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The point cloud is one of the measurement results of local measurement and is widely used because of its high measurement accuracy, high data density, and low environmental impact. However, since point cloud data from a single measurement are generally small in spatial extent, it is necessary to accurately globalize the local point cloud to measure large components. In this paper, the method of using an iGPS (indoor Global Positioning System) as an external measurement device to realize high-accuracy globalization of local point cloud data is proposed. Two calibration models are also discussed for different application scenarios. Verification experiments prove that the average calibration errors of these two calibration models are 0.12 mm and 0.17 mm, respectively. The proposed method can maintain calibration precision in a large spatial range (about 10 m × 10 m × 5 m), which is of high value for engineering applications. [ABSTRACT FROM AUTHOR]

Given that tumor microenvironment (TME) exerts adverse impact on the therapeutic response and clinical outcome, robust TME modulators may significantly improve the curative effect and increase survival benefits of cancer patients. Here, Au nanodots‐anchored CoFe2O4 nanoflowers with PEGylation (CFAP) are developed to respond to TME cues, aiming to exacerbate redox dyshomeostasis for efficacious antineoplastic therapy under ultrasound (US) irradiation. After uptake by tumor cells, CFAP with glucose oxidase (GOx)‐like activity can facilitate glucose depletion and promote the production of H2O2. Multivalent elements of Co(II)/Co(III) and Fe(II)/Fe(III) in CFAP display strong Fenton‐like activity for·OH production from H2O2. On the other hand, energy band structure CFAP is superior for US‐actuated 1O2 generation, relying on the enhanced separation and retarded recombination of e−/h+ pairs. In addition, catalase‐mimic CFAP can react with cytosolic H2O2 to generate molecular oxygen, which may increase the product yields from O2‐consuming reactions, such as glucose oxidation and sonosensitization processes. Besides the massive production of reactive oxygen species, CFAP is also capable of exhausting glutathione to devastate intracellular redox balance. Severe immunogenic cell death and effective inhibition of solid tumor by CFAP demonstrates the clinical potency of such heterogeneous structure and may inspire more relevant designs for disease therapy. [ABSTRACT FROM AUTHOR]

Zoophytophagous predators can induce plant defence responses through phytophagous feeding. Since the zoophytophagous bug Orius sauteri lays eggs into plant tissues, we hypothesised that its oviposition behaviour may also induce plant defence responses with a negative impact on subsequent herbivore attacks. Pre-inoculation of O. sauteri females on tomato plants significantly reduced the fitness and reproduction of Frankliniella occidentalis, which also preferred the non-inoculated plants in indoor micro-environments. In the field, O. sauteri pre-inoculation also caused reduced population growth of F. occidentalis. All these tendencies were weaker with male compared to female O. sauteri pre-inoculation. Next, a transcriptome analysis showed that the MAPK signalling pathway, the plant hormone signal transduction and plant-pathogen interaction of defence-related pathways were significantly enriched in plants inoculated with O. sauteri females compared to untreated plants. We showed that three key genes of the JA pathway, allene oxide synthase (AOS), jasmonate ZIM-domain 2 (JAZ2), and proteinase inhibitor 1 (PI-1), were upregulated. This is evidence of plant defence activation, the likely mechanism by which O. sauteri pre-inoculation (through feeding and oviposition activities) reduced F. occidentalis fitness in the laboratory and population densities by almost three times in a greenhouse experiment. This mechanism could be promoted in IPM strategies through the early introduction of zoophytophagous biocontrol agents activating crop plant defences to enhance biological pest control. [ABSTRACT FROM AUTHOR]

Background: Sclerotinia sclerotiorum is a highly destructive phytopathogenic fungus that poses a significant threat to a wide array of crops. The current constraints in genetic manipulation techniques impede a thorough comprehension of its pathogenic mechanisms and the development of effective control strategies. Results: Herein, we present a highly efficient genetic transformation system for S. sclerotiorum, leveraging the use of fusiform nanoparticles, which are synthesized with FeCl3 and 2,6-diaminopyrimidine (DAP). These nanoparticles, with an average longitude length of 59.00 nm and a positively charged surface, facilitate the direct delivery of exogenous DNA into the mycelial cells of S. sclerotiorum, as well as successful integration with stable expression. Notably, this system circumvents fungal protoplast preparation and tedious recovery processes, streamlining the transformation process considerably. Furthermore, we successfully employed this system to generate S. sclerotiorum strains with silenced oxaloacetate acetylhydrolase-encoding gene Ss-oah1. Conclusions: Our findings demonstrate the feasibility of using nanoparticle-mediated delivery as a rapid and reliable tool for genetic modification in S. sclerotiorum. Given its simplicity and high efficiency, it has the potential to significantly propel genetic research in filamentous fungi, offering new avenues for elucidating the intricacies of pathogenicity and developing innovative disease management strategies. [ABSTRACT FROM AUTHOR]

To deal with the highly nonlinear and time-varying characteristics of Batch Process, a model named adaptive stacking approximate kernel based broad learning system is proposed in this paper. This model innovatively introduces the approximate kernel based broad learning system (AKBLS) algorithm and the Adaptive Stacking framework, giving it strong nonlinear fitting ability, excellent generalization ability, and adaptive ability. The Broad Learning System (BLS) is known for its shorter training time for effective nonlinear processing, but the uncertainty brought by its double random mapping results in poor resistance to noisy data and unpredictable impact on performance. To address this issue, this paper proposes an AKBLS algorithm that reduces uncertainty, eliminates redundant features, and improves prediction accuracy by projecting feature nodes into the kernel space. It also significantly reduces the computation time of the kernel matrix by searching for approximate kernels to enhance its ability in industrial online applications. Extensive comparative experiments on various public datasets of different sizes validate this. The Adaptive Stacking framework utilizes the Stacking ensemble learning method, which integrates predictions from multiple AKBLS models using a meta-learner to improve generalization. Additionally, by employing the moving window method—where a fixed-length window slides through the database over time—the model gains adaptive ability, allowing it to better respond to gradual changes in industrial Batch Process. Experiments on a substantial dataset of penicillin simulations demonstrate that the proposed model significantly improves predictive accuracy compared to other common algorithms. [ABSTRACT FROM AUTHOR]

Conventional rolling is a plastic deformation process that uses compression between two rolls to reduce material thickness and produce sheet/plane geometries. This deformation process modifies the material structure by generating texture, reducing the grain size, and strengthening the material. The rolling process can enhance the strength and hardness of lightweight materials while still preserving their inherent lightness. Lightweight metals like magnesium alloys tend to lack mechanical strength and hardness in load-bearing applications. The general rolling process is controlled by the thickness reduction, velocity of the rolls, and temperature. When held at a constant thickness reduction, each pass through the rolls introduces an increase in strain hardening, which could ultimately result in cracking, spallation, and other defects. This study is designed to optimize the rolling process by evaluating the effects of the strain rate, rather than the thickness reduction, as a process control parameter. [ABSTRACT FROM AUTHOR]

