Search

Body panels have a direct influence on the static and dynamic stiffness of the body-in-white. With the introduction of CFRP in automotive body, these panels have ushered in the potential of being designed with variable-stiffness. However, due to the inherent complexity of variable-stiffness design and the need to integrate all panels to account for their interaction on BIW, the related design issue has not been solved. This paper reports a design method to achieve the successive optimization of the thickness distribution and stacking sequence for body panels, as well as ensure basic manufacturing and blending requirements of design results. This method was applied to the virtual design of the roof, floor, outer and inner side panels of a body-in-white. Compared with original constant-stiffness CFRP components, a 16.4% weight reduction was attained, the torsional and bending stiffness, first-order torsional and bending frequencies were enhanced by 5.0%, 10.9%, 4.7%, and 7%, respectively.

Combined application of steel, aluminum, and carbon fiber reinforcement plastic (CFRP) is the main direction of future lightweight body development. However, the anisotropy and additional lamination design variables of CFRP parts pose significant challenges for the development of multi-material bodies. This study establishes a parametric design method for the variable-thickness lamination scheme based on non-uniform rational B-splines, it can be coupled with existing parametric design methods for structural shapes to formulate a complete parametric design and modeling of CFRP components. On this basis, a homogenized intermediate material property is derived from classic laminate theory by introducing lamination assumptions, it enables a stepwise multi-material body optimization method to solve the challenge that components’ material design variables switching between CFRP and alloy will introduce/eliminate lamination design variables iteratively, posing a great optimization convergence difficulty. The proposed parametric modeling method for CFRP components was validated by experimental tests of a fabricated roof beam, and the proposed optimization method was applied to a vehicle body, achieving 15.9%, 23.9%, 18.6%, and 12.2% increase in bending and torsional stiffness and modal frequencies; 20.2%, 9.3%, and 12.7% reduction of weight and peak acceleration in frontal and side collisions. This study enables the forward design of multi-material bodies compatible with CFRP parts.Graphical Abstract

SiO2nanoparticles (SiO2NPs) are most widely available and coexisting with DOM at the mineral-water interface; however, the role of SiO2NPs in DOM fractionation and the underlying mechanisms have not been fully understood. Using Fourier transform ion cyclotron resonance mass spectrometry, combined with Fourier transform infrared spectroscopy and X-ray adsorption fine structure spectroscopy, was employed to investigate the adsorptive fractionation of litter layer-derived DOM on goethite coexisting with SiO2NPs under different pH conditions. Results indicated that the inhibitory effect of the coexisting SiO2NPs on OM sorbed by goethite was waning as environmental pH increased due to the reduced steric interactions and the concurrent elevated hydrogen bonding/hydrophobic partitioning interactions on the goethite surface. We observed the coexisting SiO2NPs inhibited the adsorption of high carboxylic-containing condensed aromatic/aromatics compounds on goethite under different pH conditions while improving the adsorption of highly unsaturated aliphatic/phenolic and carbohydrate-like compounds in an alkaline and/or circumneutral environment. More nitrogen-containing structures may favor the adsorption of phenolic and nonaromatic compounds to goethite by counteracting the negative effect of SiO2NPs. These findings suggest that DOM sequestration may be significantly regulated by the coexisting SiO2NPs at the mineral-water interface, which may further influence the carbon–nitrogen cycling and contaminant fate in natural environments.

To improve overall vehicle performance when using CDTire/3D model-based tyre design, a two-step approximate optimisation method that combines several representative design indices is presented. The process optimises both of the CDTire/3D model vector and scalar parameters. A vehicle road noise design index as the optimisation objective is defined using the extracted CDTire/3D model-dependent sensitive frequency subdomains, and the mean values of crucial data from vehicle ride comfort and durability simulations are used to be constraints. A sequential-updating radial basis function metamodel-based optimisation method is adopted as the solver. The proposed method is employed to develop tyres for a sport utility vehicle (SUV). Compared with typical road noise design indices-based static approximate optimisation approaches, the proposed method achieves a better overall optimisation effect. Using the developed CDTire/3D model, the road noise, ride comfort, and durability design indices of the SUV are improved by 9.8%, 6.7%, and 7.1%, respectively.

