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Aims/BackgroundLiver abscess (LA) is a serious medical condition that predisposes patients to sepsis. However, predicting sepsis in LA patients has rarely been explored. This study employed univariate and multivariate logistic regression analyses to identify independent risk factors for sepsis, which would provide guidance for clinical diagnosis and treatment.MethodsA total of 122 patients with LA treated in Peking University People’s Hospital from 1 January 2016 to 31 October 2022 were recruited. Among the cases, 35 patients had sepsis (sepsis group) while the remaining 87 did not have sepsis (non-sepsis group). Clinical data were collected for all enrolled cases. Univariate analysis was performed to identify potential predictors, which were tested in multivariable logistic analysis to pinpoint the independent risk factors for sepsis in LA patients; these findings were utilized to develop a prediction model. Receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficacy of the prediction model. Informed consent to participate was obtained from the patients or their relatives.ResultsThe incidence of shivering in the sepsis group was significantly higher than that in the non-sepsis group (p< 0.05). Through the univariate analysis, it was found that the reduction in platelet count and prothrombin time activity and the elevation of glycosylated hemoglobin (HbAlc) and procalcitonin (PCT) were more significant in the sepsis group than in the non-sepsis group (p< 0.05). Multivariate logistic regression analysis revealed that PCT and HbAlc were independent risk predictors of sepsis in LA patients within the derivation cohort (p< 0.05).ConclusionElevated levels of HbAlc and PCT were independent risk factors for sepsis associated with LA. Patients with LA exhibiting elevated PCT levels demonstrated a 21% increased susceptibility to sepsis, and those with elevated HbAlc levels showed a 38% heightened risk for sepsis.

The impact-time-angle-control guidance (ITACG) law can control the impact time and angle in a wide range of applications. The previous ITACG research generally suffer from the accuracy under the time-varying velocity condition, or the axial acceleration is required under 3-D conditions. This article develops a four-stage guidance technique based on the Bezier curve, and it is effective in both 2-D and 3-D scenarios. First, we proved a proposition that the length of the two-stage Bezier trajectory increases monotonically with the initial flight path angle. Based on this, the four-stage guidance strategy is studied. The first stage is to adjust the impact angle in the line-of-sight coordinate system. The second stage is to change the heading angle to match the Bezier trajectory with the distance-to-go. The third stage tracks the Bezier trajectory generated in the second stage. The fourth stage uses the proportional navigation guidance law to realize the end-game guidance. The proposed strategy was extended to 3-D scenarios, where the algorithm performed well even under the influence of gravity and aerodynamic drag, achieving accurate impact angle and time with high robustness. Finally, the effectiveness of the proposed algorithm is verified through four sets of simulation experiments.

Aiming at the difficulties in setting process parameters and the low accuracy of process performance prediction in electrical discharge machining (EDM) hole machining of alumina ceramics, a novel EDM process performance prediction method based on eXtreme Gradient Boosting (XGBoost) is proposed in this study. The independent variables selected include processing polarity (P), interelectrode voltage (V), peak current (I), pulse frequency (F), pulse width (T), and tool electrode and workpiece electrode gap (W), while the dependent variables are tool electrode length loss (ELW), tool electrode volume loss (EVW), and material removal rate (MRR). An L18(21×  35) orthogonal test is designed to obtain training samples for model development. The accuracy of the prediction results for ELW, EVW, and MRR by the XGBoost model is found to be 1.7%, 2.5%, and 9.4%, respectively. These results show a significant improvement compared to the linear regression model, with an improvement of 41.7%, 62.5%, and 19.4%, respectively. Furthermore, when compared to the support vector regression (SVR) model, the XGBoost model also shows improvement of 23.6%, 5.9%, and 12.0% for ELW, EVW, and MRR prediction accuracy, respectively. These findings suggest that the proposed XGBoost-based method is effective and accurate in predicting the performance of alumina ceramic EDM processes. In addition, the third generation nondominated sorting genetic algorithm (NSGA-III) is utilized to optimize the process parameters with both single objective and multi-objective approaches. The optimal parameters for ELW, EVW, and MRR, including the selection of machining polarity, interelectrode voltage, peak current, pulse frequency, pulse width, and tool electrode and workpiece electrode gap are obtained through the Pareto frontier of the three comprehensive optima. The average relative errors between the experimental results and the optimized results are found to be 6.46%, 10.45%, and 9.58% for ELW, EVW, and MRR, respectively, indicating the accuracy and effectiveness of the optimized results.

When electric discharge machining (EDM) is used to process Al2O3-TiC ceramics, it is difficult to obtain an optimal production efficiency and tool wear rate. As the target demand of processing largely relies on the process parameters, it is pertinent to conduct an in-depth study on the process parameters. In this study, the effects of certain technological parameters on the properties of EDM Al2O3-TiC ceramic materials were assessed and a mixed 2–3 horizontal orthogonal test method was designed. The rules of machining polarity, inter-electrode voltage, peak current, gain, pulse frequency, pulse width, inter-electrode gap and electrode length loss, electrode profile loss, material removal rate were studied. The effect of process parameters on process performance was determined by range analysis. In addition, the analysis of variance revealed that machining polarity has a significant effect on the electrode loss while the electrode voltage and peak current significantly influenced the material removal rate. The optimal parameter combination was determined as negative machining polarity, 130 V inter-electrode voltage, 50 A peak current, 45 gain, 150 Hz pulse frequency, 1.0 μs pulse width, and 40 μs inter-electrode gap.

