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The inverse analysis of the deformation moduli of high arch dams based on displacement monitoring data is essential for structural safety assessment. In traditional inverse analysis methods, the deformation moduli are identified based on the single‐objective optimization and the hydrostatic component derived from the statistical model. This type of method has two main shortcomings: First, it treats the essential multi‐objective optimization problem as a single‐objective problem; second, the extracted hydrostatic component may be biased due to the multicollinearity of variables in the statistical model. This paper presents a methodology for the inverse analysis of the deformation moduli of high arch dams under a multi‐objective optimization strategy. The methodology employs empirical mode decomposition to extract the aging component from displacement monitoring data. Then, thermomechanical analysis is used to reconstruct the remaining hydrostatic and temperature components, thereby avoiding the biases encountered in solving the statistical model. The adaptive polynomial chaos expansion method is embedded in the NSGA‐III algorithm to establish and solve multi‐objective functions in the inverse analysis. Additionally, a composite decision index considering errors and test information is proposed to determine acceptable deformation moduli from the Pareto solution set. A high arch dam is selected to illustrate this methodology with static and dynamic monitoring data. The results show that the identified deformation moduli have errors of 3.8% and 7.2% in displacement and acceleration, respectively. The proposed methodology can yield deformation modulus values that are more consistent with the physical implications than those of the single‐objective optimization method. [ABSTRACT FROM AUTHOR]

The fourth-order governing equation of Euler-Bernoulli beams necessitates the employment of C1 Hermite shape functions in finite element analysis. However, unlike the C0 finite element shape functions that can be easily formulated as Lagrangian polynomials in a unified manner, the Hermite shape functions of Euler-Bernoulli beam elements are usually constructed case by case. In this work, a simple and unified meshfree path is proposed to develop arbitrary order Hermite shape functions of Euler-Bernoulli beam elements. This approach is realized by proving that the Hermite reproducing kernel meshfree shape functions degenerate to the interpolatory Hermite finite element shape functions under a proper choice of support sizes. The proposed approach provides an explicit formalism for Hermite finite element shape functions, which enables an easy construction of arbitrary order Hermite beam elements. Accordingly, in addition to the conventional cubic Hermite beam elements, explicit shape functions, mass and stiffness matrices for quintic and septic Hermite beam elements are presented. Meanwhile, the frequency errors and general higher order mass matrix formulation for these Hermite beam elements are elaborated as well. These theoretical results are consistently demonstrated by numerical examples with respect to both static and free vibration analysis. [ABSTRACT FROM AUTHOR]

This paper investigates the effect of three different alkalis, namely ammonia (NH4OH), ethylenediamine (EDA), and tetrabutylammonium hydroxide (TBAOH) on the stability of colloidal silica, with pH controlled within the range of 8–11. As a result, NH4OH greatly promotes the stability of silica sol at first due to the strong solvation ability of NH4+ and then the stability begins to decrease because the electric double layer of silica is compressed. The addition of EDA into silica system leads to a reduction in the stability evidently followed by a slight increase, which can be explained by the cationic bridging effect of the ethylenediammonium cations. Meanwhile, the effect of TBAOH on silica dispersion is analogous to EDA, mainly caused by the hydrophobic and steric effects. The abovementioned relevant stability mechanisms are involved in non-DLVO theory. [ABSTRACT FROM AUTHOR]

Background: Cholesterol (CHO) is an essential component of the body. However, high CHO levels in the body can damage bone mass and promote osteoporosis. CHO accumulation can cause osteoblast apoptosis, which has a negative effect on bone formation. The pathogenesis of osteoporosis is a complicate process that includes oxidative stress, endoplasmic reticulum (ER) stress, and inflammation. Geniposide (GEN) is a natural compound with anti-osteoporotic effect. However, the roles of GEN in osteopathogenesis are still unclear. Our previous studies demonstrated that GEN could reduce the accumulation of CHO in osteoblasts and the activation of ER stress in osteoblasts. However, the molecular mechanism of GEN in inhibiting CHO-induced apoptosis in osteoblasts needs to be further investigated. Methods: MC3T3-E1 cells were treated with osteogenic induction medium (OIM). Ethanol-solubilized cholesterol (100 µM) was used as a stimulator, and 10 µM and 25 µM geniposide was added for treatment. The alterations of protein expression were detected by western blot, and the cell apoptosis was analyzed by a flow cytometer. Results: CHO promoted osteoblast apoptosis by activating ER stress in osteoblasts, while GEN alleviated the activation of ER stress and reduced osteoblast apoptosis by activating the GLP-1R/ABCA1 pathway. Inhibition of ABCA1 or GLP-1R could eliminate the protective activity of GEN against CHO-induced ER stress and osteoblast apoptosis. Conclusion: GEN alleviated CHO-induced ER stress and apoptosis in osteoblasts by mediating the GLP-1R/ABCA1 pathway. [ABSTRACT FROM AUTHOR]

Sodium-ion batteries (SIBs) and potassium-ion batteries (PIBs) are increasingly recognized as the forefront contenders for the next generation of advanced secondary batteries. The quest for suitable anode materials to enhance the performance of SIBs and PIBs remains a critical challenge. Recently, zeolitic imidazolate frameworks (ZIFs) have emerged as a significant area of research due to the high porosity, high surface area, and controllable morphology. The application of various post-treatment methods has shown that ZIF derivatives can effectively address the inherent low electronic conductivity of pristine ZIFs. This article provides a thorough review of the recent advancements in ZIFs, encompassing a range of processes for preparing ZIF derivatives and their applications in anode materials for SIBs and PIBs. Moreover, it offers a visionary outlook on future research directions for ZIFs. [ABSTRACT FROM AUTHOR]

Currently, Pt based materials are still the most efficient oxygen reduction reaction (ORR) catalysts. However, their poor stability obstructs the commercial viability of fuel cells. To lower the reaction potential barrier and enhance the stability, we constructed alloy PtNi nanoparticles (NPs) with a Pt-rich surface supported on nitrogen-doped carbon (NC) via a simple one-step solvothermal method using easily accessible reagents. The synthesized PtNi/NC exhibits enhanced mass activity (MA), specific activity (SA), and positive onset potential compared with commercial Pt/C catalysts. Meanwhile, the half-wave potential shifted negatively to only 18 mV after 5000 cycles for PtNi/NC, indicating excellent stability. The enhanced ORR performance can be ascribed to the introduction of Ni into Pt optimizing the adsorption energy of Pt towards oxygen by adjusting the d band center of the Pt atom and stronger interaction between the metal NPs and support. Our work provides a potential synthesis strategy for developing a Pt-based catalyst with a low Pt loading and high ORR performance. [ABSTRACT FROM AUTHOR]

The structural properties of braided composite laminates are significantly affected by the low‐velocity impact (LVI). In this paper, the impact resistance and damage tolerance of triaxial braid structure laminates at different positions were mainly studied. Three hybrid biaxial (B)/triaxial (T) braided composite structures of BBTT, BTTB, and TTBB were designed, and LVI test and post‐impact compression tests were carried out. The results show that the triaxial braided fabric with quasi‐isotropic on the impact side caused more significant matrix cracks along the axis yarn direction, and the matrix damage area and delamination area were larger. For triaxial braided ply with high curl levels, severe fiber fracture and relatively concentrated damage occurred first on the non‐impact side. Interestingly, triaxial braided fabrics exhibited better impact resistance in terms of mechanical response when distributed across the specimen surface. However, a global damage pattern appears at high energy levels, which seriously reduced the residual strength of the structure and showed the worst damage tolerance. The damage quantification analysis of cracks, dent depths, delamination and residual compressive strength caused by the LVI of hybrid shaft number braided laminates was carried out in this work, which provided a valuable reference for engineering failure analysis and rational structure optimization of composite laminates. [ABSTRACT FROM AUTHOR]