Graphical Abstract:

The application of lightweight structures with excellent energy absorption performance is crucial for enhancing vehicle safety and energy efficiency. Cellular structures, inspired by the characteristics observed in natural organisms, have exhibited exceptional structural utilization in terms of energy absorption compared with traditional structures. In recent years, various innovative cellular structures have been proposed to meet different engineering needs, resulting in significant performance improvements. This paper provides a comprehensive overview of novel cellular structures for energy absorption applications. In particular, it outlines the application forms and design concepts of cellular structures under typical loading conditions in vehicle collisions, including axial loading, oblique loading, bending loading, and blast loading. Cellular structures have evolved to meet the demands of complex loading conditions and diverse research methods, focusing on achieving high-performance characteristics across multiple load cases. Moreover, this review discusses manufacturing techniques and strategies for enhancing the manufacturing performance of cellular structures. Finally, current key challenges and future research directions for cellular structures are discussed. The aim of this study is to provide valuable guidelines for researchers and engineers in the development of next-generation lightweight cellular structures.

SRAM-based computing-in-memory (CIM) has been widely explored to accelerate neural networks (NNs). However, it is challenging to store all weights of many modern NNs due to limited on-chip SRAM capacity. This bottleneck induces a large amount of off-chip DRAM accesses and impedes the improvement of performance and energy efficiency. This paper proposes a new approach of computing in resistive random-access memory (ReRAM)-assisted energy- and area-efficient SRAM (CREAM) for accelerating large-scale NNs while eliminating the DRAM access. The NN weights are all stored in high-density on-chip ReRAMs and restored to the proposed non-volatile SRAM (nvSRAM) CIM cells with array-level parallelism. Furthermore, to deal with the influence of ReRAM and CMOS variations, a novel layer-wise and bit-wise weight-configuration search algorithm is proposed by leveraging different sensitivity of each layer in NN models. A data-aware weight-mapping method is also presented to efficiently map NN models to ReRAMs in CREAM for high computation parallelism. The experiment results show $10.3\times $ weight storage density over the standard 6T SRAM array. Evaluations of ResNet-18 and VGG-9 on CIFAR-10/CIFAR-100 datasets show up to $3.47\times $ and $1.70\times $ energy efficiency over two baseline designs of SRAM-CIM and ReRAM-CIM, respectively, in addition to 15.6% higher accuracy than ReRAM-CIM under device variations.

In this work, a 3D finite-element model (FEM) was developed to accurately predict the mechanical behavior of CFRP/Al self-piercing riveting (SPR) during the forming and shearing process. A continuous damage model based on the modified Hashin failure criterion was proposed to model the CFRP, and a cohesive model considering the traction-separation criterion was used to capture the delamination between CFRP layers. The simulation results were in good agreement with the experimental results. Based on the developed FEM, the damage of CFRP in the SPR forming process, the shear mechanical properties and damage of joints in the SPR shearing process, the effects of CFRP plate thickness and rivet length on the SPR forming cross-section and shear strength were investigated. The results showed that, the maximum interlock value of 0.46 mm was obtained when the rivet length was 6.5 mm and the CFRP plate thickness was 1.6 mm. The maximum shear peak load of 3611.98 N was obtained when the rivet length was 6.5 mm and the CFRP plate thickness was 2.2 mm. The interaction between rivet length and CFRP plate thickness should be considered in the actual SPR connection.

The manufacturing process of composite laminated parts has a significant influence on the final performance, especially since the draping process can change the predefined fiber orientation at local sites. With the maturing of draping-process simulation technology, an integrated structure–process–performance design has become possible. This article reports a computational framework for the integrated design of the draping process and structural performance of variable-thickness (VT) laminates. The framework is a two-loop optimization workflow. Specifically, a genetic draping optimization method features the inner optimization loop to find the optimal draping strategy and obtain the resulting fiber orientations and manufacturing layout. Structural performance optimization outlines the outer optimization loop, where a parametric method for the physical configuration of VT laminates is proposed to evaluate the structural performance considering the draping strategy. The entire framework is demonstrated by an automotive B-pillar reinforcement. The benefits of incorporating the draping design versus no draping design are illustrated by comparing the corresponding design results. The comparison results show that the integrated design not only contributes to pretreating manufacturing defects but also allows the use of reoriented fiber directions caused by draping to improve the mechanical properties and engage the load bearing. It thus achieves a further weight reduction of 18.63%. This framework facilitates resource-friendly process design, comprehensive performance evaluation, and structurally efficient laminate design.

