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In this research, a hollow mesoporous responsive nanomotor was proposed for enhanced photothermal/immunotherapy under near infrared (NIR) irradiation. HA-HMCuS/AS as the nanomotor composed of hollow mesoporous copper sulfide (HMCuS) loaded with artesunate (AS) and hyaluronic acid (HA) was utilized to induce the polarization of tumor-associated macrophages. At the beginning, ResNet18 deep learning model was utilized to predict the Brunauer–Emmett–Teller (BET) surface area of HMCuS based on the morphology data set which was obtained from our conventional research. And then, we predicted that the as-synthesized HMCuS has a large surface area, which is beneficial for drug loading. Then, upon near-infrared light irradiation, HA-HMCuS/AS exhibited significant cytotoxicity against breast cancer cells and induced tumor thermal ablation. Additionally, HA-HMCuS/AS was found to inhibit the expression of TREM2 at the tumor site, promoting the transition of M2-type macrophages to M1-type macrophages in tumor tissue and enhancing the inflammatory response at the tumor site. In addition, photothermal therapy enabled tumor cells to release tumor-associated antigen and injury-related molecular patterns, promote the maturation and metastasis of dendritic cells, and further activate the body’s specific antitumor immune response. By combining photothermal therapy with immunotherapy, the HA-HMCuS nanomotor demonstrated even more robust tumor ablation and suppression effects.

Protein hydrolysis targeted chimeras (PROTACs) represent a different therapeutic approach, particularly relevant for overcoming challenges associated with traditional small molecule inhibitors. These challenges include targeting difficult proteins that are often deemed “undruggable” and addressing issues of acquired resistance. PROTACs employ the body’s own E3 ubiquitin ligases to induce the degradation of specific proteins of interest (POIs) through the ubiquitin-proteasome pathway. This process is cyclical, allowing for broad applicability, potent protein degradation, and selective targeting. Despite their effectiveness, PROTACs can inadvertently target and degrade nonspecific proteins, potentially resulting in significant side effects and off-target toxicity. To address this concern, researchers have created stimuli-activated PROTACs that enhance targeted protein degradation while minimizing potential harm to healthy cells. These advanced PROTACs aim to improve the precision of degradation in both time and space. This article reviews the strategies for in situ activated PROTACs, highlighting key compounds and research advancements associated with various mechanisms of action. The insights presented here aim to guide further exploration in the field of activated PROTACs.

Friction is the primary cause of notching in reverse shoulder arthroplasty during internal, external rotation (IR/ER), and extension (EXT). To address notching, glenosphere eccentricity (ECC) was introduced. The primary objective of this study was to investigate different positions of glenosphere ECC to determine whether there is an optimal position for impingement-free range of motion.

Vehicle transportation of hazardous chemicals is one of the important mobile hazards in modern logistics, and its unsafe factors bring serious threats to people's lives, property and environmental safety. Although the current object detection algorithm has certain applications in the detection of hazardous chemical vehicles, due to the complexity of the transportation environment, the small size and low resolution of the vehicle target etc., object detection becomes more difficult in the face of a complex background. In order to solve these problems, the authors propose an improved algorithm based on YOLOv5 to enhance the detection accuracy and efficiency of hazardous chemical vehicles. Firstly, in order to better capture the details and semantic information of hazardous chemical vehicles, the algorithm solves the problem of mismatch between the receptive field of the detector and the target object by introducing the receptive field expansion block into the backbone network, so as to improve the ability of the model to capture the detailed information of hazardous chemical vehicles. Secondly, in order to improve the ability of the model to express the characteristics of hazardous chemical vehicles, the authors introduce a separable attention mechanism in the multi‐scale target detection stage, and enhances the prediction ability of the model by combining the object detection head and attention mechanism coherently in the feature layer of scale perception, the spatial location of spatial perception and the output channel of task perception. Experimental results show that the improved model significantly surpasses the baseline model in terms of accuracy and achieves more accurate object detection. At the same time, the model also has a certain improvement in inference speed and achieves faster inference ability. The proposed algorithm addresses challenges such as complex environments, low resolution, and small‐sized targets, making significant contributions to the detection of hazardous chemical vehicles. This is achieved through the introduction of the receptive field expansion block (RFEB) and the separable attention mechanism, enhancing the model's ability to capture detailed information and improve feature representation. Experimental results demonstrate that the improved model surpasses the baseline in terms of accuracy, achieving more precise object detection.

To facilitate a rapid and synchronous design of radiation and scattering characteristics in low-scattering phased arrays, a machine learning (ML)-enabled radiation-scattering integrated design (MLE-RSID) approach is proposed in this letter. In this MLE-RSID approach, an inverse ML model is developed, wherein the radiation and scattering characteristics (|S11| and reflection phase difference) of each two combined array elements are set as inputs, and their geometric parameters as outputs. Utilizing the proposed approach, designers can efficiently achieve phased arrays with on-demand radiation and scattering performances in near real-time. To validate the proposed approach, two antenna elements featuring a wideband scattering characteristic of (180° ± 37°) reflection phase difference and similar radiation characteristics are designed using the MLE-RSID approach, to construct a low-scattering 1 × 10 phased array.