ABSTRACTObjectivesThe aim of this study is to monitor, identify, and compare the adverse events (AEs) related to tenecteplase and alteplase, with the objective of exploring the potential safety of tenecteplase for acute ischemic stroke (AIS) and guiding its use to enhance patient safety.MethodsIn order to evaluate the disproportionality of AEs associated with tenecteplase and alteplase in real-world data, four algorithms (ROR, PRR, BCPNN, EBGM) were utilized as measures to detect signals of AEs related to both drugs. Subsequently, Breslow-Day statistical analysis was applied to compare the RORs of the main system organ classes (SOCs) and key preferred terms (PTs) between tenecteplase and alteplase.ResultsA statistical analysis was performed utilizing data gleaned from the Food and Drug Administration Adverse Event Reporting System (FAERS) database, encompassing 19,514,140 case reports from 2004Q1 to 2023Q1. There were 1,004 cases where tenecteplase was reported as the primary suspected (PS) and 2,363 tenecteplase-related adverse drug reactions (ADRs) at the PTs level were identified, the two data of alteplase were 10,945 and 25,266, respectively. The occurrence of drug-induced ADRs was analyzed across 27 organ systems, The analysis revealed several expected ADRs, such as Haemorrhage, Hypersensitivity which were consistent with the two drug-labels. It is of note that the signal strengths of ‘death,’ ‘ventricular fibrillation,’ ‘cardiogenic shock’ and ‘pneumonia aspiration’ at the PT level were markedly higher for tenecteplase than for alteplase, whereas the signal strength of ‘angioedema’ at the PT level was significantly higher for alteplase in comparison to tenecteplase. Additionally, unexpected significant ADRs associated with ocular adverse reactions and pneumonia aspiration at the PT level were identified, indicating potential AEs not currently mentioned in the drug instructions.ConclusionThis study identified and compared signals of ADRs associated with tenecteplase and alteplase, although tenecteplase is as effective as alteplase and has advantages such as ease of use and affordability, it cannot replace alteplase in the treatment of AIS until its safety profile is fully recognized. Additionally, previously unreported ocular ADRs and pneumonia were identified, providing valuable insights into the relationship between ADRs and the use of these thrombolytic drugs. These findings underscore the importance of continuous monitoring and effective detection of AEs to ultimately enhance the safety of AIS patients undergoing thrombolytic therapy.

An infrared evanescent wave sensor was developed to accurately detect ascorbic acid (vitamin C) in food and drugs. The sensor was fabricated by tapering and bending of As2S3 infrared fibers. Due to the broad transmission range (5000–1500 cm−1) of the infrared fibers, covering the characteristic absorption peak of ascorbic acid (C = O at 1760 cm−1 and C = C at 1690 cm−1), the sensor is capable of accurately identifying and detecting the concentration of ascorbic acid. Experimental results demonstrated that a conically tapered fiber sensor with a waist diameter of 50 μm, waist length of 30 mm, and a radius of 2 mm achieved a maximum sensitivity of 0.1257 (a.u./(mg·ml−1)) and a limit of detection (LoD) of 0.917 mg/ml. Furthermore, the application of this fiber sensor in various vitamin C-containing tablets and juices validated its high accuracy and minimal measurement deviation (as low as 0.19 mg/ml). Compared to traditional detection methods, the sensor not only provides a faster and cost-effective solution to identify the substance but also maintains high accuracy. It offers a new approach to quantitative and qualitative analysis of food and drugs.

In outdoor environments, ground segmentation is an important pre-processing task for the local environment perception of autonomous vehicle platforms and is the basis for obstacle detection, classification, and path planning. However, existing ground segmentation algorithms primarily focus on dense point cloud data and often struggle to achieve satisfactory accuracy when applied to sparse point cloud data. In light of this challenge, we propose a novel ground segmentation method, LR-Seg, specifically tailored for sparse point cloud data. Our method aims to address the limitations observed in previous approaches and provide improved performance, particularly on sparse point cloud data. To achieve faster processing speed, the method first divides the original point cloud into sub-regions of different sizes based on the distribution characteristics of the sparse point cloud data in the XOY plane. Then, the point clouds are appropriately assigned, and most of the non-ground points in each sub-region are removed using the PCA plane fitting method. Finally, the point cloud geometric feature information within each sub-region is used to reduce over-segmentation. The experimental results show that our method can perform ground segmentation quickly on flat and slope road sections, and the average F1 score can be maintained above 90% with an average time of 3 ms, which is suitable for various road environments.

Developing physically assistive robots capable of dressing assistance has the potential to significantly improve the lives of the elderly and disabled population. However, most robotics dressing strategies considered a single robot only, which greatly limited the performance of the dressing assistance. In fact, healthcare professionals perform the task bimanually. Inspired by them, we propose a bimanual cooperative scheme for robotic dressing assistance. In the scheme, an interactive robot joins hands with the human thus supporting/guiding the human in the dressing process while the dressing robot performs the dressing task. We identify a key feature: the elbow angle that affects the dressing action and propose an optimal strategy for the interactive robot using the feature. A dressing coordinate based on the posture of the arm is defined to better encode the dressing policy. We validate the interactive dressing scheme with extensive experiments and also an ablation study.