In this research, a direct-write 3D-printing method was utilized for the fabrication of inter-digitized solid oxide fuel cells (SOFCs) using ceramic materials. The cathode electrode was fabricated using the LSCF (La0.6Sr0.2Fe0.8Co0.2O3-δ) slurry loading and the Polyvinyl butyral (PVB) binder. The rheological parameters of slurries with varying LSCF slurry loading and PVB binder concentration were evaluated to determine their effect on the cathode trace performance in terms of microstructure, size, and resistance. Additionally, the dimensional shrinkage of LSCF lines after sintering was investigated to realize their influence on cathode line width and height. Moreover, the effect of the direct-write process parameters such as pressure, distance between the nozzle and substrate, and speed on the cathode line dimensions and resistance was evaluated. LSCF slurry with 50% solid loading, 12% binder, and 0.2% dispersant concentration was determined to be the optimal value for the fabrication of SOFCs using the direct-write method. The direct-write process parameters, in addition to the binder and LSCF slurry concentration ratios, had a considerable impact on the microstructure of cathode lines. Based on ANOVA findings, pressure and distance had significant effects on the cathode electrode resistance. An increase in the distance between the nozzle and substrate, speed, or extrusion pressure of the direct writing process increased the resistance of the cathode lines. These findings add to the ongoing effort to refine SOFC fabrication techniques, opening the avenues for advanced performance and efficiency of SOFCs in energy applications. [ABSTRACT FROM AUTHOR]

To deal with the highly nonlinear and time-varying characteristics of Batch Process, a model named adaptive stacking approximate kernel based broad learning system is proposed in this paper. This model innovatively introduces the approximate kernel based broad learning system (AKBLS) algorithm and the Adaptive Stacking framework, giving it strong nonlinear fitting ability, excellent generalization ability, and adaptive ability. The Broad Learning System (BLS) is known for its shorter training time for effective nonlinear processing, but the uncertainty brought by its double random mapping results in poor resistance to noisy data and unpredictable impact on performance. To address this issue, this paper proposes an AKBLS algorithm that reduces uncertainty, eliminates redundant features, and improves prediction accuracy by projecting feature nodes into the kernel space. It also significantly reduces the computation time of the kernel matrix by searching for approximate kernels to enhance its ability in industrial online applications. Extensive comparative experiments on various public datasets of different sizes validate this. The Adaptive Stacking framework utilizes the Stacking ensemble learning method, which integrates predictions from multiple AKBLS models using a meta-learner to improve generalization. Additionally, by employing the moving window method—where a fixed-length window slides through the database over time—the model gains adaptive ability, allowing it to better respond to gradual changes in industrial Batch Process. Experiments on a substantial dataset of penicillin simulations demonstrate that the proposed model significantly improves predictive accuracy compared to other common algorithms. [ABSTRACT FROM AUTHOR]

Introduction: Although the relationship between the number of teeth and frailty has been extensively studied, the mediating role of nutrition status in the association between the number of teeth and frailty remains to be clarified. Methods: A number of 6,664 participants lived in the communities of West China were analyzed in our study. Physical frailty was determined based on the phenotype established by Fried. Nutrition status was evaluated using the Mini Nutrition Assessment-Short Form (MNA-SF) scale. Multiple linear regression was employed to evaluate the direct relationships between the number of teeth, nutrition, and frailty. Mediation models and structural equation model (SEM) pathway analysis were used to test the mediating role of nutrition status in the relationship between the number of teeth and frailty. Results: Among the 6,664 participants aged over 50 years old, the prevalence of frailty was 6.2%. Multiple linear regression analysis showed a significant total relationship between the number of teeth (β = −0.359, 95% CI: −0.473 to −0.244, p < 0.001) and frailty. After adjusting for MNA-SF scores, the relationship between the number of teeth and frailty remained significant (β = −0.327, 95% CI: −0.443 to −0.211, p < 0.001), indicating a partial mediating effect of nutrition. Mediation analysis verified that nutrition partially mediated the relationship between the number of teeth and frailty (indirect effect estimate = −0.0121, bootstrap 95% CI: −0.0151 to −0.0092; direct effect estimate = −0.0874, bootstrap 95% CI: −0.1086 to −0.0678) in the fully adjusted model. This mediating effect occurred through influencing weight loss, low level of physical activity, and debility. SEM framework pathway analysis confirmed the association between the number of teeth, nutrition, and frailty. Conclusions: Our findings demonstrated that frailty was correlated with the number of teeth and poorer nutritional status, with nutrition partially mediating the correlation between the number of teeth and frailty. Our results supported early nutritional evaluation and intervention in oral health to decrease the risk of frailty. [ABSTRACT FROM AUTHOR]

Limited research has been conducted on the regulation of light quality on heterotrophy in in vitro-grown potato plantlets. Here, we investigated the effect of light quality on photosynthetic and heterotrophic abilities as well as microtuber formation and growth of potato plantlets (Solanum tuberosum L. cv. Shepody). Potato plantlets pre-cultivated under white light for 30 days were then transferred to grow under blue (B), green (G), red (R), yellow (Y), and white (W) lights, and parameters including dry weight, photosynthetic pigment, medium solute consumption, δ13C value, root activity, and sucrose transport (SUT) gene expression of these plantlets were measured. The results showed that B, G, and W were conducive to the rapid induction of microtubers, while R, and especially Y, delayed microtuber formation. Higher photosynthetic ability was observed in the W treatment, whereas the opposite effect was seen in the monochromatic light treatments. Microtuber growth was primarily dependent on heterotrophy, and B was conducive to microtuber growth. The delay in microtuber formation was related to the high expression of StSUT4 in the root, and better microtuber growth was associated with higher root activity, more medium solute consumption, and a higher expression level of StSUT1 in the roots. [ABSTRACT FROM AUTHOR]

Autonomous vehicles (AVs) must be thoroughly tested to ensure safety and reliability before marketing. Simulation‐based testing has gained widespread recognition as the essential approach for AV testing by providing sufficient testing scenarios in the virtual environment. Vehicle‐in‐the‐loop (VIL) simulation has the ability to perform comprehensive tests and validations for the AVs' overall behaviors while keeping significant testing accuracy and efficiency through the combination of the virtual scenarios and the physical AV. This article provides an overview of representative studies on testbench‐based VIL simulation testing for AVs, mainly focusing on utilizing testbenches to simulate realistic road conditions, and using physical signal stimulation methods and related equipment to generate sensors' physical signals. This article first summarizes current AV testing studies, identifying existing issues and flaws of the state‐of‐the‐art methods and tools. Afterward, the testbench‐based VIL is addressed around architecture, principles, advantages, and characteristics. Then, the road condition simulation and the sensor physical signal generation in VIL are discussed in depth from structure, principle, and corresponding advanced equipment. Finally, research gaps between cutting‐edge technologies and AV testing applications in industrialization are identified to facilitate future research in this direction. [ABSTRACT FROM AUTHOR]