This article is aimed at condition monitoring and fault identification for Internet of Things (IoT) devices, and proposes a multivariate statistical process control scheme. The new method aims to detect sparse mean shifts using spatial rank and an improved adaptive elastic net algorithm, which can monitor the high-dimension data stream collected by IoT devices and pinpoint faulty variables. The new method is also applicable in the presence of a non-normal distribution and insufficient reference samples. Numerical simulations verify that the proposed method has clear advantages over existing methods. The case of wind turbines shows that the method can be applied to real-time monitoring and diagnosis of real IoT devices, which could provide valuable diagnosis of root cause and optimize subsequent maintenance strategies. [ABSTRACT FROM AUTHOR]

The health of the trees in the forest affects the ecological environment, so timely detection of Standing Dead Trees (SDTs) plays an important role in forest management. However, due to the large spatial scope of forests, it is difficult to find SDTs through conventional approaches such as field inventories. In recent years, the development of deep learning and Unmanned Aerial Vehicle (UAV) has provided technical support for low-cost real-time monitoring of SDTs, but the inability to fully utilize global features and the difficulty of small-scale SDTs detection have brought challenges to the detection of SDTs in visible light images. Therefore, this paper proposes a multi-scale attention mechanism detection method for identifying SDTs in UAV RGB images. This method takes Faster-RCNN as the basic framework and uses Swin-Transformer as the backbone network for feature extraction, which can effectively obtain global information. Then, features of different scales are extracted through the feature pyramid structure and feature balance enhancement module. Finally, dynamic training is used to improve the quality of the model. The experimental results show that the algorithm proposed in this paper can effectively identify the SDTs in the visible light image of the UAV with an accuracy of 95.9%. This method of SDTs identification can not only improve the efficiency of SDTs exploration, but also help relevant departments to explore other forest species in the future. [ABSTRACT FROM AUTHOR]

To explore the enhancement mechanism of CFRP overwrap in the aluminum-CFRP square hybrid, hybrid tubes with different braided angles were investigated both experimentally and numerically. Hybrid tube of 30 degrees had severe CFRP layers splitting damage in the tube corner. As the braided angle up to 60 degrees, CFRP layers with high transverse resistance force compressed the aluminum tube and caused it to fold inward; This led to the hybrid tube failing in a distinct inward eccentric fold deformation pattern and had an obvious energy absorption improvement in its inner aluminum tube due to its increase in the corner deformation. [ABSTRACT FROM AUTHOR]

• Adaptively allocate computing resource to input regions for better network inference. • Patch sampling algorithm reduces redundant calculation costs in overlapping regions. • Comparable performance is achieved on two datasets by comparing with SOTA methods. [Display omitted] In this paper, we propose an entropy-dynamic resolution detection (EDRdet) method for object detection in aerial images. Most conventional object detection methods usually detect each region in aerial images directly with a fixed resolution, so that the resolution of the hard-to-detect region in the whole aerial image is not enough and that of the easy-to-detect region is not necessary. We argue that different resolutions of regions are required for efficient and accurate object detection in aerial images. Our EDRdet dynamically adjusts the resolution of each region via measuring the challenge of the detection process, therefore, our model can achieve a good trade-off between the computational cost and the accuracy effectively. Specifically, our EDRdet searches hard-to-detect regions through low-resolution global context information, and inputs higher-resolution patches to supplement lost feature at low resolution for fine-grained detection. We apply the entropy to determine the regions in aerial images required to be detected at higher resolutions, and where the entropy represents the uncertainty of the detection result. Moreover, we design a patch sampling algorithm to make the selected regions sparse to further improve the efficiency of patch generation. Extensive experiments on the DOTA and Visdrone2019 datasets verify that our EDRdet can reduce the computational cost and improve the model accuracy effectively. [ABSTRACT FROM AUTHOR]

It is indispensable to explore the appropriate level of surface residual stress to enhance the wear resistance of coated tools. TiAlSiN-coated carbide tools with dissimilar surface residual compressive stresses (− 300 ~ − 600 MPa) are acquired via micro-sandblasting, which are used in the high-speed sliding friction experiments and turning experiments. The influence of residual stress on the sliding wear and cutting wear performance of TiAlSiN coated tools is investigated. The results show that with the improvement of surface residual compressive stress (absolute value), the friction coefficient and wear rate are reduced. The sliding wear resistance of the coated tool is enhanced. In addition, the cutting performance of the tool is also improved with the increase of residual compressive stress (absolute value). However, the excessive surface residual compressive stresses (absolute value) can lead to severe coating peeling, which in turn impairs the capability of wear resistance. The TiAlSiN-coated carbide tool with the stress of − 500 MPa presents the best wear resistance in the high-speed sliding wear and cutting wear process. [ABSTRACT FROM AUTHOR]

Background: Hemoglobin glycation index (HGI), which is calculated by blood glucose and hemoglobin A1c(HbA1c), reflects the individual discrepancy in HbA1c. This study aimed to investigate the association between HGI/HbA1c and serum uric acid(SUA) stratified by sex and diabetes. Methods: The study recruited 33772 participants who underwent physical examinations between April 2016 and August 2021 in Beijing Chao-Yang Hospital. A random subsample of 3000 subjects was utilized to calculate the formula of HGI and data of the remaining 30772 participants were used for analysis. HGI and HbA1c were categorized according to quartiles (Q1, Q2, Q3, Q4), using Q1 as the reference. We used multiple linear regression and restricted cubic splines for data analysis. Results: 30772 participants with a mean age of 44.4 years old were included in the analysis, 48.6% (N = 14944) of which were female and 7.7% (N = 2363) with diabetes. Associations of HGI, HbA1c and SUA were modified by sex and diabetes. The relationship between SUA levels and HGI was positive in women without diabetes, with one unit increase in HGI associating with an 11.3 μmol/L increase in SUA (P < 0.001) after adjusting for other confounders. On average, each one-unit increase in HbA1c was associated with a 14.3 μmol/L decrease in SUA in women with diabetes, a 14.9 μmol/L decrease in SUA in men with diabetes, and a 16.5 μmol/L increase in SUA in women without diabetes (all P < 0.001). The SUA levels in men without diabetes showed a bell-shaped relation with HbA1c, increasing as the HbA1c rose to around 5.7% and then falling with a further increase of HbA1c (P < 0.001). Conclusions: SUA levels were inversely correlated with HbA1c in diabetic patients, also in men with prediabetes (HbA1c ≥ 5.7%), but positively correlated with HbA1c and HGI in women without diabetes. Glycemic control may help to reduce the risk of hyperuricemia in non-diabetes women. [ABSTRACT FROM AUTHOR]