In this paper, the design, manufacture and testing of an origami protective shield with a supporting frame structure are presented. It consists of an origami shield surface and a deployable supporting frame structure that needs to be portable and sufficiently stiff. First, for the design of the shield surface, a three-stage origami crease pattern is developed to reduce the shield size in the folded state. The shield surface consists of several stiff modular panels and layered with flexible fabric. The modular panels are made of a multi-layer composite where a ceramic layer is made of small pieces to improve durability as those small pieces enable restriction of crack propagation. Then, the supporting frame structure is designed as a chain-of-bars structure in order to fold into a highly compact state as a bundle of bars and deploy in sequence. Thus, a feature-driven topology structural optimization method preserving component sequence is developed where the inter-dependence of sub-structures is taken into account. A bar with semi-circular ends is used as a basic design feature. The positions of the bar’s end points are treated as design variables and the width of the bars is kept constant. Then, a constraint on the total length of the chain of bars is introduced. Finally, the modular panels made of multi-layer composite and the full-scale prototype of the origami shield are fabricated and tested to verify the bullet-proof performance.

The use of deep neural networks has revolutionised object tracking tasks, and Siamese trackers have emerged as a prominent technique for this purpose. Existing Siamese trackers use a fixed template or template updating technique, but it is prone to overfitting, lacks the capacity to exploit global temporal sequences, and cannot utilise multi‐layer features. As a result, it is challenging to deal with dramatic appearance changes in complicated scenarios. Siamese trackers also struggle to learn background information, which impairs their discriminative ability. Hence, two transformer‐based modules, the Spatio‐Temporal Fusion (ST) module and the Discriminative Enhancement (DE) module, are proposed to improve the performance of Siamese trackers. The ST module leverages cross‐attention to accumulate global temporal cues and generates an attention matrix with ST similarity to enhance the template's adaptability to changes in target appearance. The DE module associates semantically similar points from the template and search area, thereby generating a learnable discriminative mask to enhance the discriminative ability of the Siamese trackers. In addition, a Multi‐Layer ST module (ST + ML) was constructed, which can be integrated into Siamese trackers based on multi‐layer cross‐correlation for further improvement. The authors evaluate the proposed modules on four public datasets and show comparative performance compared to existing Siamese trackers.

Switched-capacitor converters (SCCs) have been developed for decades and applied successfully in many applications due to the merits of high-power density, high efficiency, and low cost. In order to explore as many as possible SCC topologies, in this article, we propose a generalized topology that can cover the existing schemes of the SCCs and enable the automatic generation of brand-new structures. Within this framework, topological interconnections or electric ratings for the power components can be determined. With more topology options with different characteristics, it is beneficial for engineers to optimize design after comprehensive comparison. Besides, all the derived topologies are verified based on the parallel simulation and experiment. The selected optimum topology is presented in detail to demonstrate its advantages. It is expected to reveal the basic laws for the SCCs optimization, especially in complicated cases, such as the large number of power elements.

In the present work, an advanced plasticity model is established to character the anisotropic and asymmetric yielding and hardening behaviors of magnesium alloy. This model is comprised of the stress invariant-based Yoon2014 yield function, non-associated flow rule and yield surface-interpolation method in the description of distortional hardening. For effectively numerical implementation of the plasticity model, a semi-explicit stress integration scheme based on the return mapping method is provided. The implemented model is then applied to simulate the inhomogeneous deformations during three-point bending of an extruded magnesium alloy. Simulation results show that there are three distinct zones in the bent plate according to its deformation patterns, i.e., the compressive inner zone, the tensile outer zone, and the central zone which experiences tensile, then compressive and again tensile deformations. Compared to the counterpart Hill48 function, Yoon2014 function has better performance in simulating the inhomogeneous deformations of magnesium alloy.

High-resolution laser additive manufacturing (LAM) significantly releases design freedom, promoting the development of topology optimization (TO) and advancing structural design methods. In order to fully take advantage of voxelated forming methods and establish the quantitative relationship between the mechanical properties of printing components and multiple process factors (laser- and process- parameters), the concurrent optimization design method based on LAM should cover the process-performance relationship. This study proposes a novel artificial intelligence-facilitated TO method for LAM to concurrently design microscale material property and macroscale structural topology of 3D components by adopting heuristic and gradient-based algorithms. The process–structure–property relationship of selective laser sintering is established by the back propagation neural network, and it is integrated into the TO algorithm for providing a systematic design scheme of structural topology and process parameter. Compared with the classical optimization method, numerical examples show that this method is able to improve the mechanical performance of the macrostructure significantly. In addition, the collaborative design method is able to be widely applied for complex functional part design and optimization, as well as case studies on artificial intelligence-facilitated product evaluation.

The main objective of this work is to characterise the prop-rotor of V/STOL (Vertical/short-take-off and landing) aircraft in various operating conditions, especially in transition flight. To this end, a BET (Blade element theory) based analytic model/tool with a low computational cost has been built to predict the aerodynamic performance of the prop-rotor. This BET-based model can predict prop-rotor thrust, torque, normal force, and yaw moment as well as their corresponding 1P load (once-per-revolution load on the single blade) regarding oblique flow. This model is verified and shows good consistency with the wind tunnel experimental data and CFD simulation results. Furthermore, by using this BET model/tool, the effects of axial advance ratio, tilt angle and collective pitch angle on these component loads on the prop-rotor are explored. Moreover, the effects of these variables on the vibratory amplitudes of the 1P load on the single blade and 3P harmonic loads on the prop-rotor due to inclined flow are presented as well.

