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We investigate the effects of boost invariance breaking on the isotropization of pressure in the glasma, using a 3+1D glasma simulation. The breaking is attributed to spatial fluctuations in the classical color charge density along the collision axis. We present numerical results for pressure and energy density at mid-rapidity and across a wider rapidity region. It is found that, despite varying longitudinal correlation lengths, the behaviors of the pressure isotropizations are qualitatively similar. The numerical results suggest that, in the initial stage, longitudinal color electromagnetic fields develop, similar to those in the boost invariant glasma. Subsequently, these fields evolve into a dilute glasma, expanding longitudinally in a manner akin to a dilute gas. We also show that the energy density at mid-rapidity exhibits a 1 / τ decay in the dilute glasma stage. [ABSTRACT FROM AUTHOR]

Rare earth oxides have been proven for their ability to refine grains and have high melting points. In this paper, different contents of rare earth oxide La2O3 were added into the Ni60/WC-Ni composite coating, in order to study its effect on the coating properties. SEM observation confirmed that the grain was refined significantly after the addition of La2O3. Energy Dispersive Spectroscopy (EDS) was applied to investigate the composition and X-Ray Diffraction (XRD) was used to measure the residual stress in the coating samples. In addition, the microhardness and wear resistance of the samples were tested. The results showed that the dilution ratio of coatings with different additions of La2O3 was in the range of 2.4 to 9.8%, and the sample with 1.0% addition of La2O3 exhibited the highest hardness of 66.1 HRC and best wear resistance with a wear volume of 9.87 × 106 μm3, and the residual stress increased from 159.4 MPa to 291.0 MPa. This implies that the performance of the coating has been obviously improved after the addition of La2O3. [ABSTRACT FROM AUTHOR]

The optimal process parameters of ultrasonic-assisted processing were studied to further improve the molding quality and mechanical properties of Ni60/WC-TiO2+La2O3 composite coating. A single-factor experiment was used to explore the influences of ultrasonic vibration frequencies on Ni60/WC-TiO2+La2O3 composite coating. The microstructure, elemental composition, phase composition, hardness, and wear resistance of the coating were studied using scanning electron microscopy (SEM), an X-ray diffractometer (XRD), an energy spectrometer, a microhardness meter, a friction and wear tester, and other equipment. Ultrasonic vibrations significantly improved the problems of pores in the coating, and the porosity was reduced from 0.13 to 0.014%. When the vibration frequency was 32 kHz in the experiment, the aspect ratio of the coating was optimized from 2.06 to 2.48, the dilution rate increased from 5.60 to 5.79%, the hardness increased from 960.25 to 988.45 HZ1.0, and the friction coefficient was reduced from 0.34 to 0.27. The coating performance was significantly improved, and the research results provide a reference for preparing excellent Ni60/WC-TiC+La2O3 composite coating. [ABSTRACT FROM AUTHOR]

The uncertainty brought about by the high proportion of distributed generations poses great challenges to the operational safety of novel distribution systems. Therefore, this paper proposes an online reactive power and voltage control method that integrates source–load uncertainty and a mechanism–data hybrid drive (MDHD) model. Based on the concept of a mechanism and data hybrid drive, the mechanism-driven deterministic reactive power optimization strategy and the stochastic reactive power optimization strategy are used as training data. By training the data-driven CNN–GRU network model offline, the influence of source–load uncertainty on reactive power optimization can be effectively assessed. On this basis, according to the online source and load predicted data, the proposed hybrid-driven model can be applied to quickly obtain the reactive power optimization strategy to enable fast control of voltage. As observed in the case studies, compared with the traditional deterministic and stochastic reactive power optimization models, the hybrid-driven model not only satisfies the real-time requirement of online voltage control, but also has stronger adaptability to source–load uncertainty. [ABSTRACT FROM AUTHOR]

Device-free indoor identification of people with high accuracy is the key to providing personalized services. Visual methods are the solution but they require a clear view and good lighting conditions. Additionally, the intrusive nature leads to privacy concerns. A robust identification and classification system using the mmWave radar and an improved density-based clustering algorithm along with LSTM are proposed in this paper. The system leverages mmWave radar technology to overcome challenges posed by varying environmental conditions on object detection and recognition. The point cloud data are processed using a refined density-based clustering algorithm to extract ground truth in a 3D space accurately. A bi-directional LSTM network is employed for individual user identification and intruder detection. The system achieved an overall identification accuracy of 93.9% and an intruder detection rate of 82.87% for groups of 10 individuals, demonstrating its effectiveness. [ABSTRACT FROM AUTHOR]

Feature matching between high-resolution satellite stereos plays an important role in satellite image orientation. However, images of changed regions, weak-textured regions and occluded regions may generate low-quality matches or even mismatches. Furthermore, matching throughout the entire satellite images often has extremely high time cost. To compute good matching results at low time cost, this paper proposes an image block selection method for high-resolution satellite stereos, which processes feature matching in several optimal blocks instead of the entire images. The core of the method is to formulate the block selection into the optimization of an energy function, and a greedy strategy is designed to compute an approximate solution. The experimental comparisons on various satellite stereos show that the proposed method could achieve similar matching accuracy and much lower time cost when compared with some state-of-the-art satellite image matching methods. Thus, the proposed method is a good compromise between matching accuracy and matching time, which has great potential in large-scale satellite applications. [ABSTRACT FROM AUTHOR]

Apache Spark is a popular open-source distributed data processing framework that can efficiently process massive amounts of data. It provides more than 180 configuration parameters for users to manually select the appropriate parameter values according to their own experience. However, due to the large number of parameters and the inherent correlation between them, manual tuning is very tedious. To solve the problem of tuning through personal experience, we designed and implemented a reinforcement-learning-based Spark configuration parameter optimizer. First, we trained a Spark application performance prediction model with deep neural networks, and verified the accuracy and effectiveness of the model from multiple perspectives. Second, in order to improve the search efficiency of better configuration parameters, we improved the Q-learning algorithm, and automatically set start and end states in each iteration of training, which effectively improves the agent's poor performance in exploring better configuration parameters. Lastly, comparing our proposed configuration with the default configuration as the baseline, experimental results show that the optimized configuration gained an average performance improvement of 47%, 43%, 31%, and 45% for four different types of Spark applications, which indicates that our Spark configuration parameter optimizer could efficiently find the better configuration parameters and improve the performance of various Spark applications. [ABSTRACT FROM AUTHOR]

