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In the process of underground resource extraction, deep rock masses are often subjected to cyclic-heating and water-cooling effects, which cause significant damage to the mechanical properties of rocks. In order to study the degradation mechanism of sandstone under cyclic heating and water cooling, many fracture experiments have been carried out. The effect of cyclic heating and water cooling at 200 °C on the tensile strength and the mixed-mode I-II fracture toughness of sandstone was investigated. The similarities and differences in the rock fracture characteristics and deterioration response of tensile properties are also examined. Meanwhile, the correlation between the microstructure and macroscopic fracture mechanical properties was revealed by observing the evolutionary pattern of rock microstructure. The findings demonstrate that the heating and water-cooling-induced variations in the tensile strength and fracture toughness of rock exhibit a consistent trend. The effective fracture toughness of sandstone decreases exponentially with increasing cycle times. The impact of the heating and water-cooling cycles on the fracture toughness of pure mode I is found to be more pronounced compared to that observed in mixed modes and pure mode II specimens. The failure mode of the rock sample undergoes a transition from abrupt to gradual, accompanied by an augmented development of cracks on the failure surface and finer fragmentation of rock debris. The results of this study can provide theoretical references for engineering practices such as the exploitation of geothermal resources and hydraulic fracturing. [ABSTRACT FROM AUTHOR]

The Gradient Richardson Number (Ri) is an important parameter for appraising the stability and turbulence exchange at the atmospheric boundary layer (ABL). However, high-resolution measurements of Ri profiles are rarely reported, especially in megacities. In this study, a Doppler wind lidar and a microwave radiometer were simultaneously utilized to measure the 2 km Ri vertical profile in downtown Beijing. These measurements were verified to have high accuracy compared with observations from a 325 m meteorological tower, with root-mean-square errors (RMSEs) of less than 1.66 K, 7.9%, and 1.45 m/s for the temperature, relative humidity, and wind speed (WS) for all altitudes and corresponding Pearson correlation coefficients (R) of 0.97, 0.93, and 0.81. The inter-comparisons of different spatial (25 m, 50 m, 100 m) and temporal resolutions (1 min, 30 min, 1 h) form a 3 × 3 resolution matrix of Ri, in which the 1 h temporal resolution of Ri overestimates the intensity and active area of turbulence. The Ri value retrieved from the 100 m spatial resolution data overestimates these by half as it misidentifies the height of the stable area at the near surface. There are significant differences between the data with a 1 min temporal resolution and a 25 m spatial resolution (defined as the standard resolution of Ri), and the rest of the data in the resolution matrix (defined as data at other resolutions), with an RMSE > 1 and an R < 0.8. The difference between data at the standard resolution and data at other resolutions increases with elevations, which results from frequent weather processes or from water-vapor blocking at higher altitudes. The Ri profiles reveal that the atmospheric layer at altitudes from 100 m to 500 m in daytime is unstable, with Ri < 0, while it is neutral, with 0 < Ri < 0.25, at night-time from 200 m to 400 m. The atmosphere above the ABL in a megacity is rather stable, with Ri > 0.25, whereas below the ABL, it is neutral or unstable, which is due to drastic changes in the WS and temperature that are affected by the topography and surface friction. [ABSTRACT FROM AUTHOR]

Enrichment of erythrocytes is a necessary step in the diagnosis of blood diseases. Due to the high deformability and viscosity of erythrocytes, they cannot be regarded as stable point-like solids, so the influence of their deformability on fluid dynamics must be considered. Therefore, by using the special effect of an I-shaped pillar (I-pillar) on erythrocytes, erythrocytes with different deformability can be made to produce different provisional distances in the chip, so as to achieve the separation of the two kinds of erythrocytes. In this study, a microfluidic chip was designed to conduct a control test between erythrocytes stored for a long time and fresh erythrocytes. At a specific flow rate, the different deformable erythrocytes in the chip move in different paths. Then, the influence of erythrocyte deformability on its movement trajectory was analyzed by two-dimensional finite element flow simulation. DLD sorting technology provides a new method for the sorting and enrichment of diseased erythrocytes. [ABSTRACT FROM AUTHOR]

In order to enhance the efficiency of agricultural machinery in orchard rows and minimize harm to personnel caused by pesticide spraying, this study developed a GNSS-based (Global Navigation Satellite System) automatic navigation driving system for tracked orchard sprayers. The tracked sprayer was used as a platform for this research. We constructed both a crawler hydraulic platform and spraying working parts based on orchard operation requirements. Additionally, we designed the hydraulic and electrical sub-control process of the crawler platform. By utilizing the motion model of the tracked mobile platform, we designed a linear path tracking control method using position deviation and heading deviation as state quantities. This allows the research platform to automatically initiate and terminate, travel in a straight line between rows, and complete spraying operations. Experimental verification confirmed that the tracked sprayer designed in this study successfully achieves automatic driving. The best automatic driving performance is achieved at a speed of 1.0 m/s. When the sprayer's speed is 1.2 m/s, the maximum value of the straight-line path tracking accuracy of the platform's automatic driving is better than 5.6 cm, with a standard deviation of 2.8 cm. This system effectively meets the requirements of automatic operation for an automatic spraying machine, thereby establishing a foundation for the implementation of automatic spraying operations in orchards. [ABSTRACT FROM AUTHOR]