Recent research advances in salient object detection (SOD) could largely be attributed to ever-stronger multi-scale feature representation empowered by the deep learning technologies. The existing SOD deep models extract multi-scale features via the off-the-shelf encoders and combine them smartly via various delicate decoders. However, the kernel sizes in this commonly-used thread are usually “fixed”. In our new experiments, we have observed that kernels of small size are preferable in scenarios containing tiny salient objects. In contrast, large kernel sizes could perform better for images with large salient objects. Inspired by this observation, we advocate the “dynamic” scale routing (as a brand-new idea) in this paper. It will result in a generic plug-in that could directly fit the existing feature backbone. This paper’s key technical innovations are two-fold. First, instead of using the vanilla convolution with fixed kernel sizes for the encoder design, we propose the dynamic pyramid convolution (DPConv), which dynamically selects the best-suited kernel sizes w.r.t. the given input. Second, we provide a self-adaptive bidirectional decoder design to accommodate the DPConv-based encoder best. The most significant highlight is its capability of routing between feature scales and their dynamic collection, making the inference process scale-aware. As a result, this paper continues to enhance the current SOTA performance. Both the code and dataset are publicly available at https://github.com/wuzhenyubuaa/DPNet. [ABSTRACT FROM AUTHOR]

In this study, steel fiber-reinforced concrete (SFRC) beam structures with regular oriented steel fibers are designed and prepared by using an assembled solenoid device. Fiber orientation is not arranged in a single direction but is similar to the direction of tensile stress of the beam in bending load conditions. With application of inductive test method and image processing technology, fiber orientation coefficient of SFRC was evaluated and increased from 0.5 to 0.7–0.8 after fiber alignment. To describe and analyze the flexural behavior of SFRC beams with regular oriented fibers and glass fiber-reinforced polymer (GFRP) rebars, 14 concrete beams with different parameters were fabricated and subjected to a four-point bending test. Specifically, the parameters included fiber content, fiber orientation, GFRP reinforcement ratio and different layer sections. The results showed that optimizing fiber orientations to the tension zone of the section led to a higher flexural strength and can well restrain the development of crack widths with comparison to randomly distributed fibers. The peak load and energy absorption increased by 7 % and 12.4 % after fiber alignment with a fiber volume fraction of 1.5 %. The combination of randomly and regularly distributed steel fibers in the form of two-layer composite members can be an efficient solution to improve the flexural performance for purpose of the optimum distribution and direction of steel fibers, and reduce the engineering cost. • A magnetic orientation device is designed for SFRC beams with regular oriented fibers. • Fibers had well arrangements with high fiber orientation coefficient. • A flexural test of the ASFRC beams was conducted and investigated. • ASFRC improved the first cracking load and initial stiffness of beams. • The experimental results indicated great flexural performance after aligning fibers to the tension zone. [ABSTRACT FROM AUTHOR]

The stable reliability analysis of dams is a challenging topic in geotechnical engineering safety. Because there are more complex system failure modes and high-dimensional parameters in large solid structures, it is difficult to calculate failure probability efficiently and accurately using general reliability analysis methods. Most methods suffer from complex iterative processes and the curse of dimensionality. In this study, an improved importance sampling method based on multi-objective optimization is proposed and applied to reliability analysis for dam sliding. The optimization function is constructed from the limit state surface and the original probability density. The optimal sampling center is selected from the optimization results by considering the dominant performance function as well as the coefficient of variation of the weight function. Four numerical examples and a case study of the sliding stability of a gravity dam demonstrate the accuracy and efficiency of the proposed method. The results show that the proposed method is not sensitive to the specific multi-objective optimization algorithm used. Simulations using multiple sampling centers perform better overall than simulations using a single sampling center. In particular, the proposed method remains applicable to parallel systems with 20 high-dimensional parameters that are virtually immune to the curse of dimensionality. [ABSTRACT FROM AUTHOR]

• Seepage failure probability of high core rockfill dams during RDD is investigated. • The randomness of the parameters is estimated by Bayesian inference. • Seepage failure is most likely to occur in the mid-term of RDD. Rapid drawdown (RDD) is an abnormal reservoir flood control operation that may cause seepage failure of high core rockfill dams. In this paper, a novel framework combining Bayesian inference and transient unsaturated seepage simulation is proposed to evaluate the seepage failure probability of the core during RDD. The dynamic hydraulic boundaries utilized in the simulation are first determined by the performance of water release structures. The extreme learning machine (ELM) is then employed as the surrogate model to establish the mapping relationship between material parameters with simulated seepage pressure and hydraulic gradient. The Bayesian inference method is applied to characterize the uncertainty of material parameters based on seepage monitoring data. Finally, the posterior distribution samples of random parameters are utilized to calculate the seepage failure probability of the core. The seepage safety of the core under different drawdown rates is studied with a real 186 m high core rockfill dam. The results reveal that a faster RDD rate can lead to an increased probability of seepage failure. The maximum failure probability is 35.56 % for the four release structures working together (about 0.316 m/h), higher than 23.16 % for the three release structures (about 0.165 m/h). It is also found that the peak of seepage failure probability often occurs in the middle stage of RDD. Therefore, to ensure the safety of the project, it is important to control the RDD rate and carefully monitor the seepage pressure. From the point of view of seepage failure, the proposed method for evaluating the safety of core rockfill dams under RDD is of high guiding significance to the risk assessment of similar projects under extreme conditions. [ABSTRACT FROM AUTHOR]

With the continuous development of computer science and technology, the level of medical image processing and analysis technology has been significantly improved. In order to further optimize the medical imaging technology and provide assistance for medical diagnosis and treatment, this study will explore the head CT image segmentation technology and three-dimensional reconstruction technology based on human anatomy, using two morphological operation methods of image expansion and image corrosion, as well as the triangulation method based on surface contour, Optimize CT image segmentation technology and three-dimensional reconstruction technology. The results show that the CT image segmentation technology based on human anatomy can obtain the more essential morphology and features of the target image, and significantly improve the image quality. The size of the threshold can have a certain impact on the 3D reconstruction effect and reconstruction time to a certain extent. The larger the threshold, the shorter the reconstruction time, but the worse the 3D reconstruction effect. This shows that the target image after fitting has a good reconstruction effect, but the threshold level should be kept at a low level. The head CT image segmentation technology and three-dimensional reconstruction technology based on human anatomy have good application effects and can be popularized and applied in clinical diagnosis and treatment. [ABSTRACT FROM AUTHOR]

This paper focuses on the in-plane compression mechanism of two-dimensional biaxial braided composite laminates under quasi-static compression and dynamic compression. Three types of specimens with different off-axial angles in respect of OA00, OA60, and OA90 were employed for compression experiments. The surface damage process of the specimen was recorded using optical camera and infrared thermal imaging technology. The internal damage morphology of the tested specimens was analyzed by X-ray computed tomography. The progressive damage behavior of braiding yarns and matrix resin was simulated by a three-dimensional meso-level finite element model. Experimental and simulation results revealed that the off-axial angle significantly affects mechanical properties and damage mechanisms. The stress distribution and compression deformation of braiding yarns are affected by the boundary conditions of load-bearing yarn, which relies on the off-axial angle. The stress of OA00 specimen is concentrated at the interface of yarn and matrix resin. The failure mode of OA00 specimen is dominated by the shear band, which is induced by the interface microcracks between yarn and matrix resin. The stress of OA60 and OA90 specimens are concentrated at the undulation position of the braiding yarn. The critical failure mode of OA60 and OA90 specimens is delamination induced by interlaminar microcracks. The OA60 specimen achieves a better compression response with respect to bearing the load and absorbing the fracture energy because the sub-laminate that is formed after delamination can continue to bear the load until its intralaminar fracture occurs. [ABSTRACT FROM AUTHOR]

