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Abstract In recent years, with the development of interfacial polymerization methodologies, the successful preparation of covalent organic framework (COF) monolayers featuring ultrahigh pore density and molecule-scale thickness has become a reality. This advancement has enabled ion transport with ultrahigh flux across extremely short paths. Owing to these processes, the COF monolayers have shown wide potential applications ranging from energy conversion and artificial mechanosensing to ion sieving. To date, nanofluidic systems based on COF monolayers are homogeneous, leading to nearly symmetric ion transport properties. Studies on the ionic transport behavior within ultrathin COF heterojunctions are limited and remain challenging. Herein, using a two-step sequential self-assembly and subsequent interfacial polymerization strategy, bi-layered heterogeneous tetraphenylporphyrin-based COF membranes composed of negatively charged and positively charged monolayers were prepared. Using an electric field as a driving force, rectified ion transport was observed for the first time. Notably, this ionic current rectification property could be modulated by the salt concentration and the structural composition of the membrane, and it was universal for various COF heterojunctions with different chemical ingredients. These findings are beneficial for promoting the development of innovative nanofluidic materials and devices.

Abstract The development of efficient and stable electrocatalysts for water oxidation in acidic media is vital for the commercialization of the proton exchange membrane electrolyzers. In this work, we successfully construct Ru–O–Ir atomic interfaces for acidic oxygen evolution reaction (OER). The catalysts achieve overpotentials as low as 167, 300, and 390 mV at 10, 500, and 1500 mA cm−2 in 0.5 M H2SO4, respectively, with the electrocatalyst showing robust stability for >1000 h of operation at 10 mA cm−2 and negligible degradation after 200,000 cyclic voltammetry cycles. Operando spectroelectrochemical measurements together with theoretical investigations reveal that the OER pathway over the Ru–O–Ir active site is near-optimal, where the bridging oxygen site of Ir–OBRI serves as the proton acceptor to accelerate proton transfer on an adjacent Ru centre, breaking the typical adsorption-dissociation linear scaling relationship on a single Ru site and thus enhancing OER activity. Here, we show that rational design of multiple active sites can break the activity/stability trade-off commonly encountered for OER catalysts, offering good approaches towards high-performance acidic OER catalysts.

Abstract Direct oxidation of KA oil (the mixture of cyclohexanone and cyclohexanol) toward ε-caprolactone is in high demand yet hard to implement in need of juggling the activation of both methyne C-H bond of cyclohexanol and α-C-C bond of cyclohexanone. Here we demonstrate that in situ formed Cu1+δ-Oδ-• active site, which originates from relay reaction at Ni(II) and Cu(I) pairs in a metal-organic framework (known as NiCu-MOF-74) with O2 and benzaldehyde (PhCHO), efficiently oxidizes KA oil toward ɛ-caprolactone along with good stability. Mechanism investigation discloses that the auxiliary Ni(II) site first adsorbs O2 for abstracting formyl hydrogen in PhCHO followed by transfer of PhCO· to react with another O2 over the major Cu(I) site, leading to formation of Cu1+δ-Oδ-• and PhCOOH. This major-auxiliary cooperative strategy will be particularly suitable for multivariate MOFs as next generation catalysts towards complex reactions.

BackgroundSome studies have shown a strong link between the central nervous system and peripheral immune system, but the prognostic implications of dynamic peripheral immune-inflammatory responses in patients with traumatic brain injury (TBI) remain unclear. This study aimed to determine the dynamic trajectory patterns of the Systemic Immune Inflammation Index (SII) in patients with TBI and assess its association with all-cause hospital mortality.MethodsThis retrospective cohort study utilized a large public database of patients with TBI sourced from the eICU Collaborative Research Database (eICU-CRD). Group-Based Trajectory Modeling (GBTM) was used to analyze daily SII trajectories during the initial 0–7 days of hospitalization. Logistic regression was employed to assess the relationship between different SII trajectory groups and hospital mortality. Receiver Operating Characteristic (ROC) curves were generated based on the logistic regression model.ResultsA total of 312 patients were included in this study, 52 of whom died during hospitalization. Using GBTM, three distinct SII trajectories were identified: Group 1 (low-level, rapid decline; 18.90%), Group 2 (moderate-level, slow decline; 60.20%), and Group 3 (sustained high-level; 20.80%). Compared to patients in Group 1, those in Groups 2 and 3 had a higher risk of all-cause hospital mortality (odds ratio [OR] 4.09; 95% confidence interval [CI] 1.21, 19.75) and (OR 5.84; 95% CI 1.52, 30.67), respectively. ROC analysis revealed an area under the curve (AUC) of 0.838, sensitivity: 75.0%, and specificity: 83.8% for mortality in this cohort.ConclusionThis study identified three distinct SII trajectories, suggesting that post-TBI SII trajectories are heterogeneous patterns associated with mortality. The sustained high-level SII trajectory may serve as a marker of disease deterioration, highlighting the need for targeted interventions. Describing the evolution of SII through GBTM and its correlation with clinical outcomes can enhance our understanding of the link between neuroinflammation and the peripheral immune system.