Photothermal therapy (PTT) may lead to healthy tissue damage, tumor metastasis, and recurrence, which makes mild photothermal therapy (mild PTT) stand out. However, overcoming heat resistance, insufficient therapeutic effect, and poor photothermal conversion efficiency has become new challenge. Herein, we report a dynamic supramolecular nanocarrier formed from amide-sericin and aldehyde-polyhydroxy glucan (denoted as SDA), the loose cavity of which can be filled by using the pharmaceutical combination of lonidamine (LND) and NIR-II photothermal agent of IR-1061, producing SDLI with a tighter inner hole, smaller and uniform particle size and excellent stability due to multiple pulling forces. Moreover, the intricate internal network structure prevents the hydrophobic IR-1061 from forming aggregates in the small cavity, and the photothermal conversion efficiency (PCE) can reach 48.9%. At the acidic tumor microenvironment of pH 6.5, the controlled release of LND can solve the problem of heat resistance of NIR-II mild PTT and significantly improve the therapeutic effect of NIR-II mild PTT. Meanwhile, SDLI also shows a reasonable tumor inhibition rate, so the synergistic strategy of inhibiting tumor energy metabolism and NIR-II mild PTT to magnify mitochondrial oxidative stress, continuous cell stress state-induced immunogenic cell death to promote the induction of tumor apoptosis is proposed to achieve more effective cancer treatment. [ABSTRACT FROM AUTHOR]

Solid oxide fuel cells (SOFCs) are a green energy technology that offers a cleaner and more efficient alternative to fossil fuels. The efficiency and utility of SOFCs can be enhanced by fabricating miniaturized component structures within the fuel cell footprint. In this research work, the parallel-connected inter-digitized design of micro-single-chamber SOFCs (µ-SC-SOFCs) was fabricated by a direct-write microfabrication technique. To understand and optimize the direct-write process, the cathode electrode slurry was investigated. Initially, the effects of dispersant Triton X-100 on LSCF (La0.6Sr0.2Fe0.8Co0.2O3-δ) slurry rheology was investigated. The effect of binder decomposition on the cathode electrode lines was evaluated, and further, the optimum sintering profile was determined. Results illustrate that the optimum concentration of Triton X-100 for different slurries was around 0.2–0.4% of the LSCF solid loading. A total of 60% of solid loading slurries had high viscosities and attained stability after 300 s. In addition, 40–50% solid loading slurries had relatively lower viscosity and attainted stability after 200 s. Solid loading and binder affected not only the slurry's viscosity but also its rheology behavior. Based on the findings of this research, a slurry with 50% solid loading, 12% binder, and 0.2% dispersant was determined to be the optimal value for the fabricating of SOFCs using the direct-write method. This research work establishes guidelines for fabricating the micro-single-chamber solid oxide fuel cells by optimizing the direct-write slurry deposition process with high accuracy. [ABSTRACT FROM AUTHOR]

Skeleton editing has rapidly advanced as a synthetic methodology in recent years, significantly streamlining the synthesis process and gaining widespread acceptance in drug synthesis and development. This field encompasses diverse ring reactions, many of which exhibit immense potential in skeleton editing, facilitating the generation of novel ring skeletons. Notably, reactions that involve the cleavage of two distinct rings followed by the reformation of new rings through ring insertion play a pivotal role in the construction of novel ring skeletons. This article aims to compile and systematize this category of reactions, emphasizing the two primary reaction types and offering a thorough exploration of their associated complexities and challenges. Our endeavor is to furnish readers with comprehensive reaction strategies, igniting research interest and injecting fresh impetus into the advancement of this domain. [ABSTRACT FROM AUTHOR]

To realise the intelligent decision-making of dynamic scheduling and reconfiguration, we studied the intelligent scheduling and reconfiguration with dynamic job arrival for a reconfigurable flow line (RFL) using deep reinforcement learning (DRL), for the first time. The system architecture of intelligent scheduling and reconfiguration in smart manufacturing is proposed, and the mathematical model is established to minimise total tardiness cost. In addition, a DRL system of scheduling and reconfiguration is proposed by designing state features, actions, and rewards for scheduling and reconfiguration agents. Moreover, the advantage actor-critic (A2C) is adapted to solve the studied problem. The training curve shows the A2C-based agents have effectively learned to generate better solutions for unseen instances. The test results show that the A2C-based approach outperforms two traditional meta-heuristics, iterated greedy (IG) and genetic algorithm (GA), in solution quality and CPU times by a large margin. Specifically, the A2C-based approach outperforms IG and GA by 57.43% and 88.30%, using only 0.46‱ and 2.20‱ CPU times of IG and GA. The trained model can generate a scheduling or reconfiguration decision within 1.47 ms, which is almost instantaneous and can satisfy real-time optimisation. Our work shows a promising prospect of using DRL for intelligent scheduling and reconfiguration. [ABSTRACT FROM AUTHOR]

Dental amalgams have been used by dentists for the restoration of posterior human teeth. However, there have been concerns about the release of mercury from amalgams into the oral cavity. The objective of the present research is to study the effect of titanium (Ti) nanoparticles on the microstructural mechanism of the release of mercury vapor in two commonly used brands of dental amalgam (the Dispersalloy: 11.8% Cu; the Sybralloy: 33% Cu). Ti powder was added to both the Dispersalloy and the Sybralloy in increments of 10 mg up to 80 mg. The addition of Ti powder to both brands of dental amalgam has been found to result in a considerable decrease in Hg vapor release. The decrease in the Hg vapor release due to Ti addition has been explained by the formation of strong Hg–Ti covalent bonds, which reduce the availability of Hg atoms for evaporation. The Ti atoms in excess of the solubility limit of Ti in Hg reside in the grain boundaries, which also reduces the evaporation of Hg from the amalgam. The binding of Hg with Ti via a strong covalent bond also results in a significant improvement in mechanical properties such as Vickers hardness. [ABSTRACT FROM AUTHOR]

Due to the robustness of skeleton data to human scale, illumination changes, dynamic camera views, and complex backgrounds, great progress has been made in skeleton‐based video anomaly detection in recent years. The spatio‐temporal graph convolutional network has been proven to be effective in modelling the spatio‐temporal dependencies of non‐Euclidean data such as human skeleton graphs, and the autoencoder based on this basic unit is widely used to model sequence features. However, due to the limitations of the convolution kernel, the model cannot capture the correlation between non‐adjacent joints, and it is difficult to deal with long‐term sequences, resulting in an insufficient understanding of behaviour. To address this issue, this paper applies the Transformer to the human skeleton and proposes the Spatio‐Temporal Enhanced Graph‐Transformer AutoEncoder (STEGT‐AE) to improve the capability of modelling. In addition, the multi‐memory model with skip connections is employed to provide different levels of coding features, thereby enhancing the ability of the model to distinguish similar heterogeneous behaviours. Furthermore, the STEGT‐AE has a single encoder‐double decoder architecture, which can improve the detection performance by the combining reconstruction and prediction error. The experimental results show that performances of STEGT‐AE is significantly better than other advanced algorithms on four baseline datasets. [ABSTRACT FROM AUTHOR]

