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Aluminium alloy 6082-T6 is known for its high strength and cost effectiveness, making it a popular choice for thin sheet structures such as automotive body panels. However, the welding-induced softening of the heat affected zone (HAZ) and the insufficient strength of the filler metal are the primary factors limiting its broader application. In this study, the lower heat input cold metal transfer welding method is employed, utilizing heat-treatable reinforced AlSi10Cu2Mg0.7Zn1 wire to weld 6082-T6 plates. Additionally, the impact of the baking paint process within the automotive manufacturing workflow on the evolution of microstructural characteristics of the HAZ and Weld Zone (WZ), as well as the mechanical properties of the joints, is investigated in detail. The findings indicate that the AlSi10Cu2Mg0.7Zn1 filled WZ contains a small quantity of θ, Q, and β phases, alongside a significant amount of reticulated eutectic Si distributed among the Al dendrites. Notably, the strength of the WZ is enhanced compared to that of conventional Al–Si and Al–Mg wires. During the baking process, the WZ exhibits the precipitation of a small amount of fine β’’ and β′ precipitates, which elevates the tensile strength of the WZ to 272 MPa and the yield strength to 179 MPa. Furthermore, the baking treatment promotes the further growth of β′ in the HAZ of the joints, while fine β’’ phases are reprecipitated within the Al matrix. The combined influence of these two precipitated phases results in an increase in the tensile strength of the HAZ by 29 MPa.

Abstract Background This retrospective cohort study aims to compare the effectiveness and safety of warfarin, rivaroxaban, and dabigatran in atrial fibrillation (AF) patients with different CHA2DS2-VASc scores in northern China. Methods A retrospective cohort study was performed to evaluate anticoagulation in AF patients at the second affiliated hospital of Harbin Medical University from September 2018 to August 2019. Patients included in this study (n = 806) received warfarin (n = 300), or rivaroxaban (n = 203), or dabigatran (n = 303). Baseline characteristics and follow-up data including adherence, bleeding events and ischemic stroke (IS) events were collected. Results Patients receiving rivaroxaban (73.9%) or dabigatran (73.6%) showed better adherence than those receiving warfarin (56.7%). Compared with warfarin-treated patients, dabigatran-treated patients had lower incidence of bleeding events (10.9% vs 19.3%, χ2 = 8.385, P = 0.004) and rivaroxaban-treated patients had lower incidence of major adverse cardiovascular events (7.4% vs 13.7%, χ2 = 4.822, P = 0.028). We classified patients into three groups based on CHA2DS2-VASc score (0–1, 2–3, ≥ 4). In dabigatran intervention, incidence of bleeding events was higher in patients with score 0–1 (20.0%) than those with score 2–3 (7.9%, χ2 = 5.772, P = 0.016) or score ≥ 4 (8.6%, χ2 = 4.682, P = 0.030). Patients with score 0–1 in warfarin or rivaroxaban therapy had a similar but not significant increase of bleeding compared with patients with score 2–3 or score ≥ 4, respectively. During the follow-up, 33 of 806 patients experienced IS and more than half (19, 57.6%) were patients with score ≥ 4. Comparing patients with score 0–1 and 2–3, the latter had an significant reduction of IS in patients prescribed warfarin and non-significant reduction in rivaroxaban and dabigatran therapy. Conclusion Compared with warfarin therapy, patients with different CHA2DS2-VASc scores receiving either rivaroxaban or dabigatran were associated with higher persistence. AF patients with score ≥ 4 were more likely to experience IS events while hemorrhagic tendency preferred patients with low score 0–1.

Twinning is widely recognized as an effective and cost-efficient method for controlling the microstructure and properties of wrought magnesium (Mg) alloys. Specifically, twins play a crucial role in initiating dynamic recrystallization (DRX), while twin regions experience rapid recrystallization during static recrystallization (SRX). The activation of twinning can lead to changes in lattice orientation, significantly impacting the final texture in Mg alloys. The active roles of twinning are influenced by various factors during the activation process, and the mobility of twin boundaries (TB) can be amplified by stress effects, dislocation interactions, and thermal effects. Conversely, annealing treatments that involve proper segregation or precipitation on TBs serve to stabilize them, restraining their motion. Events such as segregation may also alter the twinning propensity in Magnesium-rare earth (Mg-RE) alloys. While {10–11} contraction twins (CT) and {10–11}-{10–12} double twins (DT) can promote dynamic recrystallization (DRX), they also pose a risk as potential sources of voids and cracks. Additionally, understanding the nucleation and growth mechanisms of twinning is crucial, and these aspects are briefly reviewed in this article. Considering the factors mentioned above, this article summarizes the recent research progress in this field, shedding light on advancements in recent eras.

Abstract Background Compared to traditional supervised machine learning approaches employing fully labeled samples, positive-unlabeled (PU) learning techniques aim to classify “unlabeled” samples based on a smaller proportion of known positive examples. This more challenging modeling goal reflects many real-world scenarios in which negative examples are not available—posing direct challenges to defining prediction accuracy and robustness. While several studies have evaluated predictions learned from only definitive positive examples, few have investigated whether correct classification of a high proportion of known positives (KP) samples from among unlabeled samples can act as a surrogate to indicate model quality. Results In this study, we report a novel methodology combining multiple established PU learning-based strategies with permutation testing to evaluate the potential of KP samples to accurately classify unlabeled samples without using “ground truth” positive and negative labels for validation. Multivariate synthetic and real-world high-dimensional benchmark datasets were employed to demonstrate the suitability of the proposed pipeline to provide evidence of model robustness across varied underlying ground truth class label compositions among the unlabeled set and with different proportions of KP examples. Comparisons between model performance with actual and permuted labels could be used to distinguish reliable from unreliable models. Conclusions As in fully supervised machine learning, permutation testing offers a means to set a baseline “no-information rate” benchmark in the context of semi-supervised PU learning inference tasks—providing a standard against which model performance can be compared.