Abstract Photoelectrochemical (PEC) cell is an ideal platform for organic transformation because of its green benefits and minimal energy consumption. As an emerging methodology, the reaction types of photoelectrocatalytic organic synthesis (PECOS) are limited to simple oxidation and C–H activation at current stage. Metal catalysis for the construction of C(sp2)–N bonds has not been touched yet in PECOS. We introduce here a PEC method that successfully engages Ni catalysis for the mild production of aniline derivatives. Experimental and computational investigations elucidate that the addition of photoanode-generated amine radical to Ni catalyst avoids the sluggish nucleophilic attack, enabling the reaction to proceed at an ultra-low potential (–0.4 V vs. Ag/AgNO3) and preventing the overoxidation of products in conventional electrochemical synthesis. This synergistic catalysis strategy exhibits good functional group tolerance and wide substrate scope on both aryl halides and amines, by which some important natural products and pharmaceutical chemicals have been successfully modified.

Abstract Hydrogen spillover is an extraordinary effect in heterogeneous catalysis and hydrogen storage, which refers to the surface migration of metal particle-activated hydrogen atoms over the solid supports. Historical studies on this phenomenon have mostly been limited to reducible metal oxides where the long-distance proton-electron coupled migration mechanism has been established, yet the key question remains on how to surmount short-distance and defect-dependent hydrogen migration on nonreducible supports. By demerging hydrogen migration and hydrogenation reaction, here we demonstrate that the hydrogen spillover in nonreducible metal-organic frameworks (MOFs) can be finely modulated by the ligand functional groups or embedded water molecules, enabling significant long-distance (exceed 50 nm) movement of activated hydrogen. Furthermore, using sandwich nanostructured MOFs@Pt@MOFs as catalysts, we achieve highly selective hydrogenation of N-heteroarenes via controllable hydrogen spillover from Pt to MOFs-shell. We anticipate that this work will enhance the understanding of hydrogen spillover and shed light on de novo design of MOFs supported catalysts for many important reactions involving hydrogen.

Abstract The collective light-matter interaction of chiral supramolecular aggregates or molecular ensembles with confined light fields remains a mystery beyond the current theoretical description. Here, we programmably and accurately build models of chiral plasmonic complexes, aiming to uncover the entangled effects of excitonic correlations, intra- and intermolecular charge transfer, and localized surface plasmon resonances. The intricate interplay of multiple chirality origins has proven to be strongly dependent on the site-specificity of chiral molecules on plasmonic nanoparticle surfaces spanning the nanometer to sub-nanometer scale. This dependence is manifested as a distinct circular dichroism response that varies in spectral asymmetry/splitting, signal intensity, and internal ratio of intensity. The inhomogeneity of the surface-localized plasmonic field is revealed to affect excitonic and charge-transfer mixed intermolecular couplings, which are inherent to chirality generation and amplification. Our findings contribute to the development of hybrid classical-quantum theoretical frameworks and the harnessing of spin-charge transport for emergent applications.

Abstract Tungsten trioxide (WO3) has been recognized as the most promising photocatalyst for highly selective oxidation of methane (CH4) to formaldehyde (HCHO), but the origin of catalytic activity and the reaction manner remain controversial. Here, we take {001} and {110} facets dominated WO3 as the model photocatalysts. Distinctly, {001} facet can readily achieve 100% selectivity of HCHO via the active site mechanism whereas {110} facet hardly guarantees a high selectivity of HCHO along with many intermediate products via the radical way. In situ diffuse reflectance infrared Fourier transform spectroscopy, electron paramagnetic resonance and theoretical calculations confirm that the competitive chemical adsorption between CH4 and H2O and the different CH4 activation routes on WO3 surface are responsible for diverse CH4 oxidation pathways. The microscopic mechanism elucidation provides the guidance for designing high performance photocatalysts for selective CH4 oxidation.

This paper presents a novel variable stiffness mechanism, namely the SBTDTS (Spinal Biomimetic Two-Dimensional Tensegrity Structure), which is constructed by integrating bioinspiration derived from biological spinal structures with the T-Bar mechanical design within tensegrity structures. A method for determining the torsional stiffness of the SBTDTS around a virtual rotational center is established based on parallel mechanism theory. The relationship between various structural parameters is analyzed through multiple sets of typical parameter combinations. Ultimately, the PSO (Particle Swarm Optimization) algorithm is employed to identify the optimal combination of structural parameters for maximizing the stiffness ratio, Kθ_time, of SBTDTS under different constraint conditions. This optimal configuration is then compared with the RAPRPM (a type of rotational parallel mechanism) under different values of μ, with an analysis of the distinct advantages of both variable stiffness structures.

Abstract Conventional food production is restricted by energy conversion efficiency of natural photosynthesis and demand for natural resources. Solar-driven artificial food synthesis from CO2 provides an intriguing approach to overcome the limitations of natural photosynthesis while promoting carbon-neutral economy, however, it remains very challenging. Here, we report the design of a hybrid electrocatalytic−biocatalytic flow system, coupling photovoltaics-powered electrocatalysis (CO2 to formate) with five-enzyme cascade platform (formate to sugar) engineered via genetic mutation and bioinformatics, which achieves conversion of CO2 to C6 sugar (L-sorbose) with a solar-to-food energy conversion efficiency of 3.5%, outperforming natural photosynthesis by over three-fold. This flow system can in principle be programmed by coupling with diverse enzymes toward production of multifarious food from CO2. This work opens a promising avenue for artificial food synthesis from CO2 under confined environments.