The purpose of this paper is to present a shape preserving topology optimization method to prevent the adverse effects of the mechanical deformation on the Radar Cross Section (RCS). The optimization will suppress the variation of RCS on the perfect conductor surface by structural design. On the one hand, the physical optics method is utilized to calculate the structural RCS, which is based on the surface displacement field obtained from the finite element analysis of the structure. The corresponding design sensitivities of topology optimization are derived analytically and solved by the adjoint method. On the other hand, the RCS variation and mechanical performance are taken into account simultaneously by extending a standard compliance-based topology optimization model. Two optimization formulations are discussed in an illustrative example, where the influences of upper limits of the compliance and the RCS variation are considered. Two more examples are further tested to show the ability and validity of the proposed optimization method.

This paper presents a topology optimization approach for the surface flows on variable design domains. Via this approach, the matching between the pattern of a surface flow and the 2-manifold used to define the pattern can be optimized, where the 2-manifold is implicitly defined on another fixed 2-manifold named as the base manifold. The fiber bundle topology optimization approach is developed based on the description of the topological structure of the surface flow by using the differential geometry concept of the fiber bundle. The material distribution method is used to achieve the evolution of the pattern of the surface flow. The evolution of the implicit 2-manifold is realized via a homeomorphous map. The design variable of the pattern of the surface flow and that of the implicit 2-manifold are regularized by two sequentially implemented surface-PDE filters. The two surface-PDE filters are coupled, because they are defined on the implicit 2-manifold and base manifold, respectively. The surface Navier-Stokes equations, defined on the implicit 2-manifold, are used to describe the surface flow. The fiber bundle topology optimization problem is analyzed using the continuous adjoint method implemented on the first-order Sobolev space. Several numerical examples have been provided to demonstrate this approach, where the combination of the viscous dissipation and pressure drop is used as the design objective.

Multiple-laser and matrix-heat source exposures are emerging systems for powder bed fusion (PBF) to improve productivity. However, the newly available process planning such as scanning patterns may induce different transient thermal distribution and distortion in selective laser sintering or melting process. This work proposes novel scanning strategies for digital process planning in polymer PBF, including contour lines and novel inner scanning patterns. It provides detailed predictions of temperature gradient and distortion upon processing and the shrinkage/warping of printed components. Novel scanning strategies of subarea-sequential, subarea-parallel, and matrix-randomized scanning strategies are addressed here. The thermal-recrystallization-mechanical model for laser sintering is employed to reveal the underlying mechanism upon different process planning strategies. Thereafter, the matrix-randomized scanning mode can obviously reduce the building time of each layer into millisecond level. The advanced subarea-sequential scanning pattern is able to minimize shrinkage and warping. The systemic framework of digital process planning is provided through numerical investigation of scanning strategies and multi-physics modeling, which are an important aspect in the advanced digital twin of additive manufacturing.

The purpose of this paper is to demonstrate an integrated optimization scheme for a solar-powered drone structure. Consider a primary beam in the wing of large aspect ratio, where 100 lithium batteries are assembled. In the proposed integrated optimization, the batteries are considered here as parts of the load-carrying structure. The corresponding mechanical behaviors are simulated in the structural design and described with super-elements. The batteries layout and the structural topology are then introduced as mixed design variables and optimized simultaneously to achieve an accordant load-carrying path. Geometrical nonlinearity is considered due to the large deformation. Different periodic structural configurations are tested in the optimization in order to meet the structural manufacturing and assembly convenience. The optimized designs are rebuilt and tested in different load cases. Maintaining the same structural weight, the global mechanical performances are improved greatly compared with the initial design.

By integrating topology optimization and lattice-based optimization, a novel multi-scale design method is proposed to create solid-lattice hybrid structures and thus to improve the mechanical performance as well as reduce the structural weight. To achieve this purpose, a two-step procedure is developed to design and optimize the innovative structures. Initially, the classical topology optimization is utilized to find the optimal material layout and primary load carrying paths. Afterwards, the solid-lattice hybrid structures are reconstructed using the finite element mesh based modeling method. And lattice-based optimization is performed to obtain the optimal cross-section area of the lattice structures. Finally, two typical aerospace structures are optimized to demonstrate the effectiveness of the proposed optimization framework. The numerical results are quite encouraging since the solid-lattice hybrid structures obtained by the presented approach show remarkably improved performance when compared with traditional designs.

The paper presented topology optimization of 2D and 3D Nanofluid-Cooled Heat Sink (NCHS). The flow and heat transfer problem in the NCHS was treated as a single-phase nanofluid based convective heat transfer model. The temperature-dependent fluid properties were taken into account in the model due to the strong temperature-dependent features of nanofluids. An average temperature minimum problem was studied subject to the fluid area and energy dissipation constraints by using the density method. In the method, the design variable is updated according to the gradient information obtained by an adjoint based sensitivity analysis process. The effects of the energy dissipation constraint, temperature-dependent fluid properties and nanofluid characteristics on optimal configurations of NCHS were numerically investigated with following conclusions. Firstly, branched flow channels in the optimal configuration increased with the rise of the allowed energy dissipation. Secondly, temperature-dependent fluid properties were significant for obtaining the appropriate optimal results with best cooling performance. Thirdly, heat transfer performances of optimal configurations were enhanced by reducing the nanoparticle diameter or increasing the nanoparticle volume fraction. Fourthly, the optimal configuration for nanofluid had better cooling performance than that for its base fluid.

