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Transformers are widely used in computer vision areas and have achieved remarkable success. Most state-of-the-art approaches split images into regular grids and represent each grid region with a vision token. However, fixed token distribution disregards the semantic meaning of different image regions, resulting in sub-optimal performance. To address this issue, we propose the Token Clustering Transformer (TCFormer), which generates dynamic vision tokens based on semantic meaning. Our dynamic tokens possess two crucial characteristics: (1) Representing image regions with similar semantic meanings using the same vision token, even if those regions are not adjacent, and (2) concentrating on regions with valuable details and represent them using fine tokens. Through extensive experimentation across various applications, including image classification, human pose estimation, semantic segmentation, and object detection, we demonstrate the effectiveness of our TCFormer.

Segregated conductive polymer composites (CPCs) show high conductivity at low loading of filler. However, the weak interactions between fillers and polymer matrix may destroy the mechanical property of the segregated CPCs. Moreover, even with the introduction of dynamic bonds in thermoset polymers, the preparation of thermosetting CPCs remains a big challenge, as most crosslinked polymers should be ground into granules or crushed into powder with liquid nitrogen before mixing with fillers. Herein, the dynamic crosslinked polythiourethane microspheres (PTUM) are designed and synthesized. Then, a special mixing method (the mixing temperature is higher than melting temperature of soft segments of PTUM) is used to make the carbon nanotubes (CNTs) adhering closely to the surface of the crosslinked PTUM, promoting the formation of compacted conductive network. The CNT‐3%/PTUM shows the electrical conductivity of 21.9 S/m and an elongation at break of 472%. Additionally, the CNT/PTUM composites exhibit good self‐healing property, reprocessability, and close‐loop recycling property. The construction of dynamic crosslinked microspheres and compacted segregated conductive network in this work supplies a new approach to prepare thermoset CPCs with simultaneous high electrical conductivity and mechanical property, which is expected to be applied to wearable strain sensors.

Tantalum (Ta) has good potential for bone tissue engineering due to its excellent corrosion resistance and biocompatibility. However, the customization of Ta-based bone repair materials for irregularly shaped bone defects has been challenging due to their high melting point and hardness. In this work, porous tantalum granules (PTaG) were developed for the first time to repair irregularly shaped bone defects, inspired by the natural phenomenon of sand flow. PTaG were designed as a hierarchical porous structure to regulate the mechanical properties and provide a favorable space for inward growth of cells and tissues. Commercial porous titanium granules (PTiG) and Bio-Oss were used as positive controls to explore the potential of PTaG as bone substitute. The morphology, three-dimensional structure, composition, and mechanical properties of PTaG and PTiG were evaluated by SEM, X-Ray 3D imaging system, 3D laser confocal microscope, EDS, XRD, XPS, and nanoindentation. In vitro, cell compatibility and mineralization ability were evaluated for both materials. Furthermore, PTaG, PTiG, and Bio-Oss were filled in the rabbit femoral defect, and micro-CT and histological analysis were performed after 8 weeks. The results showed that the PTaG had the best bone healing effect due to the hierarchical porous structure, excellent three-dimensional connectivity and chemical stability, suitable mechanical properties and surface roughness, good biocompatibility and mineralization osteogenic activity. This work indicates that PTaG may have a positive potential for filling and repairing irregularly shaped bone defects.

Aerial intelligent reflection surface offers opportunities to increase the data freshness in challenging environments. In this paper, we investigate the integration of unmanned aerial vehicles (UAVs) and intelligent reflection surface (IRS) to assist the transmission from sensor nodes (SNs) to a base station (BS). Our goal is to minimize the Age of Synchronization (AoS) by optimizing the UAV trajectory, SNs schedule, and IRS phase-shift. An actor-critic optimization algorithm is developed to solve the mixed-integer non-convex optimization problem. Numerical results demonstrate the algorithm's superior performance in reducing the AoS compared to existing methods.

For filling higher input voltage requirement, the input-series system based on attainable power devices is prevalent in industry. Specifically, its auxiliary power supply (APS) is self-powered from submodule (SM) capacitance on medium/high voltage occasions, for easing the common mode noise and insulation problems as well as facilitating the modular design. But in the meanwhile, the APS startup problem is brought into practice. This article points out that the out-of-sync issue of APSs during the startup process is considerable since it causes a severe voltage oscillation in the input-series system with small SM capacitance, which is rarely reported before since this adverse influence is neglectable in the common system with large SM capacitance. Therefore, for the first time, the impact of the parameters on voltage oscillation is assessed in this article, which is based on a half-bridge converter equipped with designed self-powered gate drivers as a target input-series system. After establishing an analytical model, the safety and stability criteria for oscillation are identified, followed by the simulation verification. Further, a two-stage startup scheme and hardware design are proposed to ease the voltage oscillation without the penalty of matching passive component parameters. Finally, the corresponding results from experiments are provided to verify the effectiveness.