Bacterial-based self-healing concrete (BSHC) is a well-known healing technology which has been investigated for a few decades for its excellent crack healing capacity. Nevertheless, considered as costly and time-consuming, the healing performance (HP) of concrete with various types of bacteria can be designed and evaluated only in laboratory environments. Employing machine learning (ML) models for predicting the HP of BSHC is inspired by practical applications using concrete mechanical properties. The HP of BSHC can be predicted to save the time and cost of laboratory tests, bacteria selection and healing mechanisms adoption. In this paper, three types of BSHC, including ureolytic bacterial healing concrete (UBHC), aerobic bacterial healing concrete (ABHC) and nitrifying bacterial healing concrete (NBHC), and ML models with five kinds of algorithms consisting of the support vector regression (SVR), decision tree regression (DTR), deep neural network (DNN), gradient boosting regression (GBR) and random forest (RF) are established. Most importantly, 22 influencing factors are first employed as variables in the ML models to predict the HP of BSHC. A total of 797 sets of BSHC tests available in the open literature between 2000 and 2021 are collected to verify the ML models. The grid search algorithm (GSA) is also utilised for tuning parameters of the algorithms. Moreover, the coefficient of determination (R2) and root mean square error (RMSE) are applied to evaluate the prediction ability, including the prediction performance and accuracy of the ML models. The results exhibit that the GBR model has better prediction ability (R2GBR = 0.956, RMSEGBR = 6.756%) than other ML models. Finally, the influence of the variables on the HP is investigated by employing the sensitivity analysis in the GBR model. [ABSTRACT FROM AUTHOR]

Inorganic phosphate (Pi) is taken up by plant roots and translocated via phosphate transporters. Previously, we showed that phosphate transporter OsPHT1;4 in the PHT1 family participates in phosphate acquisition and mobilization; it facilitates the embryo development of Japonica rice Nipponbare. This study investigated the potential of manipulating the expression of OsPHT1;4 to increase Pi acquisition efficiency and crop productivity in rice cv. Wuyunjing 7 (WYJ 7), a cultivar widely grown in Yangtze River Delta of China. The OsPHT1;4 overexpression lines and wild-type WYJ 7 were treated under different Pi conditions in hydroponic and field experiments. Quantitative real-time RT-PCR analysis and the transgenic plants expressing GUS reporter gene indicate strong expression of OsPHT1;4 in roots and leaf collars of cv. WYJ 7. The total P contents in shoots of the OsPHT1;4-overexpressing plants were significantly higher under Pi-deficient hydroponic conditions than the wild type under Pi sufficiency and deficiency. 33Pi uptake and translocation assays confirmed the results. In the field condition, OsPHT1;4 overexpression lines had a higher P concentration in tissues than the wild type control, and the panicle performance of the overexpression lines including the grain yield was improved as well. Taken together, our results show that OsPHT1;4 plays an important role in the acquisition and mobilization of Pi in WYJ 7, especially under Pi deficiency. The study highlights the importance of OsPHT1;4 in improving the agronomic traits of the widely grown rice cultivar in China. [ABSTRACT FROM AUTHOR]

Latent reservoir persistence remains a major obstacle for curing human immunodeficiency virus type 1 (HIV-1) infection. Thus, strategies for the elimination of latent HIV-1 are urgently needed. As a bromodomain and extra-terminal (BET) inhibitor, BMS-986158 has been used in clinical trials for advanced solid tumors and hematological malignancies. Here, we found that BMS-986158 reactivated latent HIV-1 in three types of HIV-1 latency cells in vitro, and in combination antiretroviral therapy (cART)-treated patient-derived peripheral blood mononuclear cells ex vivo, without influencing global immune cell activation. BMS-986158 reactivated latent HIV-1 by increasing phosphorylation of CDK9 at Thr186 and promoting recruitment of CDK9 and RNA polymerase II to the HIV-1 long terminal repeat in J-Lat cells. Furthermore, BMS-986158 exerted strong synergism in reactivating latent HIV-1 when combined with prostratin and vorinostat and enhanced the antiviral activity of anti-HIV-1 drugs. Finally, BMS-986158 showed antiviral activity in an HIV-1 acute infection model, possibly by arresting the cell cycle in infected cells. Thus, these results suggest that BMS-986158 is a potential candidate for AIDS/HIV-1 therapy. [ABSTRACT FROM AUTHOR]

Mountains are an important research object for surveying, mapping, cartography, space science, and ecological remote sensing. Automatic mountain segmentation is one of the most critical techniques in large-scale mountain analyses. However, several factors limit the segmentation accuracy, such as the complexity of mountains, the noise of geospatial data, and the confusion in distinguishing non-mountainous objects with similar features. In order to improve the accuracy of mountain segmentation against these limiting factors, we impose the cloth constraint over the digital elevation model (DEM) with the underlying assumption that the mountain has a sizeable relative elevation and slope. We propose a robust mountain segmentation method with the cloth simulation constraint. The core algorithm extracts the relative elevation of the region using a cloth simulation filtering algorithm by transforming the mountain segmentation problem into an optimization problem based on the global energy function consisting of the relative elevation and slope. Experiments on a wide range of Earth and lunar elevation datasets with some of the aforementioned limitations show that the proposed method can extract complex mountain baselines, avoid the misclassification of lunar craters, and significantly improve the robustness and accuracy of mountain segmentation. Compared to three state-of-the-art methods (the Lunar Mountain Detection Method, the Landform Mask Method in SNAP™ from European Space Agency (located in Paris, France), and the Multiscale Segmentation Method in eCognition™ from Definiens Imaging (located in Munich, Germany), the F1 and IoU improved by 14.70% and 20.46% on average and 29.07% and 38.94% at most, respectively, which validates that the proposed method has a better all-around performance. [ABSTRACT FROM AUTHOR]

Accurate measurement of the junction temperature Tj is crucial for ensuring safe operation and evaluating the performance of silicon carbide (SiC) metal–oxide–semiconductor field-effect transistors (MOSFETs). In this paper, the junction temperature of a half-bridge is monitored by combining transient voltage and current peaks across the lower device. Both theoretical analysis and switch tests validate that the peak induced voltage and peak reversed current during the commutation transient exhibit a linear relationship with the temperature of the corresponding devices. The impact of load current and bus voltage on the measurement results is also investigated. Moreover, the effectiveness of the proposed method is confirmed by carrying out tests at different temperatures, and the feasibility of online implementation is discussed. The experimental results show that the proposed method has high linearity and sensitivity and can simultaneously provide temperature information for both devices of a half-bridge. [ABSTRACT FROM AUTHOR]