To enhance the efficiency of the training dataset construction and improve the machine learning (ML) model performance for electromagnetic (EM) devices modeling and design, an efficient training data collection method based on the evolutionary algorithm is proposed. By setting an appropriate objective function for EM response, the evolutionary algorithm guides the training samples to contain more helpful and useful information for design in each optimization iteration. Consequently, with this higher-quality dataset, the ML model achieves better performance more readily. To fully demonstrate the validity of this proposed evolutionary algorithm-based training data collection method, a topological design example for frequency selective surface (FSS) with different incident angles is presented. Results indicate that, with the same number of samples, when compared with the traditional random data collection method, the proposed method improves testing accuracy by a maximum value of 29.6%. Furthermore, if the traditional random training data collection method is used to achieve the same testing error level as the proposed training data collection method, it would require more than twice the number of full-wave EM simulations.

Headway, namely the distance between vehicles, is a key design factor for ensuring the safe operation of autonomous driving systems. There have been studies on headway optimization based on the speeds of leading and trailing vehicles, assuming perfect sensing capabilities. In practical scenarios, however, sensing errors are inevitable, calling for a more robust headway design to mitigate the risk of collision. Undoubtedly, augmenting the safety distance would reduce traffic throughput, thus highlighting the need for headway design to incorporate both sensing errors and risk tolerance models. In addition, prioritizing group safety over individual safety is often deemed unacceptable because no driver should sacrifice their safety for the safety of others. In this study, we propose a multi-objective optimization framework that examines the impact of sensing errors on both traffic throughput and the fairness of safety among vehicles. The proposed framework provides a solution to determine the Pareto frontier for traffic throughput and vehicle safety. ComDrive, a communication-based autonomous driving simulation platform, is developed to validate the proposed approach. Extensive experiments demonstrate that the proposed approach outperforms existing baselines.

Background Huanglian Jiedu decoction (HLJDD) is a classical herbal formula for treating inflammatory diseases with Traditional Chinese Medicine (TCM). However, the impacts of HLJDD on neuroinflammation and the precise mechanisms involved remain poorly understood.Purpose In the present study, HLJDD was found to inhibit lipopolysaccharide (LPS) induced neuroinflammatory responses in BV2 microglia via regulating NLRP3 inflammasome activation.Materials and Methods Anti-inflammatory activity was evaluated with ELISA assay and the polarization changes were observed using immunofluorescent staining. The levels of NLRP3 inflammasome activation were determined using qPCR and Western blotting.Results The administration of HLJDD successfully countered LPS-induced neuroinflammatory responses by attenuating microglial activation and fostering a phenotypic conversion from the pro-inflammatory M1 to the anti-inflammatory M2. Additionally, HLJDD treatment demonstrated inhibition of NLRP3 inflammasome activation.Conclusion The findings of the present study may provide insights into how HLJDD could be a therapeutic drug for neuroinflammatory diseases associated with regulating NLRP3 inflammasome and microglial activation.

Single-atom catalysts (SACs) that feature uniform metal active sites with symmetry configurations hold great promise in photocatalysis, while their catalytic efficiency is often restricted by the insufficient inherent activity. Drawing inspiration from hard–soft acid–base theory, here we propose that the delocalized electronic state of single-atom centers can be selectively modulated by adjusting their coordination symmetry, thereby optimizing the adsorption and activation of the reactant molecules. By taking ceria-based Ru–SAC (Ru–CeO2) as an example, we show that after introducing symmetry breaking, the Ru–CeO2with an asymmetric Ru–O4configuration (named P–Ru–CeO2) exhibits highly delocalized electrons with a soft acidic nature, leading to a much higher photocatalytic performance than for pristine Ru–CeO2and CeO2counterparts. The corresponding inherent mechanism was systematically investigated by spectroscopy and theoretical studies. This work provides an effective strategy for the design and controllable modulation of atomically dispersed catalysts with symmetry-broken configurations, thereby advancing applications in photocatalysis.

In response to the current challenges of low cutting efficiency and poor surface quality in processing single-crystal silicon, this article proposes the utilization of the micro-discharge abrasive cutting (MDAC) method for processing single-crystal silicon. A comprehensive comparison is conducted among the processing characteristics of the new cutting method and fixed abrasive wire sawing (FAWS) and wire electrical discharge machining (WEDM), including cutting efficiency, surface morphology, subsurface damage, surface roughness, wire saw wear, elemental spectrum analysis, and kerf width. Besides, two material removal modes at different feed rates are discussed. The results indicate that compared with WEDM, MDAC has the advantages of minimal subsurface damage, low surface roughness, low residual elements, and small kerf width. MDAC has higher cutting efficiency and shallower scratch depth than FAWS. However, compared with WEDM and FAWS, MDAC results in more significant wire saw wear and leaves a small amount of residual nickel on the silicon surface. In MDAC, the discharge occurs first. When the discharge energy within the pulse width is sufficient to remove the material within the current pulse cycle, the material is completely removed by the discharge action, the discharge products remain on the surface of the silicon wafer. When the discharge energy is insufficient to remove the material within the current pulse cycle, the silicon ingot is removed under the combined action of discharge and grinding, the diamond on wire saw surface will remove most of the discharge products.