Guided bone regeneration (GBR) technology has been demonstrated to be an effective method for reconstructing bone defects. A membrane is used to cover the bone defect to stop soft tissue from growing into it. The biosurface design of the barrier membrane is key to the technology. In this work, an asymmetric functional gradient Janus membrane was designed to address the bidirectional environment of the bone and soft tissue during bone reconstruction. The Janus membrane was simply and efficiently prepared by the multilayer self-assembly technique, and it was divided into the polycaprolactone isolation layer (PCL layer, GBR-A) and the nanohydroxyapatite/polycaprolactone/polyethylene glycol osteogenic layer (HAn/PCL/PEG layer, GBR-B). The morphology, composition, roughness, hydrophilicity, biocompatibility, cell attachment, and osteogenic mineralization ability of the double surfaces of the Janus membrane were systematically evaluated. The GBR-A layer was smooth, dense, and hydrophobic, which could inhibit cell adhesion and resist soft tissue invasion. The GBR-B layer was rough, porous, hydrophilic, and bioactive, promoting cell adhesion, proliferation, matrix mineralization, and expression of alkaline phosphatase and RUNX2. In vitro and in vivo results showed that the membrane could bind tightly to bone, maintain long-term space stability, and significantly promote new bone formation. Moreover, the membrane could fix the bone filling material in the defect for a better healing effect. This work presents a straightforward and viable methodology for the fabrication of GBR membranes with Janus-based bioactive surfaces. This work may provide insights for the design of biomaterial surfaces and treatment of bone defects.

A high-performance large-scale-integrated organic phototransistor needs a semiconductor layer that maintains its photoelectric conversion ability well during high-resolution pixelization. However, lacking a precise design for the nanoscale structure, a trade-off between photoelectric performance and device miniaturization greatly limits the success in commercial application. Here we demonstrate a photovoltaic-nanocell enhancement strategy, which overcomes the trade-off and enables high-performance organic phototransistors at a level beyond large-scale integration. Embedding a core–shell photovoltaic nanocell based on perovskite quantum dots in a photocrosslinkable organic semiconductor, ultralarge-scale-integrated (>221units) imaging chips are manufactured using photolithography. 27 million pixels are interconnected and the pixel density is 3.1 × 106units cm−2, at least two orders of magnitude higher than in existing organic imaging chips and equivalent to the latest commercial full-frame complementary metal–oxide–semiconductor camera chips. The embedded photovoltaic nanocells induce an in situ photogating modulation and enable photoresponsivity and detectivity of 6.8 × 106A W−1and 1.1 × 1013Jones (at 1 Hz), respectively, achieving the highest values of organic imaging chips at large-scale or higher integration. In addition, a very-large-scale-integrated (>216units) stretchable biomimetic retina based on photovoltaic nanocells is manufactured for neuromorphic imaging recognition with not only resolution but also photoresponsivity and power consumption approaching those of the biological counterpart.

Purpose: Intracranial hemorrhage (ICH) is a serious health problem requiring prompt and intensive medical treatment. The delineation of hemorrhage areas and the estimation of hemorrhage volume play a crucial role for subsequent treatment planning. In clinical practice, physicians need to inspect computed tomography (CT) scans carefully and delineate the hemorrhage area slice-by-slice, which is time-consuming and error-prone. Hence, we propose a segmentation framework based on deep learning to assist physicians in automatically delineating the area of intracranial hemorrhage and evaluating the volume of hemorrhage in CT scans. Methods: The framework is a two-staged encoder-decoder convolution neural network, which segments the coarse hemorrhage area in the first stage and the fine hemorrhage area in the second stage. With the segmentation result, the volume of hemorrhage is further estimated. The proposed framework was trained on a training set with 280 CT scans and tested on a blind test set with 121 CT scans. Results: Our model achieves a dice coefficient of 84.54%, which outperforms the single-stage segmentation model. As for hemorrhage volume estimation, our model is far ahead of the commonly used Tada formula in clinical practice and is able to obtain the more robust prediction for different hemorrhage situations. Conclusion: This cascade framework assists in successfully segmenting intracranial hemorrhage areas and evaluating the volume of hemorrhage in CT scans and can be used in clinical practice, which has the potential to be applied in medical practice.