Saponins are glycosides with triterpenoid or spirostane aglycones that demonstrate various pharmacological effects against mammalian diseases. To promote the research and development of anticancer agents from saponins, this review focuses on the anticancer properties of several typical naturally derived triterpenoid saponins (ginsenosides and saikosaponins) and steroid saponins (dioscin, polyphyllin, and timosaponin) isolated from Chinese medicines. These saponins exhibit in vitro and in vivo anticancer effects, such as anti-proliferation, anti-metastasis, anti-angiogenesis, anti-multidrug resistance, and autophagy regulation actions. In addition, related signaling pathways and target proteins involved in the anticancer effects of saponins are also summarized in this work. [ABSTRACT FROM AUTHOR]

This study investigates the engineering and mechanical properties of basalt fiber-reinforced (FRF) concrete, giving special attention to residual flexural strength and dynamic modal parameters. These properties, which have not been thoroughly investigated elsewhere, are a precursor to structural design applications for dynamic compliant structures (i.e., bridges, offshore platforms, railways, and airport pavement). Accordingly, the standard notched flexural tests have been carried out to assess the basalt fiber-reinforced concrete's residual flexural strength with an additional 0.125%, 0.25%, 0.375%, and 0.5% of volume fraction of basalt fiber. In addition, dynamic modal tests were then conducted to determine the dynamic modulus of elasticity (MOE) and damping of the FRF concrete beams. The results indicate that concrete's toughness and crack resistance performance are significantly improved with added fiber in basalt fiber reinforced concrete, and the optimum fiber content is 0.25%. It also exhibits the highest increment of compressive strength of 4.48% and a dynamic MOE of 13.83%. New insights reveal that although the residual flexural performance gradually improved with the addition of basalt fiber, the damping ratio had an insignificant change. [ABSTRACT FROM AUTHOR]

Relativistic magnetohydrodynamics (RMHD) provides an extremely useful description of the low-energy long-wavelength phenomena in a variety of physical systems from quark–gluon plasma in heavy-ion collisions to matters in supernova, compact stars, and early universe. We review the recent theoretical progresses of RMHD, such as a formulation of RMHD from the perspective of magnetic flux conservation using the entropy–current analysis, the nonequilibrium statistical operator approach applied to quantum electrodynamics, and the relativistic kinetic theory. We discuss how the transport coefficients in RMHD are computed in kinetic theory and perturbative quantum field theories. We also explore the collective modes and instabilities in RMHD with a special emphasis on the role of chirality in a parity-odd plasma. We also give some future prospects of RMHD, including the interaction with spin hydrodynamics and the new kinetic framework with magnetic flux conservation. [ABSTRACT FROM AUTHOR]

Few-shot object detection is a recently emerging branch in the field of computer vision. Recent research studies have proposed several effective methods for object detection with few samples. However, their performances are limited when applied to remote sensing images. In this article, we specifically analyze the characteristics of remote sensing images and propose a few-shot fine-tuning network with a shared attention module (SAM) to adapt to detecting remote sensing objects, which have large size variations. In our SAM, multi-attention maps are computed in the base training stage and shared with the feature extractor in the few-shot fine-tuning stage as prior knowledge to help better locate novel class objects with few samples. Moreover, we design a new few-shot fine-tuning stage with a balanced fine-tuning strategy (BFS), which helps in mitigating the severe imbalance between the number of novel class samples and base class samples caused by the few-shot settings to improve the classification accuracy. We have conducted experiments on two remote sensing datasets (NWPU VHR-10 and DIOR), and the excellent results demonstrate that our method makes full use of the advantages of few-shot learning and the characteristics of remote sensing images to enhance the few-shot detection performance. [ABSTRACT FROM AUTHOR]

Cracks typically develop in concrete due to shrinkage, loading actions, and weather conditions; and may occur anytime in its life span. Autogenous healing concrete is a type of self-healing concrete that can automatically heal cracks based on physical or chemical reactions in concrete matrix. It is imperative to investigate the healing performance that autogenous healing concrete possesses, to assess the extent of the cracking and to predict the extent of healing. In the research of self-healing concrete, testing the healing performance of concrete in a laboratory is costly, and a mass of instances may be needed to explore reliable concrete design. This study is thus the world's first to establish six types of machine learning algorithms, which are capable of predicting the healing performance (HP) of self-healing concrete. These algorithms involve an artificial neural network (ANN), a k-nearest neighbours (kNN), a gradient boosting regression (GBR), a decision tree regression (DTR), a support vector regression (SVR) and a random forest (RF). Parameters of these algorithms are tuned utilising grid search algorithm (GSA) and genetic algorithm (GA). The prediction performance indicated by coefficient of determination (R2) and root mean square error (RMSE) measures of these algorithms are evaluated on the basis of 1417 data sets from the open literature. The results show that GSA-GBR performs higher prediction performance (R2GSA-GBR = 0.958) and stronger robustness (RMSEGSA-GBR = 0.202) than the other five types of algorithms employed to predict the healing performance of autogenous healing concrete. Therefore, reliable prediction accuracy of the healing performance and efficient assistance on the design of autogenous healing concrete can be achieved. [ABSTRACT FROM AUTHOR]

In this paper, a new pyrroloquinoline-derivative-based fluorescent probe, PQP-1, was prepared for the selective detection of Lys in living cells and natural mineral water for drinking. PQP-1 exhibited high selectivity, low limit of detection, and a wide pH range. PQP-1 could be successfully applied for imaging Lys in living cells and in natural mineral water for drinking. We expect that PQP-1 will expand the detection reaction mechanism and the practical biological applications of Lys. [ABSTRACT FROM AUTHOR]

Carbon material is widely used and has good electrical and thermal conductivity. It is often used as a filler to endow insulating polymer with electrical and thermal conductivity. Three-dimensional printing technology is an advance in modeling and manufacturing technology. From the forming principle, it offers a new production principle of layered manufacturing and layer by layer stacking formation, which fundamentally simplifies the production process and makes large-scale personalized production possible. Conductive carbon materials combined with 3D printing technology have a variety of potential applications, such as multi-shape sensors, wearable devices, supercapacitors, and so on. In this review, carbon black, carbon nanotubes, carbon fiber, graphene, and other common conductive carbon materials are briefly introduced. The working principle, advantages and disadvantages of common 3D printing technology are reviewed. The research situation of 3D printable conductive carbon materials in recent years is further summarized, and the performance characteristics and application prospects of these conductive carbon materials are also discussed. Finally, the potential applications of 3D printable conductive carbon materials are concluded, and the future development direction of 3D printable conductive carbon materials has also been prospected. [ABSTRACT FROM AUTHOR]