Machine learning techniques have become increasingly important in analyzing single-cell RNA and identifying cell types, providing valuable insights into cellular development and disease mechanisms. However, the presence of batch effects poses major challenges in scRNA-seq analysis due to data distribution variation across batches. Although several batch effect mitigation algorithms have been proposed, most of them focus only on the correlation of local structure embeddings, ignoring global distribution matching and discriminative feature representation in batch correction. In this paper, we proposed the discriminative domain adaption network (D2AN) for joint batch effects correction and type annotation with single-cell RNA-seq. Specifically, we first captured the global low-dimensional embeddings of samples from the source and target domains by adversarial domain adaption strategy. Second, a contrastive loss is developed to preliminarily align the source domain samples. Moreover, the semantic alignment of class centroids in the source and target domains is achieved for further local alignment. Finally, a self-paced learning mechanism based on inter-domain loss is adopted to gradually select samples with high similarity to the target domain for training, which is used to improve the robustness of the model. Experimental results demonstrated that the proposed method on multiple real datasets outperforms several state-of-the-art methods.

Phosphinopyridyl ligands are used to synthesize a class of Ni(II) bis(chelate) complexes, which have been comprehensively characterized in both solid and solution phases. The structures display a square–planar configuration within the primary coordination sphere, with axially positioned labile binding sites. Their electrochemical data reveal two redox couples during the reduction process, suggesting the possibility of accessing two–electron reduction states. Significantly, these complexes serve as robust catalysts for homogeneous photocatalytic H2evolution. In a system utilizing an organic photosensitizer and a sacrificial electron donor, an optimal turnover number of 27,100 is achieved in an alcohol–containing aqueous solution. A series of photophysical and electrochemical measurements were conducted to elucidate the reaction mechanism of photocatalytic hydrogen generation. Density function theory calculations propose a catalytic pathway involving two successive one–electron reduction steps, followed by two proton discharges. The sustained photocatalytic activity of these complexes stems from their distinct ligand system, which includes phosphine and pyridine donors that aid in stabilizing the low oxidation states of the Ni center.

ABSTRACTThis study introduces a novel approach to address the limitations observed in existing random number generator (RNG) designs, focusing on the resolution for insufficiently covered problem statements and study purposes. In response to these challenges, we propose a high-throughput metastable-based RNG tailored for cryptography applications. Our design is featured with a two-layer transient effect ring oscillator (TERO) architecture, meticulously crafted to mitigate the locking phenomenon and enhance RNG randomness. Leveraging interleaved two-layer TEROs, realised through NAND and NOR SR latches, our RNG achieves unpredictable irregular sampling, crucial for cryptographic applications. To further enhance irregular sampling, the first layer of the RNG employs a multiplexer that selects inputs randomly. This multiplexer is governed by selection signals generated by the second layer of the RNG, ensuring superior randomness. Implemented using TSMC 40-nm CMOS process, our RNG passed the FIPS 140–1 randomness test at a clock frequency of 100 MHz, boasting a remarkable throughput of 50 Mbps. This study represents a significant advancement in RNG technology, addressing previous shortcomings and paving the way for high-speed communication systems’ secure cryptographic applications.

The Turpan-Hami (Tuha) Basin in Xinjiang Uygur Autonomous Region of China, holds significant strategic importance as a key economic artery of the ancient Silk Road and the Belt and Road Initiative, necessitating a holistic understanding of the spatiotemporal evolution of land use/land cover (LULC) to foster sustainable planning that is tailored to the region’s unique resource endowments. However, existing LULC classification methods demonstrate inadequate accuracy, hindering effective regional planning. In this study, we established a two-level LULC classification system (8 primary types and 22 secondary types) for the Tuha Basin. By employing Landsat 5/7/8 imagery at 5-a intervals, we developed the LULC dataset of the Tuha Basin from 1990 to 2020, conducted the accuracy assessment and spatiotemporal evolution analysis, and simulated the future LULC under various scenarios via the Markov-Future Land Use Simulation (Markov-FLUS) model. The results revealed that the average overall accuracy values of our LULC dataset were 0.917 and 0.864 for the primary types and secondary types, respectively. Compared with the seven mainstream LULC products (GlobeLand30, Global 30-meter Land Cover with Fine Classification System (GLC_FCS30), Finer Resolution Observation and Monitoring of Global Land Cover PLUS (FROM_GLC PLUS), ESA Global Land Cover (ESA_LC), Esri Land Cover (ESRI_LC), China Multi-Period Land Use Land Cover Change Remote Sensing Monitoring Dataset (CNLUCC), and China Annual Land Cover Dataset (CLCD)) in 2020, our LULC data exhibited dramatically elevated overall accuracy and provided more precise delineations for land features, thereby yielding high-quality data backups for land resource analyses within the basin. In 2020, unused land (78.0% of the study area) and grassland (18.6%) were the dominant LULC types of the basin; although cropland and construction land constituted less than 1.0% of the total area, they played a vital role in arid land development and primarily situated within oases that form the urban cores of the cities of Turpan and Hami. Between 1990 and 2020, cropland and construction land exhibited a rapid expansion, and the total area of water body decreased yet resurging after 2015 due to an increase in areas of reservoir and pond. In future scenario simulations, significant increases in areas of construction land and cropland are anticipated under the business-as-usual scenario, whereas the wetland area will decrease, suggesting the need for ecological attention under this development pathway. In contrast, the economic development scenario underscores the fast-paced expansion of construction land, primarily from the conversion of unused land, highlighting the significant developmental potential of unused land with a slowing increase in cropland. Special attention should thus be directed toward ecological and cropland protection during development. This study provides data supports and policy recommendations for the sustainable development goals of Tuha Basin and other similar arid areas.