For detecting and tracking directly from image observations without the need for any separate target detection, a novel multi-cell tracking method based on multi-Bernoulli filter using fractal measurement model is proposed. The Hurst coefficient is one of most important parameters in application of fractal theory. It is used to construct fractal measurement model in this paper, which is estimated by the rescaled range analysis method. The fractal measurement model can offer two advantages for multi-Bernoulli filter. The one is that the measurement model is easy to establish, the other is that the computation of likelihood function is simple. Experiment results show that our proposed method could achieve an accurate and joint estimate of the number of cells and their individual states especially in the case of the number of cell population varying and the cellular morphology changing. Furthermore, it shows equivalent tracking accuracy against other tracking methods.

Topology optimization was developed as an advanced structural design methodology to generate innovative lightweight and high-performance configurations that are difficult to obtain with conventional ideas. Additive manufacturing is an advanced manufacturing technique building as-designed structures via layer-by-layer joining material, providing an alternative pattern for complex components. The integration of topology optimization and additive manufacturing can make the most of their advantages and potentials, and has wide application prospects in modern manufacturing. This article reviews the main content and applications of the research on the integration of topology optimization and additive manufacturing in recent years, including multi-scale or hierarchical structural optimization design and topology optimization considering additive manufacturing constraints. Meanwhile, some challenges of structural design approaches for additive manufacturing are discussed, such as the performance characterization and scale effects of additively manufactured lattice structures, the anisotropy and fatigue performance of additively manufactured material, and additively manufactured functionally graded material issues, etc. It is shown that in the research of topology optimization for additive manufacturing, the integration of material, structure, process and performance is important to pursue high-performance, multi-functional and lightweight production. This article provides a reference for further related research and aerospace applications.

Smart morphing wing, which is equipped with smart materials and able to change structural geometry adaptively, can further improve aerodynamic efficiency of aircraft. This paper presents a new integrated layout and topology optimization design for morphing wing driven by shape memory alloys (SMAs). By simultaneously optimizing the layout of smart actuators and topology of wing substrate, the ultimately determined configuration can achieve smooth, continuous and accurate geometric shape changes. In addition, aerodynamic analysis is carried out to compare smart morphing wing with traditional hinged airfoil. Finally, the optimized smart wing structure is constructed and tested to demonstrate and verify the morphing functionality. Application setbacks are also pointed out for further investigation.

Tailsitter unmanned aerial vehicles are utilized extensively nowadays since they merge advantages of both fixed-wing unmanned aerial vehicles and rotary-wing unmanned aerial vehicles. However, their attitude control suffers from unknown nonlinearities and disturbances due to the wide flight envelope. To solve the problems, a robust attitude controller based on a newly designed flying-wing tailsitter is proposed in this paper. By employing the angular acceleration feedback to compensate unmodeled dynamics, the proportional incremental nonlinear dynamic inversion control law is first developed. The proportional incremental nonlinear dynamic inversion strengthens the conventional nominal gain incremental nonlinear dynamic inversion with a proportional term to reflect the change of the angular acceleration more directly. Therefore, the tailsitter has a quicker response and performs better in suppressing model uncertainties and external disturbances. Since the angular acceleration is difficult to measure in practice, an angular acceleration estimation method is then established to provide accurate signals for the proportional incremental nonlinear dynamic inversion. The signals are derived as complementary results of model prediction method and direct differential method. Theoretical analysis and systematic simulations are conducted to corroborate the effectiveness of the developed estimation method as well as the robustness of the proposed controller.

Misfit-layered Ca3Co4O9as a p-type semiconductor is difficult to commercialize because of its relatively poor performance. Here, Ca2.7–xLaxAg0.3Co4O9/Ag composites prepared by spark plasma sintering were systematically investigated in terms of La3+dopant levels and nano-sized Ag compacts. Multiscale microstructures of stacking fault, dislocation, and oxygen vacancy-linked defects could be recognized as an effective strategy for tuning the transport of charge carriers and phonon scattering. An increasing concentration of charge carriers was caused by the introduction of nano-sized Ag particles at the grain boundary. The multiscale structural defects served as phonon scattering centers to reduce the thermal conductivity. Finally, the Ca2.61La0.09Ag0.3Co4O9/Ag sample exhibited a maximum ZTof 0.35 at 1073 K. The results suggest that the interplay of structural defects provides an impetus for a huge improvement in thermoelectric performance.

Flexible chiral honeycomb cores generally exhibit nonlinear elastic properties due to large geometric deformation. The effective elastic moduli and Poisson’s ratio typically vary with an increase in deformation. Here, the size and shape optimization of the chiral hexagonal honeycombs was performed to keep the Young’s moduli and Poisson's ratio unchanged under large deformations. The size of the honeycomb unit cell and the position coordinates of the key points were defined simultaneously as design variables. The equivalent Young's modulus and Poisson’s ratio of chiral honeycombs were calculated through geometric nonlinear analysis. The objective was to minimize the allowable tolerance between the prescribed and actual properties within the range of the target strain. A genetic algorithm was then adopted. The optimal results demonstrate that the chiral honeycombs can maintain effective elastic properties that do not vary under large deformation. These results are meaningful to morphing aircraft designs.

