reaction potential and capacity. Electrochemical results suggest that the substitution of Cu for Ni has a positive effect on improving the cycling stability and rate capacity of nickel hexacyanoferrate when x in Cu x Ni 2– x Fe(CN) 6 is less than 0.4. Therefore, Cu 0.4 Ni 1.6 Fe(CN) 6 exhibits the best cycling performance (capacity retention of 97.54% at 0.3 C) and the highest rate capacity (41.4 mAh g–1 at 10 C) in Cu x Ni 2– x Fe(CN) 6. Additionally, the X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS) tests also reveal that the structural evolution of Cu 0.4 Ni 1.6 Fe(CN) 6 is highly reversible upon NH 4 + storage. Therefore, this work proposes a candidate material for ammonium-ion batteries and offers a novel avenue for adjusting the operating potential of the material. [Display omitted] [ABSTRACT FROM AUTHOR]

It has been demonstrated that fast convolutional sparse dictionary learning (FCSDL) is a useful instrument for diagnosing rolling bearing faults and can recover rolling bearing fault shocks unaffected by random slippage. However, although FCSDL is not impacted by random fluctuations and can rapidly reconstruct fault shock without truncating the signal, its performance for repetitive fault shock reconstruction is not optimal when dealing with strong noise vibration signals. Therefore, this article proposes cyclostationary convolutional sparse dictionary learning (CCSDL), which is guided by fault features (cyclostationarity) to achieve the greatest signal reconstruction performance. First, the proposed method is based on the rotation frequency, and various frequency-band-covering components in the vibration signal are reconstructed successively. In the meanwhile, the harmonic significance index (HSI), which can indicate the cyclostationarity of the fault shock, evaluates the fault characteristics of each reconstruction result and finally obtains the most significant reconstruction result. Compared with FCSDL and variational mode decomposition (VMD), the proposed method performs far superior in signal reconstruction when processing low SNR vibration data.

In this paper, a high-sensitivity optical fiber Fabry-Perot sensor based on all-sapphire is proposed and fabricated for pressure sensing in ultrahigh temperature environments. Compared with other works, the pressure-sensing sapphire wafer’s thickness was reduced to about $40~\mu $ m to improve the pressure sensitivity dramatically, while maintaining its dimensions to be $2\times 2$ mm. The double-side wet-etching process was adopted to thin the sapphire wafer. The roughness of the wetted sides was modified by different etching processes to optimize the optical interference fringe. The all-sapphire sensor head was assembled with a directed bonding method, and the sapphire fiber was adopted to be the lead-in fiber to increase the sensor’s tolerance to high temperatures. Detailed experiment shows that the fabricated sensor can work stably under 1200 °C in the range of 0–3 mPa. The pressure sensitivity increased from $0.877~\mu $ m/mPa under room temperature to $1.193~\mu $ m/mPa under 1200 °C. The good performance of the proposed sensor under high temperatures ensures its good application in pressure sensing of aeroengine and gas turbine.

Since the cyclostationarity in vibration signals is the key to judge the rotating machine health state, spectral harmonics-to-interference ratio (SHIR) has been used to construct single node feature learning network (SHIR-based blind deconvolution, BD-SHIR) to realize condition monitoring of machine. However, BD-SHIR still has several obvious limitations, including the need for prior fault information and the tendency to fall into local optimal solutions which will affect its feature learning and state monitoring performance. Therefore, this paper proposes a multi-node feature learning network based on BDSHIR, which does not rely on prior information, multi-node adaptive BDSHIR (MABD-SHIR). It uses each filter in the 1/3 binary tree filter bank to design each initial node (filter) in the learning network correspondingly, and introduces an adaptive fault characteristic frequency (FCF) detection process. Since this initialization can give some filters close to the fault resonant band, iterations starting from these nodes are more likely to detect the precise FCF and converge to the optimal solution. Finally, MABD-SHIR can obtain multiple local optimal solutions, among which the one with the largest SHIR is generally closer to the global optimal solution than the one obtained by BD-SHIR. Simulation and experimental data of defective rotating machinery verify the effectiveness of MABD-SHIR in machinery condition monitoring and fault feature learning. Note to Practitioners—This paper is motivated by the problems that the rotating machine condition monitoring based on BDSHIR is easy to prematurely fall into local optimal solution and requires prior fault information. These problems can be effectively solved by extending BDSHIR from single node structure to multi-node structure and introducing adaptive period detection technique (A-DPT). The core idea is to start iterations from multiple initial filters and to detect the FCF adaptively during each iteration. Multiple features conforming to filtered signals’ SHIR maximization can be finally learned. Therefore, in this paper, MABD-SHIR is proposed, which is equivalent to a multi-node feature learning network. Since the objective function of BD is usually non-convex, MABD-SHIR can obtain multiple local optimal solutions without requiring prior FCFs. This is more likely to achieve a global optimal solution than BDSHIR, which starts iteration from only one initial filter and ends up with one local optimal solution. In addition, the objective function of MABD-SHIR has lower mathematical complexity, which reduces the difficulty of optimization.

