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As a fundamental component of heatmap-based human pose estimation methods, heatmap decoding is to transform heatmaps into joint coordinates. We found that previous heatmap decoding methods generally ignored the effect of systematic errors introduced by the resolution increaseing operations in the network decoder. This work fills the gap by taking the systematic errors in heatmap decoding into consideration. We proposed a fast method to reduces the systematic and random errors in one shot by error compensation. The proposed method outperforms the previous best method on the COCO and the MPII datasets while being over 2 times faster. Extensive experiments with different networks, resolutions, metrics and datasets have proved the rationality of the proposed idea.

Continuous dynamic recrystallization (CDRX) is widely acknowledged to occur during hot forming and plays a significant role in microstructure development in alloys with moderate to high stacking fault energy. In this work, the flow stress and CDRX behaviors of the TC18 alloy subjected to hot deformation across a wide range of processing conditions are studied. It is observed that deformation leads to the formation of new low-angle grain boundaries (LAGBs). Subgrains rotate by absorbing dislocations, resulting in an increase in LAGB misorientation and the transition of some LAGBs into high-angle grain boundaries (HAGBs). The HAGBs migrate within the material, assimilating the (sub)grain boundaries. Subsequently, an internal state variable (ISV)-based CDRX model is developed, incorporating parameters such as the dislocation density, adiabatic temperature rise, subgrain rotation, LAGB area, HAGB area, and LAGB misorientation angle distribution. The values of the correlation coefficient (R), relative average absolute error (RAAE), and root-mean-square error (RMSE) between the anticipated true stress and measured stress are 0.989, 6.69%, and 4.78 MPa, respectively. The predicted outcomes demonstrate good agreement with experimental findings. The evolving trends of the subgrain boundary area under various conditions are quantitatively analyzed by assessing the changes in dynamic recovery (DRV)-eliminated dislocations and misorientation angles. Moreover, the ISV-based model accurately predicts the decreases in grain and crystallite sizes with higher strain rates and lower temperatures. The projected outcomes also indicate a transition from a stable and coarse-grained microstructure to a continuously recrystallized substructure.

In order to overcome the problems of low efficiency and poor results of classical optimization methods, an efficient, intelligent, and reliable method for optimizing the shape of the blades of the Savonius wind turbine is proposed in this paper. Firstly, the shape of the blade is parameterized by the third-order Bezier curve, and then a certain number of design schemes in the design space are obtained by the Latin hypercube sampling method and the torque coefficient of each design scheme is evaluated based on CFD simulation, and next a radial basis function surrogate model is constructed based on these design schemes and their torque coefficients, and finally the optimal design scheme is obtained by solving the surrogate model using the marine predator algorithm, and is verified using the CFD simulation. The results show that when the tip speed ratio is 1, the average torque coefficient of the optimized blade obtained by the above method is about 7% higher than that of the classical semicircular blade, and under other tip speed ratios, the average torque coefficients of the optimized blade is also significantly higher than that of the classical blade. Furthermore, compared with the classical semicircular blade, about 11% materials can be saved in the manufacturing of the optimized blade.

In the present work, the effects of aging treatment on the microstructures of a TC18 alloy are studied. The influence of aging treatment on the tensile properties and failure mechanisms is systematically analyzed. It is found that the size and morphology of the primary α (αp) phases are insensitive to aging temperature and time. Furthermore, the aging temperature and time dramatically influence the precipitation of the secondary α (αs) phases. Massive αs phases precipitate and gradually coarsen, and finally weave together by increasing the aging temperature or extending the aging time. The variations in αp and αs phases induced by aging parameters also affect the mechanical properties. Both yield strength (YS) and ultimate tensile strength (UTS) first increase and then decrease by increasing the aging temperature and time, while ductility first decreases and then increases. There is an excellent balance between the strengths and ductility. When the aging temperature is changed from 450 to 550 °C, YS varies from 1238.6 to 1381.6 MPa, UTS varies from 1363.2 to 1516.8 MPa, and the moderate elongation ranges from 9.0% to 10.3%. These results reveal that the thickness of αs phases is responsible for material strengths, while the content of α phases can enhance material ductility. The ductile characteristics of the alloy with coarser αs phases are more obvious than those with thinner αs phases. Therefore, the aging treatment is helpful for the precipitation and homogeneous distribution of αs phases, which are essential for balancing the strengths and ductility of the studied Ti alloy.