The space instable target simulator (SITS) is a vital actuator for ground verification of on-orbital capture technology, the motion performance of which directly affects simulation credibility. Different delays reduce the stability of SITS and ultimately lead to its divergence. In order to achieve high-fidelity simulation, the impacts of force measurement delay, the discrete control cycle, and simulator response delay on stability are analyzed first. Then, the dynamic equation and transfer function identification model of the hybrid simulator is constructed, and the necessary and sufficient conditions of its stability and convergence are obtained using the Routh criterion. After that, a novel switching compensator with variable gain is proposed to reduce the superimposed effects of the three delays, the compensation principle diagram of which was built, and its mathematical model including the energy observer and nonlinear tracking differentiator is also established. Finally, three sets of numerical simulations were conducted to validate the correctness of the stability analysis and effectiveness of the proposed compensation method. The simulation results show that all three types of delays can cause SITS to lose stability under critical stable motion states, and the delay in force measurement has the greatest impact, followed by the influence of the control cycle. Compared with the force applied to the simulated target, the velocity, and the recovery coefficient of the space instable target using fixed gain and linear gain compensation, the proposed compensator has significantly better performance. [ABSTRACT FROM AUTHOR]

In batch processes, existing soft sensing methodologies encounter substantial challenges when confronted with nonlinearity and multi‐phase issues. In response to these challenges, an innovative soft sensing framework known as the multi‐phase stacking ensemble model with self‐selected primary learner is proposed. The main innovation of this framework lies in the solution to the primary learner selection issue within the stacking model. To commence, the batch process is divided into multiple phases employing a Gaussian mixture model, thereby establishing local ensemble models for each phase. Subsequently, the iterative self‐selection of primary learners strategy is proposed, which iteratively selects suitable primary learners for these models, optimizing their combination of primary learners for each local model. This primary learner selection strategy effectively enhances the accuracy of predictions in the stacking ensemble model. To further enhance the performance, Bayesian optimization is utilized to tune the hyperparameters of each local ensemble model. This step guarantees optimal performance of the model across diverse phases. Extensive simulation experiments are conducted on an industrial penicillin fermentation process to validate the effectiveness of the proposed framework. According to the findings, the model demonstrated superior performance compared to existing single‐learner soft sensing methods and commonly utilized ensemble‐based soft sensing methods in terms of both R2 score (0.97584) and RMSE (0.0513). Overall, this framework offers a novel approach for selecting primary learners in stacking ensemble models and enhancing the predictive performance in batch processes for soft sensing. [ABSTRACT FROM AUTHOR]

In the field of aerospace, large and heavy cabin segments present a significant challenge in assembling space engines. The substantial inertial force of cabin segments' mass often leads to unexpected motion during docking, resulting in segment collisions, making it challenging to ensure the accuracy and quality of engine segment docking. While traditional manual docking leverages workers' expertise, the intensity of the labor and low productivity are impractical for real-world applications. Human-robot collaboration can effectively integrate the advantages of humans and robots. Parallel robots, known for their high precision and load-bearing capacity, are extensively used in precision assembly under heavy load conditions. Therefore, human-parallel-robot collaboration is an excellent solution for such problems. In this paper, a framework is proposed that is easy to realize in production, using human-parallel-robot collaboration technology for cabin segment docking. A fractional-order variable damping admittance control and an inverse dynamics robust controller are proposed to enhance the robot's compliance, responsiveness, and trajectory tracking accuracy during collaborative assembly. This allows operators to dynamically adjust the robot's motion in real-time, counterbalancing inertial forces and preventing collisions between segments. Segment docking assembly experiments are performed using the Stewart platform in this study. The results show that the proposed method allows the robot to swiftly respond to interaction forces, maintaining compliance and stable motion accuracy even under unknown interaction forces. [ABSTRACT FROM AUTHOR]

With the rapid development of communication technologies, the demand for security and automation of driving has promoted the development of the Internet of Vehicles (IoV). The IoV aims to provide users with a safer, more comfortable, and more efficient driving experience. However, the current IoV also faces a series of potential security risks and privacy breaches, which has further propelled research on trust management for vehicular networks. The introduction of the blockchain has resolved the issue of data security in IoV trust management. However, the blockchain is limited by its own performance and scalability, making it unsuitable for large-scale networks. In order to enhance the transaction-processing efficiency of blockchain-based trust management solutions and address their scalability limitations, this paper presents a graph partition-based blockchain-sharding protocol. Simulation results on real-world datasets demonstrate that the proposed scheme exhibits better scalability compared to existing blockchain-based approaches and can accommodate larger-scale device access. [ABSTRACT FROM AUTHOR]

The meso-dynamical behaviour of a high-speed rail ballast bed with under sleeper pads (USPs) was studied. The geometrically irregular refined discrete element model of the ballast particles was constructed using 3D scanning techniques, and the 3D dynamic model of the rail–sleeper–ballast bed was constructed using the coupled discrete element method–multi-flexible-body dynamics (DEM–MFBD) approach. We analyse the meso-mechanical dynamics of the ballast bed with USPs under dynamic load on a train and verify the correctness of the model in laboratory tests. It is shown that the deformation of the USPs increases the contact area between the sleeper and the ballast particles, and subsequently the number of contacts between them. As the depth of the granular ballast bed increases, the contact area becomes larger, and the contact force between the ballast particles gradually decreases. Under the action of the elastic USPs, the contact forces between ballast particles are reduced and the overall vibration level of the ballast bed can be reduced. The settlement of the granular ballast bed occurs mainly at the shallow position of the sleeper bottom, and the installation of the elastic USPs can be effective in reducing the stress on the ballast particles and the settlement of the ballast bed. [ABSTRACT FROM AUTHOR]

Magnesium-based alloys show significant promise for widespread applications owing to their lightweight nature and improved mechanical properties achieved through grain refinement via hot rolling. This investigation focuses on Mg-xAl-yCa-zMn (AXM alloys), pre-heated to temperatures of 350, 400, and 450 °C and processed via both single-pass differential speed rolling (DSR) and conventional rolling (CR). The key findings reveal the interplay between processing temperature, strain rate during single-pass rolling, and an innovative approach for incorporating varying amounts of Ca, influencing grain size, quantity of dynamic recrystallization (DRX) grains, and overall mechanical properties, including strength and ductility. A noteworthy observation is the positive correlation between an increase in the total reduction during hot rolling and a higher fraction of DRXed grains. This leads to a significant reduction in average grain size, diminishing from 60.3 ± 54.3 μm to 19.5 ± 14.2 μm at 40%, nearly a third the size of T4 grains (the initial homogenized microstructure of the AXM alloys). The resultant material strength experiences a doubling from an average of 125 ± 10.2 MPa (T4) to 260 ± 25.8 MPa (DSR rolled at 40%) for the AXM alloys with potential improvement in the ductility depending on rolling speed conditions. This study also aims to analyze the combination of rolling temperature, rolling speed, thickness reduction, speed difference and Ca content implemented across a wide range of temperatures and strain rates to provide a holistic approach to the processing parameters affecting the microstructure and mechanical properties of AXM alloys. Furthermore, this study provides a deeper understanding of DRX mechanisms, including continuous DRX (CDRX), discontinuous DRX (DDRX), and twinning induced DRX (TDRX), while each of these mechanisms plays a distinct role in the overall enhancement of formability and performance of magnesium alloys. [ABSTRACT FROM AUTHOR]