Abstract Assembly ubiquitously occurs in nature and gives birth to numerous functional biomaterials and sophisticated organisms. In this work, chiral hydrogen-bonded organic-inorganic frameworks (HOIFs) are synthesized via biomimicking the self-assembly process from amino acids to proteins. Enjoying the homohelical configurations analogous to α-helix, the HOIFs exhibit remarkable chiroptical activity including the chiral fluorescence (g lum = 1.7 × 10−3) that is untouched among the previously reported hydrogen-bonded frameworks. Benefitting from the dynamic feature of hydrogen bonding, HOIFs enable enantio-discrimination of chiral aliphatic substrates with imperceivable steric discrepancy based on fluorescent change. Moreover, the disassembled HOIFs after recognition applications are capable of being facilely regenerated and self-purified via aprotic solvent-induced reassembly, leading to at least three consecutive cycles without losing the enantioselectivity. The underlying mechanism of chirality bias is decoded by the experimental isothermal titration calorimetry together with theoretic simulation.

In the context of global climate change, promoting the low-carbon transformation of cities has become an important strategy to cope with environmental challenges. This paper takes Wenzhou Garden Expo Park area as the research object, combines its practical experience as a pilot of a national low-carbon city, and discusses how to effectively control carbon emission in the spatial planning of national territory. The study systematically evaluated the impact of different land use types and development intensities on carbon emissions, as well as the relationship between daytime temperature and carbon emissions, by constructing a carbon emission measurement model and a random forest regression model. This evaluation was based on an analysis of remote sensing data and land use changes from 2000 to 2023. The results show that between 2000 and 2023, the carbon emission from building land in the Garden Expo Park area will increase by about 70%, while the carbon emission can be reduced by more than 25% through rational land use layout and development intensity control. At the same time, the expansion of green space and forest land increases the carbon sink capacity by about 16.7%. With rising temperatures, carbon emissions exhibit a significant upward trend. This study suggests that specific optimization strategies for low-carbon planning, along with an indicator system—particularly through increasing the allocation of green spaces such as arboreal forests and parks—can significantly improve regional carbon balance. This study may provide a reference for other rapidly urbanizing regions to balance economic development and carbon emissions.

Abstract In rehabilitation medicine, real-time analysis of the gait for human wearing lower-limb exoskeleton rehabilitation robot during walking can effectively prevent patients from experiencing excessive and asymmetric gait during rehabilitation training, thereby avoiding falls or even secondary injuries. To address the above situation, we propose a gait detection method based on computer vision for the real-time monitoring of gait during human–machine integrated walking. Specifically, we design a neural network model called GaitPoseNet, which is used for posture recognition in human–machine integrated walking. Using RGB images as input and depth features as output, regression of joint coordinates through depth estimation of implicit supervised networks. In addition, joint guidance strategy (JGS) is designed in the network framework. The degree of correlation between the various joints of the human body is used as a detection target to effectively overcome prediction difficulties due to partial joint occlusion during walking. Finally, a post processing algorithm is designed to describe patients’ walking motion by combining the pixel coordinates of each joint point and leg length. Our advantage is that we provide a non-contact measurement method with strong universality, and use depth estimation and JGS to improve measurement accuracy. Conducting experiments on the Walking Pose with Exoskeleton (WPE) Dataset shows that our method can reach 95.77% PCKs@0.1, 93.14% PCKs@0.08 and 3.55 ms runtime. Therefore our method achieves advanced performance considering both speed and accuracy.

With their unique point‐contact ability with the ground and exceptional adaptability to complex terrains, quadruped robots have become a focal point in the fields of automation and robotic engineering. Significant research progress has been made in aspects such as structural design, motion planning, and balance control of these robots. However, a primary challenge in current research lies in further enhancing the dynamic performance, environmental adaptability, and payload capacity. In this article, research achievements in key technical areas of quadruped robots, encompassing structural design, gait planning, traditional control strategies, intelligent control strategies, and autonomous movement, are comprehensively discussed. The focus of the article is on analyzing trends in intelligence and technological innovation within the aforementioned areas, aiming to provide robust theoretical support and forward‐looking technical guidance for quadruped robots research. Additionally, it offers valuable references for scholars engaged in this field.

Abstract Background Influenza is a common illness for its high rates of morbidity and transmission. The implementation of non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic to manage its dissemination could affect the transmission of influenza. Methods A retrospective analysis, between 2018 and 2023, was conducted to examine the incidence of influenza virus types A and B among patients in sentinel cities located in North or South China as well as in Wuhan City. For validations, data on the total count of influenza patients from 2018 to 2023 were collected at the Central Hospital of Wuhan, which is not included in the sentinel hospital network. Time series methods were utilized to examine seasonal patterns and to forecast future influenza trends. Results Northern and southern cities in China had earlier outbreaks during the NPIs period by about 8 weeks compared to the 2018–2019. The implementation of NPIs significantly reduced the influenza-like illness (ILI) rate and infection durations. Influenza B Victoria and H3N2 were the first circulating strains detected after the relaxation of NPIs, followed by H1N1 across mainland China. The SARIMA model predicted synchronized H1N1 outbreak cycles in North and South China, with H3N2 expected to occur in the summer in southern cities and in the winter in northern cities over the next 3 years. The ILI burden is expected to rise in both North and South China over the next 3 years, with higher ILI% levels in southern cities throughout the year, especially in winter, and in northern cities mainly during winter. In Wuhan City and the Central Hospital of Wuhan, influenza levels are projected to peak in the winter of 2024, with 2 smaller peaks expected during the summer of 2023. Conclusions In this study, we report the impact of NPIs on future influenza trends in mainland China. We recommend that local governments encourage vaccination during the transition period between summer and winter to mitigate economic losses and mortality associated with influenza.