In recent years, the vitrified bond diamond grinding wheel has been applied widely in automotive, aerospace and machine tools of manufacturing industries. However, the main problems of low intensity and poor wettability between the vitrified bond and diamond abrasive limit its further application. In this study, BaO was added into the basic SiO2–B2O3–Al2O3–R2O vitrified bond system, and the impact of BaO on the wettability, thermal and mechanical behavior of vitrified bond and vitrified bond diamond composites was systematically discussed, respectively. The test indicated that when the vitrified bond containing BaO of 6 wt.% was sintered with diamond abrasive at 750 °C, a continuous barium feldspar phase transition layer between diamond abrasive and the bond was generated, which ameliorated the wet property of the bond–diamond abrasive. The contact angle varied from 59° on the blank sample to 35°, and the expansion coefficient changed from 6.24 × 10−6/K to 5.30 × 10−6/K. The Rockwell hardness and flexural strength of the vitrified bond diamond composites achieved the peaks of 117.5 MPa and 113.6 MPa, respectively, which increased by 20.2% and 16.5% compared with that of sample without the addition of BaO.

The rapid development of information technology has promoted the transformation of traditional networks into intelligent networks. Huge data traffic is generated by various types of traffic services in the intelligent networks, which can easily lead to network congestion, system instability, and other problems. These problems may incur great requirements and pose challenges for queue management algorithms. Most traditional active queue management (AQM) algorithms judge the congestion level of the network based only on the size of the average queue length while ignoring the network traffic variations. This makes these algorithms difficult to achieve effective improvement of congestion control efficiency. To address this problem, a novel network congestion control algorithm, namely the average queue length and change rate-RED (AC-RED) is proposed in this paper. AC-RED can better relieve network congestion by reconfiguring the packet loss strategy model based on the average queue length change rate. The simulation results of NS2 show that in complex and dynamic network environments, the performance of AC-RED algorithm in the average queue length, packet loss rate, delay, and delay jitter is improved in most load conditions compared with the other five algorithms.

The flat-top magnetic field (FTMF) can meet scientific experimental requirements for higher magnetic intensity, longer flat-top pulse width, and lower ripple in physics, chemistry, biology, and other scientific fields. This paper proposes an FTMF system powered by the multiple-capacitor power supply (MCPS). The MCPS consists of several capacitor banks with customizable capacitance and given voltage. These banks discharge sequentially based on the designed time-series to yield an FTMF. Compared with other methods for generating the FTMF, the MCPS can easily generate FTMFs with high parameters and flexible adjustability of the pulse width. Due to the coupled variable parameters between and within the power supply and the magnet, a hybrid algorithm based on the genetic algorithm (GA) and particle swarm optimization (PSO) is applied to calculate the discharge parameters of the MCPS. The optimal solution from the GA-PSO hybrid algorithm is visualized and selected by the basis vector method. For verifying the effectiveness of the MCPS and the optimization method, a series of FTMFs at different magnetic field strength levels are modeled in MATLAB/Simulink and achieved in the experiment. The generated FTMF with the highest field strength is 50 T, its pulse width is 70 ms, and ripple is less than 0.7%.

Spark plasma sintering is a new technology for preparing ceramic materials. In this article, a thermal-electric-mechanical coupled model is used to simulate the spark plasma sintering process of boron carbide. The solution of the thermal-electric part was based on the charge conservation equation and the energy conservation equation. A phenomenological constitutive model (Drucker-Prager Cap model) was used to simulate the densification process of boron carbide powder. To reflect the influence of temperature on sintering performance, the model parameters were set as functions of temperature. Spark plasma sintering experiments were conducted at four temperatures: 1500 °C, 1600 °C, 1700 °C, and 1800 °C, and the sintering curves were obtained. The parameter optimization software was integrated with the finite element analysis software, and the model parameters at different temperatures were obtained through the parameter inverse identification method by minimizing the difference between the experimental displacement curve and the simulated displacement curve. The Drucker-Prager Cap model was then incorporated into the coupled finite element framework to analyze the changes of various physical fields of the system over time during the sintering process.