Generating high quality histopathology images like immunohistochemistry (IHC) stained images is essential for precise diagnosis and the advancement of computer-aided diagnostic (CAD) systems. Producing IHC images in laboratory is quite expensive and time consuming. Recently, some attempts have been made based on artificial intelligence techniques (particularly, deep learning) to generate IHC images. Existing IHC stained image generation methods, still have a limited performance due to the complex structures and variations in cells shapes, potential nonspecific staining and variable antibody sensitivity. This paper proposes a novel technique known as harmonic conditional generative adversarial network (HcGAN) for generating high quality IHC-stained images. To generate the IHC images, the HcGAN model is fed with the widely available hematoxylin and eosin (H&E) images that contain cellular and morphological underlying structures of diverse cancer tissues. Such approach helps generate high quality IHC images mimicking the real ones that highlight the positive cells. The proposed HcGAN model is based generative adversarial learning with generator and discriminator networks. In HcGAN, harmonic convolution based on discrete cosine transform filter banks is employed in the generator and discriminator networks instead of the standard convolution in order to improve visual quality of the generated images and address the issue of overfitting. Our qualitative and quantitative results demonstrate that the proposed HcGAN achieved the highest performance over state-of-the-art methods using two publicly available datasets.

This paper employed squeeze-casting (SC) technology to develop a novel Al-7Si-1.5Cu-1.2Ni-0.4Mg-0.3Mn-0.15Ti heat-resistant alloy, addressing the issue of low room/high temperature elongation in traditional gravity casting (GC). Initially, the effects of SC and GC processes on the microstructure and properties of the alloy were investigated, followed by an examination of the evolution of the microstructure and properties of the SC samples over thermal exposure time. The results indicate that the SC process significantly improves the alloy’s microstructure. Compared to the GC alloy, the secondary dendrite arm spacing of the as-cast SC alloy is refined from 50.5 μm to 18.5 μm. Meanwhile, the size and roundness of the eutectic Si phase in the T6-treated SC alloy are optimized from 11.7 μm and 0.75 μm to 9.5 μm and 0.85 μm, respectively, and casting defects such as porosity are reduced. Consequently, the ultimate tensile strengths (UTSs) at room temperature and at 250 °C of the SC alloy are 5% and 4.9% higher than that of GC alloy, respectively, and its elongation at both temperatures shows significant improvement. After thermal exposure at 250 °C for 120 h, the morphology of the residual second phase at the grain boundaries in the SC alloy becomes more rounded, but the eutectic Si and nano-precipitates undergo significant coarsening, resulting in a 49% decrease in UTS.

With increasing dual pressure from global large energy consumption and environmental protection, multiple integrated energy systems (IESs) can provide more effective ways to achieve better energy utilization performance. However, in actual circumstances, many challenges have been brought to coupling multiple energy sources along with the uncertainty of each generated power to achieve efficient operation of IESs. To resolve this problem, this article reviews primary research on integrated energy optimization and scheduling technology to give constructive guidance in power systems. Firstly, the conceptual composition and classification of IESs are presented. Secondly, the coupling relationship between multiple energy sources based on mathematical expression is studied deeply. Thirdly, the scheduling of IESs with different types and regions is classified, analyzed, and summarized for clarification. Fourthly, on this basis, potential solutions for applications of key optimization technologies involved in the scheduling process in IESs can be found systematically. Finally, the future development trends to optimize scheduling integrated energy systems is explored and prospected in depth.

The advent of the digital economy has brought new opportunities to food marketing. In China, many food businesses have begun to use interactions under specific social media topics to open up new sales channels. Green food, as a representative of environmentally related topics, is increasingly influencing consumer choices through online interactions. In light of this, this study collected data from a large group of participants engaged in online green interactions to explore the psychological mechanisms behind consumers’ choices of green food in an online context. The findings indicate that online green interactions positively influence the willingness to purchase green food, with environmental self-efficacy and flow experience serving as mediators in this relationship. Information trust and consumer traits act as boundary conditions. This study not only deepens the understanding of food consumer behavior in the digital context, but also provides important references for food companies on how to more effectively utilize online interaction to promote the market expansion of green food.

Digital technology has the function of information governance, and digital transformation of enterprises may be the key way to identify and restrain ESG greenwashing. Based on the theory of digital empowerment, this study analyzes the influence and mechanism of digital transformation on restraining corporate green washing behavior from the perspective of internal and external factors. This study takes A-share listed companies in 2012–2022 as research samples and tests the effectiveness of digital transformation. Research has found that (1) digital transformation can significantly suppress corporate greenwashing behavior, and this conclusion still holds after a series of endogeneity and robustness tests. (2) In the context of high environmental awareness among executives, the inhibitory effect of digital transformation on corporate ESG greenwashing is more pronounced. (3) Mechanism analysis shows that digital transformation has inhibited the company’s greenwashing behavior by increasing the attention of investors. (4) Heterogeneity analysis shows that in state-owned enterprises, non-heavily polluting industries, high-tech industries, and enterprises located in the eastern region digital transformation has a more effective inhibitory effect on corporate greenwashing behavior. This study examines the impact of digital transformation on corporate ESG greenwashing, expands the research on the non-economic effects of digital transformation, and may provide empirical evidence for improving the quality of ESG information disclosure and sustainable development of enterprises.