A new strategy for the high-throughput characterization of the mechanical homogeneity of metallurgical materials is proposed. Based on the principle of hydrostatic transmission and the synergistic analysis of the composition, microstructure, defects, and surface profile of the chosen material, the microstrain characteristics and changes in surface roughness after isostatic pressing were analyzed. After isostatic pressing, two types of microstrains were produced: low microstrain (surface smoothening with decreasing roughness) and large microstrain (surface roughening with increasing roughness). Furthermore, the roughness of the roughened microregions could be further classified based on the strain degree. The phenomenon of weak-interface damage with a large microstrain (plastic deformation, cleavage fracture, and tearing near nonmetallic inclusions) indicated that the surface microstrain analysis could be a new method of high-throughput characterization for microregions with relatively poor micromechanical properties. In general, the effect of isostatic pressing on the surface microstrain of heat-resistant steel provides a promising strategy for achieving high-throughput screening and statistically characterizing microregions with poor micromechanical properties, such as microregions containing microcracks, nonmetallic inclusions, pores, and other surface defects. [ABSTRACT FROM AUTHOR]

Nowadays, suicide (especially adolescents' suicide) has been an increasingly prominent social problem worldwide; suicide ideation, as an important predictor, has been the focus of relevant studies and practices. Against this background, the present study aimed to examine the association between perceived family financial stress and adolescents' suicidal ideation, as well as the potential roles of depression and parent-child attachment. A sample of 526 junior middle school students was recruited voluntarily to participate in this cross-sectional study, and the results indicated that the prevalence of suicidal ideation among junior high school students was 15.45%; perceived family financial stress was positively associated with suicidal ideation, and depression could significantly mediate this relation; parent–child attachment significantly moderated the mediating effect of depression (in particular, the relation between depression and suicidal ideation); specifically, this relation was stronger among adolescents with lower values of parent–child attachment. These findings could deepen our understanding of the influences of perceived family financial condition and the risky factors of adolescents' suicidal ideation, which could provide guidance for the prevention and intervention of adolescents' depression and suicidal ideation. [ABSTRACT FROM AUTHOR]

In contrast to the past, the trade behavior of countries has become increasingly intricate, encompassing domestic trade rooted in local markets, traditional trade centered on final exports, and value chain trade reliant on intermediate goods. To tailor their strategies to their unique circumstances, nations judiciously allocate their economic focal points across these three trade modalities, engendering distinct national development models. By discerning the varying emphases placed by countries on these three trade modes, this paper employs clustering techniques to extract and analyze divergent national development models. Additionally, this paper assesses countries' performance in various trade activities and introduces a new indicator, Total Trade Ability ( T T A ), to examine the impact of these models on the economy. With our approach, one can easily distinguish how different countries develop their economies. Our findings indicate a strong correlation between economic growth and T T A . In general, countries with higher T T A tend to exhibit higher economic growth. [ABSTRACT FROM AUTHOR]

Addressing the complex issue of multi-attribute decision-making within a probabilistic dual hesitant fuzzy context, where attribute weights are unknown, a novel decision-making method based on cumulative prospect theory is proposed, named the probabilistic dual hesitant fuzzy multi-attribute decision-making method based on cumulative prospect theory. Firstly, a decision matrix is formulated, representing probabilistic dual hesitant fuzzy information. Secondly, according to the decision maker's authentic preference and non-membership information sensitivity, a comprehensive score function suitable for probabilistic dual hesitant fuzzy elements is proposed. The attribute weights are then determined using the entropy method. Next, the value function and decision weight function from the cumulative prospect theory are employed to compute the cumulative prospect value attributed to each available scheme. In addition, a cumulative prospect matrix is constructed, enabling the establishment of scheme rankings based on the comprehensive cumulative prospect value. Finally, the analysis of specific cases and a comparative assessment of methods pertaining to the selection of emergency response schemes collectively demonstrate the rationality and efficacy of the decision-making method presented in this study. [ABSTRACT FROM AUTHOR]