Cancer immunotherapy has the potential to revolutionize the treatment of malignant tumors, but its effectiveness is limited by the low immune response rate and immune‐related adverse events. Pyroptosis, as an inflammatory programmed cell death type, triggers strong acute inflammatory response and antitumor immunity, converting "cold" tumors to "hot". Particularly, biomaterials loading pyroptosis inducers targeting the tumor microenvironment to engineer pyroptosis, have achieved great progress in recent years. Herein, the design strategy, mechanism pathway, and role of biomaterials to induce pyroptosis in cancer immunotherapy are comprehensively reviewed. The present review focuses on the application of biomaterials‐induced pyroptosis in cancer immunotherapy, including nanogel, polymer prodrug, nanovesicle, and mesoporous material. Additionally, the synthesis of a series of stimuli‐responsive nanoplatforms, including glutathione‐responsive, pH‐responsive, reactive oxygen species‐responsive, and enzyme‐mimicking catalytic performance, is described. Meanwhile, it augments multiple immune response processes of cell uptake, antigen presentation, T‐cell activation, and expansion. Finally, the perspectives of pyroptosis‐mediated inflammation to break through the tumor vascular basement membrane barrier achieving efficient volcanic penetration of biomaterials are discussed. Artificial intelligence, multi‐omics analysis, and anthropogenic animal models of organoids are presented, aiming to provide guidance and assistance for constructing effective and controllable pyroptosis‐engineered biomaterials and improving tumor immunotherapy. [ABSTRACT FROM AUTHOR]

Place recognition searches the closest map node to the query node, which is an important task for vehicle localization. Traditional visual place recognition methods for underground parking lots require the deployment of additional location signals, such as WiFi, Bluetooth. This paper utilizes only front-view images to realize place recognition. First, we employ a random coefficient to reduce the dimensionality of the ConvNet features to obtain the CCFs (Condense ConvNet Features). Second, we average the CCFs of a regional zone to obtain the RACF (Regional Aggregating ConvNet Feature). Compared with WiFi, Bluetooth, RACF is extracted from the front-view image and has a superior ability to represent regional zones. Third, we propose a multiscale place recognition method that adopts a coarse-to-fine strategy that greatly reduces time consumption and accelerates precision. Finally, we evaluate the proposed method on the data collected in the underground parking lot of Hubei University of Technology. The experimental results illustrate that the proposed method has high precision with a fast speed. [ABSTRACT FROM AUTHOR]

In this study, we have developed a novel approach for synergistic tumor treatment by utilizing degradable dendritic mesoporous silica nanoparticles loaded with doxorubicin and curcumin (Cur). These nanoparticles are equipped with polydopamine (PDA) for photothermal therapy and hyaluronic acid (HA) as a targeting ligand. The incorporation of PDA not only ensures the controlled release of therapeutic agents but also confers pH and photothermal responsiveness to the nanocarriers. Cur, a natural compound, serves as an antitumor sensitizer, elevating intracellular ROS levels to promote apoptosis. In addition, the excellent biocompatibility and targeting property of nanocarriers were enhanced by the HA‐modification, improving the effective accumulation in tumor cells. Comprehensive research including MTT assay, live/dead staining and ROS generation proved that the excellent antitumor capability of dual‐drug loaded nanocarriers was a result of chemo‐photothermal synergistic therapy and Cur‐induced cellular amplified ROS generation. The cellular uptake assays further demonstrated the efficiency of HA‐mediated intracellular accumulation, accelerated uptake driven by photothermal‐induced and a precise internalization pathway of nanocarriers. Furthermore, the results of in vivo biocompatibility revealed no significant blood and organ toxicity associated to the prepared nanocarriers. Our findings underscore the substantial potential of the proposed dual‐drug loaded nanocarriers for chemo‐photothermal synergistic therapy, offering a safe and promising strategy for effective tumor treatment. [ABSTRACT FROM AUTHOR]

A facile methodology for the construction of a complex heterocycle indazolo-fused quinoxalinone has been developed via an Ugi four-component reaction (U-4CR) followed by an intramolecular Ullmann reaction. The expeditious process features an operationally simple approach, time efficiency, and a broad substrate scope. Biological activity was evaluated and demonstrated that compound 6e inhibits human colon cancer cell HCT116 proliferation with an IC50 of 2.1 μM, suggesting potential applications for developing a drug lead in medicinal chemistry. [ABSTRACT FROM AUTHOR]

Purpose: Large-scale farm management in China has developed rapidly in recent years. Large-scale farmers face substantial operating risks, requiring extensive price risk management. However, the agricultural insurance and futures markets in China are incomplete. This study aims to analyze the price-risk-management behaviors of large-scale farmers under incomplete market conditions, with a focus on the interconnections between large scale farmers' subjective preferences (risk preferences, time preferences), liquidity constraints and their price risk management. Design/methodology/approach: The authors construct an analysis framework to reveal the impact of large-scale farmers' risk preferences, time preferences and liquidity conditions on their price-risk-management behaviors under incomplete market conditions. Using data from field surveys and subjective preference experiments involving 409 large-scale grain farmers in China, an empirical analysis was conducted using the bivariate probit model. Findings: The results show that risk-averse farmers will use risk transfer (such as contract farming) and risk diversification (such as multi-period sales) to avoid price risk. However, farmers subject to liquidity constraints and strong time preferences will not choose risk diversification, and the interaction between time preferences and liquidity constraints will strengthen this decision. The larger the farm-management scale, the greater the impact. Originality/value: The authors focus on rapidly developed large-scale farm management in China. Appropriate price risk management is required by large-scale farmers due to their substantial operating risks. Considering the incomplete conditions of agricultural insurance and futures markets, the results of this study will help identify behavioral characteristics of large-scale farmers and optimize their price-risk-management strategies, further stabilizing large-scale farm management. [ABSTRACT FROM AUTHOR]