The ongoing COVID-19 pandemic has enhanced the impact of digitalisation as a driver of transformation and advancements across almost every aspect of human life. With the majority actively embracing smart technologies and their benefits, the journey of human digitalisation has begun. Will human beings continue to remain solitary unaffected beings in the middle of the whirlpool—a gateway to the completely digitalised future? This journey of human digitalisation probably started much earlier, before we even realised. This paper, in the format of an objective review and discussion, aims to investigate the journey of human digitalisation, explore the reality of domination between technology and humans, provide a better understanding of the human value and human vulnerability in this fast transforming digital era, so as to achieve valuable and insightful suggestion on the future direction of the human digitalisation journey. [ABSTRACT FROM AUTHOR]

Feature matching is to detect and match corresponding feature points in stereo pairs, which is one of the key techniques in accurate camera orientations. However, several factors limit the feature matching accuracy, e.g., image textures, viewing angles of stereo cameras, and resolutions of stereo pairs. To improve the feature matching accuracy against these limiting factors, this paper imposes spatial smoothness constraints over the whole feature point sets with the underlying assumption that feature points should have similar matching results with their surrounding high-confidence points and proposes a robust feature matching method with the spatial smoothness constraints (RMSS). The core algorithm constructs a graph structure from the feature point sets and then formulates the feature matching problem as the optimization of a global energy function with first-order, spatial smoothness constraints based on the graph. For computational purposes, the global optimization of the energy function is then broken into sub-optimizations of each feature point, and an approximate solution of the energy function is iteratively derived as the matching results of the whole feature point sets. Experiments on close-range datasets with some above limiting factors show that the proposed method was capable of greatly improving the matching robustness and matching accuracy of some feature descriptors (e.g., scale-invariant feature transform (SIFT) and Speeded Up Robust Features (SURF)). After the optimization of the proposed method, the inlier number of SIFT and SURF was increased by average 131.9% and 113.5%, the inlier percentages between the inlier number and the total matches number of SIFT and SURF were increased by average 259.0% and 307.2%, and the absolute matching accuracy of SIFT and SURF was improved by average 80.6% and 70.2%. [ABSTRACT FROM AUTHOR]

In the current work, a novel hybrid laminate with negative Poisson's ratio (NPR) is developed by considering auxetic laminate which is composed of carbon nanotube-reinforced composite (CNTRC) and fiber-reinforced composite (FRC) materials. The maximum magnitude of out-of-plane NPR is identified in the case of (20 F/20 C/−20 C/20 C) S laminate as well. Meanwhile, a method for the geometric non-linear analysis of hybrid laminated beam with NPR including the non-linear bending, free, and forced vibrations is proposed. The beam deformation is modeled by combining higher-order shear-deformation theory (HSDT) and large deflection theory. Based on a two-step perturbation approach, the asymptotic solutions of the governing equations are obtained to capture the linear and non-linear frequencies and load-deflection curves. Moreover, a two-step perturbation methodology in conjunction with fourth-order Runge–Kutta method is employed to solve the forced-vibration problem. Several key factors, such as CNT distribution, variations in the elastic foundation, and thermal stress, are considered in the exhaustive analysis. Theoretical results for some particular cases are given to examine the geometric non-linearity behavior of hybrid beam with NPR as well as positive Poisson's ratio (PPR). [ABSTRACT FROM AUTHOR]

Cracks could attenuate the service life of concrete structures because of the intrusion of hazardous substances such as water. In this study, different proportions of Duras S500 fibre were employed to investigate the self-sealing capacity of environmentally friendly, highly damped, fibre-reinforced concrete (EFHDFRC) containing 5% crumb rubber. The workability of EFHDFRC with different proportions of the fibre was investigated by mechanical properties test. The self-sealing capacity was first measured by introducing the ultrasonic pulse velocity (UPV) test combined with the damage degree in a time-dependent manner. In addition, the regained compressive strength test and visual inspection were applied as additional measures of the self-sealing capacity. The experimental results show that EFHDFRC with different proportions of fibre showed the maximum sealing degree between the 42nd and 51st days after casting the concrete. EFHDFRC with 0.1% fibre had the best performance and the maximum self-sealing degree (2.82%). In summary, it has been proven that 0.1% fibre could stimulate the self-sealing capacity of EFHDFRC by bridging cracked concrete. Moreover, it is noted that sufficient space in cracks is essential for precipitation formation, which could seal the cracks. The new insights of this innovative self-healing, high-damping material are essential for industrial applications exposed to dynamic load conditions such as railway turnout bearers and sleepers, highspeed rail track slabs, blast-resistant walls and columns, and so on. [ABSTRACT FROM AUTHOR]

Bundle adjustment of multi-view satellite images is a powerful tool to align the orientations of all the images in a unified framework. However, the traditional bundle adjustment process faces a problem in detecting mismatches and evaluating low/medium/high-accuracy matches, which limits the final bundle adjustment accuracy, especially when the mismatches are several times more than the correct matches. To achieve more accurate bundle adjustment results, this paper formulates the prior knowledge of matching accuracy as matching confidences and proposes a matching confidence based bundle adjustment method. The core algorithm firstly selects several highest-confidence matches to initially correct orientations of all images, then detects and eliminates the mismatches under the initial orientation guesses and finally formulates both the matching confidences and the forward-backward projection errors as weights in an iterative bundle adjustment process for more accurate orientation results. We compared our proposed method with the famous RANSAC strategy as well as a state-of-the-art bundle adjustment method on the high-resolution multi-view satellite images. The experimental comparisons are evaluated by image checking points and ground control points, which shows that our proposed method is able to obtain more robust and more accurate mismatch detection results than the RANSAC strategy, even though the mismatches are four times more than the correct matches and it can also achieve more accurate orientation results than the state-of-the-art bundle adjustment method. [ABSTRACT FROM AUTHOR]