The Tarim Basin, a significant region of coal-derived gas generation in China, has widely dispersed coal measure source rocks, especially in the Kuqa Depression. The typical coal measure strata from the Kuqa River and Kapushaliang River sections in the Kuqa Depression were systematically sampled. According to a conventional geochemical analysis (including materials and lithology), a total of 25 typical coal measure source rock samples were chosen for chromatography and chromatography–mass spectrometry examination. It was demonstrated that there were significant discrepancies in the coal source rock samples, particularly the coal rock from the Kuqa River section and the Kapushaliang River section. The specific performance of the Kuqa River section was characterized by a high Pr/Ph ratio (up to 9.29), a low gammacerane ratio, a low abundance of tricyclic terpane, and an "L"-type distribution of regular steranes, all of which are consistent with the properties of humic coal found in freshwater lakes that have undergone partial oxidation. The Kapushaliang River section, by contrast, exhibited a low Pr/Ph ratio (<1.0), a high gammacerane ratio, a concentration of tricyclic terpane comparable to that of hopane, and a distribution of regular steranes in a "V" shape, all of which indicate a strongly reduced saline water environment. Additionally, the degree of thermal evolution is not the key factor resulting in the above biomarker diversity within the CSRs, which was supported by the poor correlation between the maturity parameters (Ro%, Tmax °C), the sedimentary environment-related parameters (such as Pr/Ph and Ga/C31H), and the biogenic-related parameters (such as ∑tricyclic terpane/∑ hopane and ∑sterane/∑hopane). A comparative analysis suggested that transgression-related changes in the redox conditions and parent materials triggered the biomarker diversity within the CSRs, resulting in a large difference in the hydrocarbon-forming parent materials between the two abovementioned outcrop sections. These might provide some insight for hydrocarbon exploration from CSRs, which have been attracting increasing attention in China, with proven coal reserves are ranked first in the world. [ABSTRACT FROM AUTHOR]

To address the challenges of poor fluidity and low uniformity in conventional sugarcane fertilizer applicators, a novel dual-directional spiral fertilizer applicator has been developed. The working principle of the applicator is explained, and, after analyzing the agronomic requirements for sugarcane, the parameter range for key components of the applicator is determined. The spiral blade's diameter, pitch, and rotational velocity are chosen as the experimental factors, with the average fertilizer discharge uniformity as the evaluation criterion. Virtual simulation experiments are conducted using the discrete element method and a quadratic regression orthogonal rotating combined design. Regression models for the evaluation criterion and various experimental factors are obtained. Additionally, a dataset created from these experiments was then used to construct an artificial neural network (ANN) prediction model. Response surface methodology (RSM) and the ANN were both used to analyze and predict the outcomes. The results indicate that the artificial neural network outperforms response surface methodology in terms of better fitting capability and higher prediction accuracy. The determination coefficient, mean squared error, and root mean square error are 0.99629, 0.99163, 0.07763, 0.17498, 0.27862, and 0.41831, respectively. When comparing the two models, the optimal parameter combination is determined to be a diameter of 90.1669 mm, a pitch of 59.7407 mm, and a rotational speed of 53.8944 r/min, resulting in an average fertilizer discharge uniformity of 92.0670%. An experiment with these parameters confirmed the simulated findings, revealing a maximum discrepancy of 2.4%. This study offers valuable insights into optimizing spiral fertilizer applicators. [ABSTRACT FROM AUTHOR]

In this study, a mesoscale Weather Research and Forecast (WRF) model coupled with an AgI (silver iodide) cold cloud catalytic module were used to explore the potential impact of the catalytic position and rate in the catalytic module based on a ground rain enhancement operation in the Qilian Mountains, on 16 August 2020. Results show that the simulated precipitation, liquid water content (LWC), and water vapor content (PWV) are in good agreement with the observations, demonstrating that the WRF model using the coupled AgI cloud-seeding scheme is well-applicable to the precipitation simulation of the Qilian Mountains. It is also observed that there are some differences in the catalytic effect of catalysis at different cloud temperatures. The precipitation enhancement effect is the most favorable in the fifth layer of 15 km, followed by that in the fourth layer of 12 km and the sixth layer of 18 km. Considering the flight cost and catalytic efficiency, the fourth layer is highly recommended for seeding. Furthermore, the AgI seeding rate also plays a crucial impact on ground precipitation. In the case of a seeding rate of about 1.2 g·s−1, the precipitation enhancement effect tends to be stable, and the percentage of the precipitation increase reaches up to 10.4%. While in the case of a seeding rate of about 1.5 g·s−1, the percentage of ground precipitation increase is 10%, which is 0.4% lower than that of 1.2 g·s−1. In summary, the introduction of a AgI catalyst with a seeding rate of 1.2 g·s−1 can significantly increase the ground precipitation at a height of 12 km and a temperature of −3 °C in the Qilian Mountains. [ABSTRACT FROM AUTHOR]

With the development of the cloud computing era, the decision-making environment and algorithm models have become increasingly complex, and traditional decision-making methods have been unable to meet the needs of large group decision-making (LGDM) problems. Firstly, in order to solve this problem, the concept of double hierarchy interval hesitant fuzzy language (DHIHFL) is proposed. Compared with the traditional double hierarchy hesitant fuzzy language (DHHFL), it contains all elements from the lower limit to the upper limit and more comprehensively characterizes the hesitation of language information. Secondly, for LGDM problems, a self-confident double hierarchy interval hesitant fuzzy language (SC-DHIHFL) is developed, and the integration of self-confident degree can better enrich the evaluation information and promote the achievement of group consensus. Thirdly, a new two-stage LGDM method is proposed. The first stage is clustering and grouping and reaching consensus within the group, and the second stage is the integration of LGDM information. The two-stage method contains novel methods such as expert clustering algorithm, subjective and objective comprehensive weight, consensus degree, and deviation weight considering minority opinions. Finally, the proposed LGDM consensus method is applied to a practical LGDM problem, and the effectiveness is verified by comparative analysis with existing methods. [ABSTRACT FROM AUTHOR]