Al content is of great importance for the evolution of the microstructure and compressive performance of the high‐Mn and high‐Al porous steels. Herein, Fe–Mn–Al porous steels with different Al contents are prepared via powder sintering at different temperatures. The results show that the main phases of the sintered samples at 640 °C are α‐Fe, α‐Mn, and Al, while a small amount of Fe2Al5 and Al8Mn5 exists in the samples with 15 and 20 wt% Al. At 1200 °C, the sintered sample with 5 wt% Al is composed of γ‐Fe, while those with 15 and 20 wt% Al are mainly α‐Fe. The length of Mn depletion region and porosity of the samples increase with the increase of Al content. The largest total and open porosity are observed in the 1200 °C‐sintered sample with 20 wt% Al, which are 63.1 and 60.7 vol%, respectively. The compressive strength decreases sharply from 770 MPa for the sample with 5 wt% Al to 7.5 MPa with 15 wt% Al, after sintering at 1200 °C. Ductile fracture occurs in the sample with 5 wt% Al, while brittle fracture is the main fracture mode in those with 15 and 20 wt% Al. [ABSTRACT FROM AUTHOR]

Car-following (CF) behavior is a fundamental of traffic flow modeling; it can be used for the virtual testing of connected and automated vehicles and the simulation of various types of traffic flow, such as free flow and traffic oscillation. Although existing CF models can replicate the free flow well, they are incapable of simulating complicated traffic oscillation, and it is difficult to strike a balance between accuracy and efficiency. This article investigates the error variation when the traffic oscillation is simulated by the intelligent driver model (IDM). Then, it divides the traffic oscillation into four phases (coasting, deceleration, acceleration, and stationary) by using the space headway of multiple steps. To simulate traffic oscillation between multiple human-driven vehicles, a dynamic transformation CF model is proposed, which includes the long-time prediction submodel [modified sequence-to-sequence (Seq2seq)] model, short-time prediction submodel (Transformer), and their dynamic transformation strategy]. The first submodel is utilized to simulate the coasting and stationary phases, while the second submodel is utilized to simulate the acceleration and deceleration phases. The results of experiments indicated that compared to K-nearest neighbors, IDM, and Seq2seq CF models, the dynamic transformation CF model reduces the trajectory error by 60.79–66.69% in microscopic traffic flow simulations, 7.71–29.91% in mesoscopic traffic flow simulations, and 1.59–18.26% in macroscopic traffic flow simulations. Moreover, the runtime of the dynamic transformation CF model (Inference) decreased by 14.43–66.17% when simulating the large-scale traffic flow. [ABSTRACT FROM AUTHOR]

Ground test for space berthing and docking mechanism plays crucial roles in their stable operation in orbit. In this paper, a hardware-in-the-loop based ground test system is presented for economic reliability test for space berthing and docking mechanism of small spacecraft. In the system, the support and adapter unit is employed to fix the active part of the berthing and docking mechanism, and the end-effector of the manipulator connects the passive part. The manipulator is driven according to the relative motion calculated by the hardware-in-the-loop model after gravity compensation, to simulate motion of space berthing and docking mechanism in space. A Smith predictor is introduced for control system delays compensation. A berthing and docking mechanism was employed in the experiment to evaluate the performance of the test system. The results validated the effectiveness of the test system. Since only one manipulator is exploited in the test system, compared with existing systems using two manipulators, the system cost can be greatly decreased. [ABSTRACT FROM AUTHOR]

The historical route travel time data contain a few outliers composed of subpopulations signifying unusual traffic conditions. These outliers compose the right tail of the distribution against the central part occupied by most normal travel times dominating usual traffic conditions likely to be composed of subpopulations. The diverse subpopulations of two types of travel times result in the structural random changes of distribution shapes. This creates the problem for fixing the boundary between the two types of travel times. To address the problem, an adaptive Gaussian mixture model was formulated with two types of components, and an algorithm was put forward to determine the critical number of components in an iterative manner by adapting two types of components to provide an adequate fit to the two parts of the distribution respectively. The determined two types of components can not only fix the boundary to identify outliers reasonably, but also quantify the latent subpopulations of two types of route travel times. Thus, the route‐level travel time variability can be measured under usual/unusual traffic conditions. Two kinds of data were used to illustrate the good effects on the identification of outliers and to demonstrate the vital role of outliers in the measure of variation of route travel time. [ABSTRACT FROM AUTHOR]

Cingulin (CGN) is a cytoskeleton‐associated protein localized at the apical junctions of epithelial cells. CGN interacts with major cytoskeletal filaments and regulates RhoA activity. However, physiological roles of CGN in development and human diseases are currently unknown. Here, we report a multi‐generation family presenting with autosomal dominant non‐syndromic hearing loss (ADNSHL) that co‐segregates with a CGN heterozygous truncating variant, c.3330delG (p.Leu1110Leufs*17). CGN is normally expressed at the apical cell junctions of the organ of Corti, with enriched localization at hair cell cuticular plates and circumferential belts. In mice, the putative disease‐causing mutation results in reduced expression and abnormal subcellular localization of the CGN protein, abolishes its actin polymerization activity, and impairs the normal morphology of hair cell cuticular plates and hair bundles. Hair cell‐specific Cgn knockout leads to high‐frequency hearing loss. Importantly, Cgn mutation knockin mice display noise‐sensitive, progressive hearing loss and outer hair cell degeneration. In summary, we identify CGN c.3330delG as a pathogenic variant for ADNSHL and reveal essential roles of CGN in the maintenance of cochlear hair cell structures and auditory function. Synopsis: The tight junction‐related protein Cingulin (CGN) is a novel deafness gene required for maintenance of cochlear hair cell cuticular plates and hair bundles. A frameshift mutation in CGN results in progressive hearing loss in human and mouse model. A frameshift mutation in CGN co‐segregates with autosomal dominant non‐syndromic hearing loss in a Chinese family.CGN is enriched at the apical cuticular plates and circumferential belts of cochlear hair cells.CGN mutation impairs its expression and localization, alters the morphology of hair cell cuticular plates and hair bundles.CGN mutation results in progressive and noise‐sensitive hearing loss in the disease mouse model. [ABSTRACT FROM AUTHOR]

Macrophage is a critical regulator in wound healing and scar formation, and SIRT1 is related to macrophage activation and polarization, while the specific mechanism is still unclear. To explore the specific effects of SIRT1 in scarring, we established a skin incision mouse model and LPS-induced inflammation cell model. The expression of SIRT1 in tissue and macrophage was detected, and the level of SIRT1 was changed to observe the downstream effects. LPS-induced macrophages with or without SIRT1 deficiency were used for TMT-based quantitative proteomic analysis. SIRT1 was suppressed in scar while increased in macrophages of scar tissue. And macrophages were proven to be necessary for wound healing. In the early stage of wound healing, knockout of SIRT1 in macrophage could greatly strengthen inflammation and finally promote scarring. NADH-related activities and oxidoreductase activities were differentially expressed in TMT-based quantitative proteomic analysis. We confirmed that ROS production and NOX2 level were elevated after LPS stimulation while the Nrf2 pathway and the downstream proteins, such as Nqo-1 and HO-1, were suppressed. In contrast, the suppression of SIRT1 strengthened this trend. The NF-κB pathway was remarkably activated compared with the control group. Insufficient increase of SIRT1 in macrophage leads to over activated oxidative stress and activates NF-κB pathways, which then promotes inflammation in wound healing and scarring. Further increasing SIRT1 in macrophages could be a promising method to alleviate scarring. Key messages: SIRT1 was suppressed in scar while increased in macrophages of scar tissue. Inhibition of SIRT1 in macrophage leads to further activated oxidative stress. SIRT1 is negatively related to oxidative stress in macrophage. The elevation of SIRT1 in macrophage is insufficient during scarring. [ABSTRACT FROM AUTHOR]