Structural deformation caused by environmental loads (solar irradiation, snow and ice, and devices heat dissipation, etc.), could severely deteriorate the performance of high-frequency large-aperture active phased array antenna (HLAPAA). The existing sensor placement methods for monitoring the real-time status of HLAPAA have issues with slow convergence rate, limited global search ability, and low optimization accuracy, especially with large HLAPAA with tens of thousands of degrees of freedom (DOF). In this article, we propose a novel HLAPAA sensor placement method with an adaptive double population gray wolf optimizer (ADGWO). This method introduces adaptive sentinel wolves into the basic gray wolf optimizer (GWO), improves the population update strategy, and thus, improves the convergence rate plus the global search ability. First, based on four benchmark functions, the test performance of ADGWO and other popular intelligence algorithms is provided. The comparison shows that the proposed method has excellent convergence speed, higher solution quality, stronger global search ability, and better robustness, making it suitable for optimal sensor placement (OSP) of large structures. Then, the ADGWO is applied to the sensor placement of an ${X}$ -band HLAPAA. Compared with other OSP methods, the proposed method has advantages in terms of sensor distribution and computational and hardware cost. Finally, verification is carried out using an experimental antenna platform equipped with the optical fiber Bragg grating (FBG) strain sensor configuration. The measurement results show that the reconstructed shape is in good agreement with the real (deformed) shape, and the calculated radiation pattern matches the radiation pattern measured in the anechoic chamber; therefore, the proposed method and monitoring system based on ADGWO has broad application prospects in condition monitoring of large, lightweight, flexible antenna structures working in harsh environments.

Short fiber-reinforced polylactic acid composites (SFRPLA) have significant potential in diverse industries. The inherent propensity of SFRPLA to degrade at elevated temperatures underscores the critical importance of its thermal stability for high-temperature applications. In this work, poly-(butylene adipate-co-terephthalate) (PBAT) is melt-blended with PLA by incorporating nano-silica, and then reinforced with short basalt fiber (SBF) to fabricate PLA-ternary composites (SBFRPLA-ns). Then study the morphology, microstructure, and thermal properties of SBFRPLA-ns to understand the internal composition and interaction between the components and explore the modification mechanism of SBFRPLA-bio composites. The results show that the maximum tensile modulus and crystallinity of SBFRPLA-ns surpassed those of pure PLA by 103% and 185.5%, respectively. Subsequent analysis using WAXD and FTIR demonstrates that SBFRPLA-ns undergo no discernible phase change and maintain their structural similarity to PLA. Furthermore, TG/DTG at different heating rates confirms the beneficial influence of the components on the thermal stability of SBFRPLA-ns. Accordingly, breaking the key technical barriers to improve the mechanical properties and thermal stability can expand the high-temperature application potential of SBFRPLA-bio.

This work proposes a material selection decision-making method for multi-material lightweight body driven by performance to achieve that the right materials are used for the correct positions of the automotive body. The internal relationship between performance and mass, cross-sectional shape, wall thickness parameters, and material properties of a thin-walled structure is studied. The lightweight material indices driven by performance are then established. The lightweight material indices and material price are taken as the decision-making criteria for the material selection of automotive body components. A hybrid weighting method integrated with the analytic hierarchy process, fuzzy analytic hierarchy process, and quality function deployment is proposed. The difficulty of quantitatively evaluating the performance requirements of different components of the body is solved using the proposed weighting method combined with the numerical analytical results of the component performance under multiple operating conditions of the automotive body. Then, the weight of the decision-making criteria for material selection is calculated. Grey relational analysis is used to make multicriteria decision-making on a variety of candidate materials to select the best material for body components. After the lightweight material selection of the front longitudinal beam of the automotive body, the frontal collision safety performance of the body is effectively improved, and the mass of the front longitudinal beam is reduced by 45%. Material selection result of the front longitudinal beam indicates that the proposed material selection decision-making method can effectively achieve the fast material selection of components in different positions of the body.

To improve the computational cost and approximate optimization accuracy of solving engineering optimization problems with both expensive objective and constraints, a hybrid sequential approximate optimization (HSAO) method is proposed. First, the radial basis function neural network (RBFNN) and response surface method (RSM) approximate models fit the optimization objectives and constraints, respectively. Second, the fitted model searches for the optimal solution via global optimization algorithm, and then, the optimal solution is used as a newly added sample point for updating approximation model. The region of the optimal solution is iteratively updated until the optimal solution converges. The performance of the HSAO method is tested on numerical test functions. Compared with the traditional approximate model, adaptive RSM, and adaptive RBFNN approximate optimization, the results indicate that the proposed HSAO method is higher in search ability and efficiency. Finally, the method is applied in S-shaped tube crashworthiness optimization, and has achieved desired results.

To achieve lightweight of a body-in-white (BIW) structure, this study proposes a hybrid optimisation approach that integrates the subspace division of a BIW structure, relative sensitivity analysis, correlation analysis, NSGA-II (non-dominated sorting genetic algorithm II) combined with a RSM surrogate model, and design of experiments (DOE) combined with E-TOPSIS (entropy weight-technique for ordering preferences by similarity to ideal solution). The approach improves the optimisation efficiency and solves the problem of nonlinear crashworthiness optimisation. The decoupling of the linear basic performance and nonlinear collision safety performance of a BIW is achieved. According to the type of performance response, design variables are screened, and three subspace structures of the BIW are optimised. Optimisation results indicate that the mass of the BIW structure is reduced by 17.0 kg. The performance quotas of the BIW are within the specified design baseline range. Therefore, the proposed approach can be effectively applied to the lightweight design of a BIW structure.