The purpose of this paper is to present a novel topology optimization approach to control precisely the output loads under static loads and harmonic excitations. We introduce the Artificial Bar Element (ABE) at the designated output positions, where the output loads are equivalently measured and constrained with the nodal displacements of ABE. Optimization model is then formulated considering the output load constraints as well as the minimization of strain energy and dynamic displacement responses respectively under the static and dynamic conditions. The influences of the ABEs stiffness, different material usages of the design domain, widths of the output loads constraint intervals and variation ratios of output loads are discussed in detail. The proposed method is verified with several numerical examples with clear and reasonable load transfer paths.

This paper presents an extended topology optimization approach considering joint load constraints with geo-metrical nonlinearity in design of assembled structures. The geometrical nonlinearity is firstly included to reflect the structural response and the joint load distribution under large deformation. To avoid a failure of fastener joints, topology optimization is then carried out to minimize the structural end compliance in the equilibrium state while controlling joint loads intensities over fasteners. During nonlinear analysis and optimization, a novel implementation of adjoint vector sensitivity analysis along with super element condensation is introduced to address numerical instability issues. The degrees of freedom of weak regions are condensed so that their influences are excluded from the iterative Newton-Raphson (NR) solution. Numerical examples are presented to validate the efficiency and robustness of the proposed method. The effects of joint load constraints and geometrical nonlinearity are highlighted by comparing numerical optimization results.

Time-optimal (TO) control is promising in many fields since it ensures to accomplish a task in minimum time. However, its direct application may cause oscillations or even limit cycles due to the restriction of the sampling period, state measurement accuracy and so on. To solve these problems, a compound proximate time-optimal (PTO) control law is proposed by considering system bandwidth and saturation in this paper. With the help of the phase plane, the switching zone and linear zone of the PTO control law are constructed. When the system state lies outside of both switching and linear zones, the bang-bang optimal scheme is adopted to achieve rapidity. And when the system state enters the switching zone, the bang-bang suboptimal scheme is developed to avoid oscillations or even limit cycles. Proportional-derivative (PD) scheme is applied in the linear zone to obtain local asymptotic stability around the origin. Numerical simulations are carried out to corroborate the advantages of the PTO control law.

Secondary phase Bi2O3, which could promote the sintering densification of ceramics, was used to prepare (Ca0.85Ag0.1La0.05)3Co4O9thermoelectric ceramics. The mechanism of liquid−phase sintering process revealed that the diffusion rate of particles originated from the dissolved–precipitated processing for samples and suppressed the coarsening of grain growth. The effects of Bi2O3on the microstructure and thermoelectric properties were investigated. The results showed that the relative density of samples increased from 92.1% to 95.5% through the liquid−phase sintering mechanism. The band gaps were tuned and it had the profound impact on the transport of charge carriers. Electrical resistivity decreased while Seebeck coefficient increased from 110 μV/K to 190 μV/K with increasing Bi2O3. Furthermore, the peak ZTvalue of 0.25 for 6 wt% Bi2O3sample at 1073 K was obtained, resulting from low thermal conductivity of 0.92 W/(m·K). It suggests that Bi2O3additive can dramatically improve the thermoelectric properties of Ca3Co4O9.

The lifecycle of automotive electronic switches is relatively short. The products exhibit a high level of customization. The assembly production demonstrates characteristics of high variety in small batch sizes and frequent production changeovers. Traditional delivery methods often result in issues such as excessive line-side inventory, residual material accumulation, and material management disorder. In this article, a reverse planning approach is adopted to logically analyze from the material demand side to the supply side. By integrating the concepts of push and pull production control, a just-in-time delivery sequencing model is designed. Furthermore, the model was validated based on production data from Company A. The aim is to reduce line-side inventory, improve delivery accuracy, shift from ensuring material supply to efficient supply, and enhance inventory turnover. The purpose of improvement is to achieve a 20% reduction in floor space occupied by the assembly workshop at the same production level, decrease line-side inventory (including raw materials, work-in-progress, and finished goods) by 30%, increase inventory turnover by 20%, reduce the working hours required for logistics personnel by 30%, and improve the accuracy of material deployment (including quantity and part number) from 92% to 99%.

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This work evaluates the viability of a cutting-edge flexible wing prototype actuated by Shape Memory Alloy (SMA) wire actuators. Such flexible wings have garnered significant interest for their potential to enhance aerodynamic efficiency by mitigating noise and delaying flow separation. SMA actuators are particularly advantageous due to their superior power-to-weight ratio and adaptive response, making them increasingly favored in morphing aircraft applications. Our methodology begins with a detailed delineation of the fishbone camber morphing wing rib structure, followed by the construction of a multi-mode morphing wing segment through 3D-printed rib assembly. Comprehensive testing of the SMA wire actuators’ actuation capacity and efficiency was conducted to establish their operational parameters. Subsequent experimental analyses focused on the bi-directional and reciprocating morphing performance of the fishbone wing rib, which incorporates SMA wires on the upper and lower sides. These experiments confirmed the segment’s multi-mode morphing abilities. Aerodynamic assessments have demonstrated that our design substantially improves the Lift-to-Drag ratio (L/D) when compared to conventional rigid wings. Finally, two phases of flight tests demonstrated the feasibility of SMA as an aircraft actuator and the validity of flexible wing structures to adjust the aircraft attitude, respectively.