Erythrocyte enrichment is needed for blood disease diagnosis and research. DLD arrays with an I-shaped pillar (I-pillar) sort erythrocytes in a unique, accurate, and low-reagent method. However, the existing I-shaped pillar DLD arrays for erythrocyte sorting have the drawbacks of higher flow resistance and more challenging fabrication. A two-dimensional erythrocyte simulation model and the arbitrary Lagrangian–Euler equations at the cell–fluid boundary were built based on the fluid–solid coupling method to investigate the influencing factors of the erythrocyte flow path in an I-pillar DLD array and find its optimization method. Three different sizes of I-pillars were built and multiple sets of corresponding arrays were constructed, followed by finite element simulations to separately investigate the effects of these arrays on the induction of erythrocyte motion paths. This work demonstrates the motion paths of erythrocyte models in a series of I-pillar arrays with different design parameters, aiming to summarize the variation modes of erythrocyte motion paths, which in turn provides some reference for designing and optimizing the pillar size and array arrangement methods for I-pillar array DLD chips. [ABSTRACT FROM AUTHOR]

In complex orchard environments, orchard mowing robots are prone to longitudinal slippage because of the characteristics of tires and the adhesion conditions of the road surface, which makes it difficult for the robots to maintain high-precision path tracking and autonomous navigation positioning. This not only affects the accuracy of path tracking but also leads to unstable motion for the mowing robots. To solve the above problems, we take an orchard mowing robot as the control object and establish a cascaded path-tracking controller and an adaptive time domain model based on a kinematics model. By designing a linear error model, an objective function, and constraint conditions for the mowing robot, the optimal linear velocity and angular velocity of the mower are obtained and converted into the speed of the driving wheel. Then, an anti-slip driving controller is designed based on fuzzy control of the slip rate. The slip-rate-based fuzzy controller is constructed according to the real-time speed of the mower and the reference speed of the driving wheel solved by the model predictive controller, and anti-slip driving control is implemented through a combination of a PID controller and a tire dynamics model. To verify the effectiveness of the proposed method, simulation and field experiments are conducted. The experimental results show that the slip rate of the driving wheel of the mower remains within the target slip rate range in the orchard working environment, avoiding excessive driving wheel sliding. Furthermore, the average lateral error of the path-tracking controller is controlled within 0.05 m, and the average value of the longitudinal error is kept within 0.04 m, which satisfies the control accuracy requirements of lawn mower operations. The proposed method provides a reference optimization scheme for improving the path-tracking and motion stability of a mowing robot. [ABSTRACT FROM AUTHOR]

This research combines the improved reference point selection strategy and the sparrow search algorithm with an enhanced competition mechanism to create a high-dimensional multi-objective sparrow search algorithm with an incorporated improved reference point selection strategy. First, the reference point selection approach is utilized to establish the reference points and sparrow populations, and the most important reference points are dynamically chosen to increase the global search ability. Then, the size of the search population and the method of searcher position updating are dynamically adjusted according to the size of the entropy difference between two adjacent generations of the population. Following, the convergence speed is increased by improving the follower position formula and extending the competition mechanism to high-dimensional multi-objective optimization. The Corsi variation operator improves the algorithm's capacity to break out of its local optimum. Finally, we have used 12 standard benchmark test functions to evaluate the MaOISSA (Many/Multi-Objective Sparrow Search Algorithm based on Improved reference points) and compared it with many high-dimensional multi-objective algorithms. There were nine with substantial IGD values and eight with significant HV values. The findings revealed that MaOISSA had convergence and variety. The simulated results of the performance model for the defense science and technology innovation ecosystem demonstrate that MaOISSA offers a superior solution for tackling the high-dimensional, multi-objective issue, demonstrating the method's efficacy. [ABSTRACT FROM AUTHOR]

Based on the dust concentration data and meteorological environment data monitored at the open-pit mine site, the characteristics of dust concentration and the influence of temperature, humidity, wind speed, air pressure and other meteorological conditions on dust concentration were analyzed, and the causes of the change of dust concentration were clarified. Meanwhile, a dust concentration prediction model based on LSTM neural network is established. The results show that the dust concentration of the open-pit mine is high in March, November and the whole winter, and it is low in summer and autumn. The daily variation of humidity and temperature in different seasons showed the trend of "herringbone" and "inverted herringbone", respectively. In addition, the wind speed was the highest in spring and the air pressure distribution was uniform, which basically maintained at 86–88 kPa. The peak humidity gradually deviates with each month and is obviously affected by seasonality. The higher the humidity, the lower the temperature and the higher the concentration of dust. In different seasons, the wind speed is the highest around 20:00 at night, and the dust is easy to disperse. The R2 values of PM2.5, PM10 and TSP concentrations predicted by LSTM model are 0.88, 0.87 and 0.87, respectively, which were smaller than the MAE, MAPE and RMSE values of other prediction models, and the prediction effect was better with lower error. The research results can provide a theoretical basis for dust distribution law, concentration prediction and dust removal measures of main dust sources in open-pit mines. [ABSTRACT FROM AUTHOR]