A high aluminum (Al) content β-(AlxGa1−x)2O3 film was synthesized on c-plane sapphire substrate using the gallium (Ga) diffusion method. The obtained β-(AlxGa1−x)2O3 film had an average thickness of 750 nm and a surface roughness of 2.10 nm. Secondary ion mass spectrometry results indicated the homogenous distribution of Al components in the film. The Al compositions in the β-(AlxGa1−x)2O3 film, as estimated by X-ray diffraction, were close to those estimated by X-ray photoelectron spectroscopy, at ~62% and ~61.5%, respectively. The bandgap of the β-(AlxGa1−x)2O3 film, extracted from the O 1s core-level spectra, was approximately 6.0 ± 0.1 eV. After synthesizing the β-(AlxGa1−x)2O3 film, a thick β-Ga2O3 film was further deposited on sapphire substrate using carbothermal reduction and halide vapor phase epitaxy. The β-Ga2O3 thick film, grown on a sapphire substrate with a β-(AlxGa1−x)2O3 buffer layer, exhibited improved crystal orientation along the (-201) plane. Moreover, the scanning electron microscopy revealed that the surface quality of the β-Ga2O3 thick film on sapphire substrate with a β-(AlxGa1−x)2O3 intermediate buffer layer was significantly improved, with an obvious transition from grain island-like morphology to 2D continuous growth, and a reduction in surface roughness to less than 10 nm.

The hybrid electric dump truck is equipped with multiple power sources, and each powertrain component is controlled by an energy management strategy (EMS) to split the demanded power. This study proposes an EMS based on deep reinforcement learning (DRL) algorithm to extend the battery life and reduced total usage cost for the vehicle, namely the twin delayed deep deterministic policy gradient (TD3) based EMS. Firstly, the vehicle model is constructed and the optimization objective function, including battery aging cost and fuel consumption cost, is designed. Secondly, the TD3-based EMS is used for continuous action control of ICE power based on vehicle state, and the action mask is applied to filter out invalid actions. Thirdly, the simulations of the EMSs are trained under the CHTC-D driving cycle and C-WTVC driving cycle. The results show that the action mask improves the convergence efficiency of the strategies, and the proposed TD3-based EMS outperforms the deep deterministic policy gradient (DDPG) based EMS. Meanwhile, the battery life is extended by 36.17% under CHTC-D and 35.49% under C-WTVC, and the total usage cost is reduced by 4.30% and 2.49% when the EMS considers battery aging. In summary, the proposed TD3-based EMS can extend the battery life and reduce usage cost, and provides a method to solve the optimization problem for the EMS of hybrid power systems.

Skin cancer is one of the most deadly cancer types with considerable number of patients. Image analysis has largely improved the automated diagnosis accuracy for malignant melanoma and other pigmented skin lesions, compared to unaided visual examination. Recent popular solution for automated skin lesion classification is using deep neural networks, trained from large amounts of professional annotated data, but that largely limits the model’s scalability. This paper exploits transfer learning for skin lesion classification task with the help of labeled data from another domain (source), and proposes a multi-view filtered transfer learning network to strongly represent discriminative information from different image views with reasonable weighing strategy. This method also evaluates the importance for each source images, which can learn useful knowledge with neglecting negative samples from source domain. The extensive skin lesion classification experiments demonstrate our method can effectively solve Melanoma and Seborrheic Keratosis classification tasks with outstanding extensibility, and the discussion of the major components also testifies the improvements of our proposed multi-view filtered transfer learning approach.