Disease and pest stress is one of the important threats to crop growth and development, which have a very adverse impact on crop yield and quality every year, even leading to crop failure. Currently, the use of plant protection unmanned aerial vehicles (UAVs) for pesticide spraying is the most effective means of controlling crop diseases and pests. However, the areas where crop pests and diseases occur are often presented in "point-like" or "patchy" forms, and the UAV's full-coverage spraying method results in a great waste of pesticides. Therefore, there is an urgent need to research a method for identifying the area of diseases and pest stress to achieve precise targeted spraying, in order to reduce the amount of pesticides used and improve their utilization. By analyzing the low-altitude remote sensing images of crop fields taken by UAVs, real-time pesticide spraying prescription maps can be generated to meet the demand for precise targeted spraying. This review focuses on the actual needs of precise targeted spraying by plant protection UAVs. Firstly, the RS monitoring mechanism of crop diseases and pests by UAVs is studied. Secondly, a comprehensive investigation of the literature on UAV Low-altitude Remote Sensing (UAV-LARS) technology for monitoring and identifying crop diseases and pests is conducted, summarizing the research progress in monitoring and identifying crop diseases and pests, especially in wheat, cotton, and rice. Finally, the key issues to be addressed and the future development direction of UAV-LARS monitoring of crop diseases and pests are proposed. [ABSTRACT FROM AUTHOR]

Undercrossing construction can cause severe structural deformation of the above existing tunnel in operation. The induced longitudinal differential settlement between the segments can pose a huge risk to running subways, hence it is of great importance to monitor and predict the settlement. Within this study, a Wireless Sensor Network (WSN) system was implemented to obtain hourly monitoring data of settlement from the very beginning of undercrossing to post construction period. An improved direct multi-step (DMS) forecasting model called ConvRes-DLinear is proposed, which fuses monitoring data with time and process encoding bias to deeply extract and learn temporal correlation of time series. A residual LSTM model is also constructed to compare the accuracy of the improved DLinear model. The training and testing experiment on the monitoring data of longitudinal settlement obtained by WSN system shows that the ConvRes-DLinear model with time and process encoding bias performs surprisingly well with a minimum prediction error. The features of the proposed model are discussed to make the results explainable. The monitoring system and time series forecasting model proposed in this study have a guiding significance for the monitoring and prediction of longitudinal differential settlement of tunnels under environmental disturbance. [ABSTRACT FROM AUTHOR]

Given the rising popularity of post-retirement work and its potential benefits for older adults' mental health, this study examined older adults' adaptation ability as a conditional factor for the impact of post-retirement work on depression. Quantitative data from 1433 working older adults and 1433 non-working older adults were analyzed using the PROCESS macro in SPSS to test a moderated regression model with adaptation ability as the moderator. Results showed that older adults with lower adaptation ability demonstrated significantly lower depression if they worked (vs. did not work). Older adults with higher adaptation ability exhibited significantly higher depression if they worked (vs. did not work). These findings were subsequently verified with a robustness check. Overall, post-retirement work did not prevent depression for the entire sample; working only alleviated depression among older adults with limited adaptation ability. Older adults with stronger adaptation ability could better maintain their mental health by staying retired. This study fills a knowledge gap regarding the relationship between post-retirement work and mental health. Implications for aging societies are also discussed. [ABSTRACT FROM AUTHOR]

For large-scale 3D building reconstruction, there have been several approaches to utilizing multi-view satellite imagery to produce a digital surface model (DSM) for height information and extracting building footprints for contour information. However, limited by satellite resolutions and viewing angles, the corresponding DSM and building footprints are sometimes of a low accuracy, thus generating low-accuracy building models. Though some recent studies have added GIS data to refine the contour of the building footprints, the registration errors between the GIS data and satellite images are not considered. Since OpenStreetMap (OSM) provides a high level of precision and complete building polygons in most cities worldwide, this paper proposes an automatic single building reconstruction method that utilizes a DSM from high-resolution satellite stereos, as well as building footprints from OSM. The core algorithm accurately registers the building polygons from OSM with the rasterized height information from the DSM. To achieve this goal, this paper proposes a two-step "coarse-to-fine registration" algorithm, with both steps being formulated into the optimization of energy functions. The coarse registration is optimized by separately moving the OSM polygons at fixed steps with the constraints of a boundary gradient, an interior elevation mean, and variance. Given the initial solution of the coarse registration, the fine registration is optimized by a genetic algorithm to compute the accurate translations and rotations between the DSM and OSM. Experiments performed in the Beijing/Shanghai region show that the proposed method can significantly improve the IoU (intersection over union) of the registration results by 69.8%/26.2%, the precision by 41.0%/15.5%, the recall by 41.0%/16.0%, and the F1-score by 42.7%/15.8%. For the registration, the method can reduce the translation errors by 4.656 m/2.815 m, as well as the rotation errors by 0.538°/0.228°, which indicates its great potential in smart 3D applications. [ABSTRACT FROM AUTHOR]

Reported coronavirus disease 2019 (COVID-19) outcomes in persons living with HIV (PLWH) vary across cohorts. We examined clinical characteristics and outcomes of PLWH with COVID-19 compared with a matched HIV-seronegative cohort in a mid-Atlantic US healthcare system. Multivariate logistic regression was used to explore factors associated with hospitalization and death/mechanical ventilation among PLWH. Among 281 PLWH with COVID-19, the mean age was 51.5 (SD 12.74) years, 63% were male, 86% were Black, and 87% had a HIV viral load <200 copies/mL. Overall, 47% of PLWH versus 24% (p < 0.001) of matched HIV-seronegative individuals were hospitalized. Rates of COVID-19 associated cardiovascular and thrombotic events, AKI, and infections were similar between PLWH and HIV-seronegative individuals. Overall mortality was 6% (n = 18/281) in PLWH versus 3% (n = 33/1124) HIV-seronegative, p < 0.0001. Among admitted patients, mortality was 14% (n = 18/132) for PLWH and 13% (n = 33/269) for HIV-seronegative, p = 0.75. Among PLWH, hospitalization associated with older age aOR 1.04 (95% CI 1.01, 1.06), Medicaid insurance aOR 2.61 (95% CI 1.39, 4.97) and multimorbidity aOR 2.98 (95% CI 1.72, 5.23). Death/mechanical ventilation associated with older age aOR 1.06 (95% CI 1.01, 1.11), Medicaid insurance aOR 3.6 (95% CI 1.36, 9.74), and multimorbidity aOR 4.4 (95% CI 1.55, 15.9) in adjusted analyses. PLWH were hospitalized more frequently than the HIV-seronegative group and had a higher overall mortality rate, but once hospitalized had similar mortality rates. Older age, multimorbidity and insurance status associated with more severe outcomes among PLWH suggesting the importance of targeted interventions to mitigate the effects of modifiable inequities. [ABSTRACT FROM AUTHOR]