To quantify the interaction between anthropogenic activities and the atmospheric environment in North China, spatiotemporal characteristics, transmission, source apportionment and a health risk assessment of the elements were analyzed in regional and background atmospheric research stations during a period of 2017. This study found that the concentration of PM2.5 and 16 elements in the regional station was 1.5 and 2.8 times higher, respectively, than that in the background station. Under the combined influence of human activities and the dusty weather in Spring, the concentration of 16 elements in the regional station was up to 3 times that in the background station. In terms of the transmission of PM2.5, the potential source regions of PM2.5 in North China were mainly the central and southern parts of Beijing-Tianjin-Hebei (BTH), as well as parts of northern Henan and western Shandong. The source apportionment of the elements proved that the elements in North China were mainly derived from soil dust (29.9–68.2%), followed by traffic (8.8–26.3%), with coal combustion and oil consumption accounting for 5.8–24.5% and 4.1–12.9%, respectively. Although the proportion was not the largest, traffic posed the highest health risk to people, which should draw the attention of the relevant authorities. [ABSTRACT FROM AUTHOR]

The efficiency of DNA computation is closely related to the design of DNA coding sequences. For the purpose of obtaining superior DNA coding sequences, it is necessary to choose suitable DNA constraints to prevent potential conflicting interactions in different DNA sequences and to ensure the reliability of DNA sequences. An improved matrix particle swarm optimization algorithm, referred to as IMPSO, is proposed in this paper to optimize DNA sequence design. In addition, this paper incorporates centroid opposition-based learning to fully preserve population diversity and develops and adapts a dynamic update on the basis of signal-to-noise ratio distance to search for high-quality solutions in a sufficiently intelligent manner. The results show that the proposal of this paper achieves satisfactory results and can obtain higher computational efficiency. [ABSTRACT FROM AUTHOR]

To improve intelligent construction standard systems in coal mines, we must promote the high-quality development of the coal mining industry. The current intelligent construction of coal mines is inefficient. Considering the complexity and diversity of coal mine intelligent construction index factors, this paper proposes an intelligent coal mine construction evaluation model that integrates the fuzzy decision-making trial and evaluation laboratory (FDEMATEL) and the analytical network process (ANP). Firstly, the evaluation index system is established based on the intelligent construction of coal mines. Secondly, the FDEMATEL is applied to deal with the fuzziness in the evaluation process and determine the influence relationship between the evaluation indexes of coal mine intelligent construction to draw the ANP network structure diagram. Finally, super decision software is used to calculate the weight of coal mine intelligent construction evaluation indexes, and then obtain the combination weight and correlation degree of each evaluation index. By applying the evaluation model to conduct a comprehensive evaluation of coal mine intelligent construction, the results show that there is a significant correlation between the indexes affecting the intelligent construction of coal mines. Basic platform intelligence and safety monitoring intelligence are the two most important aspects of intelligent coal mine construction. Database construction, mobile internet construction, big data support, and model algorithm support are the key indexes affecting the intelligent construction of coal mines. [ABSTRACT FROM AUTHOR]

High-amylose starch has unique functional properties and nutritional values in food applications. This type of starch is generally resistant to enzymatic digestion in the gastrointestinal tract, and contains an increased fraction of resistant starch (RS), which is a type of dietary fiber. The digestion and fermentation of high-amylose starch in the gut are of current research interest, as the processes are related to its nutritional functionality. This review summarizes recent in vitro and in vivo studies on the digestion and fermentation of high-amylose starches from different botanical sources and those that have been obtained by modifications. The RS content and fermentation properties are compared among high-amylose starches. This review aims to provide a current understanding of the relationship between high-amylose starch structures and fermentation-related nutritional properties. The results of these studies suggest that both modifications and food processing of high-amylose starch result in distinct fermentation products and nutritional properties. The review provides insight into the potential future applications of diverse high-amylose starches as bioactive compounds to modulate colonic fermentation. [ABSTRACT FROM AUTHOR]

To achieve high-frequency and effective inter-vehicle communication between harvesters and transport vehicles during cooperative harvesting, a protocol for wireless communication was designed by analyzing actual communication requirements. Two different wireless communication modes (radio and 4G) were selected for the hardware design; then, a Kalman Filter was designed based on real-time Dead-reckoning and inter-vehicle Communication data after delay Compensation (KFDCC). Finally, the relative longitudinal deviation between two vehicles was obtained and updated steadily at a 10 Hz frequency. By using the relative longitudinal deviation of two vehicles, calculated after aligning the UTC stamp with the local GNSS data from the harvester and transport vehicle as a comparative metric, accuracy evaluation experiments were conducted regarding radio and 4G. The maximum absolute errors of the KFDCC output value were 0.03783 and 0.07381 m, respectively, and the mean square errors were 0.00392 and 0.01317 m, respectively. Compared with systems without the KFDCC method, the mean square errors were reduced by 88.76% and 90.60%, respectively. The KFDCC method can also effectively solve the problems of data delay, packet loss, blockage, error, and so on, in wireless communication, and has short-time breakpoint endurance capabilities. Field experiments showed that the proposed method can provide accurate data support for the dynamic alignment and unloading processes of harvesters and transport vehicles, and it can also provide algorithmic support for real-time communication data fusion between different wireless communication modes. Overall, the inter-vehicle communication mode and data-processing method designed in this paper have good effects and adaptability, and they can guarantee that the whole process of autonomous harvesting operates properly. [ABSTRACT FROM AUTHOR]