The energy management strategy of hybrid electric vehicles is of significant importance to improve the fuel economy. In this regard, two energy management strategies are designed for power-split hybrid electric city bus (HECB), which are based on the linear time-varying stochastic model predictive control (LTV-SMPC) and stochastic model predictive control based on Pontriagin’s minimum principle (PMP-SMPC). In the present study, the Markov chain and long short-term memory (LSTM) forecast demand torque and velocity respectively are applied to establish a combination forecast model. Then several processes, including linear approximation, processing simplified control model, the proposed nonlinear vehicle model is converted into a linear time-varying model. Meanwhile, the energy management problem in a linear quadratic programming problem is solved. Considering linearization error and limitations of the quadratic optimization, Pontriagin’s minimum principle (PMP) is applied to optimize the nonlinear model predictive control. Based on the reference theory, the range of coordinated variables is derived, and the optimal coordination variable is searched by dichotomy to realize the rolling optimization of the model predictive control (MPC). Finally, the effectiveness of the proposed energy management strategy is verified by simulating several case studies. Obtained results show that compared with the rule-based (RB) control strategy, the fuel economy of LTV-SMPC and PMP-SMPC increases by 8.79% and 14.42%, respectively. Meanwhile, it is concluded that the two strategies have real-time computing potential.

The tooth-marked tongue refers to the tongue with the edge featured in jagged teeth marks, which is a significant indicator for reflecting the conditions of patients' internal organs in Traditional Chinese Medicine (TCM). From the perspective of computer vision, due to the small variance in the global region (original image) but the large variance in the local region (tongue region), especially in the differential region (tongue edge region around landmarks), the recognition of the tooth-marked tongue is a naturally fine-grained classification task. To address this challenging task, a two-stage method based on tongue region detection and tongue landmark detection via deep learning is proposed in this paper. In the first stage, we introduce a cascaded convolutional neural network to detect the tongue region and tongue landmarks simultaneously for minimizing the redundancy information and maximizing discriminative information explicitly. In the second stage, we send not only the detected tongue region but also the detected tongue landmarks to a fine-grained classification network for the final recognition. Conclusively, our method is highly consistent with human perception. Moreover, to the best of our knowledge, we are the first attempt to manage the tooth-marked tongue recognition via deep learning. We conducted extensive experiments with the proposed method. The experimental results demonstrate the effectiveness of the proposed method.

In recent years, residual learning has shown excellent performance on convolutional neural network (CNN)-based single-image super-resolution (SISR) tasks. However, CNN-based SISR approaches have focused mainly on the design of deep architectures, and the rectified linear units (ReLUs) used in these networks hinder shallow-to-deep information transfer. As a result, these methods are unable to utilize some shallow information, and improving model performance is difficult. To solve the above issues, this paper proposes an image SR reconstruction method based on a generative adversarial network with a residual dense architecture. First, before ReLU activation, the number of feature channels is expanded by a factor of 6~9 using a 1 × 1 convolutional layer, which improves the utilization of shallow information. Next, the original discriminator is replaced with a relativistic average discriminator, thereby improving the authenticity of the discriminative network. Finally, preactivation features are used to improve the perceptual loss, thus providing stronger monitoring for brightness consistency and texture restoration. Experimental results show that the proposed algorithm improves the utilization of shallow information in a deep network. Structural similarity (SSIM) index evaluations show that the overall utilization of shallow information is increased by 105.52%. In addition, the average runtime is 0.42 sec/frame, nearly 3.6 times faster than those of traditional methods. Moreover, the recovered images have an average natural image quality evaluator value of 3.4 and high perceptual quality, showing that the proposed method is suitable for image reconstruction applications in fields such as agriculture and medicine.

Bowel cancer, which is easily affected by diet and drugs, has some restrictive factors such as the fecal occult blood test (FOBT) in the routine detection and the high cost and inconvenience of microscopy. In order to break through these restrictive factors, a possible alternative method of FOBT is sought. In this paper, error back propagation neural network (BPNN) algorithm is used, and expression spectrum is used as an auxiliary method to detect medical images, and a colorectal cancer (CRC) diagnosis model based on neural network is constructed. The results show that the accuracy of the model on the training set and the test set are 0.943 and 0.935, respectively, the AUC reaches more than 0.95. Therefore, the CRC diagnosis model based on neural network provides a possible alternative method of FOBT. Experimental results show that the proposed algorithm have high robustness and accuracy, which meets the current clinical needs.