Body sizes and head anatomical characteristics play the major role in the head injuries sustained by vulnerable road users (VRU) in traffic accidents. In this study, in order to study the influence mechanism of body sizes and head anatomical characteristics on head injury, we used age, gender, height, and Body Mass Index (BMI) as characteristic parameters to develop the personalized human body multi-rigid body (MB) models and head finite element (FE) models. Next, using simulation calculations, we developed the VRU head injury dataset based on the personalized models. In the dataset, the dependent variables were the degree of head injury and the brain tissue von Mises value, while the independent variables were height, BMI, age, gender, traffic participation status, and vehicle speed. The statistical results of the dataset show that the von Mises value of VRU brain tissue during collision ranges from 4.4 kPa to 46.9 kPa at speeds between 20 and 60 km/h. The effects of anatomical characteristics on head injury include: the risk of a more serious head injury of VRU rises with age; VRU with higher BMIs has less head injury in collision accidents; height has very erratic and nonlinear impacts on the von Mises values of the VRU’s brain tissue; and the severity of head injury is not significantly influenced by VRU’s gender. Furthermore, we developed the classification prediction models of head injury degree and the regression prediction models of head injury response parameter by applying eight different data mining algorithms to this dataset. The classification prediction models have the best accuracy of 0.89 and the best R2 value of 0.85 for the regression prediction models.

The high momentum tail (HMT) of nucleons, as a signature of the short-range correlations in nuclei, has been investigated by the high-energy bremsstrahlung γ rays produced in 86Kr+124Sn at 25 MeV/nucleon. The energetic photons are measured by a CsI(Tl) hodoscope mounted on the spectrometer CSHINE. The energy spectrum ≥35 MeV can be reproduced by the Isospin- and Momentum-Dependent Boltzmann-Uehling-Uhlenbeck model calculations incorporating the photon production channel from np process in which the HMTs of nucleons is considered. A non-zero HMT ratio of about 15% is favored by the data. The effect of the capture channel np→dγ is demonstrated.

Summary: Correlates of protection (CoPs) are key guideposts that both support vaccine development and licensure as well as improve our understanding of the attributes of immune responses that may directly provide protection. Unfortunately, factors such as low rate of exposure and low efficacy can result in low power to discover correlates in field trials—making it difficult to identify these guideposts for the pathogens against which there is greatest need for further insights. To address this gap, we examine the ability of positive-unlabeled (PU) learning approaches to use immunogenicity data and infection status outcomes to accurately predict protection status. We report a combination of PU bagging and two-step reliable negative techniques that accurately classify the protection status of unlabeled (uninfected) samples from synthetic and real-world humoral immune response profiles in human trials and animal models and lead to the discovery of CoPs that are “missed” using conventional infection status case-control analysis.

Abstract The shape and internal structure of an atomic nucleus can change significantly with increasing excitation energy, angular momentum, or isospin asymmetry. As an example of this structural evolution, linear-chain configurations in carbon or heavier isotopes have been predicted for decades. Recent studies have found non-stability of this structure in 12C while evidenced its appearance in 16C. It is then necessary to investigate the linear-chain molecular structures in 14C to clarify the exact location on the nuclear chart where this structure begins to emerge, and thus to benchmark theoretical models. Here we show a cluster-decay experiment for 14C with all final particles coincidentally detected, allowing a high Q-value resolution, and thus a clear decay-path selection. Unambiguous spin-parity analyses are conducted, strongly evidencing the emergence of the π-bond linear-chain molecular rotational band in 14C. The present results encourage further studies on even longer chain configurations in heavier neutron-rich nuclei.

The integration of visual algorithms with infrared imaging technology has become an effective tool for industrial gas leak detection. However, existing research has mostly focused on simple scenarios where a gas plume is clearly visible, with limited studies on detecting gas in complex scenes where target contours are blurred and contrast is low. This paper uses a cooled mid-wave infrared (MWIR) system to provide high sensitivity and fast response imaging and proposes the MWIRGas-YOLO network for detecting gas leaks in mid-wave infrared imaging. This network effectively detects low-contrast gas leakage and segments the gas plume within the scene. In MWIRGas-YOLO, it utilizes the global attention mechanism (GAM) to fully focus on gas plume targets during feature fusion, adds a small target detection layer to enhance information on small-sized targets, and employs transfer learning of similar features from visible light smoke to provide the model with prior knowledge of infrared gas features. Using a cooled mid-wave infrared imager to collect gas leak images, the experimental results show that the proposed algorithm significantly improves the performance over the original model. The segment mean average precision reached 96.1% (mAP50) and 47.6% (mAP50:95), respectively, outperforming the other mainstream algorithms. This can provide an effective reference for research on infrared imaging for gas leak detection.

The effective connection of 1050 Al and AZ31 Mg was realized by magnetic pulse welding. The maximum tensile-shear force of the dissimilar Al/Mg metal lap joint reached 97% of that of the 1050 Al alloy base material by optimizing the process parameters. The microstructure of dissimilar Al/Mg welded joints was analysed by Scanning Electron Microscope (SEM), Energy Dispersive Spectrometer (EDS) and Electron Backscattered Diffraction (EBSD). The results show that the key to obtaining high shear strength of Al/Mg dissimilar metal joints is mainly due to the following two reasons. On the one hand, grain refinement and element interdiffusion occur at the interface. On the other hand, no intermetallic compounds are formed at the interface.

Abstract Due to the significant effects of the human anatomical characteristics on the injury mechanism of passenger in traffic accidents, it is necessary to develop human body FEM (Finite Element Model) with detailed anatomical characteristics. However, traditional development of a human body FEM is an extremely complicated process. In particular, the meshing of human body is a huge and time-consuming project. In this paper, a new fast methodology based on CPD (Coherent Point Drift) and RBF (Radial Basis Function) was proposed to achieve the rapid developing the FEM of human bone with detailed anatomical characteristics. In this methodology, the mesh morphing technology based the RBF was used to generate FEM mesh in the geometry extracted from the target CT (Computed Tomography) data. In order to further improve the accuracy and speed of mesh morphing, the target geometric feature points required in the mesh morphing process were realized via the rapid and automatic generation based on the point-cloud registration technology of the CPD algorithm. Finally, this new methodology was used to generate a 3-year-old ribcage FEM consisting of a total of 27,728 elements with mesh size 3–5 mm based on the THUMS (Total Human Model for Safety) adult model. In the entire process of generating this new ribcage model, it only took about 2.7 s. The average error between the new FEM and target geometries was only about 2.7 mm. This indicated that the new FEM well described the detailed anatomical characteristics of target geometry, thus importantly revealing that the mesh quality of the new FEM was basically similar to that of source FEM.