This article presents a new unconditionally stable finite-difference time-domain (FDTD) method combined with the artificial neural network (ANN) for multiscale problems. The input of ANN-FDTD is the point location of spatial grid division in FDTD, and the output is the field data at these points. At each time step, the hypothetical solution of Maxwell’s equations is constructed using a known forced excitation source and the output of ANN. The error of ANN is determined by the gradient of its output with respect to the input vector. This error value is used to update ANN parameters with the backpropagation (BP) algorithm, so the training does not have to involve labeled samples. Here, ANN is trained to ensure that the hypothetical solution satisfies the boundary conditions. ANN is trained individually at each time-marching step, and thus, the results of the previous step do not affect those of the next step. The time step of ANN-FDTD can be chosen much larger than that of the traditional FDTD for microwave components with fine structures. Moreover, all time steps can be divided into blocks to execute parallel computation. Three numerical examples are provided to validate the accuracy and efficiency of the proposed method.

High precision and high efficiency are the main characteristics of contact scanning probes, but the deterioration of dynamic measurement accuracy caused by high-speed and high-frequency excitation is becoming increasingly prominent. Therefore, this article proposes a dynamic compensation method for high-frequency errors based on dual-parameter feedback optimization of pole configuration. Initially, the original dynamic characteristics of the probe are analyzed by combining with a kinematic model, including transient impact and steady-state resonance characteristics, revealing the impact of damping ratio and first-order natural frequency on dynamic performance. Then, a dynamic compensator based on gain matrix dual-parameter feedback optimization is developed to achieve high-frequency-adaptive high-precision compensation by dynamically adjusting pole configuration. Subsequently, through numerical simulation and comparative experimental analysis, the proposed method is validated for its high-precision compensation effect on wideband dynamic signals, particularly high-frequency signals.

In this article, a conditional deep convolutional generative adversarial network (cDCGAN) for the metasurface (MTS) to reduce the radar cross section (RCS) of the phased array is proposed. To reduce the calculation burden of collecting training data for cDCGAN, an equivalent circuit model is presented based on the mechanism of MTS. This method suppresses the sample space and largely reduces the number of collected samples from full-wave simulations. The designed MTS is loaded with resistors, and it absorbs the incident wave and transports the radiating wave. The resistor is represented as a binary image during the training procedure. The trained cDCGAN is utilized to generate satisfying meta-atoms. Two low-RCS wideband scanning arrays are designed, and the validity of the proposed method is verified with simulated and measured results. The monostatic RCSs of the arrays are reduced by 10 dB from 2.5 to 7.6 GHz and from 3.1 to 7.5 GHz, and the insert losses of MTSs are below 1.4 and 1.1 dB.

Rectangular microchannel heat sinks (MCHS) are widely used to cool high-heat-flux electronic devices. However, previous studies focused mainly on MCHS with uniform channels (UCs). This study considers a microchannel heat sink with non-uniform channels (NUCs). A mathematical model is developed based on energy equations and the Darcy flow principle. Explicit expressions for total thermal resistance and coolant pressure drop are derived using the thermoelectric analogy. Experiments and numerical simulations are performed to verify the mathematical model. As non-uniformity increases, total coolant pressure drop decreases but at the cost of higher thermal resistance. The overall performance of NUCs is better than that of UCs because of their lower ratio of pumping power to cooling power. Heat transfer performance of NUCs changes little for more than 120 channels and depends mainly on channel arrangement. A multi-objective optimization is conducted to minimize the thermal resistance and pumping power of an NUC. An optimal NUC saves 64% pumping power compared with a conventional UC for the total thermal resistance of 0.1°C/W, indicating that the use of non-uniform channels could be very helpful to reduce the flow resistance of MCHS.

Despite recent advances in lane detection methods, scenarios with limited- or no-visual-clue of lanes due to factors such as lighting conditions and occlusion remain challenging and crucial for automated driving. Moreover, current lane representations require complex post-processing and struggle with specific instances. Inspired by the DETR architecture, we propose LDTR, a transformer-based model to address these issues. Lanes are modeled with a novel anchor-chain, regarding a lane as a whole from the beginning, which enables LDTR to handle special lanes inherently. To enhance lane instance perception, LDTR incorporates a novel multi-referenced deformable attention module to distribute attention around the object. Additionally, LDTR incorporates two line IoU algorithms to improve convergence efficiency and employs a Gaussian heatmap auxiliary branch to enhance model representation capability during training. To evaluate lane detection models, we rely on Fréchet distance, parameterized Fl-score, and additional synthetic metrics. Experimental results demonstrate that LDTR achieves state-of-the-art performance on well-known datasets.