We develop a method to compute synthetic kilonova light curves that combine numerical relativity simulations of neutron star mergers and the SNEC radiation–hydrodynamics code. We describe our implementation of initial and boundary conditions, r-process heating, and opacities for kilonova simulations. We validate our approach by carefully checking that energy conservation is satisfied and by comparing the SNEC results with those of two semi-analytic light-curve models. We apply our code to the calculation of colour light curves for three binaries having different mass ratios (equal and unequal mass) and different merger outcome (short-lived and long-lived remnants). We study the sensitivity of our results to hydrodynamic effects, nuclear physics uncertainties in the heating rates, and duration of the merger simulations. We find that hydrodynamics effects are typically negligible and that homologous expansion is a good approximation in most cases. However, pressure forces can amplify the impact of uncertainties in the radioactive heating rates. We also study the impact of shocks possibly launched into the outflows by a relativistic jet. None of our models match AT2017gfo, the kilonova in GW170817. This points to possible deficiencies in our merger simulations and kilonova models that neglect non-LTE effects and possible additional energy injection from the merger remnant and to the need to go beyond the assumption of spherical symmetry adopted in this work. [ABSTRACT FROM AUTHOR]

Polyamines are critical metabolites involved in various cellular processes and often dysregulated in cancers. Kaposi's sarcoma-associated Herpesvirus (KSHV), a defined human oncogenic virus, leads to profound alterations of host metabolic landscape to favor development of KSHV-associated malignancies. In our studies, we identified that polyamine biosynthesis and eIF5A hypusination are dynamically regulated by KSHV infection through modulation of key enzymes (ODC1 and DHPS) of these pathways. During KSHV latency, ODC1 and DHPS are upregulated along with increase of hypusinated eIF5A (hyp-eIF5A), while hyp-eIF5A is further induced along with reduction of ODC1 and intracellular polyamines during KSHV lytic reactivation. In return these metabolic pathways are required for both KSHV lytic reactivation and de novo infection. Further analysis unraveled that synthesis of critical KSHV latent and lytic proteins (LANA, RTA) depends on hypusinated-eIF5A. We also demonstrated that KSHV infection can be efficiently and specifically suppressed by inhibitors targeting these pathways. Collectively, our results illustrated that the dynamic and profound interaction of a DNA tumor virus (KSHV) with host polyamine biosynthesis and eIF5A hypusination pathways promote viral propagation, thus defining new therapeutic targets to treat KSHV-associated malignancies. Author summary: Understanding virus-host interactions is crucial to develop and improve therapies. Kaposi's sarcoma associated Herpesvirus (KSHV) is a human gamma-herpesvirus which deeply modulates the host metabolism and is associated with various cancers of endothelial and lymphoid origin. Polyamines are critical metabolites often dysregulated in cancers. In this study we demonstrated KSHV dynamically modulates polyamine metabolism to favor eIF5A hypusination and translation of critical KSHV latent and lytic proteins (LANA, RTA). Consequently, we found KSHV lytic switch from latency and de novo infection were dependent on polyamines and hypusination and pharmacological inhibition efficiently and specifically restricted KSHV infection. Our study provides new insights into KSHV alteration of the host metabolism and describe new therapeutic targets to treat KSHV-associated malignancies. [ABSTRACT FROM AUTHOR]

We investigate privacy-preserving, video-based action recognition in deep learning, a problem with growing importance in smart camera applications. A novel adversarial training framework is formulated to learn an anonymization transform for input videos such that the trade-off between target utility task performance and the associated privacy budgets is explicitly optimized on the anonymized videos. Notably, the privacy budget, often defined and measured in task-driven contexts, cannot be reliably indicated using any single model performance because strong protection of privacy should sustain against any malicious model that tries to steal private information. To tackle this problem, we propose two new optimization strategies of model restarting and model ensemble to achieve stronger universal privacy protection against any attacker models. Extensive experiments have been carried out and analyzed. On the other hand, given few public datasets available with both utility and privacy labels, the data-driven (supervised) learning cannot exert its full power on this task. We first discuss an innovative heuristic of cross-dataset training and evaluation, enabling the use of multiple single-task datasets (one with target task labels and the other with privacy labels) in our problem. To further address this dataset challenge, we have constructed a new dataset, termed PA-HMDB51, with both target task labels (action) and selected privacy attributes (skin color, face, gender, nudity, and relationship) annotated on a per-frame basis. This first-of-its-kind video dataset and evaluation protocol can greatly facilitate visual privacy research and open up other opportunities. Our codes, models, and the PA-HMDB51 dataset are available at: https://github.com/VITA-Group/PA-HMDB51 [ABSTRACT FROM AUTHOR]

A 2π equivalent circuit model of serpentine interconnect fabricated by transfer printing technology is proposed considering the stretching deformation of the interconnect. The S parameters are measured, and the RLC parameters are extracted by quasi‐linearization method at various elongations. A V‐shaped model is built to illustrate the change of Lc increasing by 9.7% due to elongation. A branch of Rf and Lf in parallel is utilized to model the impact of high‐frequency effects, Rf decreases by 56.7%, and Lf remains ~0.65 nH. A branch of Rsub and Csub characterizing the electrical loss of substrate increases due to substrate thinning with elongation, Rsub increases by 87.1%, and Csub remains 100 fF. S parameters are measured and compared to verify the accuracy of the model and study the effect of elongation on the transmission characteristics; it is found that the amplitude at S11 minimum decreases from −7 to −10 dB during stretching, and the corresponding frequency points also decrease. [ABSTRACT FROM AUTHOR]

The development and utilization of wind energy can help promote the carbon neutrality. Condition monitoring and fault diagnosis based on supervisory control and data acquisition (SCADA) can effectively improve wind turbine reliability and reduce operation and maintenance (O&M) costs. However, the complex data characteristics (e.g., time-varying and distribution-free) make it difficult to use a unified model to comprehensively evaluate the turbine state. To finely detect and analyze the turbine faulty mode, a hierarchical condition monitoring and fault diagnosis (CMFD) method is proposed, which consists of variable-level and turbine-level. First, the SCADA data is divided into two blocks: the nonstationary part with time-varying distribution, and the stationary part with time-invariant but non-Gaussian distribution. At the variable-level, this paper proposed a local monitoring unit based on sparse cointegration analysis (SCA) and independent component analysis (ICA) to detect faulty samples of different variable blocks. And the fault-related variables are isolated based on the proposed distributed reconstruction. At the turbine-level, the evaluation results from variable-level are fused: on the one hand, Bayesian inference is used to integrate monitoring statistics of stationary and nonstationary parts to comprehensively evaluate the operational state of the turbine; on the other hand, the root fault subsystem is deduced from the fault-related variables isolated from the two parts based on Gaussian process regression. The proposed method is applied in the operation and maintenance of a real wind farm, and the performance advantages are verified by the comparison with popular benchmarks. • An innovative hierarchical CMFD strategy for wind turbines is proposed which consists of variable-level and turbine-level methods. • At the variable-level, the monitoring and isolated methods are proposed based on SCA and ICA. • At the turbine-level, Bayesian inference is used to integrate variable-level monitoring results, and the root faulty subsystem is deduced based on Gaussian process regression. [ABSTRACT FROM AUTHOR]