Despite the industrial significance of grain size for enhancing mechanical properties and formability, the in-depth deformation mechanisms at elevated temperature are still unclear. To investigate the functions of grain size on hot workability and deformation mechanisms, three groups of Mg-1.2Zn-0.2Y alloy specimens with different grain sizes were hot compressed and then studied by combining constitutive model, processing map and microstructural observations. The results showed that the enhanced hot workability accompanying low deformation activation energy and small instability regime was obtained with refined grain size. During hot deformation, the decreased grain size in Mg-1.2Zn-0.2Y alloy mainly improved the plastic deformation homogeneity, especially for the weakened local straining around grain boundaries. As a result, the dynamic recrystallization nucleation and texture development at lower strain level were influenced by the initial grain size. At higher strain magnitude, the growth and coarsening of dynamic recrystallized grains would further release strain localization and improve hot workability, while the texture was less impacted. Further, unlike the primary basal slip and deformation twinning in the specimen with coarse grain at low temperature, non-basal slips of dislocations were initiated with less deformation twins in the specimens with refined grain size.

Microcellulose materials are increasingly considered multifunctional candidates for emerging energy applications. Microcellulose fibers (MCF) are a kind of bio-based reinforcement in composites, and their hydrophilic character hinders their wide application in industry. Thus, in the present work, MCF was hybrid-modified by amino silicone oil-phosphorylated to fabricate hydrophobic, thermal stability, and flame-retardant microcellulose fibers for potential application in vehicle engineering. The results showed that the amino silicone oil-phosphorylated (ASOP) hybrid modification could transform the surface property of microcellulose from hydrophilic to hydrophobic and improve the compatibility between MCF and resin matrix. Meanwhile, the ASOP treatment led to the formation of an amino silicone oil film layer on the surface of the microcellulose, which improved the thermal stability of the MCF. Furthermore, the ASOP hybrid modification microcellulose fibers paper (100% microcellulose fibers paper) was transformed from flammable to flame-retardant and showed self-extinguishing behavior after burning under flame for 2 s. The flame-retardant mechanism was attributed to the formation of the char layer in the condensed phase and the production of non-combustible gases in the gaseous phase.

Electric wheel-drive multi-axle heavy-duty vehicles have the characteristics of strong maneuverability and good passability, thereby they are widely used in heavy equipment transportation. However, current research on the composite braking of multi-axle heavy-duty vehicles is rare, which is not conducive to improving braking performance and braking energy utilization efficiency. This work proposes a multi-mode composite braking control strategy for the five-axle distributed electric wheel-drive heavy-duty vehicle. Firstly, given the differences in braking dynamics between two-axle vehicles and multi-axle vehicles, the brake dynamics characteristics of multi-axle vehicles are analyzed, and the vehicle dynamics model of multi-axle vehicles is constructed. Next, a multi-mode composite braking control strategy including a fully electric braking state and hybrid electro–hydraulic braking state is proposed in order to improve the braking energy recovery and braking stability. Finally, a hardware-in-the-loop simulation system is established, and the single-braking conditions and China heavy-duty commercial vehicle test cycle-heavy truck (abbreviated as CHTC-HT) are conducted to verify the performance of the braking control strategy. The results indicate that the recaptured braking energy and braking stability are significantly increased by applying the control strategy proposed in this work.

Human‐like and creature‐like systems are one of the most representative imaginary blueprints of future robots. To fulfill this blueprint, the development of high‐performance actuators across different length scales is indispensable. Owing to their mechanical compliance and conformability to curvy surfaces of living organisms, flexible actuators have emerged as an essential direction of next‐generation actuators. This review focuses on thin‐film‐shaped flexible actuators (TFFAs), a rising family of flexible actuators, aiming to provide an overview of the state‐of‐art status in this exciting direction. The designs, manufacturing, and mechanisms of various TFFAs are summarized, according to their key composing materials/mechanisms, including, for example, nanomaterials, liquid crystal elastomers, shape memory polymers/alloys, hydrogels, biohybrids, and other mechanisms/materials. The representative applications of TFFAs are introduced, ranging from biomedical uses, robots for environment explorations, to haptic interfaces and reconfigurable electronics. Finally, the grand challenges and open opportunities are discussed in detail.

Four-wheel-drive system with multiple motors has been one of the important development trends of electric vehicles, which has an important impact on the regenerative braking of electric vehicles. Aiming at the configuration characteristics of a composite braking system for the four-wheel-drive electric vehicle with multiple motors, this paper proposed a novelty composite braking control strategy based on braking energy recovery optimization. Firstly, the characteristics and constraints of braking dynamics of four-wheel-drive electric vehicles with multiple motors were analyzed. Secondly, the composite braking control strategy to optimize braking energy recovery was developed. The control strategy mainly optimized the motor braking of the front and rear axle according to the braking energy recovery efficiency within the range of motor braking to maximize the recovery of braking energy. And the motor and hydraulic braking forces were concertedly distributed to improve braking stability. Finally, the semi-physical simulation platform is used to verify the composite braking control strategy under single braking conditions and CLTC-P cycle condition. The test results demonstrate that the composite braking control strategy proposed in this paper can effectively improve braking energy recovery and braking stability.