Macroporous characteristics have been shown to play a key role in the osteoinductivity of hydroxyapatite ceramics, but the physics underlying the new bone formation and distribution in such scaffolds still remain elusive. The work here has emphasized the osteoinductive capacity of porous hydroxyapatite scaffolds containing different macroporous sizes (200–400 μm, 1200–1500 μm) and geometries (star shape, spherical shape). The assumption is that both the size and shape of a macropore structure may affect the microfluidic pathways in the scaffolds, which results in the different bone formations and distribution. Herein, a mathematical model and an animal experiment were proposed to support this hypothesis. The results showed that the porous scaffolds with the spherical macropores and large pore sizes (1200–1500 μm) had higher new bone production and more uniform new bone distribution than others. A finite element analysis suggested that the macropore shape affected the distribution of the medium–high velocity flow field, while the macropore size effected microfluid speed and the value of the shear stress in the scaffolds. Additionally, the result of scaffolds implanted into the dorsal muscle having a higher new bone mass than the abdominal cavity suggested that the mechanical load of the host tissue could play a key role in the microfluidic pathway mechanism. All these findings suggested that the osteoinduction of these scaffolds depends on both the microfluid velocity and shear stress generated by the macropore size and shape. This study, therefore, provides new insights into the inherent osteoinductive mechanisms of bioceramics, and may offer clues toward a rational design of bioceramic scaffolds with improved osteoinductivity. [ABSTRACT FROM AUTHOR]

The development of smart, efficient and multifunctional material systems for diseases treatment are imperative to meet current and future health challenges. Nanomaterials with theranostic properties have offered a cost effective and efficient solution for disease treatment, particularly, metal/oxide based nanotheranostic systems already offering therapeutic and imaging capabilities for cancer treatment. Nanoparticles can selectively generate/scavenge ROS through intrinsic or external stimuli to augment/diminish oxidative stress. An efficient treatment requires higher oxidative stress/toxicity in malignant disease, with a minimal level in surrounding normal cells. The size, shape and surface properties of nanoparticles are critical parameters for achieving a theranostic function in the microenvironment. In the last decade, different strategies for the synthesis of biocompatible theranostic nanostructures have been introduced. The exhibition of therapeutics properties such as selective reactive oxygen species (ROS) scavenging, hyperthermia, antibacterial, antiviral, and imaging capabilities such as MRI, CT and fluorescence activity have been reported in a variety of developed nanosystems to combat cancer, neurodegenerative and emerging infectious diseases. In this review article, theranostic in vitro behaviour in relation to the size, shape and synthesis methods of widely researched and developed nanosystems (Au, Ag, MnOx, iron oxide, maghemite quantum flakes, La2O3−x, TaOx, cerium nanodots, ITO, MgO1−x) are presented. In particular, ROS-based properties of the nanostructures in the microenvironment for cancer therapy are discussed. The provided overview of the biological behaviour of reported metal-based nanostructures will help to conceptualise novel designs and synthesis strategies for the development of advanced nanotheranostic systems. [ABSTRACT FROM AUTHOR]

Laser peen forming (LPF) is suitable for shaping sheet metals without the requirement for die/mold and without causing high temperatures. An analytical model for estimating the bending curvatures of LPF is convenient and necessary for better understanding of the physical processes involved. In this paper, we describe a new analytical model based on internal force balance and the energy transformation in LPF. Experiments on 2024 aluminum alloy sheets of 1–3 mm thickness were performed to validate the analytical model. The results showed that for 1 mm and 3 mm thick–thin plates, the curvature obtained by the analytical model changes from −14 × 10−4 mm−1 and −1 × 10−4 mm−1 to 55 × 10−4 mm−1 and −21 × 10−4 mm−1, respectively, with the increase of laser energy, which is consistent with the experimental trend. So, when either the stress gradient mechanism (SGM) or the shock bending mechanism (SBM) overwhelmingly dominated the forming process, the analytical model could give relatively accurate predicted curvatures compared with the experimental data. Under those conditions where SGM and SBM were comparable, the accuracy of the model was low, because of the complex stress distributions within the material, and the complex energy coupling process under these conditions. [ABSTRACT FROM AUTHOR]

Traditional stereo dense image matching (DIM) methods normally predefine a fixed window to compute matching cost, while their performances are limited by the matching window sizes. A large matching window usually achieves robust matching results in weak-textured regions, while it may cause over-smoothness problems in disparity jumps and fine structures. A small window can recover sharp boundaries and fine structures, while it contains high matching uncertainties in weak-textured regions. To address the issue above, we respectively compute matching results with different matching window sizes and then proposes an adaptive fusion method of these matching results so that a better matching result can be generated. The core algorithm designs a Convolutional Neural Network (CNN) to predict the probabilities of large and small windows for each pixel and then refines these probabilities by imposing a global energy function. A compromised solution of the global energy function is utilized by breaking the optimization into sub-optimizations of each pixel in one-dimensional (1D) paths. Finally, the matching results of large and small windows are fused by taking the refined probabilities as weights for more accurate matching. We test our method on aerial image datasets, satellite image datasets, and Middlebury benchmark with different matching cost metrics. Experiments show that our proposed adaptive fusion of multiple-window matching results method has a good transferability across different datasets and outperforms the small windows, the median windows, the large windows, and some state-of-the-art matching window selection methods. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) printing of continuous fiber-reinforced composites has been developed in recent decades as an alternative means to handle complex structures with excellent design flexibility and without mold forming. Although 3D printing has been increasingly used in the manufacturing industry, there is still room for the development of theories about how the process parameters affect microstructural properties to meet the mechanical requirements of the printed parts. In this paper, we investigated continuous carbon fiber-reinforced polyphenylene sulfide (CCF/PPS) as feedstock for fused deposition modeling (FDM) simulated by thermocompression. This study revealed that the samples manufactured using a layer-by-layer process have a high tensile strength up to 2041.29 MPa, which is improved by 68.8% compared with those prepared by the once-stacked method. Moreover, the mechanical–microstructure characterization relationships indicated that the compactness of the laminates is higher when the stacked CCF/PPS are separated, which can be explained based on both the void formation and the nanoindentation results. These reinforcements confirm the potential of remodeling the layer-up methods for the development of high-performance carbon fiber-reinforced thermoplastics. This study is of great significance to the improvement of the FDM process and opens broad prospects for the aerospace industry and continuous fiber-reinforced polymer matrix materials. [ABSTRACT FROM AUTHOR]