The failure mode is a dominant factor of mechanical performance of braided composite tube subjected to axial compression. The failure mode was modified by tailoring stack-up sequence of composite tubes hybridized by bi/tri-axial braided laminate. The thermal photographing and micro-CT scanning methods were employed to monitor the in-situ damage progression and post-mortem specimen, respectively. The results show that the delamination was easy to be induced at the interface nearby tri-axial braided laminate. It attributes to the combination between the crack caused by local buckling of the axial yarns and the crack caused by shear deformation of braiding yarns. By the illumination of this behavior, delamination is controlled to occur at the different position along thickness direction of braided tube by adjusting the stacking sequence of braided laminates. When delamination position transfer from the inner/outer sides to the middle of tube wall, the failure mode transfer from the progressive folding to splaying, which increase the specific energy absorption by 45.77%. [ABSTRACT FROM AUTHOR]

The powder immersion reaction assisted coating (PIRAC) method was used to fabricate a coating on the graphite surface. The graphite was immersed into Cr powder and annealed at 1173–1373 K. The iodine powder was admixed with the Cr powder to investigate the contribution of iodine to the growth and mechanical properties of the coating. After 4 h of PIRAC treatment with or without iodine, Cr7C3- and Cr23C6-based coatings were formed, respectively. The iodinated samples had a higher hardness and thickness, and the friction coefficient was slightly higher than that of the iodine-free sample. The mechanisms for the formation of PIRAC-chromium carbide coatings are explained, and the variations in the produced coatings are discussed. [ABSTRACT FROM AUTHOR]

Objective. To explore the effects of epidural anesthesia and general anesthesia on perioperative cognitive function and deep vein thrombosis (DVT) in patients undergoing total knee arthroplasty (TKA). Methods. Total of 68 patients undergoing TKA in our hospital from September 2019 to March 2021 were selected and divided into the control group under general anesthesia and the observation group under epidural anesthesia according to the different anesthesia methods, 34 patients in each group. TKA was selected in both groups and performed by the same group of physicians, anesthesiologists, and nursing staff. The mean arterial pressure (MAP), heart rate (HR), and blood oxygen saturation (SpO2) were observed immediately before anesthesia (T1), 30 min after anesthesia (T2), after surgery (T3), and 1 d after surgery (T4). The changes of platelet (PLC), fibrinogen (Fbg), prothrombin time (PT), activated partial thrombin time (APTT), and other coagulation indicators were recorded. The Montreal Cognitive Assessment (MoCA) scores before surgery and 1 d and 3 d after surgery were observed. The blood samples of the two groups were collected before surgery and 1 d and 3 d after surgery, and the levels of brain-derived neurotrophic factor (BDNF) and nerve growth factor (NGF) in the peripheral blood of the two groups were measured by ELISA. The number of postoperative mental disorders and DVT in the two groups was calculated. Results. The MAP and HR of T4 were lower than those of T1, T2, and T3. The MAP of T2, T3, and T4 in the observation group was lower than that in the control group (P < 0.05), and the SpO2 of T1–T4 in the two groups did not change significantly, and there was no significant difference between the two groups (P > 0.05). Compared with T1, there was no significant difference in PLC, Fbg, and PT in the observation group T4 (P > 0.05), and APTT was lower than T1 (P < 0.05). The PLC, PT, and APTT of T4 in the control group were all lower than those of T1 (P < 0.05), and there was no significant difference between Fbg and T1 (P > 0.05). The PLC, Fbg, and PT of T4 in the observation group were higher than those in the control group, while APTT was lower than that in the control group (P < 0.05). The MoCA scores of patients in both groups on the 1st and 3rd day after operation were lower than those before operation, and the observation group was higher than that in the control group (P < 0.05). The BDNF and NGF of patients in both groups were lower than those before operation on the 1st day after operation, and the BDNF and NGF in the observation group were higher than those in the control group on the 1st and 3rd day after operation (P < 0.05). The mental disorder (2.94%) and DVT incidence (2.94%) in the observation group were lower than those in the control group (29.41%, 26.47%) (P < 0.05). Conclusion. Epidural anesthesia for patients with TKA can obtain better clinical effects, maintain stable hemodynamic and coagulation states, reduce stress response of patients at the same time, and reduce perioperative cognitive dysfunction and the incidence of DVT in patients. [ABSTRACT FROM AUTHOR]

Fully convolutional neural networks (FCNs) have shown great effects in the saliency detection task. In this paper, we present an FCN based model to aggregate multi-level convolutional features for salient object detection. The main contribution of this work is to make full use of the information extracted by the network while preserving the characteristics of each level. We first propose a novel Intra-stage Feature Aggregating (IFA) module to enhance the detailed information by integrating feature maps from different layers within the same stage of the ResNet-50. We then design an Inter-stage Feature Fusion (IFF) module which exploits contextual information by combining the features from neighboring stages to make each feature map more discriminative. Finally, we propose a Deeply Supervised Group Recursive Prediction (DGR) module to refine the details of the predicted saliency maps and generate the final saliency map. Our approach performs favorably against state-of-the-art methods on five popular datasets and achieves a real-time speed of 30 fps. [ABSTRACT FROM AUTHOR]

Background: Bladder cancer is the second most common malignant tumor in the urogenital system. The research investigated the prognostic role of immune‐related long non‐coding RNA (lncRNA) in bladder cancer. Methods: We extracted 411 bladder cancer samples from The Cancer Genome Atlas database. Single‐sample gene set enrichment analysis was employed to assess the immune cell infiltration of these samples. We recognized differentially expressed lncRNAs between tumors and paracancerous tissues, and differentially expressed lncRNAs between the high and low immune cell infiltration groups. Venn diagram analysis detected differentially expressed lncRNAs that intersected the above groups. LncRNAs with prognostic significance were identified by regression analysis. Multivariate Cox analysis was used to establish the risk score model. Then we established and evaluated the nomogram. Additionally, we performed gene set enrichment analysis to explore the potential functions of the screened lncRNAs in tumor pathogenesis. Results: Three hundred and twenty differentially expressed lncRNAs were recognized. We randomly divided patients into the training data set and the testing data set at a 2: 1 ratio. In the training data set, 9 immune‐related lncRNAs with prognostic significance were identified. The risk score model was constructed to classify patients as high‐ and low‐risk cohorts. Patients in the low‐risk cohort had better survival outcomes than those in the high‐risk cohort. The nomogram was established based on the indicators including age, gender, tumor‐node‐metastases stage, and risk score. The model's predictive performance was confirmed by the receiver operating characteristic curve analysis, concordance index method, calibration curve, and decision curve analysis. The testing data set also achieved similar results. Bioinformatics analysis suggested that the 9‐lncRNA signature was involved in the modulation of various immune responses, antigen processing and presentation, and T cell receptor signaling pathway. Conclusions: Our study uncovered the prognostic value of immune‐related lncRNAs for bladder cancer and showed that they may regulate tumor pathogenesis in various ways. [ABSTRACT FROM AUTHOR]

A new testing method (oscillating wave method) has been proposed as a solution to the problem of short maintenance time in the traction power supply system, which could complete tests of winding and insulation via one connection and improve the maintenance efficiency of the transformer. Through the method being proved highly sensitive to winding faults, the variation under different winding states, however, remains unclear. This article aims to analyze oscillating wave signatures in the occurrence of axial displacement (AD). First, the modeling method will be proposed that can simulate oscillating wave signatures in different winding states. Then, the time-domain feature and the new proposed frequency-domain feature are applied to analyze the fault extent and location of AD fault. The results show that this model accurately simulates oscillating wave signatures. Moreover, the time-domain feature [Euclidean distance (ED)] is closely related to fault extent. The combined use of the time-domain feature (ED) and the frequency-domain feature (W-Energy entropy in Z-Score) is effective in the identification of fault location. These features, therefore, can provide the reference for winding state evaluation. [ABSTRACT FROM AUTHOR]