Hydraulic power take-off (PTO) is increasingly favored as energy regulation and transmission system in wave energy converters (WEC), significantly smoothing the inherent randomness and fluctuation of wave energy. This paper designed a novel hydraulic PTO system composition of a double-acting hydraulic cylinder pump and accumulators. The dynamic process sub-division principle in an operating period of the hydraulic cylinder pump and accumulator and the mathematical model for explaining the fluctuations of pressure and flow rate in the hydraulic pump and accumulator circuit by means of the sub-division principle are put forward. The MATLAB/Simulink simulation model used to analyze pressure fluctuation in the hydraulic PTO system is established based on the mathematical model. The numerical results and MATLAB simulation results are mutually verified about the fine analysis of the accumulator smoothing fluctuation in the hydraulic PTO system. The results show that the pressure fluctuation amplitude of a hydraulic circuit is negatively correlated with the accumulator pre-charge pressure and the accumulator volume, and is positively correlated with the operating period of a hydraulic pump. The energy transfer efficiency of the hydraulic PTO system with accumulator fine compensation can be above 90%. The theory and model in this paper will serve as a valuable reference for designing fluctuation compensation parameters in hydraulic systems.

The electrochemical oxygen reduction reaction (ORR) is pivotal in energy conversion via a 4e− ORR pathway and green hydrogen peroxide production via 2e− ORR pathway. Transition metal single atom catalysts (TM SACs) have attracted intense attention in recent years for ORR due to their high activity and near maximum metal atom utilization. The future development of TM SACs for ORR requires improved understanding of reaction pathways, since currently the true origin of activity remains contentious owing to the lack of qualitative/quantitative information about active sites. Knowledge-guided design is imperative for the optimization of TM SACs performance in terms of activity and selectivity. This review focuses on the latest progress in the design of TM SACs for ORR, placing particular attention on efforts to elucidate reaction mechanisms. Experimental evidence based on in-situ/operando characterization measurements, along with theoretical predictions, are summarized to deepen understanding of the structure-performance relationships at both atomic and molecular level. Finally, some perspectives are offered relating to the fundamental science needed for TM SACs to find practical application in energy storage and conversion devices. We hope this review will inspire the development of new synthetic routes towards high-performance ORR electrocatalysts for the energy sector.

The exoskeleton robot is a wearable electromechanical device inspired by animal exoskeletons. It combines technologies such as sensing, control, information, and mobile computing, enhancing human physical abilities and assisting in rehabilitation training. In recent years, with the development of visual sensors and deep learning, the environmental perception of exoskeletons has drawn widespread attention in the industry. Environmental perception can provide exoskeletons with a certain level of autonomous perception and decision-making ability, enhance their stability and safety in complex environments, and improve the human–machine–environment interaction loop. This paper provides a review of environmental perception and its related technologies of lower-limb exoskeleton robots. First, we briefly introduce the visual sensors and control system. Second, we analyze and summarize the key technologies of environmental perception, including related datasets, detection of critical terrains, and environment-oriented adaptive gait planning. Finally, we analyze the current factors limiting the development of exoskeleton environmental perception and propose future directions.

Abstract Staircases are some of the most common building structures in urban environments. Stair detection is an important task for various applications, including the environmental perception of exoskeleton robots, humanoid robots, and rescue robots and the navigation of visually impaired people. Most existing stair detection algorithms have difficulty dealing with the diversity of stair structure materials, extreme light and serious occlusion. Inspired by human perception, we propose an end-to-end method based on deep learning. Specifically, we treat the process of stair line detection as a multitask involving coarse-grained semantic segmentation and object detection. The input images are divided into cells, and a simple neural network is used to judge whether each cell contains stair lines. For cells containing stair lines, the locations of the stair lines relative to each cell are regressed. Extensive experiments on our dataset show that our method can achieve 81.49 $$\%$$ % accuracy, 81.91 $$\%$$ % recall and 12.48 ms runtime, and our method has higher performance in terms of both speed and accuracy than previous methods. A lightweight version can even achieve 300+ frames per second with the same resolution.

Dendrite growth has been the main trouble preventing the practical application of Li metal anodes. Herein, we present how an Fe3O4-PVDF composite network prepared by using electrospinning has been designed to protect lithium metal anodes effectively. In the symmetrical cells test, the cell with the Fe3O4-PVDF composite network maintains good cycle performance after 600 h (500 cycles) at a current density of 1 mA cm−2 and a plating/stripping capacity of 1 mAh cm−2. The bulky Li dendrite is suppressed and a uniform Li deposition remains after long cycling. The characteristics of this engineered separator are further demonstrated in Li-S full cells with a good cycle performance (capacity of 419 mAh g−1 after 300 cycles at 0.5 C). This work provides a new idea for the protection of lithium metal anodes.

Lung cancer is a leading cause of cancer-related death worldwide, with a very poor overall five-year survival rate. The intrinsic limitations associated with the conventional diagnosis and therapeutic strategies used for lung cancer have motivated the development of nanotechnology and nanomedicine approaches, in order to improve early diagnosis rate and develop more effective and safer therapeutic options for lung cancer. Cancer nanomedicines aim to individualize drug delivery, diagnosis, and therapy by tailoring them to each patient’s unique physiology and pathological features—on both the genomic and proteomic levels—and have attracted widespread attention in this field. Despite the successful application of nanomedicine techniques in lung cancer research, the clinical translation of nanomedicine approaches remains challenging due to the limited understanding of the interactions that occur between nanotechnology and biology, and the challenges posed by the toxicology, pharmacology, immunology, and large-scale manufacturing of nanoparticles. In this review, we highlight the progress and opportunities associated with nanomedicine use for lung cancer treatment and discuss the prospects of this field, together with the challenges associated with clinical translation.