Abstract Adjuvants can alter the magnitude, characteristics, and persistence of the humoral response to protein vaccination. HIV vaccination might benefit from tailored adjuvant choice as raising a durable and protective response to vaccination has been exceptionally challenging. Analysis of trials of partially effective HIV vaccines have identified features of the immune response that correlate with decreased risk, including high titers of V1V2-binding IgG and IgG3 responses with low titers of V1V2-binding IgA responses and enhanced Fc effector functions, notably antibody-dependent cellular cytotoxicity (ADCC) and antibody-dependent cellular phagocytosis (ADCP). However, there has been limited opportunity to compare the effect of different adjuvants on these activities in humans. Here, samples from the AVEG015 study, a phase 1 trial in which participants (n = 112) were immunized with gp120SF-2 and one of six different adjuvants or combinations thereof were assessed for antibody titer, biophysical features, and diverse effector functions. Three adjuvants, MF59 + MTP-PE, SAF/2, and SAF/2 + MDP, increased the peak magnitude and durability of antigen-specific IgG3, IgA, FcγR-binding responses and ADCP activity, as compared to alum. While multiple adjuvants increased the titer of IgG, IgG3, and IgA responses, none consistently altered the balance of IgG to IgA or IgG3 to IgA. Linear regression analysis identified biophysical features including gp120-specific IgG and FcγR-binding responses that could predict functional activity, and network analysis identified coordinated aspects of the humoral response. These analyses reveal the ability of adjuvants to drive the character and function of the humoral response despite limitations of small sample size and immune variability in this human clinical trial.

Abstract Sensors based on triboelectric nanogenerators (TENGs) have gained worldwide interest owing to their advantages of low cost and self‐powering. However, the detection of most triboelectric vibration sensors (TVS) is restricted to low frequency, whereas high‐frequency vibration signals are successfully measured in recent studies; their sensitivity still requires improvement. Hence, a highly sensitive vibration sensor based on TENG (HSVS‐TENG) with ultrawide frequency response is presented. This study is the first to introduce a quasi‐zero stiffness structure into the TENG to minimize the driving force by optimizing the magnetic induction intensity and the weight of the moving part. The results show that the HSVS‐TENG can measure vibrations with frequencies ranging from 2.5 to 4000 Hz, with a sensitivity ranging from 0.32 to 134.9 V g−1. Moreover, the sensor exhibits a good linear response versus the applied acceleration, and the linearity ranges from 0.08 to 2.81 V g−1. The self‐powered sensor can monitor the running state and fault type of the key components with a recognition accuracy of 98.9% by leveraging machine‐learning algorithms. The results reach a new height for the ultrawide frequency response and high sensitivity of the TVS and provide an idea for a follow‐up high‐resolution TVS.

In this study, the effects of Ni content on the microstructure and mechanical properties of Al-5.0Cu-1Mn-based alloys were investigated. The results demonstrate that Ni alloying refines the grain size and enhances castability. At low Ni content, some of Ni atoms participate in forming Al7Cu4Ni at the grain boundaries. The rest Ni segregates to the interface between α-Al and AlCu3Mn2 phase, which inhibits AlCu3Mn2 growth. The amount of Ni-rich phase at grain boundaries increases significantly as Ni concentration increases at the expense of the decline precipitates. After T6 treatment, the lamellar Ni-rich phase spheroidized into discrete particles that were uniformly distributed along the grain boundaries. Ni-rich phase of particles can effectively pin grain boundaries and prevent grain rotation. The optimum mechanical property is achieved with 1% Ni addition, predominantly due to the synergistic strengthening of eutectic strengthening and precipitation strengthening. The ultimate tensile strength, yield strength and elongation reach 505 MPa, 348 MPa and 16.5% respectively, which far exceed the current commercial cast aluminium alloys. With increasing Ni concentration, the fracture mode of the alloy changes from ductile fracture to brittle-ductile mixed fracture, because the high amount of Ni-rich phase intermetallic compounds increase the sites for localized brittle fractures.

Triboelectric nanogenerators (TENG), renowned for their remarkable capability to harness weak mechanical energy from the environment, have gained considerable attention owing to their cost-effectiveness, high output, and adaptability. This review provides a unique perspective by conducting a comprehensive and in-depth analysis of magnetically assisted TENGs that encompass structures, materials, and self-powered sensing systems. We systematically summarize the diverse functions of the magnetic assistance for TENGs, including system stiffness, components of the hybrid electromagnetic-triboelectric generator, transmission, and interaction forces. In the material domain, we review the incorporation of magnetic nano-composites materials, along with ferrofluid-based TENG and microstructure verification, which have also been summarized based on existing research. Furthermore, we delve into the research progress on physical quantity sensing and human-machine interface in magnetic-assisted TENGs. Our analysis highlights that magnetic assistance extends beyond the repulsive and suction forces under a magnetic field, thereby playing multifaceted roles in improving the output performance and environmental adaptability of the TENGs. Finally, we present the prevailing challenges and offer insights into the future trajectory of the magnetic-assisted TENGs development.

With the continuous improvement in people’s living standards and the change in consumption concept, green food is favored by more and more consumers. Consumer repurchase behavior is a necessary condition to activate the market, expand the consumption scale and stabilize the continuous growth of the market. Repurchase intention is the most direct factor affecting consumers’ green food repurchase intention. Therefore, it is necessary to study consumers green food repurchase intentions. This study collects data from 303 consumer surveys on green food consumption to explore the impact of consumer satisfaction with consumption experience on green food repurchase intention and further explore the mechanisms and influence boundaries. The results show that (1) consumer experience satisfaction positively affects green food repurchase intention; (2) consumer experience satisfaction can improve consumers’ green food repurchase intention through consumer perceptions of social value, green self-efficacy and warm glow; (3) the higher the degree of consumer inertia, the stronger the influence of green self-efficacy and warm glow on consumers’ green food repurchase intention; and (4) the higher the degree of consumer subjective norms, the stronger the influence of consumer perceived social value, green self-efficacy and warm glow on the consumer’s green food repurchase intention. This study provides a new perspective and theoretical framework for promoting consumers’ green food repurchase intention, and it may have certain theoretical significance and practical impact on green food market growth, sustainable carrying of the ecological environment and high-quality development of agriculture.