Simple Summary: Corn stover pellets are of poor quality (regarding physical and mechanical properties), and wet soybean dregs are viscous, which may improve the pelleting process. Furthermore, the soybean dregs have high nutritional value, which may increase the quality of feed. The relaxed density, dimensional stability coefficient and hardness of feed pellets were measured and analyzed in this study to clarify the influence mechanism of wet-fermented soybean dregs on the pelleting of corn stover. The results showed that fermented soybean dregs had a significant effect on corn stover pelleting. The comprehensive analysis showed that the addition of 5–10% fermented soybean dregs to corn stover improved the relaxed density, dimensional stability coefficient, and hardness of feed pellets by 10.76–23.51%, 7.32–15.74%, and 33.39–454.47%, respectively. The results could provide guidance on the research and development of high-quality forming technology and instruments using the mixture of corn stover and fermented soybean dregs and offer a new route for the utilization of high-moisture materials. Pelleting experiments were carried out in this study to clarify the influence mechanism of wet-fermented soybean dregs on the forming of corn stover. The effects of addition of water or wet fermented soybean dregs on the forming quality of corn stover were comparatively studied under different corn stover particle sizes and compression displacement. The fermented soybean dregs significantly affected the relaxed density, dimensional stability coefficient, and hardness of feed pellets. The relaxed density, dimensional stability coefficient, and hardness of feed pellets increased first and then decreased with the increase of fermented soybean dregs. The forming quality of corn stover added with fermented soybean dregs was higher than that of corn stover added with the same amount of water. The mechanism allowed soybean dregs to strengthen the bonding between corn stover particles and thus improved the quality of feed pellets. A certain amount of water was favorable for corn stover pelleting, but excessive water may decrease the quality of pellets. The comprehensive analysis showed that the addition of 5–10% fermented soybean dregs to the corn stover improved the relaxed density, dimensional stability coefficient, and hardness of feed pellets by 10.76–23.51%, 7.32–15.74%, and 33.39–454.47%, respectively. [ABSTRACT FROM AUTHOR]

Zinc oxide (ZnO) is a potential thermoelectric material with good chemical and thermal stability as well as an excellent Seebeck coefficient. However, the extremely low carrier concentration brings poor electrical transport properties. Although Gallium (Ga) doping could increase the carrier concentration of ZnO film, its thermoelectric performance is still limited due to the deteriorated Seebeck coefficient and enhanced thermal conductivity. Interface engineering is an effective strategy to decouple electron-phonon interaction for thermoelectric materials. Thus, in this work, GZO (Ga-doped ZnO)/NAZO (Ni, Al co-doped ZnO) multilayer films were designed to further improve the thermoelectric properties of GZO films. It was found that GZO/NAZO multilayer films possessed better electrical conductivity, which was attributed to the increased carrier concentration and Hall mobility. Meanwhile, benefiting from the energy filtering that occurred at GZO/NAZO interfaces, the density of states effective mass increased, resulting in comparable Seebeck coefficient values. Ultimately, an enhanced power factor value of 313 μW m−1 K−2 was achieved in the GZO/NAZO multilayer film, which is almost 46% larger than that of GZO film. This work provides a paradigm to optimize the thermoelectric performance of oxide films and other thermoelectric systems by multilayer structure design with coherent interfaces. [ABSTRACT FROM AUTHOR]

The identification method of rice seedling rows based on machine vision is affected by environmental factors that decrease the accuracy and the robustness of the rice seedling row identification algorithm (e.g., ambient light transformation, similarity of weed and rice features, and lack of seedlings in rice rows). To solve the problem of the above environmental factors, a Gaussian Heatmap-based method is proposed for rice seedling row identification in this study. The proposed method is a CNN model that comprises the High-Resolution Convolution Module of the feature extraction model and the Gaussian Heatmap of the regression module of key points. The CNN model is guided using Gaussian Heatmap generated by the continuity of rice row growth and the distribution characteristics of rice in rice rows to learn the distribution characteristics of rice seedling rows in the training process, and the positions of the coordinates of the respective key point are accurately returned through the regression module. For the three rice scenarios (including normal scene, missing seedling scene and weed scene), the PCK and average pixel offset of the model were 94.33%, 91.48%, 94.36% and 3.09, 3.13 and 3.05 pixels, respectively, for the proposed method, and the forward inference speed of the model reached 22 FPS, which can meet the real-time requirements and accuracy of agricultural machinery in field management. [ABSTRACT FROM AUTHOR]