In this paper, the hot tensile deformation of a GH3230 superalloy double-sheet was conducted under deformation temperatures ranging from 1123~1273 K and strain rates ranging from 0.001~0.2 s−1. The flow behavior of the GH3230 superalloy double-sheet was analyzed in detail. The hot tensile deformation process of the GH3230 superalloy double-sheet includes four stages of elastic deformation, strain hardening, steady state and fracture. The true stress decreases with the increasing deformation temperature and decreasing strain rate. The variation of the strain rate sensitivity index and strain hardening index with processing parameters were discussed. The average apparent activation energy for hot tensile deformation is 408.53 ± 46.96 kJ·mol−1. A combined Johnson-Cook and Hensel-Spittle model considering the couple effect of strain hardening, strain rate hardening and thermal softening was established to describe the hot tensile behavior of the GH3230 alloy double-sheet. Compared to Johnson-Cook model and Hensel-Spittle model, this model has the highest predicting accuracy. The average absolute relative error of true stress between the experimental and the predicted is only 2.35%.

Chip clogging is one of the fundamental issues faced by the dentists during the drilling of cortical bone. The chip clogging can lead to significant increase in temperature, thrust force and torque, and can even cause the drill bit to break. In this study, a modified chip-evacuation model to estimate the thrust force was developed for drilling of the cortical bone. First, the modified chip-evacuation force model with two parameters was developed based on the flute geometry. A set of drilling tests was conducted to calibrate the model parameters. Secondly, the parameter models were established as functions of spindle speed and feed, and the coefficients were then determined through nonlinear-regression analysis. The importance of cutting parameters on the model parameters was investigated via analysis of variance. Thirdly, the validity of the force model was analyzed by experimental results. Subsequently, based on the gradient of the chip-evacuation force, the chip-clogging prediction was conducted, and the prediction accuracy was assessed by the validation tests. Finally, the effect of cutting condition on the chip clogging was explored. The modified chip-evacuation force model proved to be an effective tool for predicting the thrust force, and the chip clogging could be estimated accurately with a relative error less than 10%. The spindle speed and the feed both had significant effects on the chip-evacuation force and chip clogging. The modified model could provide a better approach to finding how a pecking cycle can be implemented, and then, it can assist surgeons in selecting cutting conditions.

The defects such as voids and debonding within the bonding interface between the composite and the substrate are the key factors affecting the safe use of materials. In this study, Industrial Computed Tomography (ICT) was used to perform non-destructive tests of bonding defects. High-precision characterization methods are required to quantify the defects. This paper proposes an improved image segmentation processing method for accurate extraction of defect edges and quantitative characterization. With the proposed method combining mathematical morphology and Fuzzy C-Means (FCM) threshold segmentation, the irregular defects of voids and debonding can be visualized and established with corresponding quantitative indicators. The experimental results show that the defect area error of the tested bonding layer is within ±6%, which satisfies the requirements of accurate detection and characterization of interface bonding layer defects of composite components. This work provides a strong technical basis for the reliability assessment of new ceramic matrix composites (CMCs) bonded structural members.

The present study integrated a discrete search equation to develop a discrete improved artificial bee colony (DiABC) algorithm. A random binary population was added in the initialization. The current optimal solution was then introduced into the search equations for worker and onlooker bees to effectively enhance the convergence accuracy of the algorithm. And a new selection method on scout bees was proposed. Eighteen benchmark knapsack problems (KPs) experiments were conducted, of which the results validated the effectiveness of the DiABC algorithm in optimizing the KPs.