This article focuses on distributed sliding-mode consensus tracking control of fuzzy multiagent systems (MASs) under time-varying transmission delays and hybrid cyber-attacks, including deception and denial-of-service (DoS) attacks. Impulsive and switching signals are adopted to describe the dynamic process of the fuzzy MAS subject to hybrid cyber-attacks. In order to address the hybrid cyber-attacks, a novel integral-type sliding surface function grounded on lumped consensus tracking errors is presented, and a resilient control scheme based on this surface is designed accordingly. By adopting the analysis method of hybrid systems and finite-time control theory, sufficient conditions are deduced to guarantee that the tracking error dynamics can achieve the intended sliding surface in a finite time and remain on the surface thereafter. Meanwhile, the quantitative relationships between the influence of hybrid cyber-attacks on the convergence rate of the control strategy are clearly revealed. Moreover, the stabilities of the corresponding sliding-mode dynamics are investigated. Finally, the example of cooperative fuzzy robot manipulators is used to illustrate the effectiveness and validity of the proposed theoretical results.

This article investigates one of the most challenging tasks in dynamic manipulation—catching large-momentum moving objects. Beyond the realm of quasi-static manipulation, dealing with highly dynamic objects can significantly improve the robot's capability of interacting with its surrounding environment. Yet, the inevitable motion mismatch between the fast moving object and the approaching robot will result in large impulsive forces, which lead to the unstable contacts and irreversible damage to both the object and the robot. To address the above problems, we propose an online optimization framework to: 1) estimate and predict the linear and angular motion of the object, 2) search and select the optimal contact locations across every surface of the object to mitigate impact through sequential quadratic programming, 3) simultaneously optimize the end-effector motion, stiffness, and contact force for both robots using multimode trajectory optimization (MMTO), and 4) realise the impact-aware catching motion on the compliant robotic system based on indirect force controller. We validate the impulse distribution, contact selection, and impact-aware MMTO algorithms in simulation and demonstrate the benefits of the proposed framework in real-world experiments including catching large-momentum moving objects with well-defined motion, constrained motion, and free-flying motion.

The variability caused by the proliferation of distributed energy resources (DERs) and the significant growth in unbalanced three-phase loads pose unprecedented challenges to distribution network operations. This paper focuses on how a distribution system operator (DSO), taking over the distribution grid and market operations, would develop a risk-aware flexibility market to mitigate uncertainties in an unbalanced three-phase power distribution network. First, a distributionally robust chance constraint (DRCC) method is devised to solve the unbalanced three-phase optimal power flow using a semidefinite programming (SDP) model. The DSO can apply the proposed solution to jointly clear energy and flexibility markets. Then, the DRCC model accuracy is improved by an information-sharing mechanism characterized by spatially-correlated uncertainties in the distribution grid. Further, a novel system-wide response function is derived to make the DRCC model tractable. Using the duality theory, the paper further investigates the physical composition of the DSO’s cleared flexibility prices to guide the unbalanced distribution network operation. Finally, the effectiveness of the risk-aware flexibility market is verified in a modified three-phase IEEE 34-node system. Results demonstrate that the flexibility market can quantify the impact of spatially correlated uncertainties and facilitate the utilization of flexible resources to mitigate uncertainties across the network.

Study Design network meta-analysisObjective To compare the clinical efficacy and safety of endoscopic lumbar interbody fusion (Endo-LIF), minimally invasive transforaminal lumbar interbody fusion (MIS-TLIF), and open transforaminal lumbar interbody fusion (OTLIF) in the treatment of lumbar degenerative diseases (LDDs).Method A literature search was conducted in the PubMed, Embase, and Cochrane Library databases. Studies comparing Endo-LIF, MIS-TLIF and OTLIF published from September 2017 to September 2022 for the treatment of LDD were retrieved. Data were extracted from preset clinical outcome measures, including operation time, estimated intraoperative estimated blood loss (EBL), length of hospital stay (LOS), complications, visual analog scale (VAS) score, Oswestry disability index (ODI) score, etc.Result Thirty-one studies with 3467 patients were included in this study. Network meta-analysis showed that in the comparison of the 3 procedures, Endo-LIF was superior to MIS-TLIF and OTLIF in terms of reducing EBL, LOS, time to ambulation, and VAS score of back pain. MIS-TLIF was superior to Endo-LIF in terms of ODI improvement, and OTLIF required the shortest intraoperative fluoroscopy time. There was no significant difference in operative time, complication rate, fusion rate, VAS score of leg pain, or JOA score among the 3 procedures.Conclusion Endo-LIF, MIS-TLIF and OTLIF each have their own advantages and disadvantages and show similar results in many respects, except for better early outcomes achieved with the more minimally invasive procedure.