In this work, a method combining nanomechanical characterization and finite element analysis (FEA) was applied to reveal the influence mechanism and quantify the effects of the substrate on the mechanical properties of the ultrathin polystyrene (PS) film. SiOx, polylactic acid (PLA), and polydimethylsiloxane (PDMS), respectively, were chosen as the substrate. Results show that PS film exhibits a Young's modulus that is completely different from that of the bulk when the thickness is lower than the end‐to‐end distance of PS, and the value is directly related to the substrate to which the film is attached. The equivalent Young's moduli of the 20 nm PS films attached to SiOx, PLA, and PDMS substrates are 4.9, 3.4, and 1.0 GPa, respectively, which is positively correlated with the Young's modulus of the substrates. It suggests that the substrate has non‐negligible and important influences on the mechanical properties of supported polymer ultrathin films. The results provide new insight into the unique physical properties of polymer ultrathin films and favor the design of polymer‐based nanodevices.

Ideal medical implant materials should have the qualities of osteoinduction, osteoconduction, nontoxicity, non-sensitivity and non-carcinogenicity to the body. Although its high melting point and high elastic modulus pose difficulties for processing technology, tantalum has become a popular research topic in the field of medical implant materials because of its excellent biocompatibility and bone inductivity. In this paper, the performance of the bone-inducing properties of tantalum in different applications, including its combination with other elements, its use as a coating, its porous structure and its surface modification ability, are summarized. The mechanism of promoting bone formation and the clinical application of tantalum are reviewed.

Energy recovery from biomass gasification has attracted increasing attention. However, carbon content in biomass is low relative to plastics and fossil fuels, causing a significant reduction in the calorific value of the produced syngas. The addition of a high-carbon fuel (e.g., coal and polyethylene) to biomass can improve the quality of the syngas. In this study, a comprehensive process model was developed for co-gasification of rice husk (RH) and polyethylene (PE) using Aspen Plus simulator under steady-state condition. The model is first validated by comparing with experimental data collected from a fluidized bed co-gasification system. Subsequently, the effects of gasification temperature, steam-to-fuel ratio (STF) and PE ratio (PR) on the produced gas composition, syngas yield, producer gas caloric value, tar content in the producer gas, H2/CO, H2yield and gasification efficiencies are studied. The results showed that, increasing PR could improve the gas yield, H2/CO and the higher heating value (HHV) of the syngas. Higher PR also enhanced the production of H2and tar conversion. However, as STF increased, the HHV of the syngas, and gasification performance reduced. Higher temperature was beneficial for H2production, tar conversion, and gas yield, but reduced H2/CO.

There are many flow behaviors in solid tumors, including intravascular, bloodstream, and interstitial convection. Studies have shown that tumor interstitial fluid (TIF) is an important part of tumor microenvironment regulation and affects drug delivery and metabolism between tumor cells. Magnetic resonance imaging (MRI) is suitable for detecting the flow rates of liquids in tissues. Clinical phase contrast PC-MRI technology has been designed to observe the blood flow in large vessels such as arteries and veins; however, it is not sensitive enough to deal with slow flow velocity. Our previously developed vertical plane echo PC-MRI technology, the Velocity Mapping sequence, improved the signal-to-noise ratio (SNR) for measuring slow interstitial fluid rate. In this study, this sequence was used to determine the TIF flow rate in MDA-MB-231 human breast tumor cells used in BALB/c nude male mice. Two different sizes of contrast agents were intravenously injected, and the relationship between their distribution and the TIF flow rate was studied for the first time. Combining the results of clinical scanning showed that small-molecule DTPA-Gd (diethylenetriaminepentaacetic acid-gadolinium) was distributed immediately around the tumor margin after the injection. This distribution was positively correlated to the high flow rate area of the TIF before administration. In contrast, nanoparticles NaGdF4–PEG (polyethylene glycol) entered the tumor and reached their peak at 3 h. Drug distribution was negatively correlated with the high-flow-rate region of the TIF. Investigation of the TIF velocity can help better understand the fluid behavior in tumors and its role in drug delivery.