AbstractAim:Malignant vasovagal syncope in children seriously affects their physical and mental health. Our study aimed to explore the efficacy of catheter ablation in ganglionated plexus with malignant vasovagal syncope children.Conclusion:Catheter ablation of ganglionated plexus was safe and effective in children with malignant vasovagal syncope and can be used as a treatment option for these children.Methods:A total of 20 children diagnosed with malignant vasovagal syncope were enrolled in Beijing Children’s Hospital, affiliated with Capital Medical University. All underwent catheter ablation treatment of ganglionated plexus. Ganglionated plexuses of the left atrium were identified by high-frequency stimulation and/or anatomic landmarks being targeted by radiofrequency catheter ablation. The efficacy of the treatment was evaluated by comparing the remission rate of post-operative syncopal symptoms and the rate of negative head-up tilt results. Safety and adverse events were evaluated.Results:After follow-up for 2.5 (0.6–5) years, the syncope symptom scores were decreased significantly compared with before treatment [3 (2–4) versus 5 (3–8) scores, P < 0.01]. Eighty-five per cent (17/20) children no longer experienced syncope, whilst 80% (16/20) children showed negative head-up tilt test after treatment. No adverse effects such as cardiac arrhythmia occurred in the children.

Co-based superalloys have attracted attention to replace Ni-based superalloys in high temperature structural applications because of their higher melting temperature and corrosion resistance. Strengthening is provided by Co3(Al, X) (X = W, Cr, Mo, Ni) phases and optimization of their properties requires detailed information about the free energies of the different phases and of the phase diagram of the Co–Al system. This is achieved in this paper through first principles calculations in combination with statistical mechanics. Configurational entropic contributions to the free energy were included through Monte Carlo simulations using the cluster expansion formalism. The vibrational entropy of each phase was determined through the length-stiffness relationship while the magnetic entropy was also included through the Heisenberg Hamiltonian. The computed phase diagram is compared with the currently accepted experimental phase diagram and the different contributions to the stability of each phase are analyzed independently. The potential of this strategy to predict phase diagrams of magnetic systems is clearly established.

This study aims to investigate the phase composition, microstructure, and mechanical properties of three refractory high-entropy alloys (RHEAs): Al7(NbTiZr)93, Al7(NbTiZrMo)93, and Al7(NbTiZrTa)93. The as-cast Al7(NbTiZr)93alloy exhibits a single-phase BCC structure, with a yield strength (YS) of 1050 MPa at room temperature. The addition of Mo and Ta leads to the formation of Al7(NbTiZrMo)93and Al7(NbTiZrTa)93alloys with BCC-B2and B2-FCC dual-phase structures, whose YS values reach 1741 MPa and 2350 MPa, respectively. At 800 °C, trace amounts of ordered B2phase are generated in the Al7(NbTiZr)93matrix, and the YS decreases from 1050 MPa at room temperature to 219 MPa at 800 °C. In the Al7(NbTiZrMo)93alloy, the 10 nm B2precipitates grow to several hundred nanometers, and the YS decreases to 431 MPa after compression at 800 °C. In the Al7(NbTiZrTa)93alloy, the grid B2-FCC structure at room temperature transforms into a disordered BCC structure at 800 °C, yet possessing the YS of 908 MPa and demonstrating acceptable ductility. Moreover, Ta exhibits the better structural and performance matching than Mo in AlNbTiZr-based RHEAs. Therefore, it opens up new prospects for regulating the structure and high-temperature performance of AlNbTiZr-based RHEAs.

This cross‐sectional study aimed to determine the epidemiology of olfactory and gustatory dysfunctions related to COVID‐19 in China. This study was conducted by 45 tertiary Grade‐A hospitals in China. Online and offline questionnaire data were obtained from patients infected with COVID‐19 between December 28, 2022, and February 21, 2023. The collected information included basic demographics, medical history, smoking and drinking history, vaccination history, changes in olfactory and gustatory functions before and after infection, and other postinfection symptoms, as well as the duration and improvement status of olfactory and gustatory disorders. Complete questionnaires were obtained from 35,566 subjects. The overall incidence of olfactory and taste dysfunction was 67.75%. Being female or being a cigarette smoker increased the likelihood of developing olfactory and taste dysfunction. Having received four doses of the vaccine or having good oral health or being a alcohol drinker decreased the risk of such dysfunction. Before infection, the average olfactory and taste VAS scores were 8.41 and 8.51, respectively; after infection, they decreased to 3.69 and 4.29 and recovered to 5.83 and 6.55 by the time of the survey. The median duration of dysosmia and dysgeusia was 15 and 12 days, respectively, with 0.5% of patients having symptoms lasting for more than 28 days. The overall self‐reported improvement rate was 59.16%. Recovery was higher in males, never smokers, those who received two or three vaccine doses, and those that had never experienced dental health issues, or chronic accompanying symptoms. The incidence of dysosmia and dysgeusia following infection with the SARS‐CoV‐2 virus is high in China. Incidence and prognosis are influenced by several factors, including sex, SARS‐CoV‐2 vaccination, history of head‐facial trauma, nasal and oral health status, smoking and drinking history, and the persistence of accompanying symptoms.