Repetitive or ambiguous environment, where structures are highly similar and distinct geometric features are not sufficient, is one of the critical and challenging scenarios for mobile robots to perform global localization or simultaneous localization and mapping (SLAM) tasks. The robots are easy to get lost or mismatched to wrong places in such environments. The existing solutions either rely heavily on pre-installed infrastructures which are inflexible and expensive or rely on single sensor-based global localization methods whose initialization module is not capable enough to provide distinctive information. Thus, this article proposes a hierarchical probabilistic framework that addresses the problem of infrastructure-free mobile robot global localization in GPS-challenged repetitive environments by leveraging both the measured magnetic field (MF) and Light Detection and Ranging (LiDAR) information. The proposed hierarchical system mainly consists of: 1) coarse localization: MF-based localization and 2) fine localization: LiDAR-based localization (LBL). The ambient MF functions as a substitute for the GPS signal, which is considered not available or severely challenged in indoor/semi-indoor local environments. Based on the pre-built MF database, multiple coarse candidate robot poses can first be determined by the proposed initial pose estimation algorithm, which incorporates multivariate Gaussian observation model and random sample consensus (RANSAC) algorithm. Then, using the obtained multiple candidate poses as initialization, the robot can be localized more accurately by LiDAR-based fine localization. Extensive real-world experiments demonstrate that the proposed system achieves over 95% localization success rate and takes less than 2.0-m average traveling distance to localize the mobile robot. [ABSTRACT FROM AUTHOR]

Well-shaped sulfated titania nanofibers were prepared by impregnation of hydrogen titanate with (NH4)2S2O8 and calcination. Various characterization results showed that sulfated titania nanofibers consisted of smooth-surface and long-size fibers with the diameter of dozens of nanometers. Moreover, acid characteristics of sulfated titania nanofibers exhibited the high acid content, multi-strength acid sites and large Brønsted/Lewis acid ratio. In low-viscosity esterification, sulfated titania nanofibers had 1.2 ~ 2.1 times the reaction rate of other sulfated titania and other typical solid acids. Simultaneously, sulfated titania nanofibers possessed the higher DS value of product than those by other solid acids in highly viscous cellulose acetylation (2.41 versus 2.06 ~ 2.21). Besides these, sulfated titania nanofibers could show the excellent recycling performance in viscous acid-catalytic reactions. [ABSTRACT FROM AUTHOR]

A multi-turbine condition monitoring method using supervisory control and data acquisition (SCADA) data for large-scale wind farm is proposed. The method takes the difference between the SCADA data of each turbine with the median of other remaining turbines, and establishes condition vector consisting of the differences. Considering the autocorrelation of turbine SCADA data, vector autoregression (VAR) model is used to remove the autocorrelation in the condition vector of wind farm. Hotelling and multivariate exponentially weighted moving average (MEWMA) control chart are applied to monitor the residual vector. An industrial wind farm example is given to illustrate the proposed method. Compared with the existing turbine condition monitoring charts, the false alarm of proposed method is reduced for considering the autocorrelation of operation data, and monitoring strategy using MEWMA improves detected rate and expedites alarm time compared with Hotelling. The proposed method realizes monitoring multiple turbines simultaneously in farm by a fault indicator, which has important theoretical and engineering significance to the practical operations and maintenance activities in large-scale wind farm. • Through the cross-reference between the turbines in wind farm, the operation state would be detected more conveniently. • VAR-Hotelling and VAR-MEWMA were established to monitor the wind farm and locate the faulty turbine. • An industry example verified that proposed method could detect the faulty turbines and expedite fault warning. [ABSTRACT FROM AUTHOR]

Background: Pseudomonas aeruginosa (PA) is a pathogenic bacterium that causes severe pneumonia in critically ill and immunocompromised patients. Peptidylarginine deiminase (PAD) 2, PAD4, and caspase-1 are important enzymes in mediating host response to infection. The goal of this study was to determine the interplay between PAD2, PAD4, and caspase-1 in PA pneumonia-induced sepsis.Methods: Pneumonia was produced in wild-type, Pad2-/-, and Pad4-/- mice by intranasal inoculation of PA (2.5 × 106 colony-forming units per mouse), and survival (n = 15/group) was monitored for 10 days. Bone marrow-derived macrophages (BMDMs) were isolated for in vitro studies. Samples were collected at specific timepoints for Western blot, bacterial load determination, and flow cytometry analysis.Results: Caspase-1-dependent inflammation was diminished in PA-inoculated Pad2-/- mice, contributing to reduced macrophage death and enhanced bacterial clearance. In addition, Pad2-/- mice exhibited improved survival and attenuated acute lung injury after PA infection. In contrast, Pad4-/- mice did not display diminished caspase-1 activation, altered bacterial loads, or improved survival.Conclusions: Peptidylarginine deiminase 2 plays an essential role in the pathogenesis of pulmonary sepsis by mediating caspase-1 activation. This goes against previous findings of PAD4 in sepsis. Our study suggests that PAD2 is a potential therapeutic target of PA pneumonia-induced sepsis. [ABSTRACT FROM AUTHOR]

The instability of wind power has a serious impact on the power grid system, and an accurate wind power forecasting is greatly demanded in practice. Due to the time-varying nature, the distribution of weather variables such as wind speed, wind direction and temperature and/or the functional relationship of the power output relating to these weather variables are likely to change over time, which are often known as "concept drifts". However, most of the prediction models usually fail to adapt to such concept drifts. This would deteriorate the prediction accuracy, especially for the short-term prediction with high accuracy requirements. Motivated by this, this paper proposes an adaptive short-term wind power prediction method, aimed at automatically detecting the occurrence of concept drifts and updating the forecast model accordingly. The proposed method consists of three main steps, extract sample-related and sequence-related features of the weather data, cluster the data based on the similarity of the features extracted, and develop a new power prediction model with automatic drift detection and adaptation for each cluster. The comparison results show that the prediction performance of the proposed method is superior to that of the competitive methods. • Develop a new method, DDA-LightGBM, for very short-term. • The new method can take weather drifts into account. • Compare DDA-LightGBM with a wide variety of prediction models. • Show that DDA-LightGBM is able to provide a better forecasting accuracy. [ABSTRACT FROM AUTHOR]

• The Si-O-Cs groups are identified as active sites. • The promotion of Brønsted acid sites in Cs dispersion was revealed. • The maximum MA production reaches up to 7.23 mmol·g Cat −1·h−1. • The deactivation behavior of catalyst was demonstrated. With the gradual depletion of petroleum resources and overcapacity of coal industry, the one-step synthesis of methyl acrylate (MA) from the coal-based methyl acetate (Ma) and formaldehyde via aldol condensation, regarded as a supplementary route for the petroleum-based propene selective oxidation, receives significant attention in decades. Herein, a series of Cs/Beta zeolite catalysts were developed for this transformative process using incipient wetness impregnation with varied Cs loading amount and SiO 2 /Al 2 O 3 ratio. The physicochemical properties of both prepared Cs/Beta zeolite catalyst series and Beta zeolite supports were characterized by using FT-IR, ICP, XRF, physical N 2 adsorption–desorption isotherms, NH 3 -TPD, Py-IR, 31P-MAS NMR, XRD and XPS, in combination with catalytic evaluation experiments. The effects of Brønsted acid sites, SiO 2 /Al 2 O 3 ratio and Cs loading on the catalytic performance and catalyst deactivation were systematically investigated. The Brønsted acid sites on Beta zeolite support showed promotional effects on the dispersion of loaded Cs species and formation of (Al-O)-Si-O-Cs groups which were crucial to the improvement of catalytic activity and selectivity. However, the excessive Brønsted acid sites on Cs/Beta or Beta zeolite would lead to the heavy side reactions, including the hydrolysis of Ma and dehydration and further aromatization of methanol (MeOH). As a result, the highest formation rate of 7.23 mmol·g Cat −1·h−1 for MA could be achieved with a relatively high selectivity of 85% on the optimal 17.5Cs/Beta40 catalyst. While the undesired catalytic deactivation behavior resulting from the accumulation of alkyl-substituted single-ring aromatic species in the micropores of catalyst was found during the reaction by employing FT-IR, GC–MS, TGA and UV–vis analysis, which could be easily regenerated by thermal treatment at 400 °C and air atmosphere. [ABSTRACT FROM AUTHOR]