ABSTRACTObjective: To investigate the effect of femoral component valgus/varus angle (FCVA) on the mid-term outcome after unicondylar knee arthroplasty. Methods: Patients who underwent unicompartmental knee replacement in the Department of Bone and Joint Surgery, were retrospectively analyzed. According to the postoperative femoral prosthesis internal and external rotation angle, patients were divided into six groups the postoperative femoral prosthesis internal and external rotation angle: standard group, mildly abnormal group. The patients were followed up for 12 months, and complications were recorded during the follow-up period. The Range of motion (ROM), visual analog scale (VAS), and Knee society score (KSS) were compared between the six groups. Results: One hundred twenty-four patients with 124 knees were included in this study. There were no statistical differences in age, gender, body mass index, preoperative ROM, or preoperative VAS, KSS-C, and KSS-F scores among the six groups. Comparison of postoperative outcomes and efficacy grades showed that: (1) the differences in ROM grades, as well as VAS, KSS-C, and KSS-F efficacy at 12 months postoperatively were statistically significant in all six groups by rank sum test; and patients in the normal group had better ROM grades and KSS-F grades than those in the mild and severe abnormality groups; (2) During the follow-up, no patient records had other serious complications such as periprosthetic infection, prosthetic loosening, periprosthetic fracture, and spacer dislocation. Conclusion: Controlling the femoral prosthetic internal rotation angle between 6° and 0° in unicompartmental knee replacements can lead to better mid-term outcomes for patients.

Clear cell renal cell carcinoma (ccRCC), the most common subtype of renal cell carcinoma, often exhibits resistance to tyrosine kinase inhibitors (TKIs) when used as monotherapy. However, the integration of PD-1 blockade with TKIs has significantly improved patient survival, making it a leading therapeutic strategy for ccRCC. Despite these advancements, the efficacy of this combined therapy remains suboptimal, necessitating a deeper understanding of the underlying regulatory mechanisms. Through comprehensive analyses, including mass spectrometry, RNA sequencing, lipidomic profiling, immunohistochemical staining, and ex vivo experiments, we explored the interaction between PTPRZ1 and RNF26 and its impact on ccRCC cell behavior. Our results revealed a unique interaction where PTPRZ1 stabilized RNF26 protein expression by dephosphorylating it at the Y432 site. The modulation of RNF26 levels by PTPRZ1 was found to be mediated through the proteasome pathway. Additionally, PTPRZ1, via its interaction with RNF26, activated the TNF/NF-κB signaling pathway, thereby promoting cell proliferation, angiogenesis, and lipid metabolism in ccRCC cells. Importantly, inhibiting PTPRZ1 enhanced the sensitivity of ccRCC to TKIs and PD-1 blockade, an effect that was attenuated when RNF26 was simultaneously knocked down. These findings highlight the critical role of the PTPRZ1-RNF26 axis in ccRCC and suggest that combining PTPRZ1 inhibitors with current TKIs and PD-1 blockade therapies could significantly improve treatment outcomes for ccRCC patients.

In this paper, we propose a novel UAV aided ground nodes (GNs) localization and communication integrated framework, where the age of information (AoI) is introduced to evaluate the system timeliness. We aim to jointly optimize the UAV trajectory, localization accuracy, bandwidth and beamwidth, to guarantee the information freshness. Specifically, we give a two-stage method, where a low complexity initial UAV trajectory searching algorithm is firstly proposed, based on the rough position information of GNs. Afterwards, we formulate a joint UAV location and resource optimization problem. This essential mixed integer problem can be solved by efficient successive convex approximation based iterative algorithm. Simulations show that the localization and communication integrated framework can obtain about 50% performance gain compared with the UAV communication aid only solution, and over 37% performance gain via proper resource allocation. Moreover, the analysis reveals that our proposed scheme strikes a balance among the time durations of localization, data transmission and UAV movement. Additionally, we conduct practical experiments to draw valuable insights into the system design and implementations (An experimental can be found on the supplementary materials, or online at https://youtu.be/OX6Bgz6naUA).

Rail-track detection is a crucial function for an active obstacle avoidance system in trains. However, existing methods face challenges in effectively detecting rail-tracks, particularly in turnout scenarios. This study introduces a novel rail-track detection approach using a key-point estimate network. The network treats the rail-track as a pair and constructs a dedicated model for detection. Additionally, a pseudo-attention mechanism leverages the detection output from previous stages, enabling the network to focus on the rail-track region. Also, a dislocation assignment mechanism is proposed to address label assignment confusion at turnouts. Moreover, a rail-track generalized IoU is also introduced, treating the rail-track as a pair and adds a correction term to enhance detection performance. Experimental results demonstrate that the proposed method achieves a remarkable mF1 score of 69.42%, establishing it as the state-of-the-art (SOTA) in this field. Furthermore, the effectiveness of the proposed method has been validated and applied in real-world testing on the Hong Kong Metro Tsuen Wan Line.