With more commercialized automated vehicles (AVs) shortly entering the market, evaluating their fuel economy has become an important topic. The traditional fixed-profile test methods only indicate the effect of powertrain efficiency on fuel economy and cannot reflect the influences of control algorithms or driving behaviors on fuel consumption under real-world traffic conditions. Therefore, a data-driven simulation method for evaluating the real driving fuel economy of automated vehicles is developed. It utilizes naturalistic driving data to reconstruct test cycles. This method can inspect the performance of automated vehicle control algorithms under realistic traffic conditions in terms of fuel economy. The naturalistic driving data collected on urban expressways and freeways were used to model the longitudinal driving scenarios. Then, the fuel consumption of automated and human-driven vehicle was evaluated under the simulated scenarios via the Monte Carlo integration approach. It was found that the fuel consumption rate of the automated vehicle was 11.944 L/100 km under the car-following scenarios. The human-driven vehicle had a fuel consumption rate of 10.124 L/100 km under the same traffic conditions. The tested automated vehicle control algorithm is tuned to achieve better performance in terms of safety and travel efficiency and hence it tends to maintain a relatively steady time headway to the leading vehicle. It applies more frequent accelerating/braking cycles and higher average speed compared to typical human drivers. These characteristics lead to a higher fuel consumption rate of the automated vehicle. The presented method provides an accurate and efficient way to analyze the fuel economy performance of automated vehicles under practical conditions. This method can easily be scaled for large-scale traffic flow analyses. It can also be used to study the effects of human driving styles on fuel economy.

The present study was focused on the relationship between an α platelet microstructure and the properties of TC21 alloy, and the tensile deformation process was revealed by in situ observation. To obtain the α platelet microstructures, the samples were administered a solution treatment (1000 °C for 15 min) and then cooled to room temperature by different cooling methods (furnace cooling (FC), open-door furnace cooling (OFC), air cooling (AC), and water quench (WQ), corresponding to an increased cooling rate). It is found that α platelets become thinner and colonies become narrower with the increase in cooling rate. The formation of the platelet microstructure is based on the preferred Burgers orientation relationship of {110}β//{0001}α and β//2¯0>α. The α platelets orientation changes with the cooling rate. These differences in α platelets thickness and orientation result in the excellent ductility of the sample with thick platelets and the high strength of the samples with thin platelets. During the in situ tensile deformation process, the crack propagation path is deflected in the presence of grain boundaries, α platelets, and α colonies with different orientations. The fracture of the sample with thick α platelets shows better ductility compared to those with thin α platelets.

Billions of devices are connected in the Internet of Things (IoT)-based sensor networks and they continuously generate a large volume of data. In order to get access to specific data, which is crucial to enable a myriad of new intelligent applications, efficient information retrieval becomes an imminent need for IoT. However, sensor information in the physical world can be heterogeneous, high dimensional, and voluminous due to the complex and dynamic environments. In this paper, we first investigate several IoT search scenarios and propose a uniform representation model for sensor information recordings. Four query models are designed to represent all possible information query styles. With these models, we develop information retrieval architecture for IoT. In essence, an indexing mechanism called efficiency maximization and cost minimization is proposed to solve the property selection problem in the process of index construction and update. Meanwhile, a novel real-time grid R-tree structure is designed to support historical and realtime search for spatiotemporal observation data. Simulation results based on real-world IoT data sets show that storage space is considerably reduced with the sensor model. Furthermore, the proposed indexing mechanisms can improve retrieval efficiency and accuracy, and ensure scalability for large-sized data simultaneously.

Plants frequently suffer from different types of stress and their combination. Timely and effective monitoring of plant stresses is necessary for the precision management of crops and environmental protection. Hyperspectral remote sensing may help monitoring demand based on spectral feature analysis. However, existing studies are still insufficient for the spectral identification of plant stress types, including the combined stress type. In this paper, drought, copper, and their combined stresses on maize seedlings were designed to analyze differences of plant parameters and spectral indices by comparing with a control group. The experimental results indicate that: 1) chlorophyll content, leaf area, and relative water content could be used as key parameters to express the inter-type stress differences, and in particular, chlorophyll content was the most important bio-parameter due to its unique characteristic to distinguish a combined stress from drought stress; 2) red-edge position, the first derivative at the red edge and shortwave infrared water stress index was found to be effective for characterizing the three plant parameters under plant stresses because they could minimize the effect of variations of stress types on the prediction of these parameters; and 3) the three spectral indices might be used to identify the three stress types of maize seedlings by a decision tree analysis. The results may be useful for the precision management of crops and for environmental protection and monitoring as well.