Electrical discharge wire sawing (EDWS) is a new composite processing method that combines wire electrical discharge machining with fixed abrasive wire sawing. During processing, relying on the grinding effect of the abrasive grains, craters and recast layers generated by discharge are removed. The surface roughness of the workpiece is reduced while the cutting efficiency is improved. In this study, the relationship between discharge voltage and workpiece surface roughness in composite machining is investigated. A single-input single-output control system model with applied resistance as the input and discharge voltage as the output is established. Based on the theory of system identification and parameter estimation, the order of the model is identified by calculating the variance of the model residuals and using the F-test method. The model parameters of the control system are estimated online using a recursive least squares algorithm with a forgetting factor. A generalized predictive controller is designed on the basis of the predictive control theory and the identified discharge voltage control system model. Using LabVIEW software, a discharge voltage prediction control system is constructed for the processing of single-crystal silicon by EDWS, and its prediction control performance is experimentally verified. The results show that the discharge voltage control system designed in this study can successfully control the composite machining of single-crystal silicon in real time. Under the same machining conditions, the surface roughness of the workpiece machined with predictive control was 23.5 % lower than that without predictive control.

To improve the thermal barrier properties of epoxy resin coatings, a composite waterborne aerogel multifunctional coating that possesses both mechanical properties and controllable barrier ability was prepared in this study. This coating incorporates granular silica aerogel, organic montmorillonite, and two flame retardants: aluminum hydroxide (ATH) and magnesium hydroxide (MH). The results showed that montmorillonite, modified with stearyl trimethyl ammonium bromide (STAB), exhibited antisymmetric and symmetric stretching vibrational infrared spectral absorption peaks of –CH3,–CH2, characteristic of the long-chain alkane quaternary ammonium salt. This indicated that the quaternary ammonium salt was intercalated into the montmorillonite interlayer, enhancing the surface lamellae. The coating effectively absorbed energy when stressed, resulting in a strength increase of 146.3%. Incorporating high porosity silica aerogel significantly improved the barrier behavior of the composite coating, enabling controlled thermal insulation and excellent corrosion resistance. The coating reduced heat transfer paths and effectively impeded heat transfer, maintaining a high corrosion inhibition efficiency (99.9%) in a 3.5% NaCl solution. Additionally, the individual actions of the two flame retardants led to highly effective flame retardant properties, with the total heat release rate (THR) reduced by about 43.5% and the total smoke production (TSP) by 40.1%.

• Microcracks in LPBF-processed HEA were significantly eliminated via HIP treatment. • LPBF-processed HEA after HIP exhibits high UTS of 1149 MPa and FE of 22.9%. • The property improvement is attributed to the crack closure and precipitation strengthening. Hot isostatic pressing (HIP) is usually applied to reduce the defects including cracks and pores in the materials prepared by laser powder bed fusion (LPBF). In the present research, in order to improve the relative density and mechanical property, HIP was employed on the LPBF-processed Al-Cr-Fe-Ni-V high-entropy alloy (HEA) with microcracks and pores. The microstructure evolution and property improvement induced by HIP were investigated. In the LPBF-processed HEA, the microcracks were caused by residual stress and element segregation, and these microcracks as well as the pores reduced significantly after HIP treatments. Remarkably, HIP temperature has a more critical effect on the microcrack closure than the holding time, thus, microcracks and pores still existed after HIP-1 treatment (1273 K, 8 h), while HIP-2 treatment (1473 K, 4 h) could close the microcracks significantly. The crack closure was attributed to the interfacial diffusion of the alloying element under high temperature accompanied by high pressure, and the degree of element diffusion at both interfaces of the cracks determined the bonding strength after crack closure. Higher temperatures at high pressure induced more adequate element diffusion and higher bonding strength. The above high temperature and high pressure also induced the growth of the L1 2 phase and the precipitation of the B2 phase in HEA. Consequently, the tensile strength and elongation of the LPBF-processed HEA after HIP-2 treatment were simultaneously enhanced (80.7% and 222.5% higher than that of LPBF-processed HEA, respectively). This could be attributed to the combined effect of microcrack/pore closure and precipitation strengthening. The strengthening effect of the B2 phase and L1 2 phase accounted for 53% (dislocation by-pass mechanism) and 47% (dislocation shearing mechanism) of the total precipitation strengthening, respectively. [ABSTRACT FROM AUTHOR]

Low-dimensional hybrid metal halides are an emerging class of materials with highly efficient photoluminescence (PL), but the problems of poor stability remain challenging. Sn(IV)-based metal halides show robust structure but exhibit poor PL properties, and the structure–luminescence relationship is elusive. Herein, two Sn(IV)-based metal halides (compounds 1and 2) with the same constituent ((C6H16N2)SnCl6) but different crystal structures have been prepared, which however show poor PL properties at room temperature due to the absence of active ns2electrons. To improve materials’ PL properties, Sb3+with active 5s2electrons was embedded into the lattice of Sn4+-based hosts. As a result, efficient emissions were achieved for Sb3+-doped compounds 1and 2with a maximum PL efficiency of 14.28 and 62%, respectively. Experimental and calculation results reveal that the smaller distorted lattice structure of the host could result in the blueshift of the emission from Sb3+. Thus, a tunable color from red to orange was realized. Benefiting from the broadband efficient emission from Sb3+-doped compound 2, an efficient white light-emitting diode with a high color rendering index of up to 92.3 was fabricated to demonstrate its lighting application potential. This work promotes the understanding of the influence of robust Sn(IV)-based host lattice on the PL properties of Sb3+, advancing the development of environmentally friendly, low-cost, and high-efficiency Sn(IV)-based metal halides.