The Bayesian inversion of electromagnetic data can obtain key information on the uncertainty of subsurface resistivity. However, due to its high computational cost, Bayesian inversion is largely limited to 1-D resistivity models. In this study, a fast Bayesian inversion method is implemented by introducing the spatial correlation as prior information. The contributions of this article mainly include: 1) explicitly introduce the expression of spatial correlation prior information and provide a method to determine the parameters in the expression through the variogram theory. The influence of parameters in the spatial correlation prior information on the inversion results is systematically analyzed with the 1-D synthetic model. 2) The information entropy theory of continuous functions is introduced to quantify the degrees of freedom (DOF) of the parameters of the spatial correlation prior model. The analysis shows that the DOF of model parameters are significantly smaller than the number of model parameters when spatial correlation prior information is introduced, which is the main reason for the rapid Bayesian inversion. 3) Introducing the Sengpiel fast imaging algorithm, combined with the variogram theory, realized the direct acquisition of spatial correlation prior information from the observation data, minimizing the dependence on other information. The inversion results of 1-D and 2-D synthetic models and field datasets show that considering the spatial correlation prior information, hundreds of thousands of Markov chain Monte Carlo sampling steps are needed to enable the inversion of up to thousands of model parameters. This result provides a possible idea for future Bayesian inversion of complex 3-D models.

Currently, Coronavirus Disease 2019 (COVID-19) is still endangering world health and safety and deep learning (DL) is expected to be the most powerful method for efficient detection of COVID-19. However, patients’ privacy concerns prohibit data sharing between medical institutions, leading to unexpected performance of deep neural network (DNN) models. Fortunately, federated learning (FL), as a novel paradigm, allows participating clients to collaboratively train models without exposing source data outside original location. Nevertheless, the current FL-based COVID-19 detection methods prefer optimizing secondary objectives including delay, energy consumption and privacy, while few works focus on improving the model accuracy and stability. In this paper, we propose a federated learning framework with dynamic focus for COVID-19 detection on CXR images, named FedFocus. Specifically, to improve the training efficiency and accuracy, the training loss of each model is taken as the basis for parameter aggregation weights. As training layer deepens, a constantly updated dynamic factor is designed to stabilize the aggregation process. In addition, to highly restore the real dataset, the training sets in our experiments are divided based on the population and the infection of three real cities. Extensive experiments conducted on the real-world CXR images dataset demonstrate that FedFocus outperforms the baselines in model training efficiency, accuracy and stability.

Stroke is a leading cause of disability and fatality in the world, with ischemic stroke being the most common type. Digital Subtraction Angiography images, the gold standard in the operation process, can accurately show the contours and blood flow of cerebral vessels. The segmentation of cerebral vessels in DSA images can effectively help physicians assess the lesions. However, due to the disturbances in imaging parameters and changes in imaging scale, accurate cerebral vessel segmentation in DSA images is still a challenging task. In this paper, we propose a novel Edge Regularization Network (ERNet) to segment cerebral vessels in DSA images. Specifically, ERNet employs the erosion and dilation processes on the original binary vessel annotation to generate pseudo-ground truths of False Negative and False Positive, which serve as constraints to refine the coarse predictions based on their mapping relationship with the original vessels. In addition, we exploit a Hybrid Fusion Module based on convolution and transformers to extract local features and build long-range dependencies. Moreover, to support and advance the open research in the field of ischemic stroke, we introduce FPDSA, the first pixel-level semantic segmentation dataset for cerebral vessels. Extensive experiments on FPDSA illustrate the leading performance of our ERNet.

Humanized immune system (HIS) mice have been developed and used as a small surrogate model to study human immune function under normal or disease conditions. Although variations are found between models, most HIS mice show robust human T cell responses. However, there has been unsuccessful in constructing HIS mice that produce high-affinity human antibodies, primarily due to defects in terminal B cell differentiation, antibody affinity maturation, and development of primary follicles and germinal centers. In this review, we elaborate on the current knowledge about and previous attempts to improve human B cell development in HIS mice, and propose a potential strategy for constructing HIS mice with improved humoral immunity by transplantation of human follicular dendritic cells (FDCs) to facilitate the development of secondary follicles.