Rapidly developing single-cell sequencing analyses produce more comprehensive profiles of the genomic, transcriptomic, and epigenomic heterogeneity of tumor subpopulations than do traditional bulk sequencing analyses. Moreover, single-cell techniques allow the response of a tumor to drug exposure to be more thoroughly investigated. Deep learning (DL) models have successfully extracted features from complex bulk sequence data to predict drug responses. We review recent innovations in single-cell technologies and DL-based approaches related to drug sensitivity predictions. We believe that, by using insights from bulk sequence data, deep transfer learning (DTL) can facilitate the use of single-cell data for training superior DL-based drug prediction models. A comprehensive understanding of heterogeneous tumor subpopulations will benefit drug sensitivity prediction and combination drug treatment design. Deep learning models are powerful and extensively used in drug sensitivity prediction and in inferring drug–target interactions. Single-cell sequencing techniques offer precise and accurate profiling of tumor subpopulations and reveal subtle differences in their response to drug treatments. Applying deep transfer learning to predict drug sensitivity allows us to not only take advantage of prior knowledge obtained from massive bulk sequencing data but also utilize the heterogeneous landscapes generated by single-cell sequencing techniques. The integration of single-cell multi-omic data for drug sensitivity prediction using transfer learning methods poses a special challenge. [ABSTRACT FROM AUTHOR]

Data-driven fault diagnostics of industrial systems suffer from class-imbalanced problems, which is a common challenge for machine learning algorithms as it is difficult to learn the features of the minority class samples. Synthetic oversampling methods are commonly used to tackle these problems by generating minority class samples to balance the majority and minority classes. Two major issues will influence the performance of oversampling methods which are how to choose the most appropriate existing minority seed samples, and how to synthesize new samples from seed samples effectively. However, many existing oversampling methods are not accurate and effective enough to generate new samples when dealing with high-dimensional faulty samples with different imbalanced ratios, since they do not take these two factors into consideration at the same time. This article develops a novel adaptive oversampling technique: expectation maximization (EM)-based local-weighted minority oversampling technique for industrial fault diagnostics. This method uses a local-weighted minority oversampling strategy to identify hard-to-learn informative minority fault samples and an EM-based imputation algorithm to generate fault samples based on the distribution of minority samples. To validate the performance of the developed method, experiments were conducted on two real-world datasets. The results show that the developed method can achieve better performances, in terms of F-measure, Matthews correlation coefficient (MCC), and Mean (average of F-measure and MCC) values, on multiclass imbalanced fault diagnostics in different imbalance ratios than state-of-arts’ baseline sampling techniques. [ABSTRACT FROM AUTHOR]

In this article, a new testing method for the diagnosis of winding faults based on the resonant frequency of the transformer is proposed. The system that consists of the high-voltage dc power and high-voltage insulated gate bipolar transistor (IGBT) could charge the transformer periodically. The response measured from the bushing of the winding is the oscillating wave that can be used for evaluating the status. Carrying out the new test on a 220-kV three-winding, three-phase transformer in the field, the tests of insulation and the winding have been completed in one test. The calculated frequency verifies the feasibility and applicability of the method in theory and the voltage does not affect the resonant frequency in the test. The three-winding, three-phase model transformer was built to simulate the various parameters of the power supply, which proves that the features of the oscillating waves are brought about by the properties of the transformer. Also, the new testing method provides a new opportunity to do parameter identification of axial displacement and short-circuit (SC) fault. The abovementioned research shows that the proposed method can conduct the test of winding and insulation in one test for a short time, which proves a reference in the field. [ABSTRACT FROM AUTHOR]

Endotoxemia induced by lipopolysaccharide (LPS) is an extremely severe syndrome identified by global activation of inflammatory responses. Neutrophil extracellular traps (NETs) play an important role in the development of endotoxemia. Histone hypercitrullination catalyzed by peptidylarginine deiminases (PADs) is a key step of NET formation. We have previously demonstrated that simultaneous inhibition of PAD2 and PAD4 with pan-PAD inhibitors can decrease NETosis and improve survival in a mouse model of LPS-induced endotoxic shock. However, the effects of PAD2 specific inhibition during NETosis and endotoxic shock are poorly understood. Therefore, in the present study, we aimed to investigate the effect of the specific PAD2 or PAD4 inhibitor on LPS-induced endotoxic shock in mice. We found that PAD2 inhibition but not PAD4 inhibition improves survival. Also, the levels of proinflammatory cytokines and NETosis were significantly reduced by PAD2 inhibitor. To our knowledge, this study demonstrates for the first time that PAD2 inhibition can reduce NETosis, decrease inflammatory cytokine production, and protect against endotoxin-induced lethality. Our findings provided a novel therapeutic strategy for the treatment of endotoxic shock. [ABSTRACT FROM AUTHOR]

In this study, the performance of braided composite tubes under low-velocity transverse impact loading at mid-span was investigated using both numerical and experimental methods. Three types of braided composite tubes with different braiding angles (30°, 45°, and 60°) were manufactured. The transverse punch behavior of the tubes was examined on a low-velocity imspact test bench. A meso-level finite element model of the composite tube was also established for identifying the damage initiation and development. The numerical results showed a good correlation with the experimental data. The mechanical response including force–time histories, force–displacement histories, and fracture morphologies was compared between three types of composite tubes for analyzing the influence of braiding angle on the impact response and failure mode. Although suffering from the low bending stiffness depends on fiber volume fraction at initial impact stage, the braided tube with 30° angle engaged more portion to resist impact loading in subsequent process and thus presented higher peak loading than the one with large angle. In addition, there are distinct different failure modes between composite tubes with various braiding angles. Shear yarn breakage underneath the punch was prone to occur in 30° sample because the braiding yarn was closer to the axial direction of tube. In contrast, the resin was deboned severely from the braiding yarn and then the braiding yarn exhibits plastic deformation in 60° sample due to the stress concentration caused by the large braiding angle. [ABSTRACT FROM AUTHOR]

A numerical model of the equivalent time conversion to DC stressing for electromigration failures under various current waveforms is proposed based on the vacancy diffusion and damage healing theory. The Joule heating effect and the intrinsic stress-induced voiding are included in the calculation of interconnect reliability. The simulation results show that the cumulative lifetimes are strongly affected by current frequency and vacancy lifetime and a frequency shift to the right occurs for small vacancy lifetimes under AC stressing. For pulsed-DC stressing, the temperature rise dependent on the current frequency dominates the interconnect reliability. The average temperature rise due to Joule heating is reduced by two times when the frequency is larger than approximately the reciprocal of the thermal time of interconnect structures. [ABSTRACT FROM AUTHOR]