The utilization of lower extremity exoskeletons has witnessed a growing presence across diverse domains such as the military, medical treatment, and rehabilitation. This paper introduces a novel design of a lower extremity exoskeleton specifically tailored for individuals engaged in heavy object carrying tasks. The exoskeleton incorporates an impressive 12 degrees of freedom (DOF), with four of them being effectively controlled through hydraulic cylinders. To achieve optimal control of this intricate lower extremity exoskeleton system, the authors propose an adaptive dynamic programming (ADP) algorithm. Several crucial components are established to implement this control scheme. These include the formulation of the state equation for the lower extremity exoskeleton system, which is well-suited for the ADP algorithm. Additionally, a corresponding performance index function based on the tracking error is devised, along with the game algebraic Riccati equation. By employing the value iteration ADP scheme, the lower extremity exoskeleton demonstrates highly effective tracking control. This research not only highlights the potential of the proposed control approach but also showcases its ability to enhance the overall performance and functionality of lower extremity exoskeletons, particularly in scenarios involving heavy object carrying. Overall, this study contributes to the advancement of lower extremity exoskeleton technology and offers valuable insights into the application of ADP algorithms for achieving precise and efficient control in demanding tasks.

A novel structural design of a wave energy converter (WEC) is proposed, utilizing a gyroscope as the main component for energy absorption. A vacuum-sealed, high-speed flywheel is implemented within the gyroscope to convert energy and reduce energy loss from air resistance. A dynamic model that considers the response of the floater, gyroscope, and power take-off (PTO) system with multi-degree-of-freedom (DoF) wave excitations is created. The primary parameters such as flywheel speed, damping, and stiffness of the WEC system that affect the gyroscope and PTO on energy absorption are analyzed through numerical simulations. The simulation results demonstrate that changes to these parameters can significantly impact the energy-absorption peak value. Furthermore, an improved multi-objective evolutionary algorithm based on the decomposition (MOEA/D) algorithm is applied to optimize key parameters, such as the power conversion and gyro precession of the WEC system, using a hybrid constraint handling strategy for enhanced diversity and the HV value as the evaluation criterion. The optimal design solution is selected from the Pareto solution set using a technique for order preference by similarity to the ideal solution (TOPSIS) method based on entropy weighting so that technical guidance can be provided for the design and control of the WEC system.

Nanoparticles are being explored as a potential method to target Aβ aggregation in Alzheimer’s disease. Here, the authors develop gold nanoparticles that were capped with chiral L or D-glutathione which has been shown to improve BBB permeability and demonstrate their ability to improve cognitive function in a mouse model of AD.

Stairs are common vertical traffic structures in buildings, and stair detection tasks are important in environmental perception for autonomous mobile robots. Most existing algorithms have difficulty combining the visual information from binocular sensors effectively and ensuring reliable detection at night and in the case of extremely fuzzy visual clues. To solve these problems, we propose a stair detection network with red-green-blue (RGB) and depth inputs. Specifically, we design a selective module, which can make the network learn the complementary relationship between the RGB feature maps and the depth feature maps and fuse the features effectively in different scenes. In addition, we propose several postprocessing algorithms, including a stair line clustering algorithm and a coordinate transformation algorithm, to obtain the stair geometric parameters. Experiments show that our method has better performance than existing the state-of-the-art deep learning method, and the accuracy, recall, and runtime are improved by 5.64%, 7.97%, and 3.81 ms, respectively. The improved indexes show the effectiveness of the multimodal inputs and the selective module. The estimation values of stair geometric parameters have root mean square errors within 15 mm when ascending stairs and 25 mm when descending stairs. Our method also has extremely fast detection speed, which can meet the requirements of most real-time applications.

Electrocatalysis is at the heart of many significant chemical transformation processes and advanced clean energy technologies. Traditional noble/transition metal oxides are widely used as electrocatalysts; however, they often suffer from intrinsic disadvantages, including low atom utilization, small surface area, and unfavorable tunability. Metal–organic frameworks (MOFs), as a new family of catalytic materials, are attracting extensive attention due to their unique physicochemical properties. The tremendous pristine MOF‐based materials are created using various synthetic approaches and further used for important energy conversions. Herein, a systematic overview on the unique merits and the state‐of‐the‐art design of MOF‐based electrocatalysts is offered. This review also presents recent advances in the development of various pristine MOFs and MOF‐based host–guest composite catalysts for electrocatalysis (i.e., oxygen reduction reaction, hydrogen oxidation reaction, hydrogen evolution reaction, oxygen evolution reaction, and CO2 reduction reaction) and discusses the future challenges and opportunities in this emerging field.