The scheduling problems of distributed production have received increasing attention in recent years. To coordinate the scheduling of production and transportation, we propose a novel distributed assembly permutation flowshop scheduling problem with flexible assembly and batch delivery (DAPFSP-FABD). The objective is to minimise the total cost of delivery and tardiness. Given that the problem involves six successive decisions, several batch allocation strategies for both production and distribution are proposed, and four neighbourhood structures are designed to search for the best batch sequence, batch allocation, product sequence, and job sequence. Seven algorithms, including four heuristics, a variable neighbourhood descent (VND) algorithm, and two iterated greedy (IG) algorithms, are proposed. In the IG, two types of destruction and construction operations are present to facilitate escape from local optima, thereby resulting in two IG algorithms: IG_desP and IG_desJ. Finally, extensive computational and statistical experiments are carried out. The results show that the proposed batch allocation strategies can improve the solution significantly. Additionally, the heuristics can obtain reasonable solutions in a short time, while IG_desJ performs the best regarding the quality of solutions. This study can help cross-regional companies coordinate the scheduling of production and transportation and enhance the performance of supply chains. [ABSTRACT FROM AUTHOR]

Vitamin B3 is an essential vitamin that the human body cannot synthesize in sufficient amounts. Thus, it must be supplemented through food and medicine. The vitamin B3 content of foods and drugs must therefore be determined to identify its main sources. However, vitamin B3 is a strongly polar substance, and its detection in complex actual samples has certain challenges. In this study, a double column tandem method was established for the determination of vitamin B3 in fruit and drug samples. The chromatographic retention of the vitamin was investigated using different chromatographic single-column and double-column tandem models. The best separation and determination effect was obtained under a dual-column tandem mode consisting of a C18 short column and a HILIC column. The chromatographic conditions for vitamin B3 detection were as follows: 10:90 (v:v) acetonitrile–0.1% acetic acid water solution mobile phase, 30 ℃ column temperature, 0.4 mL/min flow rate, and 262 nm detection wavelength. This method was used to detect vitamin B3 in three fruits and two drugs. The detected content ranged between 1.50×102 and 1.35×103 mg/kg, and the recovery for spiked samples was 85–102%. This high performance liquid chromatography with two columns tandem can effectively determine vitamin B3. [ABSTRACT FROM AUTHOR]

Safety helmet detection is a hot topic of research in the field of industrial safety for object detection technology. Existing object detection methods still face great challenges for the detection of small-scale safety helmet object. In this paper, we propose a safety helmet detection method based on the fusion of semantic guidance and feature selection. The method is able to consider the balance between detection performance and efficiency. First, a multi-scale non-local module is proposed to establish internal correlations between different scales of deep image features as well as to aggregate semantic context information to guide the information recovery of decoder network features. Then the feature selection fusion structure is proposed to adaptively select deep features and underlying key features for fusion to make up for the missing semantic and spatial detail information of the decoding network and improve the spatial location expression capability of the decoding network. Experimental analysis shows that the method in this paper has good detection performance on the expanded safety helmet wearing dataset with 5.12% improvement in mAP compared to the baseline method CenterNet, and 6.11% improvement in AP for the safety helmet object. [ABSTRACT FROM AUTHOR]

The rapid development of farmers' cooperatives in rural China cannot be separated from government support. To ensure the growth of farmers' cooperatives, the Chinese government uses it as a key performance indicator for its local institutions. Superficially, rural China's cooperative population and membership size witnessed rapid growth during the first decade after the Farmers' Specialized Cooperatives Law was enacted in 2007; however, such government intervention also leads to non‐standard phenomena due to market distortion. Using nationally representative survey data from 504 cooperatives in Jiangsu, Jilin, and Sichuan provinces, this paper provides a reliable estimate of the "shell cooperative" rate around 2014. The empirical study sheds further light on the role of government during the period of rapid but chaotic growth by a subset containing 241 marketing cooperatives. Results show that direct administrative intervention leads to the emergence of many shell cooperatives (approximately 37%). Further study also confirms that task‐oriented policy support is only positively associated with the nominal coverage ratio but has no significant relationship with cooperatives' function. This study provides new insights into the formation of shell cooperatives and suggests that direct administrative intervention may not be a good strategy for promoting the sustainable development of farmers' cooperatives. [ABSTRACT FROM AUTHOR]

Conventional fuel consumption prediction (FCP) models using neural networks usually adopt driving parameters, such as speed and acceleration, as the training input, leading to a low prediction accuracy and a poor correlation between fuel consumption and driving behavior. To address this issue, the present study introduced jerk (an acceleration derivative) as an important variable in the training input of four selected neural networks: long short-term memory (LSTM), recurrent neural network (RNN), nonlinear auto-regressive model with exogenous inputs (NARX), and generalized regression neural network (GRNN). Furthermore, the root-mean-square error (RMSE), relative error (RE), and coefficient of determination (R2) were used to evaluate the prediction performance of each model. The results from the comparison experiment show that the LSTM model outperforms the other three models. Specifically, the four selected neural network models exhibited an improved accuracy in fuel consumption prediction after the jerk was added as a new variable to the training input. LSTM exhibited the greatest improvement under the high-speed expressway scenario, in which the RMSE decreased by 14.3%, the RE decreased by 28.3%, and the R2 increased by 9.7%. [ABSTRACT FROM AUTHOR]

Located in Dunhuang, northwest China, the Mogao Grottoes are a cultural treasure of China and the world. However, after more than 2000 years of weathering and destruction, many murals faded and were damaged. This treasure of human art is in danger. Mural inpainting through deep learning can permanently preserve mural information. Therefore, a digital restoration method combining the Deformable Convolution (DCN), ECANet, ResNet and Cycle Generative Adversarial Network (CycleGAN) is proposed. We name it DC-CycleGAN. Compared with other image digital inpainting methods, the proposed DC-CycleGAN based mural image color inpainting method has better inpainting effects and higher model performance. Compared with the current repair network, the Frechet Inception Distance (FID) value and the two-image structural similarity metric (SSIM) value are increased by 52.61% and 7.08%, respectively. Image color inpainting of Dunhuang murals can not only protect and inherit Chinese culture, but also promote academic research and development in related fields. [ABSTRACT FROM AUTHOR]