An integrated optimization method that comprehensively considers draping factors such as fiber reorientations and cutting of layers is proposed for designing CFRP B-pillar reinforcement with a variable thickness. A laminate parameterization scheme, the local shared layer parameterization scheme (LSL-PS), is developed to parameterize the physical composition of laminates with variable-thickness. Kinematic draping simulations and preform designs are introduced to evaluate fiber reorientations and eliminate manufacturing defects. The optimization design of the B-pillar reinforcement is integrated with a LSL-PS, draping-simulation and preform-design, a RBF surrogate model and GA. At the same time, a comparative optimization without the consideration of draping factors is performed in parallel. The comparison results show that considering draping not only helps designers eliminate manufacturing defects but also helps to obtain a further weight reduction of 13.33% because fiber reorientations are fully utilized to improve the structural performance.

This study presents a hybrid approach to integrate the comprehensive sensitivity analysis method, support vector machine technology, modified non-dominated sorting genetic algorithm-II method and the technique for order preference by similarity to ideal solution, which have been applied to multi-objective lightweight optimization of the B-pillar structure of an automobile. First, numerical models of the static–dynamic stiffness and the crashworthiness performance of automobile are established and validated by experimental testing. Then, the comprehensive sensitivity analysis method is used to define the final optimization variables. Experimental design and support vector machine based surrogate model techniques are introduced to establish the approximate model; subsequently, the modified non-dominated sorting genetic algorithm-II algorithm is applied to the multi-objective lightweight optimization design of the B-pillar structure, and the non-dominated solution set is determined. The principal component analysis method is applied to determine the weight of each objective. Finally, the technique for order preference by similarity to ideal solution method is used to rank Pareto front from best to worst to obtain the optimal solution; furthermore, a comparison between the original model and optimized design denotes that the mass of the B-pillar being reduced by 22.55% under the other impacting indicators is well guaranteed. Therefore, the proposed hybrid approach provided promising prospects in the lightweight and crashworthiness optimization application of the B-pillar.

China is experiencing significant public health challenges related to social and demographic transitions and lifestyle transformations following unprecedented economic reforms four decades ago. Of particular public health concern is the fourfold increase in overweight and obesity rates in the nation’s youth population, coupled with the low prevalence of adolescents meeting recommended levels of physical activity. Improving the overall health of China’s more than 170 million children and adolescents has become a national priority. However, advancing nationwide health initiatives and physical activity promotion in this population has been hampered by the lack of a population-specific and culturally relevant consensus on recommendations for achieving these ends. To address this deficiency and inform policies to achieve Healthy China 2030goals, a panel of Chinese experts, complemented by international professionals, developed this consensus statement. The consensus was achieved through an iterative process that began with a literature search from electronic databases; in-depth reviews, conducted by a steering committee, of the resulting articles; and panel group evaluations and discussions in the form of email correspondence, conference calls and written communications. Ultimately, the panel agreed on 10 major themes with strong scientific evidence that, in children and adolescents aged 6–17, participating in moderate to vigorous physical activities led to multiple positive health outcomes. Our consensus statement also (1) highlights major challenges in promoting physical activity, (2) identifies future research that addresses current knowledge gaps, and (3) provides recommendations for teachers, education experts, parents and policymakers for promoting physical activity among Chinese school-aged children and adolescents. This consensus statement aligns with international efforts to develop global physical activity guidelines to promote physical activity and health and prevent lifestyle-related diseases in children and adolescents. More importantly, it provides a foundation for developing culturally appropriate and effective physical activity interventions, health promotion strategies and policy initiatives to improve the health of Chinese children and adolescents.

Wind noise is a significant source of noise when vehicles are driven at high speeds. While wind tunnel experiments are commonly method to study real automotive wind noise, anechoic wind tunnel laboratory are prohibitive for automakers on account of their high costs and limited experimental period. Therefore, this paper proposes to extract automotive wind noise through combining road experiment and drum experiment, define significant wind noise prime regions, and verify its effectiveness. Subsequently, the Long Short-Term Memory Neural Network algorithm (LSTM) is employed to reveal the complex nonlinear relationship between wind noise and its impact areas, in order to establish an identification model for automotive wind noise. The performance of the model is then compared with Backpropagation Neural Networks (BPNN) and Support Vector Regression (SVR) models. The results indicate that the LSTM wind noise identification model exhibits higher accuracy, shorter training time, and stronger generalization ability, thus demonstrating the superiority of this model.