This work systematically studies the dynamic recrystallization (DRX) behavior of as-cast AZ91 magnesium alloy under hot compression deformation at 350 °C. These results were also compared with the former results obtained at 300 °C in order to evaluate the effect of deformation temperature on DRX behavior of as-cast AZ91 alloy. The results show that the “{10–12} extension twin DRX” in original grains, “original grain boundary bulging (BLG) accompanied by progressive lattice misorientation” at the original grain boundaries and “particle-stimulated nucleation (PSN)” around massive eutectic β phases are observed during compression at 350 °C. The DRX mechanism of as-cast AZ91 alloy during hot compression at 350 °C is continuous DRX. Compared with the hot compression at 300 °C, the main twin type changes from 38° {10–11}–{10–12} double twin (300 °C) to 86° {10–12} extended twins (350 °C) at the beginning of hot compression (ε = 10%). And the 30° {10–11}–{10–12} double twin forms in the sample compressed at 350 °C with further compressive strain (ε = 20%). The DRX grain size and DRX volume fraction increase after hot compression at 350 °C compared with hot compression at 300 °C.

Abstract Background While antibodies can provide significant protection from SARS-CoV-2 infection and disease sequelae, the specific attributes of the humoral response that contribute to immunity are incompletely defined. Methods We employ machine learning to relate characteristics of the polyclonal antibody response raised by natural infection to diverse antibody effector functions and neutralization potency with the goal of generating both accurate predictions of each activity based on antibody response profiles as well as insights into antibody mechanisms of action. Results To this end, antibody-mediated phagocytosis, cytotoxicity, complement deposition, and neutralization were accurately predicted from biophysical antibody profiles in both discovery and validation cohorts. These models identified SARS-CoV-2-specific IgM as a key predictor of neutralization activity whose mechanistic relevance was supported experimentally by depletion. Conclusions Validated models of how different aspects of the humoral response relate to antiviral antibody activities suggest desirable attributes to recapitulate by vaccination or other antibody-based interventions.

For unmanned electric drive chassis parameter optimization problems, an unmanned electric drive chassis model containing power systems and energy systems was built using CRUISE, and as the traditional genetic algorithm is prone to falling into the local optima, an improved isolation niche genetic algorithm based on KOHONEN network clustering (KIGA) is proposed. The simulation results show that the proposed KIGA can reasonably divide the initial niche populations. Compared with the traditional genetic algorithm (GA) and the isolation niche genetic algorithm (IGA), KIGA can achieve faster convergence and a better global search ability. The comprehensive performance of the unmanned electric drive chassis in terms of power and economy was increased by 8.26% with a set of better solutions. The results show that simultaneous power system and energy system parameter optimization can enhance unmanned electric drive chassis performance and that KIGA is an efficient method for optimizing the parameters of unmanned electric drive chassis.

This paper investigates an event-triggered adaptive neural tracking control issue for flexible-joint robot (FJR) systems subject to unknown dynamic and input saturation. To enable the backstepping design framework to be implemented, the input saturation nonlinearity is replaced by a smooth function. In the control design, the dynamic surface control (DSC) and adaptive neural techniques are used to handle the “explosion of complexity” issue and unknown dynamics, respectively. Furthermore, to reduce the calculated burden caused by the adaptive neural reconstruction technique, three virtual parameters are updated by using the single-parameter-learning approach. To decrease the frequency of actuator response to the control command for reducing the mechanical wear of actuator, an event triggering mechanism is established between the control law and actuator. Finally, an event-triggered adaptive neural tracking control solution is proposed, which is endowed the advantages as: (1) it does not need any priori knowledge of FJR systems; 2) it only needs to update three unknown parameters; 3) it can reduce the transmission frequency of the control commands and the response rate of the actuator. Using the Lyapunov stability theory, the proposed event-triggered control solution ensures that all signals of the closed-loop tracking control system are bounded. Finally, the simulation results verify the effectiveness and superiority of the proposed control scheme.

Equity incentive, as an institutional arrangement for the coordination of the interests of shareholders and managers, has been widely implemented by public companies in developed capital markets throughout Europe and America. However, does it work and/or when might it be more effective in emerging market economies such as China? We aimed to understand the effects of equity incentive plans implemented by listed companies in China and the potential influence of the general characteristics of contracts on the effectiveness of equity incentive plans. Based on behavioral decision theory, this paper adopts a multivariate linear regression model to analyze the 1695 equity incentive plans implemented in Chinese listed companies between 2010 and 2018 with their two-year lagged performance data. The empirical results show that the operational performance of companies after implementing equity incentive plans shows a trend of polarization. In the 95% confidence interval, the effect of restrictive stock incentive and exercise-constrained variables is not significant, while the validity period has a significant positive correlation and incentive intensity has a significantly negative correlation with the company's operational performance. Furthermore, the negative effects mentioned above become more obvious with a longer plan implementation period. Based on these conclusions, we suggest that companies could adopt equity incentive plans with a relatively longer validity period and more reasonable incentive intensity. Additionally, it would be better for companies to select non-restricted stocks as incentive tools if there is no obvious preference.

Purpose: This study was aimed at evaluating the safety and efficacy of warfarin versus rivaroxaban in patients with atrial fibrillation (AF) and different CHA2DS2-VASc score subgroups in northern China. Methods: A retrospective cohort study was conducted to evaluate 387 patients with AF who received treatment at our institution between September 2018 and August 2019. The patients were divided into two groups receiving either warfarin (n=194) or rivaroxaban (n=193). Follow-up data were collected, including adherence, bleeding and ischemic stroke events. Results: The group receiving rivaroxaban showed better adherence than the group receiving warfarin. In the warfarin-treated group, bleeding incidents declined with increasing scores. In the warfarin-treated group, patients with scores of 2–3 had greater adherence and fewer stroke occurrences. The events of bleeding and stroke did not significantly differ in patients in the rivaroxaban-treated group with different scores. Conclusions: Compared with patients in the warfarin group with different CHA2DS2-VASc scores, those in the rivaroxaban group had greater compliance, and fewer bleeding and stroke events. Regardless of economic considerations, rivaroxaban is preferable for anticoagulative AF treatment in northern Chinese patients.