The flow behavior and microstructure change of the Ti-55511 alloy are investigated by thermal compression experiments with stepped strain rates. The phase transformation features, the dynamic recrystallization (DRX) behavior of the β matrix, the dynamic spheroidization mechanism of the lamellar α phase and the evolution of the β sub-grain size are quantitatively analyzed. A unified constitutive model is constructed to characterize the hot deformation features of the Ti-55511 alloy. In the established model, the work hardening effect is taken into account by involving the coupled effects of the equiaxed and lamellar α phases, as well as β substructures. The dynamic softening mechanisms including the dynamic recovery (DRV), DRX and dynamic spheroidization mechanisms are also considered. The material parameters are optimized by the multi-objective algorithm in the MATLAB toolbox. The consistency between the predicted and experimental data indicates that the developed unified model can accurately describe the flow features and microstructure evolution of the hot compressed Ti-55511 at stepped strain rates.

A supply of minerals is critical to socioeconomic development. However, such a supply also induces negative impacts on environment and ecology, e.g., leading to dust emission and deposition. An ultra-low-grade magnetite has been exploited as a new iron type since 2001 in China. In this paper, two Landsat images were used for monitoring foliar dust in Changhe River Mining Area, China. First, models were established to estimate foliar dust using vegetation indices (VIs) differences according to laboratory spectral measurements; normalized differenced VI was selected as an optimal VI for estimating foliar dust amount based on both field and laboratory spectral measurements (RMSE = 6.58 g/m2), and finally, the spatial patterns of foliar dust were analyzed by using ancillary high-resolution data. The result showed that most foliar dust distributed near ore transportation roads and around mining sites and tailings ponds, which was related to ultra-low-grade characteristics of the iron ore due to large-area extraction and tailings occupation, and large-amount dust emission released from ore transportation. The remote sensing method for estimating foliar dust may be beneficial for environmental management in mining areas.

Big data has potential to unlock novel groundbreaking opportunities in power grid that enhances a multitude of technical, social, and economic gains. As power grid technologies evolve in conjunction with measurement and communication technologies, this results in unprecedented amount of heterogeneous big data. In particular, computational complexity, data security, and operational integration of big data into power system planning and operational frameworks are the key challenges to transform the heterogeneous large dataset into actionable outcomes. In this context, suitable big data analytics combined with visualization can lead to better situational awareness and predictive decisions. This paper presents a comprehensive state-of-the-art review of big data analytics and its applications in power grids, and also identifies challenges and opportunities from utility, industry, and research perspectives. The paper analyzes research gaps and presents insights on future research directions to integrate big data analytics into power system planning and operational frameworks. Detailed information for utilities looking to apply big data analytics and insights on how utilities can enhance revenue streams and bring disruptive innovation are discussed. General guidelines for utilities to make the right investment in the adoption of big data analytics by unveiling interdependencies among critical infrastructures and operations are also provided.

Surface topography and roughness significantly affect the functional properties of engineering parts. In this study, a mathematical model simulating the surface topography in end milling is presented and verified by milling experiments. The three dimensional (3D) surface amplitude parameters (arithmetic average deviation Sba and root mean square deviation Sq) of the milled surface were calculated by using the model and the effects of the product (p) and ratio (r) of radial depth of cut ae and feed per tooth fz on amplitude parameters were researched. To evaluate the lateral characteristics of the milled surface, one dimensional (1D) power spectral densities (PSD) along both feed and step-over direction were calculated and investigated. It was found that fz affects 1D PSD along both directions, whereas ae affects 1D PSD along the step-over direction. An angular spectrum, derived from the area power spectral density (APSD), was employed to research the spatial distribution of spectral energy on the milled surface. Furthermore, the influences of p and r on the PSD properties were researched. It was found that r is the significant factor that influences the direction of surface energy spectrum distribution.