Photothermal materials exhibit significant potential for marine applications, yet their efficiency and durability require enhancement. This study presents a dual-layer photothermal device that integrates a polyurethane light absorption layer with a wood sponge layer, designed for water transport and thermal insulation. The wood sponge’s hydrophilicity and effective water transport properties improve water supply while minimizing heat loss. Under sunlight, the device achieves a surface temperature of 63.2°C and an evaporation rate of 1.86 kg/m2/h for seawater desalination. The porous design of both layers ensures salt resistance during extended desalination processes. Furthermore, the incorporation of disulfide bonds into the polyurethane enhances the material’s self-healing properties, enabling sustained performance even after prolonged use, thus extending its service life. Additionally, the device demonstrates effective crude oil adsorption capabilities attributed to its photothermal efficiency and porosity. This innovative design provides valuable insights for advancing solar-driven evaporation devices and crude oil adsorption systems in marine environments.

An efficient closed-loop voltage tracking control (VTC) for a grid-forming-based converter is required for precise voltage regulation. However, few papers designed a VTC exclusively for modular multilevel converter (MMC) with a full understanding of the MMC's internal dynamic coupling and presented a detailed analysis of the controller. In contrast, traditional voltage–current double loop control or its derivatives are employed without considering the impact of the coupled fluctuating capacitor voltage and with insufficient stability analysis under various operating conditions, especially for the wide range of grid short-circuit ratio. To solve these problems on MMC, this article systematically analyzes the effect of each component of capacitor voltages on the output voltage. In this article, an easy-implemental VTC is proposed which takes the dominant factor of capacitor voltage into the feedforward path (CVF). The proposed CVF-VTC with an appropriate feedforward coefficient shows a high damping effect under varying grid strength with the eigenvalue proof of the established small signal model. Moreover, the PI parameters of the VTC show a low effect on system stability when CVF is applied. Experiment results are performed to verify the proposed method.

Deep neural networks (DNN) supported by multicenter large-scale Chest X-Ray (CXR) datasets can efficiently perform tasks such as disease identification, lesion segmentation, and report generation. However, the non-ignorable inter-domain heterogeneity caused by different equipment, ethnic groups, and scanning protocols may lead to dramatic degradation in model performance. Unsupervised domain adaptation (UDA) methods help alleviate the cross-domain discrepancy for subsequent analysis. Nevertheless, they may be prone to: 1) spatial negative transfer: misaligning non-transferable regions which have inadequate knowledge, and 2) semantic negative transfer: failing to extend to scenarios where the label spaces of the source and target domain are partially shared. In this work, we propose a classification-based framework named attention-guided partial domain adaptation (AGPDA) network for overcoming these two negative transfer challenges. AGPDA is composed of two key modules: 1) a region attention discrimination block (RADB) to generate fine-grained attention value via lightweight region-wise multi-adversarial networks. 2) a residual feature recalibration block (RFRB) trained with class-weighted maximum mean discrepancy (MMD) loss for down-weighing the irrelevant source samples. Extensive experiments on two publicly available CXR datasets containing a total of 8598 pneumonia (viral, bacterial, and COVID-19) cases, 7163 non-pneumonia or healthy cases, demonstrate the superior performance of our AGPDA. Especially on three partial transfer tasks, AGPDA significantly increases the accuracy, sensitivity, and F1 score by 4.35%, 4.05%, and 1.78% compared to recently strong baselines.

A steady-state Aspen Plus model was established to numerically study biomass (ramie residues) gasification using air as gasification agent. This study proposed a comprehensive model which used the thermodynamic equilibrium and chemical kinetics to accurately simulate the char combustion and char/volatiles gasification, respectively, to search for optimal gasifier operating conditions. The proposed model correctly predicted the products yield, gasifier performance and the composition of the produced syngas, and therefore the higher heating value (HHV). The effects of air temperature and oxygen content in gasification agent on the gasification characteristics were discussed. The gas yield slightly increased with the increase of air temperature, while char conversion efficiency (CCE) firstly increased and then declined attributing to the slight increase of tar cracking with the increase of air temperature. The results also indicate that higher oxygen ratio (OR) leads to higher H2production and lower tar yield. Compared to the maximum H2content (11.17 vol%) and the syngas HHV (6.22 MJ/Nm3) at OR = 31.4%, they are respectively reduced to about 35.45% and 21.7% when OR drops to 21%. The present model opens the possibility of predicting the gasification characteristics using different gasification agents and feedstocks based on their chemical compositions.