This paper presents a novel working mechanism of a micropump using micropaddles (MPs) to actively manipulate fluid based on 3D printing technology. The novel working principle is systematically discussed using analysis, computation and experiment methods. A theoretical model is established to research the working mechanism and crucial parameters for driving ability, such as MPs shape, size, vibration amplitude and frequency. Two different 3D printing techniques that simplify the multi-step process into only one step are introduced to manufacture the prototype pump for investigating the principle experimentally. A testing system is designed to evaluate the flow rate of pumps with eight different vibrating paddles. A maximum flux of 127.9 mL/min is obtained at an applied voltage of 9 V. These experiments show that the active-type mechanical pump could not only freely control flow direction but also change flux by adopting different shapes or distribution ways. The advantage of the novel micropump is the application of the MP structure into the micropump system to actively manipulate fluid with flexibility and high driving ability at fairly low power. Keywords: Micropump, Manipulate fluid, Vibrating micropaddles, 3D printing, Fluid-structure interaction

In recent years, the lower limb exoskeleton has been more and more widely used in military, medical and other fields. In this paper, the muscle–bone model of the lower limb during the human walking process is analyzed, and a lower limb exoskeleton with the purpose of loadbearing is designed. The exoskeleton is driven by four hydraulic cylinders to the hip and knee joints whose design load is 50 kg. The kinematic and dynamic model of the exoskeleton designed in this paper is established and analyzed, and it is simulated. Finally, the experiments were carried out on the exoskeleton test platform to verify that the stability, bearing capacity, tracking effect and durability of the exoskeleton can meet the requirements.

Aiming at the difficulty of debugging the exoskeleton control system driven by a hydraulic cylinder, a research method of a nonlinear control strategy for the hydraulic exoskeleton system with 1 degree of freedom (DOF) joint is proposed. Based on a hardware-in-the-loop (HIL) simulation, this method establishes the dynamic model of the 1-DOF joint system of the hydraulic driven exoskeleton, constructs the HIL simulation test platform based on the Linux real-time kernel patch, and studies the nonlinear control strategy of the 1-DOF joint system on this platform system. The control effects of the PID control algorithm and the backstepping method on nonlinear control are compared, and the controller parameters are tested on the HIL simulation platform. From the experimental results of the HIL simulation, the research method has the advantages of low cost, high efficiency of system development, safety, and reliability. It has important reference value for the development and debugging of a hydraulic exoskeleton control system.

BackgroundPneumocystis pneumonia (PCP) has a significant impact on the mortality of immunocompromised patients. It is not known whether the prophylactic application of trimethoprim-sulfamethoxazole (TMP-SMZ) can reduce the incidence of PCP and mortality in the human immunodeficiency virus (HIV)-negative immunodeficient population. The safety profile is also unknown. There have been few reports on this topic. The aim of this study was to systematically evaluate the efficacy and safety of the use of TMP-SMZ for the prevention of PCP in this population of patients from the perspective of evidence-based medicine.MethodsA comprehensive search without restrictions on publication status or other parameters was conducted. Clinical randomized controlled trials (RCTs) or case-control trials (CCSs) of TMP-SMZ used for the prevention of PCP in HIV-negative immunocompromised populations were considered eligible. A meta-analysis was performed using the Mantel-Haenszel fixed-effects model or Mantel-Haenszel random-effects model, and odds ratios (ORs) with 95% confidence intervals (CIs) were calculated and reported.ResultsOf the 2392 records identified, 19 studies (n = 4135 patients) were included. The efficacy analysis results indicated that the PCP incidence was lower in the TMP-SMZ group than in the control group (OR = 0.27, 95% CI (0.10, 0.77), p = 0.01); however, the rate of drug discontinuation was higher in the TMP-SMZ group than in the control group (OR = 14.31, 95% CI (4.78, 42.91), pConclusionsTMP-SMZ has a better effect than other drugs or the placebo with regard to preventing PCP in HIV-negative immunocompromised individuals, but it may not necessarily reduce the rate of mortality, the rate of drug discontinuation or AEs. Due to the limitations of the research methodologies used, additional large-scale clinical trials and well-designed research studies are needed to identify more effective therapies for the prevention of PCP.

Abstract As an emerging building unit, carbon dots (CDs) have been igniting the revolutionaries in the fields of optoelectronics, biomedicine, and bioimaging. However, the difficulty of synthesizing CDs in aqueous solution with full‐spectrum emission severely hinders further investigation of their emission mechanism and their extensive applications in white light emitting diodes (LEDs). Here, the full‐color‐emission CDs with a unique structure consisting of sp3‐hybridized carbon cores with small domains of partially sp2‐hybridized carbon atoms are reported. First‐principle calculations are initially used to predict that the transformation from sp3 to sp2 hybridization redshifts the emission of CDs. Guided by the theoretical predictions, a simple, convenient, and controllable route to hydrothermally prepare CDs in a single reaction system is developed. The prepared CDs have full‐spectrum emission with an unprecedented two‐photon emission across the whole visible color range. These full‐color‐emission CDs can be further nurtured by slight modifications of the reaction conditions (e.g., temperature, pH) to generate the emission color from blue to red. Finally a flexible LEDs with full‐color emission by using epoxy CDs films is developed, indicating that the strategy affords an industry translational potential over traditional fluorophores.

Recently, the advent of generative adversarial networks (GANs) in synthesizing identity-preserving faces has aroused the considerable interest of many scholars. However, face attribute representation learning, which is explicitly disentangled from identity feature and synthesizes identity-preserving face images with high diversity and quality in other datasets, still remains challenging. To cope with that, this paper proposes multi-process training GAN, or MP-GAN for short, which significantly improves the disentangled representation, diversity, and quality. Unlike other existing single-process models that map noise to a final output resolution image in a single training process, MP-GAN divides training into multiple processes. The main idea is to first generate lower resolution images that contain lower frequency feature information through competition and then extract their disentangled facial features to generate a higher resolution image. Furthermore, an identity-preserving image with real identity feature and disentangled facial feature could be generated at the final output resolution training process. The distinct benefits are not only getting diverse facial feature generation but also achieving disentangled representation from the lower resolution training processes and rendering a photo-realistic image that contains high diversity but preserves identity at the final output resolution training process. The high performance of this method is highlighted by quantitative and qualitative comparisons. We conclude that MP-GAN can generate face images featuring high diversity and quality while efficiently preserving identity, thereby significantly outperforming most modern advanced methods.