Current treatment options for patients infected with hepatitis B virus (HBV) are suboptimal, because the approved drugs rarely induce cure due to the persistence of the viral DNA genome in the nucleus of infected hepatocytes, and are associated with either severe side effects (pegylated interferon-alpha) or require life-long administration (nucleos(t)ide analogs). We report here the evaluation of the safety and therapeutic efficacy of a novel, humanized antibody (hzVSF) in the woodchuck model of HBV infection. hzVSF has been shown to act as a viral entry inhibitor, most likely by suppressing vimentin-mediated endocytosis of virions. Targeting the increased vimentin expression on liver cells by hzVSF after infection with HBV or woodchuck hepatitis virus (WHV) was demonstrated initially. Thereafter, hzVSF safety was assessed in eight woodchucks naïve for WHV infection. Antiviral efficacy of hzVSF was evaluated subsequently in 24 chronic WHV carrier woodchucks by monotreatment with three ascending doses and in combination with tenofovir alafenamide fumarate (TAF). Consistent with the proposed blocking of WHV reinfection, intravenous hzVSF administration for 12 weeks resulted in a modest but transient reduction of viral replication and associated liver inflammation. In combination with oral TAF dosing, the antiviral effect of hzVSF was enhanced and sustained in half of the woodchucks with an antibody response to viral proteins. Thus, hzVSF safely but modestly alters chronic WHV infection in woodchucks; however, as a combination partner to TAF, its antiviral efficacy is markedly increased. The results of this preclinical study support future evaluation of this novel anti-HBV drug in patients. [ABSTRACT FROM AUTHOR]

As current interventions for chronic hepatitis B (CHB) rarely induce cure, more effective drugs are needed. Short-term treatment of woodchucks with the novel immunomodulator AIC649, a parapoxvirus-based stimulator of toll-like receptor 9 dependent and independent pathways, has been shown to reduce viral DNA and surface antigen via a unique, biphasic response pattern. The present study evaluated long-term AIC649 treatment in combination with Entecavir for potency and safety in woodchucks. AIC649 monotreatment induced modest reductions in serum viral DNA and surface and e antigens that were associated with the same biphasic response pattern previously observed. Entecavir monotreatment reduced transiently viremia but not antigenemia, while AIC649/Entecavir combination treatment mediated superior viral control. Undetectability of viral antigens and elicitation of antibodies in AIC649/Entecavir-treated woodchucks correlated with the expression of interferons and suppression of viral replication in liver. Combination treatment was well tolerated, and liver enzyme elevations were minor and transient. It was concluded that the AIC649-mediated effects were most likely based on an improvement and/or reconstitution of antiviral immune responses that are typically deficient in CHB. As a combination partner to Entecavir, the antiviral efficacy of AIC649 was markedly enhanced. This preclinical study supports future evaluation of AIC649 for treatment of human CHB. [ABSTRACT FROM AUTHOR]

Acute myeloid leukemia (AML) is a highly heterogeneous hematopoietic malignancy characterized by excessive proliferation and accumulation of immature myeloid blasts in the bone marrow. AML has a very poor 5-year survival rate of just 16% in the UK; hence, more efficacious, tolerable, and targeted therapy is required. Persistent leukemia stem cell (LSC) populations underlie patient relapse and development of resistance to therapy. Identification of critical oncogenic signaling pathways in AML LSC may provide new avenues for novel therapeutic strategies. The phosphatidylinositol-3-kinase (PI3K)/Akt and the mammalian target of rapamycin (mTOR) signaling pathway, is often hyperactivated in AML, required to sustain the oncogenic potential of LSCs. Growing evidence suggests that targeting key components of this pathway may represent an effective treatment to kill AML LSCs. Despite this, accruing significant body of scientific knowledge, PI3K/Akt/mTOR inhibitors have not translated into clinical practice. In this article, we review the laboratory-based evidence of the critical role of PI3K/Akt/mTOR pathway in AML, and outcomes from current clinical studies using PI3K/Akt/mTOR inhibitors. Based on these results, we discuss the putative mechanisms of resistance to PI3K/Akt/mTOR inhibition, offering rationale for potential candidate combination therapies incorporating PI3K/Akt/mTOR inhibitors for precision medicine in AML. [ABSTRACT FROM AUTHOR]

In this study, graphene-oxide (GO)-reinforced Ti–Al–Sn–Zr–Mo–Nb–Si high-temperature titanium-alloy-matrix composites were fabricated by powder metallurgy. The mixed powders with well-dispersed GO sheets were obtained by temperature-controlled solution mixing, in which GO sheets adsorb on the surface of titanium alloy particles. Vacuum deoxygenating was applied to remove the oxygen-containing groups in GO, in order to reduce the introduction of oxygen. The compact composites with refined equiaxed and lamellar α phase structures were prepared by hot isostatic pressing (HIP). The results show that in-situ TiC layers form on the surface of GO and GO promotes the precipitation of hexagonal (TiZr)6Si3 particles. The composites exhibit significant improvement in strength and microhardness. The room-temperature tensile strength, yield strength and microhardness of the composite added with 0.3 wt% GO are 9%, 15% and 27% higher than the matrix titanium alloy without GO, respectively, and the tensile strength and yield strength at 600 °C are 3% and 21% higher than the matrix alloy. The quantitative analysis indicates that the main strengthening mechanisms are load transfer strengthening, grain refinement and (TiZr)6Si3 second phase strengthening, which accounted for 48%, 30% and 16% of the improvement of room-temperature yield strength, respectively. [ABSTRACT FROM AUTHOR]

Biomass reflects the state of forest management and is critical for assessing forest benefits and carbon storage. The effective crown is the region above the lower limit of the forest crown that includes the maximum vertical distribution density of branches and leaves; this component plays an important role in tree growth. Adding the effective crown to biomass equations can enhance the accuracy of the derived biomass. Six sample plots in a larch plantation (ranging in area from 0.06 ha to 0.12 ha and in number of trees from 63 to 96) at the Mengjiagang forest farm in Huanan County, Jiamusi City, Heilongjiang Province, China, were analyzed in this study. Terrestrial laser scanning (TLS) was used to obtain three-dimensional point cloud data on the trees, from which crown parameters at different heights were extracted. These parameters were used to determine the position of the effective crown. Moreover, effective crown parameters were added to biomass equations with tree height as the sole variable to improve the accuracy of the derived individual-tree biomass estimates. The results showed that the minimum crown contact height was very similar to the effective crown height, and an increase in model accuracy was apparent (with R a 2 increasing from 0.846 to 0.910 and root-mean-square error (RMSE) decreasing from 0.372 kg to 0.286 kg). The optimal model for deriving biomass included tree height, crown length from minimum contact height, crown height from minimum contact height, and crown surface area from minimum contact height. The novelty of the article is that it improves the fit of individual-tree biomass models by adding crown-related variables and investigates how the accuracy of biomass estimation can be enhanced by using remote sensing methods without obtaining diameter at breast height. [ABSTRACT FROM AUTHOR]