Food safety is an important basis for promoting economic development, ensuring social stability and maintaining national security. Research on the evaluation of food security is the basis by which to accurately grasp the food security situation and to establish national food security policies in a scientific manner. Based on China’s agricultural economic data from 2001 to 2020, this research uses an entropy weight TOPSIS model to start from the new connotations and goals of food security in the new development stage, takes quantity security, structural security, ecological security of resources, economic security and policy security as the breakthrough points, builds a food security evaluation system containing 25 indicators, and aims to conduct evaluation and research on the evolution and current situation of China’s food security. The results show that China’s food security level drops first and then rises, that China attaches increasing importance to the ecological security and policy security of food resources, and that China’s food security level is restricted by such factors as resources, modes of production, circulation, storage, transportation, trade and structure. This article puts forward some policies and suggestions in terms of resources, technology and foreign trade to safeguard China’s food security.

Following radiotherapy, patients have decreased bone mass and increased risk of fragility fractures. Diabetes mellitus (DM) is also reported to have detrimental effects on bone architecture and quality. However, no clinical or experimental study has systematically characterized the bone phenotype of the diabetic patients following radiotherapy. After one month of streptozotocin injection, three-month-old male rats were subjected to focal radiotherapy (8 Gy, twice, at days 1 and 3), and then bone mass, microarchitecture, and turnover as well as bone cell activities were evaluated at 2 months post-irradiation. Micro-computed tomography results demonstrated that DM rats exhibited greater deterioration in trabecular bone mass and microarchitecture following irradiation compared with the damage to bone structure induced by DM or radiotherapy. The serum biochemical, bone histomorphometric, and gene expression assays revealed that DM combined with radiotherapy showed lower bone formation rate, osteoblast number on bone surface, and expression of osteoblast-related markers (ALP, Runx2, Osx, and Col-1) compared with DM or irradiation alone. DM plus irradiation also caused higher bone resorption rate, osteoclast number on bone surface, and expression of osteoclast-specific markers (TRAP, cathepsin K, and calcitonin receptor) than DM or irradiation treatment alone. Moreover, lower osteocyte survival and higher expression of Sost and DKK1 genes (two negative modulators of Wnt signaling) were observed in rats with combined DM and radiotherapy. Together, these findings revealed a higher deterioration of the diabetic skeleton following radiotherapy, and emphasized the clinical importance of health maintenance.

Resting-state functional magnetic resonance imaging (rs-fMRI) has been used to construct functional connectivity (FC) in the brain for the diagnosis and analysis of brain disease. Current studies typically use the Pearson correlation coefficient to construct dynamic FC (dFC) networks, and then use this as a network metric to obtain the necessary features for brain disease diagnosis and analysis. This simple observational approach makes it difficult to extract potential high-level FC features from the representations, and also ignores the rich information on spatial and temporal variability in FC. In this paper, we construct the Latent Space Representation Network (LSRNet) and use two stages to train the network. In the first stage, an autoencoder is used to extract potential high-level features and inner connections in the dFC representations. In the second stage, high-level features are extracted using two perspective feature parses. Long Short-Term Memory (LSTM) networks are used to extract spatial and temporal features from the local perspective. Convolutional neural networks extract global high-level features from the global perspective. Finally, the fusion of spatial and temporal features with global high-level features is used to diagnose brain disease. In this paper, the proposed method is applied to the ANDI rs-fMRI dataset, and the classification accuracy reaches 84.6% for NC/eMCI, 95.1% for NC/AD, 80.6% for eMCI/lMCI, 84.2% for lMCI/AD and 57.3% for NC/eMCI/lMCI/AD. The experimental results show that the method has a good classification performance and provides a new approach to the diagnosis of other brain diseases.

Peptide antioxidants are becoming increasingly popular in the food industry. This study reported antioxidant and anticancer properties of papain-hydrolyzed sorghum kafirin. The optimum hydrolysis conditions were determined to be 360 kU/g enzyme-to-substrate ratio, and 4 h of reaction time. After ultrafiltration, the 1–3 kDa hydrolysate fraction demonstrating promising antioxidative activities as well as yield was selected for further analyses. In an oil-in-water emulsion system, the incorporation of the selected fraction of hydrolysates at 10 mg/mL resulted in an average inhibition of primary and secondary oxidation products by 53.7% and 43.0%, respectively, without significantly changing emulsion turbidity or stability. In a ground meat system, an average of 32.1% inhibition towards lipid peroxidation was also detected when incorporated at 1.0 mg/g. The kafirin hydrolysates also effectively reduced HepG2 cell growth via non-toxic mechanisms, indicating its anticancer potential. Further, the 1–3 kDa peptides were fractionated and 13 peptide sequences were identified using RP-HPLC analysis followed by MALDI-TOF/TOF MS from the most potent fraction.

Convalescent plasma has been deployed globally as a treatment for COVID-19, but efficacy has been mixed. Better understanding of the antibody characteristics that may contribute to its antiviral effects is important for this intervention as well as offer insights into correlates of vaccine-mediated protection.