Unmanned aerial vehicles (UAVs) are attractive in some Internet of Things (IoT) applications, due to their flexible deployment and extended coverage. In this paper, we consider an UAV-assisted network where the UAV flies between the resource-limited sensor nodes (SNs) and collects their status updates. The UAV trajectory and SN scheduling are jointly optimized to minimize the Age of Synchronization (AoS). In contrast to the conventional Age of Information (AoI), AoS takes into account both the freshness and the content of the information, which makes AoS a more suitable design criterion for information collection in an energy-constrained wireless network. Since the formulated problem is challenging to solve due to its non convexity, we reformulate the problem as a Markov decision process (MDP) and propose a deep reinforcement learning (DRL) algorithm to obtain the optimal solution with various action and state spaces. Our simulation results show the fast convergence rate of the proposed DRL algorithm and demonstrate that our proposed scheme can improve the performance of the UAV-assisted network compared to AoI-based schemes.

Passive clamping snubbers for voltage balancing (VB) series-connected power devices exhibit strong applicability and high robustness; moreover, they are particularly suitable for the emerging fast-switching silicon-carbide (SiC) metal-oxide-semiconductor field-effect transistors (mosfets). However, the compromise still exists as a better VB performance comes at a penalty of a larger loss of snubber. Consequently, in this article, novel adaptive-impedance “snubbers” are proposed for series-connected SiC mosfets on the basis of converter-based self-powered gate driver design, and a better tradeoff is achieved between loss and VB both in static and dynamic states. Further, the proposed passive VB strategy could be combined with an active delay control strategy by introducing an extra closed-loop controller. Benefiting from a more accurately established small-signal system model, the closed-loop numerical parameters are easier to design. As a result, well-balanced voltage distribution is realized during the continuously switching process of series-connected SiC mosfets. To verify the effectiveness, a comprehensive analysis is first provided as guidance, followed by the corresponding detailed hardware and software design. Finally, the experiments are conducted by using two SiC mosfets, which show excellent VB performance at a 110 kV/μs switching speed.

Power synchronization control (PSC), one of the common grid-forming (GFM) controls, is an emerging grid support technique for converters in the converter-dominated power system. However, its application to modular multilevel converters (MMCs) with dynamic grid conditions and varying grid strength, exposed some instability issues. This article studies the essence of the MMC internal voltage coupling and the degree of influence on the reference voltage under different grid short-circuit ratios (SCRs). It is found that the key reason for instability is that the submodule (SM) capacitor voltage modifies the reference voltage through the nonlinear switching process and eventually generates the voltage error, up to 61.3% with high SCR = 25. Based on the above findings, an easy-to-implement modulation compensation is proposed to enhance the PSC-MMC stability in changing SCR scenarios. A simplified selection scheme of the compensation coefficient, which is affected by the practical main circuit parameters is presented according to the established small-signal model (SSM). Both the simulation and the experimental results verify the effectiveness and accuracy of the proposed method.

Roadside light detection and ranging (LiDAR) is commonly used to record the traffic data of the whole intersection scene or road segment in intelligent transportation systems (ITSs). However, general deep-learning object detection methods do not adequately consider the static background captured by roadside LiDAR. Moreover, critical issues remain to be overcome in object detection using roadside LiDAR: false alarms caused by complex background interference and multiscale objects with limited characteristics. To this end, a feature enhancement and cascade network (FecNet) is proposed to alleviate the problems. From the perspective of feature enhancement, FecNet improves foreground feature discrimination by extracting foreground information and fusing it with feature maps of multiple stages. Also, from the perspective of feature cascade, a feature cascade backbone is proposed to enhance the localization and contextual information of multiscale objects with limited characteristics. Comprehensive experiments are conducted using a roadside LiDAR dataset. The experimental results suggest that FecNet is superior to the benchmark detectors and achieves better computational efficiency and detection accuracy.