Object detection has attracted great attention in recent years. Many experts and scholars have proposed efficient solutions to address object detection problems and achieve perfect performance. For example, coordinate-based anchor-free (CBAF) module was proposed recently to predict the category and the adjustments to the box of the object by its feature part and its contextual part features, which are based on feature maps divided by spatial coordinates. However, these methods do not work very well for some particular situations (e.g., small object detection, scale variation, deformations, etc.), and the accuracy of object detection still needs to be improved. In this paper, to address these problems, we proposed ODEM-GAN based on CBAF, which utilizes generative adversarial networks to implement the detection of a deformed object. Specifically, ODEM-GAN first generates the object deformation features and then uses these features to enhance the learning ability of CBFA for improving the robustness of the detection. We also conducted extensive experiments to validate the effectiveness of ODEM-GAN in the simulation of a parachute opening process. The experimental results demonstrate that, with the assistance of ODEM-GAN, the AP score of CBAF for parachute detection is 88.4%, thereby the accuracy of detecting the deformed object by CBAF significantly increases.

Tracking aircraft and parachutes plays a vital role in airdrop experiments. It is necessary to study a parachute’s open state and flight trajectory. More scholars are looking into how to efficiently and accurately obtain parachute deformation data and trajectory data. At present, the actual data collection primarily involves experimenters holding high-definition high-speed cameras to track and shoot parachutes to obtain the image sequences of the parachutes during the airdrop process. However, these methods cannot obtain the trajectories of the parachutes and they are susceptible to interference from human factors. In this paper, we designed TuSeSy, an intelligent turntable servo system that can track the aircraft and parachutes in airdrop tests automatically. Specifically, TuSeSy generates the control commands according to the differences between the actual taken images and the inferred images by tracking algorithms (so as to actually track the target). In addition, we propose an effective multi-target tracking switch algorithm based on the image frame difference and optical flow, to achieve real-time switching from the aircraft to the parachute in an airdrop test. To evaluate the performance of TuSeSy, we conducted extensive experiments; the experimental results show that TuSeSy not only solves the problem of wrong target tracking, but it also reduces computational overhead. Moreover, the multi-target tracking switch algorithm has higher computing efficiency and reliability compared to other tracking switch approaches, ensuring the practical applications of the turntable servo system.

Molecular aggregation is a widespread and important process in physiological metabolism, but details regarding conformational changes during the process are hard to probe. Here, the authors use circular dichroism to monitor in-situ the conformational changes occurring during molecular aggregation.

Traditional video object segmentation often has low detection speed and inaccurate results due to the jitter caused by the pan-and-tilt or hand-held devices. Deep neural network (DNN) has been widely adopted to address these problems; however, it relies on a large number of annotated data and high-performance computing units. Therefore, DNN is not suitable for some special scenarios (e.g., no prior knowledge or powerful computing ability). In this paper, we propose RoiSeg, an effective moving object segmentation approach based on Region-of-Interest (ROI), which utilizes unsupervised learning method to achieve automatic segmentation of moving objects. Specifically, we first hypothesize that the central n × n pixels of images act as the ROI to represent the features of the segmented moving object. Second, we pool the ROI to a central point of the foreground to simplify the segmentation problem into a classification problem based on ROI. Third but not the least, we implement a trajectory-based classifier and an online updating mechanism to address the classification problem and the compensation of class imbalance, respectively. We conduct extensive experiments to evaluate the performance of RoiSeg and the experimental results demonstrate that RoiSeg is more accurate and faster compared with other segmentation algorithms. Moreover, RoiSeg not only effectively handles ambient lighting changes, fog, salt and pepper noise, but also has a good ability to deal with camera jitter and windy scenes.

The gas–liquid slug flow characteristics in a novel honeycomb microchannel reactor were investigated numerically and experimentally. Computational fluid dynamics (CFD) modeling was carried out with Comsol finite element software using the phase-field method, and the simulation results were verified by micro-particle image velocimetry (micro-PIV) analysis. The breakups of liquid slugs at the bifurcations of current honeycomb microchannel followed a complex behavior, leading to non-uniformity in each branch. The pressure distribution inside the microreactor was closely related to the phase distribution. The increasing inlet gas velocity increased the gas phase volume fraction, as well as the gas slug length. Higher gas velocity resulted in stronger turbulence of the liquid phase flow field and a deviation of residence time distribution from normal distribution, but it was favorable to even more residence time during the liquid phase. There also exists a secondary flow in the gas–liquid interface. This work reveals the intrinsic intensified effect of honeycomb microchannel, and it provides guidance on future microreactor design for chemical energy conversion.

Power technology has long been the main problem that has plagued the realization of ocean exploration by autonomous underwater vehicles (AUVs). This paper introduces a new wave energy conversion device for AUV, which is sealed inside a closed floating body to avoid interaction with the marine environment. The system uses the gyroscopic effect to continuously convert the pitching motion of waves into electrical energy through flywheel rotation, and thus theoretically extend the endurance time of AUVs. In this paper, a mathematical model of the power generation device is established, and the effects caused by different parameters on the system behavior and energy output are analyzed. In order to reduce the cost of experiments, the energy conversion device is installed on an experimental platform that can simulate wave motion to observe its energy generation performance. The experimental results show that the established mathematical model can accurately reflect the real behavior of the power generation device on the platform under different wave conditions, and the energy output error is only 9.91%.