Supplemental Digital Content is available in the text.Antibiotic (ATB) use seems to negatively affect the outcomes of immune checkpoint inhibitors (ICIs). The aim of this review is to clarify whether ATB use influences the efficacy of ICI treatment in cancer patients. Databases of MEDLINE, Embase, and Cochrane Library were searched for reports published in English between January 2007 and December 2019. We included studies that compared the outcomes of ATB use and no-ATB use in cancer patients using ICIs. Two reviewers independently selected eligible studies and extracted the data. Meta-analysis was performed with pooling of unadjusted hazard ratios (HRs) for overall survival (OS) and progression-free survival (PFS), and with pooling of odds ratios (ORs) for objective response rate (ORR). Thirty-eight studies involving 8409 patients were finally included for qualitative or quantitative analyses. Cancer types included renal cell carcinoma, non–small cell lung cancer, urothelial carcinoma, melanoma, gastrointestinal cancer, and others. Meta-analyses revealed that ATB use was associated with poor OS [HR: 1.80, 95% confidence interval (CI): 1.44–2.26, P<0.001], PFS (HR: 1.55, 95% CI: 1.26–1.91, P<0.001) and ORR (OR: 0.63, 95% CI: 0.42–0.95, P=0.03). Subgroup analysis found that these relationships were not influenced by cancer type or ICI regimens, but were dependent on the timing of ATB use. Narrative results of multivariable analyses further confirmed the negative effects of ATB use on OS and PFS. In cancer patients using ICIs, pre-ATB use close to the start of ICI treatment (within 60 d) was detrimental to outcomes in terms of OS, PFS, and ORR.

A novel bottom corrugated cross-beam (S-beam) structure improved the dynamic and static performance of a container based on the combination of a modified non-dominated sorting genetic algorithm (MNSGA-II) and grey relational analysis. First, a parametric model was established and used to verify the structure’s validity through static physical testing. Eight design variables for the S-beam container structure were also defined according to the parametric model technology. Second, MNSGA-II was used for the multi-objective lightweight optimization design of an S-beam container to obtain the optimal combination of design parameters that are considerably affected by weight reduction under peak bending stress and peak loading deflection as well as first-order natural frequency variations within the allowable range. A set of non-dominated solutions was used to obtain a multi-objective optimization design. Finally, grey relational analysis and grey entropy theory are applied to rank all solutions and determine the best compromise solution. The comparison of the technique for the order of preference by similarity to ideal solution method with grey relational analysis demonstrates the extraordinary reliability and superiority of the latter. In addition, the combined method can achieve a weight reduction of up to 23.54%, which can enhance the utilization of materials and demonstrates the superiority of the combined method relative to the initial model.

This study deals with the multi-objective lightweight optimization of the front end structure of an automobile body, as the main assembly to withstand impact force and protect occupants from injuries in frontal collision, based on entropy-based grey relational analysis (EGRA). First, basic noise, vibration, and harshness (NVH) models of the automobile body and crashworthiness models of the vehicle are established and then validated by corresponding actual experiments; hence the lightweight controlling quotas are extracted. Next, the contribution analysis method determines the final parts for lightweight optimization, for which both continuous thickness variables and discrete material variables are simultaneously taken into account. Subsequently, design of experiment (DoE) using the optimal Latin hypercube sampling (OLHS) method is carried out, considering the total mass and the torsional stiffness of the automobile body, the maximum intrusion deformation on the firewall, the maximum impact acceleration at lower end of the B-pillar, and the total material cost of the selected optimization parts as five competing optimization objectives. After that, the optimal combination of thickness and material parameters of the optimization parts is determined using EGRA and confirmed by technique for order preference by similarity to ideal solution (TOPSIS). Finally, a comparison between the original design and the post-lightweight design, namely the optimized design, further confirms the effectiveness of the lightweight optimization. According to the outcomes, the automobile body is lightweight optimized with a mass decrease of 4.98 kg on the basis of well guaranteeing other relevant mechanical performance. Accordingly, the EGRA could be well employed to the multi-objective lightweight optimization of the automobile body.