In light of increasing consumer purchasing power and the growing diversification of demands, the emergence of small-batch and multi-variety requirements has posed substantial challenges to production manufacturing, particularly in on-site production logistics management. This study aims to address the issues of effective material flow and resource allocation in production logistics management by investigating a method for the rapid and accurate allocation of supply logistics resources. Leveraging the extensive logistics resource allocation data available in the Plan For Every Part system, this research utilizes the Term Frequency-Inverse Document Frequency algorithm to filter out less discriminative data. Additionally, a recommender system based on machine learning, specifically the Recsys (Recommender System) employing similarity algorithms, is employed to construct a logistics resource allocation recommendation model. The effectiveness of this approach is validated through a case study that involves material resource allocation recommendations in a specific remote control production workshop. The validation results indicate that, compared to traditional logistics resource allocation processes, the proposed model optimizes resource allocation recommendations in production logistics, resulting in a significant improvement in material delivery punctuality and overall operational efficiency in enterprises.

This paper presents a multiscale design method for simultaneous topology optimization of both macrostructures and microstructures. Geometric features are extended as design primitives at both macro and micro scales and represented by Level Set Functions (LSFs). Parameters related to the locations, sizes, and orientations of macro and micro features are considered as design variables and optimized simultaneously. In the overlapping areas of different macro features, embedded microstructures are optimally figured out as the solution of the corresponding sub-optimization problem. In this study, the eXtended Finite Element Method (XFEM) is implemented for structural and sensitivity analyses with respect to design variables. This method has the advantage of using a fixed grid independent of the topology optimization process. The homogenization procedure is applied to calculate the effective properties of considered microstructures in each macro feature. Numerical examples are presented to illustrate the effectiveness of the proposed method. Results depict that the multiscale design cannot obviously improve structural stiffness compared with a solid-material design under the linear elastic condition.

This work is devoted to the aeronautical application of topology optimization for modular structures with multiple assemblies that consist of repeated standard modules and optional reinforcements. These kinds of structures are widely used owing to their transportability, reconfigurability, low manufacturing and service costs. In this work, the design of airborne shelves with modular structures characterized by the standard module configuration is formulated for the first time as a topology optimization problem of multiple assemblies and multiple load cases subjected to the volume constraint. It is shown that the weighted compliance design of multiple assemblies is a compromising solution compared to the optimization result of each individual assembly of standard modules. Meanwhile, the performance of optimized airborne shelves with the modular structures can effectively be ameliorated with the help of reinforcements.

The modular multilevel converter (MMC) is now the most attractive topology for medium and high voltage power conversion applications with several advantages over the traditional voltage source converter (VSC). However, due to a large number of sub-modules (SMs) in the MMC, system reliability is a big challenge in its practical application, where each SM may be considered as a potential point of failure. In this paper, a reliability evaluation based on the Markov model is proposed for the MMC. The failure rates of the power electronic devices and SMs are firstly analyzed. Then, the Markov model and the state transition equation of the system are built in detail. A general reliability evaluation function is established, in which the mean time to failure and reliability evaluation of the MMC with redundant SMs are also discussed. Finally, a practical direct current (DC) distribution example for reliability evaluation is analyzed, and the results verify that the reliability evaluation based on the Markov model could provide a useful reference for project design.

Structural topology optimization subjected to stationary random base acceleration excitations is investigated in this paper. In the random response analysis, the Large Mass Method (LMM) which attributes artificial large mass values at each driven nodal Degree Of Freedom (DOF) to transforming the base acceleration excitations into force excitations is proposed. The Complete Quadratic Combination (CQC) which is commonly used to calculate the random responses in previous optimization has been proven to be computationally expensive especially for large-scale problems. In order to conquer this difficulty, the Pseudo Excitation Method (PEM) and the combined method of PEM and Mode Acceleration Method (MAM) are adopted into the dynamic topology optimization, and random responses are calculated using these two methods to ascertain a high efficiency over the CQC. A density-based topology optimization method minimizing dynamic responses is then formulated based on the integration of LMM and PEM or the combined method of PEM and MAM. Numerical examples are presented to verify the accuracy of the proposed schemes in dynamic response analysis and the quality of the optimized design in improving dynamic performance.

Neutrophil extracellular traps (NETs) have been shown to worsen acute pulmonary injury including after lung transplantation. The breakdown of NETs by DNAse-1 can help restore lung function, but whether there is an impact on allograft tolerance remains less clear. Using intravital 2-photon microscopy, we analyzed the effects of DNAse-1 on NETs in mouse orthotopic lung allografts damaged by ischemia-reperfusion injury. Although DNAse-1 treatment rapidly degrades intragraft NETs, the consequential release of NET fragments induces prolonged interactions between infiltrating CD4+T cells and donor-derived antigen presenting cells. DNAse-1 generated NET fragments also promote human alveolar macrophage inflammatory cytokine production and prime dendritic cells for alloantigen-specific CD4+T cell proliferation through activating toll-like receptor (TLR) — Myeloid Differentiation Primary Response 88 (MyD88) signaling pathways. Furthermore, and in contrast to allograft recipients with a deficiency in NET generation due to a neutrophil-specific ablation of Protein Arginine Deiminase 4 (PAD4), DNAse-1 administration to wild-type recipients promotes the recognition of allo- and self-antigens and prevents immunosuppression-mediated lung allograft acceptance through a MyD88-dependent pathway. Taken together, these data show that the rapid catalytic release of NET fragments promotes innate immune responses that prevent lung transplant tolerance.