Understanding humoral immune responses to SARS-CoV-2 infection will play a critical role in the development of vaccines and antibody-based interventions. We report systemic and mucosal antibody responses in convalescent individuals who experienced varying severity of disease. Whereas assessment of neutralization and antibody-mediated effector functions revealed polyfunctional antibody responses in serum, only robust neutralization and phagocytosis were apparent in nasal wash samples. Serum neutralization and effector functions correlated with systemic SARS-CoV-2-specific IgG response magnitude, while mucosal neutralization was associated with nasal SARS-CoV-2-specific IgA. Antibody depletion experiments support the mechanistic relevance of these correlations. Associations between nasal IgA responses, virus neutralization at the mucosa, and less severe disease suggest the importance of assessing mucosal immunity in larger natural infection cohorts. Further characterization of antibody responses at the portal of entry may define their ability to contribute to protection from infection or reduced risk of hospitalization, informing public health assessment strategies and vaccine development efforts.

Real-time noninvasive monitoring of crop water information is an important basis for water-saving irrigation and precise management. Nano-electronic technology has the potential to enable smart plant sensors to communicate with electronic devices and promote the automatic and accurate distribution of water, fertilizer, and medicine to improve crop productivity. In this work, we present a new flexible graphene oxide (GO)-based noninvasive crop water sensor with high sensitivity, fast responsibility and good bio-interface compatibility. The humidity monitoring sensitivity of the sensor reached 7945 Ω/% RH, and the response time was 20.3 s. We first present the correlation monitoring of crop physiological characteristics by using flexible wearable sensors and photosynthesis systems, and have studied the response and synergistic effect of net photosynthetic rate and transpiration rate of maize plants under different light environments. Results show that in situ real-time sensing of plant transpiration was realized, and the internal water transportation within plants could be monitored dynamically. The synergistic effect of net photosynthetic rate and transpiration of maize plants can be jointly tested. This study provides a new technical method to carry out quantitative monitoring of crop water in the entire life cycle and build smart irrigation systems. Moreover, it holds great potential in studying individual plant biology and could provide basic support to carry out precise monitoring of crop physiological information.

As Beryllium is an important element in primary nucleosynthesis, its nuclear reaction cross-sections induced by charged projectiles need to be measured precisely. The astrophysical S(Ei) factors were measured in the ultra-low energy range from Elab=18 to 100 keV in 2-keV increments (42 points). Both the astrophysical S(E) curve (Sbare(0)=16.2±1.8MeVb) and the screening energy (Us=545±98eV) were deduced. In regard to astrophysical applications of the Sbare(E) curve, the present results are consistent with Zahnow's direct measurement. For the long-standing problem of ‘abnormal’ screening effect, our screening energy result also agrees with that obtained using the Trojan-horse method (Us=676±86eV) but smaller than Zahnow's value (900±50eV), although all results are much larger than that obtained in the adiabatic limit (∼264 eV). Keywords: p–9Be reaction, Screening potential, Astrophysical factor

In the present study, mechanical property, microstructure and texture of an extruded Mg–1.0Al–1.5Ca–1.0Mn (wt.%) flat-oval tube in both the flat and the oval regions were systematically studied. Our results show that there exists a great difference in microstructure and texture between the flat and the oval regions. Both the flat and the oval regions have fine DRXed grains of 3–4 µm, while more row stacked grains are observed in the flat region. A large number of fine particles with a size of about 0.5 µm are dispersed in both the grain boundary and grain interior. The particles exhibit a uniform distribution in the oval region, while they tend to aggregate into bands along the extrusion direction (ED) in the flat region. The texture in the oval region is similar to that in extruded Mg rods with a preferred distribution of prismatic planes and random distribution of (0002) poles around the ED, while that in the flat region is close to that in extruded sheets containing two texture components, //normal direction (ND) and //transverse direction (TD). A high fraction of //TD component, about 52–62%, is observed in the flat region. The flat region has a tensile yield strength of 254 MPa, an ultimate tensile strength of 290 MPa and an elongation to failure of 4.3%. Keywords: Magnesium alloy, Extrusion, Microstructures, Mechanical properties

Sorghum has a significant amount of proteins, especially kafirin; however, limited information is available on evaluating its potential for peptide antioxidants. The objectives of this study were to: (1) investigate the effects of two key variables, enzyme-to-substrate ratio and reaction time on kafirin hydrolysis using Alcalase; (2) evaluate the antioxidant performances of the hydrolysates and fractions from membrane ultrafiltration and gel filtration; and (3) identify peptide sequences in the antioxidant fraction using MALDI-TOF/TOF MS. Kafirin hydrolysates prepared at enzyme-to-substrate ratio of 0.4 Au/g and 4 h had a good balance of antioxidant activity, yield, and economic efficiency. Medium-sized fraction of hydrolysates (5–10 kDa) from membrane filtration possessed the highest antioxidant activities among various fractions. The fraction also unveiled a good inhibition effect against lipid oxidation in emulsion and ground meat systems. Smaller-sized fraction (F3) collected through gel-filtration chromatography had significantly stronger antioxidant activities than other fractions, and 26 representative peptide sequences were identified in the fraction.

Purple membranes (PM) of the bacteria Halobacterium salinarum are a unique natural membrane where bacteriorhodopsin (BR) can convert photon energy and pump protons. Elucidating the electronic properties of biomembranes is critical for revealing biological mechanisms and developing new devices. We report here the electric properties of PMs studied by using multi-functional electric force microscopy (EFM) at the nanoscale. The topography, surface potential, and dielectric capacity of PMs were imaged and quantitatively measured in parallel. Two orientations of PMs were identified by EFM because of its high resolution in differentiating electrical characteristics. The extracellular (EC) sides were more negative than the cytoplasmic (CP) side by 8 mV. The direction of potential difference may facilitate movement of protons across the membrane and thus play important roles in proton pumping. Unlike the side-dependent surface potentials observed in PM, the EFM capacitive response was independent of the side and was measured to be at a dC/dz value of ~5.25 nF/m. Furthermore, by modification of PM with de novo peptides based on peptide-protein interaction, directional oriented PM assembly on silicon substrate was obtained for technical devices. This work develops a new method for studying membrane nanoelectronics and exploring the bioelectric application at the nanoscale.