Lightweight optimization and design on body in white (BIW) has a significant effect on reducing emissions, fuel consumption, and protecting the environment. Much lightweight study on complete BIW and single BIW parts has been done in recent years. However, the lightweight study of sub-modules is difficult for considering the complete BIW performances. The efficiency of sub-module lightweight optimization based on complete BIW is not high. A new method is proposed to conduct frontal sub-module lightweight optimization. Firstly, the implicit parametric frontal BIW structure is constructed by SFE-CONCEPT software. Then, the coupling BIW model is constructed by combining the above frontal BIW structure and rear finite element structure. The shape and thickness variables are extracted from the frontal BIW structure, and the performance of complete BIW can be analyzed. The efficiency of constructing BIW and the complete performances are all considered by the new method. In addition, a modular method is adopted to analyze performance. It can achieve the concept of “analysis leads design” to conduct lightweight optimization and design for frontal structure by combining with ISIGHT software. After lightweight optimization and design of BIW frontal structure, the weight is reduced by 4.91 kg, which is as much as 5.70%. The performances have little change; the improved maximum performance is 3.32%, and the reduced maximum performance is 1.81%.

Crashworthiness and lightweight design are two main challenges in the early body in white (BIW) design stage. An implicit parametric model of BIW was built by using SFE-CONCEPT to allow for larger geometrical modifications and more flexible design space. A physical test was then conducted to verify the validity of the implicit parametric model. A hybrid method coupling the particle swarm optimization (PSO) algorithm with the bacterial foraging optimization (BFO) algorithm has been proposed to improve the crashworthiness and lightweight design. This method aims to obtain the Pareto sets to guide designers in selecting the optimal solution according to different demand. Meanwhile, a comparison between the proposed hybrid method and other widely used intelligent methods (PSO, BFO, and PSO–GA) was performed to illustrate the advantages of the hybrid method in solving complex, nonlinear, and multi-objective optimization problems. The results indicate that the proposed hybrid method has significant potential to optimize the crashworthiness and to guide lightweight design of BIW.

In this study, an effective optimization approach was proposed to improve acoustical behaviors of PU foams. The important parameters of PU foams: content of water, silicone oil and catalyst A1 were chosen and their effects on sound absorption coefficient and transmission loss of PU foams were studied by using Taguchi methods. In addition, bamboo chips were incorporated into PU foams as fillers to improve the acoustical properties of PU foams. Four controlled factors: the content of water, silicone oil, catalyst A1 and bamboo chips with three levels for each factor were chosen and Taguchi method based on orthogonal array L9(34) was employed to conduct the experiments. Based on the results of Taguchi’s orthogonal array L9(34), signal-to noise (S/N) analysis was used and developed to determine an optimal formulation of PU-bamboo-chips foam composites.

Mycobacterium avium subspecies paratuberculosis(MAP) causes chronic, wasting, and progressive enteritis in cattle, bringing significant economic losses in livestock industries. MAP has spread worldwide mainly due to movement of animals. The objective of this study was to determine the MAP seroprevalence in cattle in the Xinjiang Uygur Autonomous Region, Northwest China, and evaluate the difference between intensive farming herds (cattle number in a herd is more than 200, and the cattle cannot have access to pasture) and free-range herds (the cattle are bred by individual households, a herd is defined as the cattle are bred in a village or town in this study). A total of 3157 serum specimens were collected from 42 herds in nine different regions. This included 1481 specimens from 18 intensive farming herds in four regions and 1676 specimens from 24 free-range herds in six regions. Antibody against MAP was tested with commercial ELISA test kits. The results showed that the overall apparent prevalence was 4.8% (95% CI, 4.1 to 5.6%) at animal level, and 50.0% (21/42) at herd level. The apparent prevalence in intensive farming herds and free-range herds were 9.5% (141/1481) and 0.7% (11/1676) at the animal-level, 88.9%(16/18) and 20.8% (5/24) at herd level, respectively, with a significant statistical difference between these two farming modes (p< 0.01). Cattle in intensive farming herds had a relatively higher risk to be infected with MAP than those in free-range herds (RR = 14.4). This study demonstrates that apparent prevalence of MAP infection in dairy cattle differs with farming modes at the animal level and herd level, and farming density could be an important risk factor associated with the presence of MAP infected cattle. This study provides important epidemiological data for bovine MAP control in Xinjiang, Northwest China.

With the advancement of safety awareness, the automobile brake tester is widely applied to automobile brake performance testing. The brake tester is composed of four major subsystems: one or more energy supply devices, electric motors, transmission assemblies and a control device. This paper reviews various patents of the automobile brake tester which employs the different devices and structures to obtain new functions in recent years. Compared with the new structures, the traditional brake tester is not versatile, compact and modular. Moreover, it is hard to fix the vehicle on the brake tester. Thus, several kinds of new structures of brake testers have been designed to solve these problems, including the limited function and the complex structure. The new types of brake testers are more compact, simple and versatile. The vehicle can be easily fixed on the brake tester as well. Furthermore, the cost of the brake tester will be reduced by importing the new structures and devices.