Doping of nitrogen is a promising approach to improve the electrical conductivity of 3C-SiC and allow its application in various fields. N-doped, -oriented 3C-SiC bulks with different doping concentrations were prepared via halide laser chemical vapour deposition (HLCVD) using tetrachlorosilane (SiCl4) and methane (CH4) as precursors, along with nitrogen (N2) as a dopant. We investigated the effect of the volume fraction of nitrogen (ϕN2) on the preferred orientation, microstructure, electrical conductivity (σ), deposition rate (Rdep), and optical transmittance. The preference of 3C-SiC for the orientation increased with increasing ϕN2. The σ value of the N-doped 3C-SiC bulk substrates first increased and then decreased with increasing ϕN2, reaching a maximum value of 7.4 × 102 S/m at ϕN2 = 20%. Rdep showed its highest value (3000 μm/h) for the undoped sample and decreased with increasing ϕN2, reaching 1437 μm/h at ϕN2 = 30%. The transmittance of the N-doped 3C-SiC bulks decreased with ϕN2 and showed a declining trend at wavelengths longer than 1000 nm. Compared with the previously prepared -oriented N-doped 3C-SiC, the high-speed preparation of -oriented N-doped 3C-SiC bulks further broadens its application field.

Highly-oriented polycrystal 3C-SiC bulks were ultra-fast fabricated via halide chemical vapor deposition (CVD) using tetrachlorosilane (SiCl4) and methane (CH4) as precursors. The effects of deposition temperature (Tdep) and total pressure (Ptot) on the orientation and surficial morphology were investigated. The results showed that the growth orientation of 3C-SiC columnar grains was strongly influenced by Tdep. With increasing Tdep, the columnar grains transformed from - to -oriented. The arrangement of -oriented columnar grains was controlled by Ptot. Lotus-, turtle-, thorn-, and strawberry-like surface morphologies were naturally contributed by different arrangements of -oriented grains, and the deposition mechanism was discussed. The wetting behaviors of CVD-SiC samples by molten aluminum were also examined at 1173 K in a high vacuum atmosphere.

Direct-current magnetron sputtering (DCMS) was applied to prepare vanadium (V) films on Si substrate. The influence of substrate temperature (Ts) and target⁻substrate distance (Dt⁻s) on phase structure and surface morphology of V films were investigated by X-ray diffraction (XRD), scanning electron microscopy (SEM), atomic force microscope (AFM) and transmission electron microscopy (TEM). The results show that the crystallinity of the V films increases with increasing Ts and decreasing Dt⁻s. The film deposited at Ts = 400 °C and Dt⁻s = 60 mm exhibits the best crystallinity and preferred orientation with a regular tetrahedral surface morphology. Oxidation behavior of the V thin films has also been studied by X-ray photoelectron spectroscopy (XPS).

In this study, we demonstrated the chemical vapor deposition (CVD) of vertically standing molybdenum disulfide (MoS2) nanosheets, with an unconventional combination of molybdenum hexacarbonyl (Mo(CO)6) and 1,2-ethanedithiol (C2H6S2) as the novel kind of Mo and S precursors respectively. The effect of the distance between the precursor’s outlet and substrates (denoted as d) on the growth characteristics of MoS2, including surface morphology and nanosheet structure, was investigated. Meanwhile, the relationship between the structure characteristics of MoS2 nanosheets and their catalytic performance for hydrogen evolution reaction (HER) was elucidated. The formation of vertically standing nanosheets was analyzed and verified by means of an extrusion growth model. The crystallinity, average length, and average depth between peak and valley (Rz) of MoS2 nanosheets differed depending on the spatial location of the substrate. Good crystalized MoS2 nanosheets grown at d = 5.5 cm with the largest average length of 440 nm, and the highest Rz of 162 nm contributed to a better HER performance, with a respective Tafel slope and exchange current density of 138.9 mV/decade, and 22.6 μA/cm2 for raw data (127.8 mV/decade and 19.3 μA/cm2 for iR-corrected data).