Non-isothermal oxidation is one of the important issues for the safe application of Ti-Al alloys, so this study aimed to illustrate the non-isothermal oxidation behaviors and the corresponding mechanisms of a TiAl-based alloy in comparison with a Ti3Al-based alloy. The non-isothermal oxidation behaviors of Ti-46Al-2Cr-5Nb and Ti-24Al-15Nb-1.5Mo alloys in pure oxygen were comparatively investigated with a thermogravimetry-differential scanning calorimetry (TGA/DSC) simultaneous thermal analyzer heating from room temperature to 1450 °C with a heating rate of 40 °C/min. When the temperature rose above 1280 °C, the oxidation rate of the Ti-46Al-2Cr-5Nb alloy sharply increased and exceeded that of the Ti-24Al-15Nb-1.5Mo alloy owing to the occurrence of internal oxidation. When the temperature was higher than 1350 °C, the oxidation rate of the Ti-46Al-2Cr-5Nb alloy decreased obviously due to the generation of an oxygen-barrier β-Al2TiO5-rich layer by a chemical reaction between Al2O3 and TiO2 in the oxide scale. Based on Wagner's theory of internal oxidation, the reason for the occurrence of internal oxidation in the Ti-46Al-2Cr-5Nb alloy is the formation of the α phase in the subsurface, while no internal oxidation occurred in the Ti-24Al-15Nb-1.5Mo alloy due to the existence of the β phase in the subsurface with the enrichment of Nb and Mo. [ABSTRACT FROM AUTHOR]

The influence of processing parameters on the micro-hardness and wear resistance of a Ni-based alloy and titanium carbide (TiC) composite cladding layer was studied. Mathematical models were developed to predict the micro-hardness and wear resistance of the cladding layer by controlling the laser cladding processing parameters. Key processing parameters were the laser power, scanning speed, gas flow, and TiC powder ratio. The models were validated by analysis of variance and parameter optimization. Results show that the micro-hardness is positively correlated with laser power and TiC powder ratio, where the TiC powder ratio shows the most significant impact. The wear volume decreased with an increasing TiC powder ratio. The targets for the processing parameter optimization were set to 62 HRC for micro-hardness and a minimal volume wear. The difference between the model prediction value and experimental validation result for micro-hardness and wear volume were 1.87% and 6.33%, respectively. These models provide guidance to optimize the processing parameters to achieve a desired micro-hardness and maximize wear resistance in a composite cladding layer. [ABSTRACT FROM AUTHOR]

Acupuncture is a practice of treatment based on influencing specific points on the body by inserting needles. According to traditional Chinese medicine, the aim of acupuncture treatment for pain management is to use specific acupoints to relieve excess, activate qi (or vital energy), and improve blood circulation. In this context, the Hegu point is one of the most widely-used acupoints for this purpose, and it has been linked to having an analgesic effect. However, there exists considerable debate as to its scientific validity. In this pilot study, we aim to identify the functional connectivity related to the three main types of acupuncture manipulations and also identify an analgesic effect based on the hemodynamic response as measured by functional near-infrared spectroscopy (fNIRS). The cortical response of eleven healthy subjects was obtained using fNIRS during an acupuncture procedure. A multiscale analysis based on wavelet transform coherence was employed to assess the functional connectivity of corresponding channel pairs within the left and right somatosensory region. The wavelet analysis was focused on the very-low frequency oscillations (VLFO, 0.01–0.08 Hz) and the low frequency oscillations (LFO, 0.08–0.15 Hz). A mixed model analysis of variance was used to appraise statistical differences in the wavelet domain for the different acupuncture stimuli. The hemodynamic response after the acupuncture manipulations exhibited strong activations and distinctive cortical networks in each stimulus. The results of the statistical analysis showed significant differences ( p < 0.05 ) between the tasks in both frequency bands. These results suggest the existence of different stimuli-specific cortical networks in both frequency bands and the anaesthetic effect of the Hegu point as measured by fNIRS. [ABSTRACT FROM AUTHOR]

Non-isothermal oxidation is one of the important issues related to the safe application of high-temperature titanium alloys, so this study focuses on the non-isothermal oxidation behavior and mechanism of near-α titanium alloys. The thermogravimetry-differential scanning calorimetry (TGA/DSC) method was used to study the non-isothermal oxidation behavior of TA29 titanium alloy heated from room temperature to 1450 °C at a heating rate of 40 °C/min under pure oxygen atmosphere. The results show that non-isothermal oxidation behavior can be divided into five stages, including no oxidation, slow oxidation, accelerated oxidation, severe oxidation and deceleration oxidation; for the three-layer TiO2 scale, Zr, Nb, Ta are enriched in the intermediate layer, while Al is rich in the inner layer and Sn is segregated at the oxide-substrate interface, which is related to their diffusion rates in the subsurface α case. The oxidation mechanism for each stage is: oxygen barrier effect of a thin compact oxide film; oxygen dissolution; lattice transformation accelerating the dissolution and diffusion of oxygen; oxide formation; oxygen barrier effect of recrystallization and sintering microstructure in outer oxide scale. The alloying elements with high valence state and high diffusion rate in α-Ti are favorable to slow down the oxidation rate at the stage governed by oxide formation. [ABSTRACT FROM AUTHOR]

Die-cutting tools have been widely applied in industrial production. However, different forms of failure on a blade, such as wear and fracture, can greatly reduce its service life. In this research, the die-cutting tool was selected as the object, a mixture of high speed steel powder and 304 stainless steel powder was coated as a gradient cladding layer onto the surface of AISI/SAE 1045 steel by laser cladding. The central composition design of the response surface methodology was adopted to establish a mathematical model between the pore area of the multi-layer, multi-track cladding, and its processing parameters: Laser Power (LP), Scanning Speed (SS), Gas Flow (GF), and Overlapping Rate (OR). This model was validated by variance analysis and inspection indicators. The actual experiment value by processing parameters optimization for achieving the smallest pore area showed a 4.41% error compared with the predicted value. The internal structure of the cladding layer is uniform. The defects, such as pores and cracks, meet the requirements. The wear resistance on the cutting edge is about 4.5 times compared with the substrate. The results provide a theoretical guidance for the controlling and prediction of the laser cladding forming quality on a two-dimensional gradient material and the optimization of the processing parameters. [ABSTRACT FROM AUTHOR]