Introduction: Sulfur-oxidizing bacteria (SOB) play a key role in the biogeochemical cycling of sulfur. Objectives: To explore SOB diversity, distribution, and physicochemical drivers in five volcanic lakes and two springs in the Wudalianchi volcanic field, China. Methods: This study analyzed microbial communities in samples via high-throughput sequencing of the soxBgene. Physical-chemical parameters were measured, and QIIME 2 (v2019.4), R, Vsearch, MEGA7, and Mothur processed the data. Alpha diversity indices and UPGMA clustering assessed community differences, while heat maps visualized intra-sample variations. Canoco 5.0 analyzed community-environment correlations, and NMDS, Adonis, and PcoA explored sample dissimilarities and environmental factor correlations. SPSS v.18.0 tested for statistical significance. Results: The diversity of SOB in surface water was higher than in springs (more than 7.27 times). We detected SOB affiliated to β-proteobacteria (72.3 %), α-proteobacteria (22.8 %), and γ-proteobacteria (4.2 %) distributed widely in these lakes and springs. Rhodoferax and Cupriavidus were most frequent in all water samples, while Rhodoferax and Bradyrhizobium are dominant in surface waters but rare in springs. SOB genera in both habitats were positively correlated. Co-occurrence analysis identified Bradyrhizobium, Blastochloris, Methylibium, and Metyhlobacterium as potential keystone taxa. Redundancy analysis (RDA) revealed positive correlations between SOB diversity and total carbon (TC), Fe2+, and total nitrogen (TN) in all water samples. Conclusion: The diversity and community structure of SOB in volcanic lakes and springs in the Wudalianchi volcanic group were clarified. Moreover, the diversity and abundance of SOB decreased with the variation of water openness, from open lakes to semi-enclosed lakes and enclosed lakes.

Existing adaptive optimal consensus approaches for high-order nonlinear multiagent systems (MASs) are limited by their complicated and computation-intensive identifier–actor–critic structure and ignore the conflict of interest between agents. This article proposes a graphical game-based adaptive fuzzy optimal bipartite containment scheme that removes these restrictions. The optimal containment is formulated as an $N$-player game over the communication topology for high-order nonlinear MASs by defining a cost function that integrates the agent's control inputs, including those of its neighbors, and the local tracking errors. To seek the Nash equilibrium, integral reinforcement learning is adopted, which does not involve the system drift dynamic. This approach eliminates the need for an identifier network and simplifies the control scheme using adaptive critic learning. To drive the online learning mechanism, the Bellman residual error is utilized, and a fuzzy logic system is used to approximate the optimal value functions of the critic networks. The updating laws incorporate an experience stack, resulting in an easy-to-check persistence excitation condition. It is proven that the synchronization error is uniformly ultimately bounded, and the bipartite containment of the outputs of followers is achieved. An illustrative example is presented to verify the effectiveness of the developed control scheme.

This letter presents an accurate and explicit linear model of bus impedance for a power network with unknown branch status. Firstly, an equivalent network of the power network is constructed, and the relationship between bus voltage and bus impedance is established. Then, the linear expression of bus impedance with unknown branch status is built. This model can be easily included as constraints in various optimization problems with network topology adjustment involving short circuit calculation.

Decomposition techniques generally have two limitations. First, for adaptive correlated kurtogram and variational mode decomposition, etc., the definition of the decomposition target has no direct relationship with the features of fault impacts, i.e., the decomposition is not guided by health indices used to characterize bearing faults, resulting in fault impacts cannot be completely separated from vibration. Second, it is hard to accurately determine the decomposition stop parameter (the number of final output modes), resulting in over- or under-decomposition. These are not conducive to bearing fault detection in engineering practice. Therefore, a new decomposition, multi-objective sparse maximum mode decomposition (MOSMMD), is proposed. Firstly, by analyzing the Fourier spectrum of vibration, MOSMMD constructs multiple finite impulse response filters covering different frequency bands to obtain initial candidate modes. Then, aiming at maximizing L2/L1 norm of envelope spectrum (ES), the upper and lower cutoff frequencies of each filter are adjusted adaptively through iterative optimization, and the frequency bands covered by each mode are adjusted accordingly. Finally, by using that fault feature harmonics have periodic natures in ES, the redundant modes are eliminated and only the modes with obvious fault features are output. The decomposition target of MOSMMD is directly related to fault features, i.e., cyclostationarity, which makes the extraction of fault impacts more accurate. The number of final output modes of MOSMMD is determined adaptively and does not need to be set as input before application. Even when processing concurrent fault signals, MOSMMD can decompose fault modes from vibration without prior fault information.