This study discusses the mechanism of social trust and legal institutions and their impact on farmers' contract selection in the farmland transfer market from the perspective of contract governance. Using data from a survey of 128 villages in Heilongjiang, Henan, Zhejiang, and Sichuan provinces, this study empirically tests the impact of social trust and legal institutions on the binding force of contracts, and the proportions of paper and long-term contracts in the farmland transfer market. The results showcase, first, that improvement in social trust and legal institutions can strengthen the binding force of farmland contracts. The strength of legal institutions, as embodied in regulation files and execution, and of social trust, as embodied in village neighborhood relations and loan relations, have significant positive impacts on the binding force of contracts in the farmland transfer market. Second, the binding force of contracts positively impacts both paper and long-term contracts in the farmland transfer market. Whether contract execution or dispute resolution rates are selected as the proxy variables for the binding force of contracts, the stronger the contract binding force, the higher the proportion of both paper and long-term contracts in the farmland transfer market. Therefore, improving formal and informal social systems to enhance contractual binding force is of great importance in standardizing contracts and improving the efficiency of market resource allocation. [ABSTRACT FROM AUTHOR]

Degradation in farmland quality owing to overuse emphasizes the current need for the adoption of protective technologies to ensure food security and sustainable resource utilization. This study employs plot survey data from Heilongjiang, Henan, Zhejiang, and Sichuan provinces in China to investigate how the spatial attributes of transferred plots influence the current adoption of farmland protection methods, such as deep tillage and straw-returning. Findings reveal that larger or interconnected transferred plots significantly increase the likelihood of farmers adopting conservation tillage technologies. However, the influence of the plot's location on technology adoption varies among different plots. As the farmland transfer market expands, the spatial features of these plots emerge as critical determinants in the use of protective technologies. This underscores the pressing need for an integrated farmland transfer trading system and strengthened policy measures promoting land consolidation to foster widespread adoption of these conservation strategies. [ABSTRACT FROM AUTHOR]

Purpose: High dose rate (HDR) brachytherapy rapidly delivers dose to targets with steep dose gradients. This treatment method must adhere to prescribed treatment plans with high spatiotemporal accuracy and precision, as failure to do so may degrade clinical outcomes. One approach to achieving this goal is to develop imaging techniques to track HDR sources in vivo in reference to surrounding anatomy. This work investigates the feasibility of using an isocentric C‐arm x‐ray imager and tomosynthesis methods to track Ir‐192 HDR brachytherapy sources in vivo over time (4D). Methods: A tomosynthesis imaging workflow was proposed and its achievable source detectability, localization accuracy, and spatiotemporal resolution were investigated in silico. An anthropomorphic female XCAT phantom was modified to include a vaginal cylinder applicator and Ir‐192 HDR source (0.5 × 0.5 × 5.0 mm3), and the workflow was carried out using the MC‐GPU Monte Carlo image simulation platform. Source detectability was characterized using the reconstructed source signal‐difference‐to‐noise‐ratio (SDNR), localization accuracy by the absolute 3D error in its measured centroid location, and spatiotemporal resolution by the full‐width‐at‐half‐maximum (FWHM) of line profiles through the source in each spatial dimension considering a maximum C‐arm angular velocity of 30° per second. The dependence of these parameters on acquisition angular range (θtot = 0°–90°), number of views, angular increment between views (Δθ = 0°–15°), and volumetric constraints imposed in reconstruction was evaluated. Organ voxel doses were tallied to derive the workflow's attributable effective dose. Results: The HDR source was readily detected and its centroid was accurately localized with the proposed workflow and method (SDNR: 10–40, 3D error: 0–0.144 mm). Tradeoffs were demonstrated for various combinations of image acquisition parameters; namely, increasing the tomosynthesis acquisition angular range improved resolution in the depth‐encoded direction, for example from 2.5 mm to 1.2 mm between θtot = 30o and θtot = 90o, at the cost of increasing acquisition time from 1 to 3 s. The best‐performing acquisition parameters (θtot = 90o, Δθ = 1°) yielded no centroid localization error, and achieved submillimeter source resolution (0.57 × 1.21 × 5.04 mm3 apparent source dimensions, FWHM). The total effective dose for the workflow was 263 µSv for its required pre‐treatment imaging component and 7.59 µSv per mid‐treatment acquisition thereafter, which is comparable to common diagnostic radiology exams. Conclusions: A system and method for tracking HDR brachytherapy sources in vivo using C‐arm tomosynthesis was proposed and its performance investigated in silico. Tradeoffs in source conspicuity, localization accuracy, spatiotemporal resolution, and dose were determined. The results suggest this approach is feasible for localizing an Ir‐192 HDR source in vivo with submillimeter spatial resolution, 1–3 second temporal resolution and minimal additional dose burden. [ABSTRACT FROM AUTHOR]

Herein, a porous Fe–Al intermetallic material with ultrahigh open porosity is prepared via the multistep sintering process using a Fe–Al–Mn powder mixture as the raw material. The microstructure, phase composition, Mn sublimation, and open porosity of the sintered Fe–Al–Mn compacts are investigated. The compacts sintered at temperatures ≤700 °C are composed of the newly formed Fe/Mn‐based solid solutions, intermetallic phases, and the original elemental particles without the sublimation of Mn. The open porosities of the 650‐ and 700 °C‐sintered compacts are ≈39.83 and ≈47.19 vol%, respectively. After sintering at 1250 °C, Mn is rarely left in the porous body, and a single Fe–Al intermetallic phase with an even chemical composition is generated. The open porosity of the 1250 °C‐sintered compacts is ≈66.67 vol%, superior to other reported porous Fe–Al materials, and its strength is ≈22.5 MPa, which is competitive as well. This work may open a new strategy to design and optimize the high‐quality porous Fe–Al alloy in future. [ABSTRACT FROM AUTHOR]

The feeding and weighing of oxidizer agents is the key process of solid rocket motor propellant preparation, and its accuracy directly affects the burning performance of solid rocket motors. At present, the existing multi-batch feeding methods have the problem of low accuracy and high time consumption of the oxidizer agent. In this paper, an improved mass flow rate regulation method based on variable frequency control is proposed to improve accuracy and reduce time consumption. The nonlinear variation process of the mass flow rate during the opening and closing process of the air-operated pinch valve is analyzed. The periodic opening and closing frequency of the air-operated pinch valve is introduced to establish the mathematical model of the mass flow rate and frequency, and then, the model parameters are obtained through the discrete element method. The plan of the method of variable frequency regulation and the frequency parameters were determined using the multi-objective optimization method. The experiments are carried out, and the results show that compared to the existing multi-batch feeding method, optimized with the improved mass flow rate regulation methods based on the variable frequency control method, improved the feeding and weighing accuracy by 0.37% and reduced time consumption by 25.6%. [ABSTRACT FROM AUTHOR]