The combination of near-infrared (NIR) response and imaging response is a hot research area in which the functions of nanomaterials are maximized. However, the types of such materials reported so far present problems such as requiring complex synthesis. In this study, hydrophilic, porous, and hollow cobalt/manganese oxide (CMO) nanocrystals (NCs) were successfully prepared via a facile and green hydrothermal route. The CMO NCs show strong near-infrared (NIR) absorption, which results from their defect structure due to the coexistence of Co2+ and Co3+ in the as-prepared CMO NCs. Thus, the CMO NCs exhibit excellent photothermal performance, showing photothermal efficiency of up to 43.2%. In addition, the CMO NCs possess good magnetic resonance (MR) imaging performance, with longitudinal relaxivity (r1) of up to 3.48 mM−1 s−1. Finally, for the first time, we prove that the CMO nanocrystals are a promising photothermal agent. Our work provides insights into the application of Mn-based control agents and photothermal agents for photothermal theragnosis therapy.

An improved Maximum Power Point Tracking (MPPT) method based on a purely mechanical wave energy converter (WEC) of gyroscope precession is proposed. The method adopts dynamic perturbation step adjustment, which improves the stability of power output and reduces steady-state oscillation. The paper introduces the principle of the device, establishes the mathematical model, and obtains the complete expression of power. The effect of wave frequency, pitch amplitude, power take-off (PTO) damping coefficient, and flywheel rotating speed on power output is analyzed. The output regression equation is established, and the extraction conditions of the maximum power are summarized and predicted. Aiming at the time-varying nature of actual ocean waves, a variable step size modified maximum power point (MPP) tracking control algorithm based on perturbation and observation (P&O) method is proposed. The algorithm has a unique technology to dynamically change the perturbation size, which not only improves the dynamic response but also reduces the oscillation. Besides, the boundary conditions ensure that the algorithm will not deviate from the motion trajectory, and the average filtering method and steady-state judgment can further reduce steady-state oscillation. In various ocean conditions, the proposed method has better output stability compared with other variable step size algorithms. Finally, different wave working conditions are given in the experiment, and the results verify the effectiveness of the proposed MPPT control strategy in experimental equipment. The device will be suitable for distributed power sources in small islands and ports.

In this article, methods for the attitude control optimization of large-load plant-protection quadrotor unmanned aerial vehicles (UAVs) are presented. Large-load plant-protection quadrotors can be defined as quadrotors equipped with sprayers and a tank containing a large amount of water or pesticide, allowing the quadrotors to water plants or spray pesticide during flight. Compared to the control of common small quadrotors, two main points need to be considered in the control of large-load plant-protection quadrotors—first, the water in the tank gradually diminishes during flight and the physical parameters change during this process. Second, the size and mass of the rotors are especially large, which greatly slows the response rate of the rotors. We present an extended-state reinforcement learning (RL) algorithm to solve these problems. The moment of inertia (MOI) of the three axes and the dynamic response constant of the rotors are included in the state list of the quadrotor during the training process, so that the controller can learn these changes in the models. The controlling laws are automatically generated and optimized, which greatly simplifies the tuning process compared to those of traditional control algorithms. The controller in this article is tested on a 10 kg class large-load plant-protection quadrotor, and the flight performance verifies the effectiveness of our work.

Ordered self-assembly is a promising way to realize collective properties in nanocrystals, but reliable routes to such macroscopic structures are missing. Here the authors make cm-scale ordered superlattice films from gold nanoclusters, correlating film properties with the shape of the building blocks

In this paper, methods are presented for designing a quadrotor attitude control system with disturbance rejection ability, wherein only one parameter needs to be tuned for each axis. The core difference between quadrotor platforms are extracted as critical gain parameters (CGPs). Reinforcement learning (RL) technology is introduced in order to automatically optimize the controlling law for quadrotors with different CGPs, and the CGPs are used to extend the RL state list. A deterministic policy gradient (DPG) algorithm that is based on an actor-critic structure in a model-free style is used as the learning algorithm. Mirror sampling and reward shaping methods are designed in order to eliminate the steady-state errors of the RL controller and accelerate the training process. Active disturbance rejection control (ADRC) is applied to reject unknown external disturbances. A set of extended state observers (ESOs) is designed to estimate the total disturbance to the roll and pitch axes. The covariance matrix adaptation evolution strategy (CMA-ES) algorithm is used to automatically tune the ESO parameters and improve the final performance. The complete controller is tested on an F550 quadrotor in both simulation and real flight environments. The quadrotor can hover and move around stably and accurately in the air, even with a severe disturbance.

Four nanostructured Au electrodes were prepared by a simple and templateless electrochemical deposition technique. After complexing with a biotinylated polythiophene derivative (PTBL), photocurrent generation and performance of PTBL/Au-nanostructured electrodes as photoelectrochemical biosensors were investigated. Among these four nanostructured Au electrodes, vertically oriented nanobranches on the electrode significantly improved the photoelectric conversion, because the vertically oriented nanostructures not only benefit light harvesting but also the transfer of the photogenerated charge carriers. Owing to this advantaged nanostructure, the PTBL/Au-nanobranch electrode showed higher sensitivity and faster response times in the photoelectrochemical detection of a streptavidin-biotin affinity reaction compared to a PTBL/Au-nanoparticle electrode.