Graphene functionalization has been widely used in improving the thermal transport of graphene/epoxy composites owing to its performance in enhancing thermal conductivity and facilitating the formation of graphene networks. However, the heat transfer law and mechanism between functionalized graphene remain unclear. In this paper, the effects of distribution, type, and coverage of functional groups on the graphene–graphene interfacial thermal resistance (ITR) are analyzed by nonequilibrium molecular dynamics simulations. The results demonstrate that the graphene–graphene ITR depends on the synthetic effect of the graphene–graphene interaction, graphene–functional group interaction, and interaction between functional groups. The heat transfer contributions of those three interactions vary with the relative positions between functional groups. Then, the potential distribution of graphene is evaluated to compare the effects of different types of functional groups. It indicates that the potential difference is mainly attributed to functional groups. Compared with methyl, amino, and fluorine groups, hydroxyl groups have the largest potential difference and interaction. As the coverage of functional groups increases, the ITR of methylated graphene, aminated graphene, and hydroxylated graphene decreases inversely, whereas that of fluorinated graphene increases first and then decreases inversely. This work provides practical importance for the design and application of functionalized graphene in composite materials.

ABSTRACT This paper reports the results of a detailed experimental study conducted to investigate the influence of mixed chlorides on sulfate-induced corrosion in cast-in-situ concrete. Concrete specimens with or without [...]

At the core of plasmonic-enhanced applications lies photon energy harvesting and conversion over plasmonic/non-plasmonic multicomponent hybrid materials. The direct excitation of local interfacial electronic states that arise from the chemical bonding between plasmonic metal and attached functional entity opens up an exceptionally efficient energy transfer channel. However, there is a lack of a general strategy to manipulate these states and insights into the impact of such electronic modulations on light-driven functionalities. We demonstrate in a plasmonic-metal/molecule hybrid system that by precisely manipulating microfacets of nanostructures, it is possible to engineer metal-adsorbate-hybridized interfacial states in terms of both energy gap opening and permissible electronic excitations. Low-index microfacets feature much more efficient interfacial electronic transitions than high-index ones, retarding plasmonic relaxation and entailing enhanced photocatalytic activity toward molecular coupling reactions. This study contributes to pushing forward frontiers of plasmonic research from geometric control to nanostructure engineering and enlightens new strategies for plasmonic-enhanced applications.

In ground-penetrating radar (GPR) data, clutter and noise are commonly observed in B-scan images, which can seriously affect the interpretability of the GPR data. In this article, we propose wavelet-GAN, a deep-learning network that integrates generative adversarial network (GAN) and discrete wavelet transform (DWT). Wavelet-GAN could decompose the GPR image into multiple frequency subimages and remove clutter. Additionally, we solve the problem of error handling when there are no features in the dataset through micro datasets, dataset fine-tuning, high-speed training, and multiple feature generalization. Our method decomposes GPR image by DWT, then convolutional neural network (CNN) and GAN are, respectively, used to reconstruct low-frequency and high-frequency target signal information. Finally, the information of different frequency bands is combined into a new GPR image by inverse DWT (IDWT). Wavelet-GAN uses a small-scale dataset for training, which enables it to make rapid adjustments to process new target types, even if we only have one typical target data. We compare our method with traditional methods and other deep-learning based methods and demonstrate that our wavelet-GAN performs better in real data processing. Finally, we apply this method as a data-preprocessing tool for machine learning inversion and tested its feasibility.

In this paper, we propose a stochastic Levenberg–Marquardt algorithm based on trust region for stochastic nonlinear least squares problems, where the stochastic Jacobians and gradients are used instead of the exact Jacobians and gradients. We show that the estimates and models of the objective function are probabilistically accurate if the number of samples at each iteration is chosen appropriately. Further, we prove that at least one accumulation point of the sequence generated by the proposed algorithm is a stationary point of the objective function with probability one.

Dynamic brain network has the advantage over static brain network in characterizing the variation pattern of functional brain connectivity, and it has attracted increasing attention in brain disease diagnosis. However, most of the existing dynamic brain networks analysis methods rely on extracting features from independent brain networks divided by sliding windows, making them hard to reveal the high-order dynamic evolution laws of functional brain networks. Additionally, they cannot effectively extract the spatio-temporal topology features in dynamic brain networks. In this paper, we propose to use optimal transport (OT) theory to capture the topology evolution of the dynamic brain networks, and develop a multi-channel spatio-temporal graph convolutional network that collaboratively extracts the temporal and spatial features from the evolution networks. Specifically, we first adaptively evaluate the graph hubness of brain regions in the brain network of each time window, which comprehensively models information transmission among multiple brain regions. Second, the hubness propagation information across adjacent time windows is captured by optimal transport, describing high-order topology evolution of dynamic brain networks. Moreover, we develop a spatio-temporal graph convolutional network with attention mechanism to collaboratively extract the intrinsic temporal and spatial topology information from the above networks. Finally, the multi-layer perceptron is adopted for classifying the dynamic brain network. The extensive experiment on the collected epilepsy dataset and the public ADNI dataset show that our proposed method not only outperforms several state-of-the-art methods in brain disease diagnosis, but also reveals the key dynamic alterations of brain connectivities between patients and healthy controls.