In this study, a low-cost, self-balancing crop spectral reflectance sensor (CSRS) was designed for real-time, nondestructive monitoring of the spectral reflectance and vegetation index of crops such as tomato and rapeseed. The sensor had a field of view of 30°, and a narrow-band filter was used for light splitting. The filter's full width at half-maximum was 10 nm, and the spectral bands were 710 nm and 870 nm. The sensor was powered by a battery and used WiFi for communication. Its software was based on the Contiki operating system. To make the sensor work in different light intensity conditions, the photoelectric conversion automatic gain circuit had a total of 255 combinations of amplification. The gimbal of the sensor was mainly composed of an inner ring and an outer ring. Under the gravity of the sensor, the central axis of the sensor remained vertical, such that the up-facing and down-facing photosensitive units stayed in the horizontal position. The mechanical components of the sensor were designed symmetrically to facilitate equal mass distribution and to meet the needs of automatic balancing. Based on the optical signal transmission process of the sensor and the dark-current characteristics of the photodetector, a calibration method was theoretically deduced, which improved the accuracy and stability of the sensor under different ambient light intensities. The calibration method is also applicable for the calibration of other crop growth information sensors. Next, the standard reflectance gray scale was taken as the measurement variable to test the accuracy of the sensor, and the results showed that the root mean square error of the reflectance measured by the sensor at 710 nm and 870 nm was 1.10% and 1.27%, respectively; the mean absolute error was 0.95% and 0.89%, respectively; the relative error was below 4% and 3%, respectively; and the coefficient of variation was between 1.0% and 2.5%. The reflectance data measured by the sensor under different ambient light intensities suggested that the absolute error of the sensor was within ±0.5%, and the coefficients of variation at the two spectral bands were 1.04% and 0.39%, respectively. With tomato and rapeseed as the monitoring targets, the proposed CSRS and a commercial spectroradiometer were used to measure at the same time. The results showed that the reflectance measured by the two devices was very close, and there was a linear relationship between the normalized difference vegetation index of the CSRS and that of the commercial spectroradiometer. The coefficient of determination (R2) for tomato and rapeseed were 0.9540 and 0.9110, respectively. [ABSTRACT FROM AUTHOR]

This study deploys environmental and positive psychology models to develop and test the influence of substantive and communicative staging of airport servicescape (i.e., SSoS and CSoS) on passengers' emotional and subsequent behavioral responses. Furthermore, we examined the extent to which the strength of these associations is contingent upon passengers' travel frequency (passengers' familiarity with airport facilities and processes). The study's sample (n = 387) was drawn from passengers departing from Shanghai Hongqiao International Airport (SHA). The results indicate that airport servicescape robustly engenders passengers' positive emotion and satisfaction (with SSoS having more potent effects), facilitating intentions to repurchase, recommend, pay more, and partly, spend more. The interaction effects demonstrate that while pleasant CSoS induces higher satisfaction in frequent flyers, pleasing SSoS generates higher satisfaction in infrequent flyers. In addition, positive emotion appears more vital in predicting infrequent passengers' behavioral intentions to repeat purchase, recommend, and pay more. Passenger satisfaction seems relevant for different passengers regarding their familiarity levels depending on the kind of behavioral response under consideration. Thus, satisfied frequent travelers are more inclined to repeat purchase and pay more; however, satisfied infrequent travelers are more likely to recommend and spend more at airport terminals. The summary, interpretation, and implication of the results conclude the study. [ABSTRACT FROM AUTHOR]

Credit is an important means to promote economic development, while green credit is conducive to the sustainable development of industry. This paper aims to build a multiple linear regression model and a dynamic panel data GMM estimation model to analyze the important factors that affect the optimization of the industrial structure. We then use an analytic hierarchy process to explore the relationship between green credit and industrial optimization. We compare this with the optimization rate of the industrial structure according to the real data, and then obtain the effectiveness of the hierarchical analysis of the three major industries in the eastern, central and western regions. Finally, neural networks are used to forecast the total amount and distribution of green credit in 2021. The final results show that there are regional and industrial differences in the influence of green credit on industrial structure optimization, and in the process of using green credit to promote the optimization and upgrading of industrial structure. [ABSTRACT FROM AUTHOR]

To explore the drying characteristics of soybean dregs and a nondestructive moisture content test method, in this study, soybean dregs were dried with hot air (80 °C), the moisture content was measured using the drying method, water status was analyzed using low-field nuclear magnetic resonance (LF-NMR) and the moisture content prediction models were built and validated. The results revealed that the moisture contents of the soybean dregs were 0.57 and 0.01 g/g(w.b.), respectively, after drying for 5 and 7 h. The effective moisture diffusivity increased with the decrease in moisture content; it ranged from 5.27 × 10−9 to 6.96 × 10−8 m2·s−1. Soybean dregs contained bound water (T21), immobilized water (T22) and free water (T23 and T23'). With the proceeding of drying, all of the relaxation peaks shifted left until a new peak (T23') appeared; then, the structure of soybean dregs changed, and the relaxation peaks reformed, and the peak shifted left again. The peak area may predict the moisture content of soybean dregs, and the gray values of images predict the moisture contents mainly composed of free water or immobilized water. The results may provide a reference for drying of soybean dregs and a new moisture detection method. [ABSTRACT FROM AUTHOR]