A series of La-containing Mg–Al hydrotalcite-like (HTl) precursors with different La contents (Mg2+ : Al3+ : La3+= 3 : 1 : x, where xvaries from 0 to 1.0) were synthesized using a co-precipitation method followed by hydrothermal treatment. X-ray diffraction and thermogravimetric measurements demonstrated that the yield of the HTl phase decreased with increasing La content. The La-modified Mg–Al mixed oxides (HTC-La) were then obtained by thermal decomposition of the corresponding HTl precursors, and the mesoporous structure was formed during calcination. It was demonstrated that the structure and surface basic properties of the HTC-La samples strongly depended on the amount of La additive. Simultaneously, the resulting HTC-La materials were used as solid base catalysts for the synthesis of dimethyl carbonate (DMC) from methyl carbamate (MC) and methanol. Then, the correlation between their basic properties and catalytic performance was studied in detail. The incorporation of a suitable amount of La into HTC-La catalysts was beneficial for the production of DMC, and a DMC yield of 54.3% with a high DMC selectivity of 80.9% could be achieved when xwas tuned to 0.5 under the optimized reaction conditions. In addition, the HTC-La catalyst could be readily recycled while maintaining high catalytic activity and selectivity for DMC. Furthermore, in situFTIR experiments were carried out to elucidate the adsorption behaviours of the reactants. On the basis of the experimental results, a plausible basic catalytic mechanism wherein MC and methanol were activated simultaneously on the basic sites of the catalyst was proposed for this catalytic reaction.

The synthesis of glycerol carbonate (GC) from glycerol and urea was investigated over a series of lanthanum compounds. Among them, LaCl3showed the best catalytic activity. The conversion of glycerol could be 95.4 % with 99.9 % selectivity towards GC under the optimum reaction conditions. Then, it was found that for these catalysts, their solubility in the reaction system during reaction was the key to achieve high glycerol conversion and GC selectivity. Meanwhile, catalytic tests and FTIR measurements of the liquid phases after reaction confirmed that this approach could be divided into two steps, and the second one was the key step. Interestingly, it was revealed that some La-containing complex species could be formed via NH3coordination to La3+during the reaction. Furthermore, a possible reaction mechanism was proposed based on these systematic experimental results.

Static shearing, drawing, and dynamic impact test schemes of carbon fiber reinforced polymer (CFRP)/aluminum alloy (Al) bolt joint were designed. The finite element model of the CFRP/Al bolt joint was established, and the failure modes of the joints under the static and dynamic impact conditions were analyzed. The structure, lay-up, and connection parameters of the joint were defined as design variables, and the static and dynamic impact performance indicators of the joint and the lay-up numbers of the CFRP sheet were defined as optimization objectives. Integrated multiobjective optimization was conducted for joints, employing the radial basis function neural network (RBFNN) surrogate model, elitist nondominated sorting genetic (NSGA-II) algorithm, and entropy-technique for order preference by similarity to ideal solution (E-TOPSIS) decision method. The best trade-off solution was obtained, and the optimal design variables were determined. The optimized joint was fabricated, and static and dynamic impact tests were carried out. The test and simulation results were compared to verify the effectiveness of simulation and optimization.

Most of recent research on carbody lightweighting has focused on substitute material and new processing technologies rather than structures. However, new materials and processing techniques inevitably lead to higher costs. Also, material substitution and processing lightweighting have to be realized through body structural profiles and locations. In the huge conventional workload of lightweight optimization, model modifications involve heavy manual work, and it always leads to a large number of iteration calculations. As a new technique in carbody lightweighting, the implicit parameterization is used to optimize the carbody structure to improve the materials utilization rate in this paper. The implicit parameterized structural modeling enables the use of automatic modification and rapid multidisciplinary design optimization (MDO) in carbody structure, which is impossible in the traditional structure finite element method (FEM) without parameterization. The structural SFE parameterized model is built in accordance with the car structural FE model in concept development stage, and it is validated by some structural performance data. The validated SFE structural parameterized model can be used to generate rapidly and automatically FE model and evaluate different design variables group in the integrated MDO loop. The lightweighting result of body-in-white (BIW) after the optimization rounds reveals that the implicit parameterized model makes automatic MDO feasible and can significantly improve the computational efficiency of carbody structural lightweighting. This paper proposes the integrated method of implicit parameterized model and MDO, which has the obvious practical advantage and industrial significance in the carbody structural lightweighting design.