MicroRNAs (miRNAs) have emerged as powerful biomarkers for disease diagnosis and screening. Traditional miRNA analytical techniques are inadequate for point-of-care testing due to their reliance on specialized expertise and instruments. Graphene field-effect transistors (GFETs) offer the prospect of simple and label-free diagnostics. Herein, a GFET biosensor based on tetrahedral DNA nanostructure (TDN)-assisted catalytic hairpin assembly (CHA) reaction (TCHA) has been fabricated and applied to the sensitive and specific detection of miRNA-21. TDN structures are assembled to construct the biosensing interface, facilitating CHA reaction by providing free space and preventing unwanted entanglements, aggregation, and adsorption of probes on the graphene channel. Owing to synergistic effects of TDN-assisted in situ nucleic acid amplification on the sensing surface, as well as inherent signal sensitization of GFETs, the biosensor exhibits ultrasensitive detection of miRNA-21 down to 5.67 × 10–19M, approximately three orders of magnitude lower than that normally achieved by graphene transistors with channel functionalization of single-stranded DNA probes. In addition, the biosensor demonstrates excellent analytical performance regarding selectivity, stability, and reproducibility. Furthermore, the practicability of the biosensor is verified by analyzing targets in a complex serum environment and cell lysates, showing tremendous potential in bioanalysis and clinical diagnosis.

Adsorption is one of the promising techniques for effectively removing dyes from wastewater due to its low cost and high efficiency. However, most reported adsorbents still suffer from low adsorption capacity and stability under harsh conditions, which severely limit their practical application. Herein, we report a novel method to prepare robust aramid nanofiber (ANF) microbead adsorbents using electrospraying technology and solvent replacement method. The structure of the ANF microbeads can be easily manipulated by changing the coagulation baths, thus achieving fabricated ANF microbeads with a uniform and highly porous microstructure. The as-prepared ANF microbeads show high removal efficiency toward methylene blue (MB) with a maximum adsorption capacity of up to 267 mg/g, which can be attributed to the strong π–π stacking, hydrogen bonding, and electrostatic attraction between MB and the microbeads. Moreover, ANF microbeads show outstanding stability within a wide pH range (from 2 to 12) with an MB removal efficiency over 90% even after five adsorption–desorption cycles at pH = 12. Additionally, the ANF microbeads can maintain their structure integrity after the adsorption process and can be easily separated from the solution by static sedimentation because of their robust structure. This work provides a new strategy to design robust microbeads with excellent adsorption performance and stability, demonstrating their great potential in various environmental engineering applications.

Introducing dynamic covalent bonds into cross-linked polymer endows thermosetting polymers with thermoplastic properties such as reprocessability and recyclability, providing a solution to the environmental problems induced by thermosetting polymers. However, unlike thermoplastic polymers, the synthesis and molding processes of most thermosetting polymers with covalent adaptable networks still maintain together, affecting the development of industrial production. Herein, the cross-linked polythiourethane microspheres (PTUMs) with dynamic covalent bonds have been prepared by suspension polymerization. The PTUMs can be closed-loop-recycled and shows no significant performance degradation after recycling, paving an easy and efficient way for the recycling of cross-linked polymers. Furthermore, as with the thermoplastic film prepared from polymer pellets, a thermosetting polythiourethane film (PTUMF) can be obtained by simple compression molding of PTUMs. Importantly, by mixing PTUMs with fillers and then compression molding, the composites with a segregated structure and excellent electrical conductivity can be fabricated. Such composites can be used as wearable strain sensors to monitor human motions. Notably, the preparation and molding of thermosetting polymer materials in this work are separated, which may be conducive to the development of industrial production.

Antibacterial materials are urgently needed to combat bacterial contamination, growth, or attachment on contact surfaces, as bacterial infections remain a public health crisis worldwide. Here, a novel ultrasound-assisted method is utilized for the first time to fabricate oxidative chlorine-loaded AH@PBAT/PBF-Cl films with more superior grafting efficiency and rechargeable antibacterial effect than those from conventional techniques. The films remarkably inactivate 99.9999% Escherichia coliand Staphylococcus aureuscells, inducing noticeable cell deformations and mechanical instability. The specific antibacterial mechanism against E. coliused as a model organism is unveiled using several cell envelope structural and functional analyses combined with proteomics, peptidoglycomics, and fluorescence probe techniques. Film treatment partially neutralizes the bacterial surface charge, induces oxidative stress and cytoskeleton deformity, alters membrane properties, and disrupts the expression of key proteins involved in the synthesis and transport of the lipopolysaccharide and peptidoglycan, indicating the cell envelope as the primary target. The films specifically target lipopolysaccharides, resulting in structural impairment of the polysaccharide and lipid A components, and inhibit peptidoglycan precursor synthesis. Together, these lead to metabolic disorders, membrane dysfunction, structural collapse, and eventual death. Given the films’ antibacterial effects via the disruption of key cell envelope components, they can potentially combat a wide range of bacteria. These findings lay a theoretical basis for developing efficient antibacterial materials for food safety or biomedical applications.