With the remarkable success of digital histopathology and the deep learning technology, many whole-slide pathological images (WSIs) based deep learning models are designed to help pathologists diagnose human cancers. Recently, rather than predicting categorical variables as in cancer diagnosis, several deep learning studies are also proposed to estimate the continuous variables such as the patients’ survival or their transcriptional profile. However, most of the existing studies focus on conducting these predicting tasks separately, which overlooks the useful intrinsic correlation among them that can boost the prediction performance of each individual task. In addition, it is sill challenge to design the WSI-based deep learning models, since a WSI is with huge size but annotated with coarse label. In this study, we propose a general multi-instance multi-task learning framework (HistMIMT) for multi-purpose prediction from WSIs. Specifically, we firstly propose a novel multi-instance learning module (TMICS) considering both common and specific task information across different tasks to generate bag representation for each individual task. Then, a soft-mask based fusion module with channel attention (SFCA) is developed to leverage useful information from the related tasks to help improve the prediction performance on target task. We evaluate our method on three cancer cohorts derived from the Cancer Genome Atlas (TCGA). For each cohort, our multi-purpose prediction tasks range from cancer diagnosis, survival prediction and estimating the transcriptional profile of gene TP53. The experimental results demonstrated that HistMIMT can yield better outcome on all clinical prediction tasks than its competitors.

Background: Patients with end-stage renal disease (ESRD) undergoing dialysis have increased risks of atrial fibrillation (AF). Objective: To investigate dialysis-related incident AF and associated outcomes. Methods: Patients with dialysis were retrieved using data from Taiwan National Health Insurance Research Database during 2001–2013. Patients were separated into peritoneal dialysis (PD) and hemodialysis (HD) according to their initial modality. Primary outcome was new-onset AF. Secondary outcomes were AF-associated ischemic stroke (IS)/systemic embolism (SE) and hemorrhagic stroke. Results: A total of 158,910 dialytic patients were retrieved. After exclusion criteria, a total of 117,023 patients with ESRD undergoing dialysis were separated into 12,659 patients on PD and 104,364 patients on HD. There were 458 PD patients with subsequent development of AF, and 6216 HD patients with subsequent development of AF. At end of follow-up, patients on PD and HD had AF incidence densities of 7.8 and 8.8 events per 1000 person-years, the SHR of PD versus HD was 0.83 (95% CI 0.73–0.94). The SHR of PD versus HD was 1.07 (95% CI 0.80–1.44) for IS/SE and the SHR of PD versus HD was 0.34 (95% CI 0.13–0.90) for hemorrhagic stroke. Conclusion: In patients with ESRD undergoing dialysis, PD had lowered risks of new onset of AF compared to HD. Subsequently, these AF patients in PD group had comparable incidence of ischemic stroke but decreased incidence of hemorrhagic stroke compared to AF patients in HD group. PD could be the most suitable modality in patients at risk for the onset of AF.

In this letter, a multifunctional active frequency selective surface (AFSS) is proposed. In addition to forming several continuously tunable transmission windows in the operating band, it can also maintain low radar cross section (RCS) characteristics out-of-band. Besides, we propose a systematic design method introduced the complete circuit for active lumped elements, which enables the rapid and accurate design of the multifunctional AFSS. The prototype is fabricated and measured. The measured results are in good agreement with the simulated ones. The proposed AFSS can regulate the scattering and radiation characteristics simultaneously and independently, and realize a tunable transmission passband from 3.4 to 6.4 GHz (with a relative bandwidth of 61.2%) and RCS reduction within 8–12.1 GHz. It provides a practical scheme to achieve low RCS while ensuring wideband or multiband antenna radiation performance and a balance between the operating bandwidth with the exposure risk.

Photocatalytic cycloadditions of CO2with epoxides are emerging as a significant platform for the green synthesis of valuable carbonates, where efficient catalytic systems with spatially separated charge carriers are demanded. Herein, a single p-block metal atom is proposed to be a candidate for photogenerated electron localization on a metal oxide substrate. By taking the Bi single-atom supported on ZnO nanosheet (Bi1/ZnO) as an example, we show that the Bi atom plays the role of an electron island in the sea of delocalized holes within ZnO. Meanwhile, the as-formed electron island could readily promote the rate-determining ring-opening process of cycloaddition reactions. Benefiting from the unique spatially separated electrons and holes, the Bi1/ZnO achieves a high yield of cyclic carbonates with almost 100% selectivity. This study provides a pioneering strategy for enhancing the performances of photocatalytic CO2chemical fixation.