Abstract: Obesity with excessive levels of circulating free fatty acids (FFAs) is tightly linked to the incidence of type 2 diabetes. Insulin resistance of peripheral tissues and pancreatic β-cell dysfunction are two major pathological changes in diabetes and both are facilitated by excessive levels of FFAs and/or glucose. To gain insight into the mitochondrial-mediated mechanisms by which long-term exposure of INS-1 cells to excess FFAs causes β-cell dysfunction, the effects of the unsaturated FFA linoleic acid (C 18:2, n-6) on rat insulinoma INS-1 β cells was investigated. INS-1 cells were incubated with 0, 50, 250 or 500 μM linoleic acid/0.5% (w/v) BSA for 48 h under culture conditions of normal (11.1 mM) or high (25 mM) glucose in serum-free RPMI-1640 medium. Cell viability, apoptosis, glucose-stimulated insulin secretion, Bcl-2, and Bax gene expression levels, mitochondrial membrane potential and cytochrome c release were examined. Linoleic acid 500 μM significantly suppressed cell viability and induced apoptosis when administered in 11.1 and 25 mM glucose culture medium. Compared with control, linoleic acid 500 μM significantly increased Bax expression in 25 mM glucose culture medium but not in 11.1 mM glucose culture medium. Linoleic acid also dose-dependently reduced mitochondrial membrane potential (ΔΨm) and significantly promoted cytochrome c release from mitochondria in both 11.1 mM glucose and 25 mM glucose culture medium, further reducing glucose-stimulated insulin secretion, which is dependent on normal mitochondrial function. With the increase in glucose levels in culture medium, INS-1 β-cell insulin secretion function was deteriorated further. The results of this study indicate that chronic exposure to linoleic acid-induced β-cell dysfunction and apoptosis, which involved a mitochondrial-mediated signal pathway, and increased glucose levels enhanced linoleic acid-induced β-cell dysfunction.

Abstract: Ghrelin is a peptide hormone produced in the stomach and a range of other tissues, where it has endocrine, paracrine and autocrine roles in both normal and disease states. Ghrelin has been shown to be an important growth factor for a number of tumours, including prostate and breast cancers. In this study, we examined the expression of the ghrelin axis (ghrelin and its receptor, the growth hormone secretagogue receptor, GHSR) in endometrial cancer. Ghrelin is expressed in a range of endometrial cancer tissues, while its cognate receptor, GHSR1a, is expressed in a small subset of normal and cancer tissues. Low to moderately invasive endometrial cancer cell lines were examined by RT–PCR and immunoblotting, demonstrating that ghrelin axis mRNA and protein expression correlate with differentiation status of Ishikawa, HEC1B and KLE endometrial cancer cell lines. Moreover, treatment with ghrelin potently stimulated cell proliferation and inhibited cell death. Taken together, these data indicate that ghrelin promotes the progression of endometrial cancer cells in vitro, and may contribute to endometrial cancer pathogenesis and represent a novel treatment target.

Abstract: Estrogen plays several important physiological and pathological functions in not only reproductive system but many other systems as well. Its transcriptional activation has been traditionally described as being mediated by classic nuclear estrogen receptors (ERs). It is however established recently that a novel functional estrogen transmembrane receptor, G protein-coupled receptor 30 (GPR30), modulates both rapid non-genomic events and genomic transcriptional events of estrogen. It has been demonstrated that GPR30 promotes the progress of estrogen-related tumors through mitogen-activated protein kinase (MAPK) signaling pathways. Effects mediated by GPR30 are maintained when classic ERs are absent or blocked. In addition, GPR30 is involved in drug resistance, which is often occurring during cancer treatments. All these new findings strongly imply that GPR30 may be an important therapeutic target for estrogen-related tumors. Simultaneously blocking both GPR30 and classic ERs may be a better strategy for the treatment of estrogen-related tumors.

Description of personality traits by Chinese adjectives: A trial on university studentsStudies on personality-related Chinese adjectives suggest either a five-factor or seven-factor structure. In the current investigation, we selected a bigger adjective pool of personality-related adjectives, and tested them on the university students in Northern, Southern, Western and Eastern China. In Study 1, we administered the the self-rating scales of the 650 adjectives in 610 subjects. Five factors emerged clearly, and named as "Intelligent", "Emotional", Conscientious", "Unsocial" and "Agreeable". We then selected 20 adjectives with highest target loadings for each factor to develop a short version of the self-report rating scales, the Chinese Adjective Descriptors of Personality (CADP). In study 2, we administered the 100-adjective CADP to 720 university students in the four areas of China. Again, five-factor structures were confirmed. Loadings of the individual adjectives on the target factor were satisfactory, and the internal alphas for each personality scale were high. Most CADP scales were intercorrelated. There were, however, no significant gender differences in regard to CADP scales. The five-factor structures found in our report were comparable to the Openness to Experiences (or Intellect), Neuroticism, Conscientiousness, Extraversion and Agreeableness found in other cultures. The normative data of the CADP is presented.