To investigate the effect and molecular mechanism of methionine (Met) on the growth of hair follicles (HFs) in Rex rabbits. A total of 200 weaning Rex rabbits were divided into four groups and fed varying levels of Met‐supplemented diets. We measured the HF density on dorsal skin and the Wnt/β‐catenin pathway protein expression level. Meanwhile, whole HFs were isolated from Rex rabbit skins and cultured with Met in vitro to measure hair shaft growth. The relationship between Met and the Wnt/β‐catenin signalling pathway was also characterized by using the Wnt/β‐catenin signalling inhibitor, XAV‐939. The results showed that the addition of dietary Met could significantly increase the HF density on dorsal skin (p <.05) and enhance the protein expression level of Wnt10b (p <.05), β‐catenin (p <.05) and DSH (p <.05). Methionine stimulation could also prolong the hair shafts growth in vitro (p <.05). And inhibition of Wnt/β‐catenin signalling using XAV‐939 could eliminate this phenomenon. In summary, Met can increase the density of HFs on dorsal skin in vitro and prolong the hair shaft growth of HFs in vivo via the Wnt/β‐catenin signalling pathway. [ABSTRACT FROM AUTHOR]

We aimed to determine whether treatment effect size differed between randomised controlled phase II trials and subsequent phase III trials and examine potential predictor of positive phase III trials. We searched MEDLINE for randomised controlled phase II studies published from January 2006 to December 2015. Matched phase III trials that investigated same intervention in the same setting of the same cancer were identified through Web of Science, ClinicalTrials.gov and conference proceedings. For each pair of phase II and phase III trials, we extracted hazard ratios (HRs) with 95% confidence intervals (CIs) for both overall survival (OS) and progression-free survival (PFS) and evaluated the differences by ratio of HRs (rHRs): the HR for phase II trial to that for phase III trial. A summary rHR was obtained through a random-effect meta-analysis. Univariable analyses were conducted to identify predictors of positive phase III trials. We identified 57 pairs of phase II and phase III trials. Compared with phase III trials, treatment effect sizes of PFS were, on average, 26% larger in phase II trials (rHR = 0.74, P < 0.001, 95% CI: 0.68–0.80). Treatment effect sizes of OS were 27% greater in phase II trials than in phase III trials (rHR = 0.73, P < 0.001, 95% CI: 0.66–0.79). Fifteen (26.3%) phase III trials were positive, and the only predictor of positive phase III trials was positive phase II trials Treatment effects in randomised controlled phase II trials were greater than those in matched phase III trials. Caution must be taken when interpreting promising results from randomised controlled phase II trials. • Progression-free survival benefit was 26% larger in phase II than phase III trial. • Overall survival benefits was 27% larger in phase II than phase III trial. • The only predictor of positive phase III trials was positive phase II trials. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the effects and mechanisms of hematopoietic-substrate-1-associated protein X-1 (HAX-1) on liver cancer cells. Information on HAX-1 from liver cancer patients was analyzed by the Cancer Genome Atlas (TCGA) program. Cell migration and invasion abilities were respectively tested by scratch assay and transwell assay. Tube formation assay was applied to detect angiogenesis protein and mRNA was determined using quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot. We found that the median month survival of HAX-1 overexpressing liver cancer patients was shorter than that of HAX-1 normal liver cancer patients. HAX-1 was overexpressed in liver cancer tissues and cells, and HAX-1 overexpression promoted the liver cancer cells growth, migration, and invasion, whereas silencing HAX-1 produced the opposite results. Inhibition of Akt by LY294002 reversed the migration and invasion abilities of liver cancer cells, and inhibited the ability of cells growth and angiogenesis. Silencing PIK3CA enhanced the inhibitory effects of HAX-1 silencing on the viability, migration, and invasion of liver cancer cells. HAX-1 affected liver cancer cells metastasis and angiogenesis by affecting Akt phosphorylation and FOXO3A expression. [ABSTRACT FROM AUTHOR]

The endoplasmic reticulum (ER) is the main site for protein synthesis, folding, and secretion, and accumulation of the unfolded/misfolded proteins in the ER may induce ER stress. ER stress is an important participant in various intracellular signaling pathways. Prolonged- or high-intensity ER stress may induce cell apoptosis. Osteoporosis, characterized by imbalanced bone remodeling, is a global disease caused by many factors, such as ER stress. ER stress stimulates osteoblast apoptosis, increases bone loss, and promotes osteoporosis development. Many factors, such as the drug's adverse effects, metabolic disorders, calcium ion imbalance, bad habits, and aging, have been reported to activate ER stress, resulting in the pathological development of osteoporosis. Increasing evidence shows that ER stress regulates osteogenic differentiation, osteoblast activity, and osteoclast formation and function. Various therapeutic agents have been developed to counteract ER stress and thereby suppress osteoporosis development. Thus, inhibition of ER stress has become a potential target for the therapeutic management of osteoporosis. However, the in-depth understanding of ER stress in the pathogenesis of osteoporosis still needs more effort. [Display omitted] • ER stress is implicated in bone remodeling. • ER stress mediates osteogenic differentiation and osteoblast activity. • ER stress regulates osteoclast formation and function. • ER stress has become a target for osteoporosis treatment. [ABSTRACT FROM AUTHOR]

The electric resistance CoFe[SUB2]O[SUB4] nanocrystals of different sizes, obtained by the coprecipitation method was investigated under pressure up to 20 GPa in a diamond anvil cell at ambient temperature. The experimental results indicate that the phase transition (from the spinel to a tetragonal structure) takes place at 7.5 and 12.5 GPa for 6 and 80 nm, nanocrystals, respectively, in agreement with the prediction of scaling theory of the phase transition of nanocrystals. The relatively low transition pressure for CoFe[SUB2]O[SUB4] is related to the high d-electron coupling between magnetic ions and to the small band gap in CoFe[SUB2]O[SUB4] as compared to the other spinel ferrites. These results are discussed in terms of decreasing band gap with increasing pressure. The smaller the size of nanocrystals, the higher the phase transition pressure. The resistance of CoFe[SUB2]O[SUB4] nanocrystal decreases exponentially under pressure, according to R∝exp(-CP), where C = 0.7634 Gpa[SUP-1] for large nanocrystals (80 nm) and C = 0.5124 Gpa[SUP-1] for CoFe[SUB2]O[SUB4] of 6 nm. [ABSTRACT FROM AUTHOR]

To solve the problem that the individual differences and the measurement errors affect the accuracy of life estimation in accelerated degradation test, the inverse Gauss process with stochastic parameters is applied in the accelerated degradation test with the consideration of the influence of individual differences, and the analysis of measurement uncertainty is carried out. An inverse Gauss accelerated degradation model considering both individual differences and measurement errors is established. In the maximum likelihood estimation of parameters, Genetic Algorithm and Monte Carlo integral are used to solve the problems caused by complex integral and the unobservable measurement errors in the calculation process. Finally, the proposed method is verified by the Monte Carlo simulation under the constant accelerated stress and step accelerated stress and the illustrative example of electrical connectors under the constant acceleration stress, respectively. The results show that the modeling tool is useful for improving the accuracy of the life prediction and the reliability evaluation. [ABSTRACT FROM AUTHOR]