Neutrophil extracellular traps (NETs) have been shown to worsen acute pulmonary injury including after lung transplantation. The breakdown of NETs by DNAse‐1 can help restore lung function, but whether there is an impact on allograft tolerance remains less clear. Using intravital 2‐photon microscopy, we analyzed the effects of DNAse‐1 on NETs in mouse orthotopic lung allografts damaged by ischemia‐reperfusion injury. Although DNAse‐1 treatment rapidly degrades intragraft NETs, the consequential release of NET fragments induces prolonged interactions between infiltrating CD4+T cells and donor‐derived antigen presenting cells. DNAse‐1 generated NET fragments also promote human alveolar macrophage inflammatory cytokine production and prime dendritic cells for alloantigen‐specific CD4+T cell proliferation through activating toll‐like receptor (TLR) — Myeloid Differentiation Primary Response 88 (MyD88) signaling pathways. Furthermore, and in contrast to allograft recipients with a deficiency in NET generation due to a neutrophil‐specific ablation of Protein Arginine Deiminase 4 (PAD4), DNAse‐1 administration to wild‐type recipients promotes the recognition of allo‐ and self‐antigens and prevents immunosuppression‐mediated lung allograft acceptance through a MyD88‐dependent pathway. Taken together, these data show that the rapid catalytic release of NET fragments promotes innate immune responses that prevent lung transplant tolerance. Neutrophil extracellular trap fragments generated by DNAse treatment trigger toll‐like receptor signaling in antigen‐presenting cells that inhibit immunosuppression‐mediated lung transplant tolerance. See the videos online.

For the needs of target acquisition and edge extraction under complex scenes, a method based on layer by layer segmentation is proposed. First of all, morphological reconstruction technology is combined with ant colony edge detection method to execute pre-segmentation to get the foreground that targets locate. Secondly, line scanning technique is used to divide foreground into several parts for further segmentation. And then, mean difference characteristic is adopted to determine seed point, with which region growing algorithm is applied to find true targets. Finally, active contour method is used to find out the edge of targets. The experimental results show that our method is effective in finding the target and extracting its edge under complex scenes.

To solve the contradiction between the optimality of the energy management strategy and the adaptability of the driving conditions of the hybrid electric vehicle. This paper proposes a hierarchical energy management strategy for plug-in hybrid electric trucks based on the state of charge (SOC). In the upper hierarchical layer, the deep deterministic policy gradient (DDPG) algorithm is used to generate the SOC reference value for the future driving section based on the historical driving condition information, to guide the SOC of the battery and make the SOC run in a reasonable range. Ultimately, it enables optimal battery discharge for plug-in hybrid electric trucks throughout the journey. In the lower hierarchical layer, the long short-term memory (LSTM) neural network algorithm is used to predict vehicle speed in the short term. Meanwhile, the model predictive control of the vehicle is constructed to distribute the required power of the plug-in hybrid electric truck in real-time, and the penalty factor is introduced into the objective function to accurately follow the SOC reference value. The simulation results show that the control strategy in this paper saves 16.34 % of fuel compared with the charge-depleting/charge-sustaining (CD/CS) strategy, which greatly improves fuel economy. At the same time, the simulation results of different driving conditions show that the fuel-saving rate is about 16 %, which verifies the robustness of the proposed method.

The advantages of laminated composite structures make them attractive in industrial applications. To achieve the maximum advantages of the composite structures, the design optimization is of great importance. This paper classifies and compares various optimization problems and methods in laminated composite structure design. Three kinds of problems are illustrated in this paper: constant stiffness design, variable stiffness design, and topology optimization. The main optimization methods used in laminated composite structure design, including the gradient-based methods, heuristic methods, and hybrid methods, are presented. The advantages and shortcomings of each method are discussed in detail. Finally, constant and variable stiffness design and topology optimization of laminated composite structures which have been solved in literature are reviewed. Graphical abstractThis paper reviews the problems and methods in constant and variable shiffness design and topology optimization of laminated composite structures.

Multi-sensing and correlation analyses are essential for online process evaluation and optimization to improve the quality of as-fabricated components. Defect-free process control is important for additively manufactured (AM) continuous fiber-reinforced composites (CFRP) because the number of defects and poor-quality control in AM-fabricated CFRP restrict their mechanical performance and product service life. In this study, a framework of multi-sensor fusion for CFRP additive manufacturing is proposed for in-situ process evaluation and to establish correlations between process parameters/pattern features with layer wise defects and surface quality. Infrared (IR), visual cameras, force, and laser-displacement sensors were integrated with the printing head to obtain online datasets. Multiple signal denoising, feature extraction, and classification were performed to incorporate deep-learning neural networks and correlation analyses using feature-level fusion approaches. The critical features of these signals were extracted for a quantitative analysis of the layer wise surface roughness, level of fiber misalignment (LoM), and number of defects. Multi-sensor fusion is an effective approach to online monitoring and process evaluation. The established knowledge base is helpful for predicting and adjusting the localized process parameters during the fabrication process.

In this study, the robust transition trajectory optimization was conducted for tail-sitter UAVs. The correlated stochastic uncertainties are different from existing deterministic optimal transition studies and are considered for the first time. Simulation results show that the robustness of the derived transition trajectories is improved. In the future, we will study more complicated unknown uncertainties. In addition, the robust control law for the tail-sitter transition phases will also be studied.