Alfalfa (Medicago sativa L.) is a perennial leguminous forage cultivated globally. Fusarium spp.-induced root rot is a chronic and devastating disease affecting alfalfa that occurs in most production fields. Studying the disease resistance regulatory network and investigating the key genes involved in plant–pathogen resistance can provide vital information for breeding alfalfa that are resistant to Fusarium spp. In this study, a resistant and susceptible clonal line of alfalfa was inoculated with Fusarium proliferatum L1 and sampled at 24 h, 48 h, 72 h, and 7 d post-inoculation for RNA-seq analysis. Among the differentially expressed genes (DEGs) detected between the two clonal lines at the four time points after inoculation, approximately 81.8% were detected at 24 h and 7 d after inoculation. Many DEGs in the two inoculated clonal lines participated in PAMP-triggered immunity (PTI) and effector-triggered immunity (ETI) mechanisms. In addition, transcription factor families such as bHLH, SBP, AP2, WRKY, and MYB were detected in response to infection. These results are an important supplement to the few existing studies on the resistance regulatory network of alfalfa against Fusarium root rot and will help to understand the evolution of host–pathogen interactions. [ABSTRACT FROM AUTHOR]

In recent years, healthcare has attracted much attention, which is looking for more and more data analytics in healthcare to relieve medical problems in medical staff shortage, ageing population, people living alone, and quality of life. Data mining, analysis, and forecasting play a vital role in modern social and medical fields. However, how to select a proper model to mine and analyze the relevant medical information in the data is not only an extremely challenging problem, but also a concerning problem. Tuberculosis remains a major global health problem despite recent and continued progress in prevention and treatment. There is no doubt that the effective analysis and accurate forecasting of global tuberculosis prevalence rates lay a solid foundation for the construction of an epidemic disease warning and monitoring system from a global perspective. In this paper, the tuberculosis prevalence rate time series for four World Bank income groups are targeted. Kruskal–Wallis analysis of variance and multiple comparison tests are conducted to determine whether the differences of tuberculosis prevalence rates for different income groups are statistically significant or not, and a novel combined forecasting model with its weights optimized by a recently developed artificial intelligence algorithm—cuckoo search—is proposed to forecast the hierarchical tuberculosis prevalence rates from 2013 to 2016. Numerical results show that the developed combination model is not only simple, but is also able to satisfactorily approximate the actual tuberculosis prevalence rate, and can be an effective tool in mining and analyzing big data in the medical field. [ABSTRACT FROM AUTHOR]

Population agglomeration and haze pollution are two major problems that urban development will inevitably face in the future. Population agglomeration has a spatial impact on smog pollution through scale and intensive effects. This paper uses panel data from 236 prefecture-level cities in China from 2001 to 2012 to verify the impact of urban population agglomeration on haze pollution and its mechanism based on a spatial lag model. The research shows that: (1) China's urban haze pollution has a significant positive spatial spillover effect, and presents a spatial distribution state of high-high and low-low agglomeration. (2) There is a significant "N-type" nonlinear relationship between urban population agglomeration and haze pollution. (3) At present, the scale effect of urban population agglomeration in China is greater than the intensification effect, and the scale effect as well as intensification effect have opposite effects on haze pollution. This shows that urban layout should be scientifically planned, urban population should be reasonably controlled, production efficiency should be improved, and green development should be promoted to deal with haze pollution. (4) The spillover effect of urban population agglomeration on haze pollution is significantly greater than the direct effect, indicating that local haze pollution is more likely to be affected by spatially related regions, indicating that strengthening regional coordination and cooperation and joint prevention and control are necessary to control haze pollution. [ABSTRACT FROM AUTHOR]

Smart farming uses advanced tools and technologies such as intelligent agricultural machines, high-precision sensors, navigation systems, and sophisticated computer systems to increase the economic benefits of agriculture and reduce the associated human effort. With the increasing demands of individualized farming operations, the internet of things is a crucial technique for acquiring, monitoring, processing, and managing the agricultural resource data of precision agriculture and ecological monitoring domains. Here, an internet of things-based system scheme integrating the most recent technologies for designing a management platform for agricultural machines equipped with automatic navigation systems is proposed. Various agricultural machinery cyber-models and their corresponding sensor nodes were constructed in a pre-production phase. Three key enabling technologies—multi-optimization of agricultural machinery scheduling, development of physical architecture and software, and integration of the controller-area-network with a mobile network—were addressed to support the system scheme. A demonstrative prototype system was developed and a case study was used to validate the feasibility and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Mo:BiVO4 nanoparticles were employed as an optical modulator in a Q-switched all-solid-state Tm:YAP laser for the first time. The nonlinear optical parameters of Mo:BiVO4 nanoparticles in the 2-μm region were characterized by measuring nonlinear transmission. Saturation intensity was 718 MW/cm2, and the modulation depth was 12.3%. A stable pulse sequence was acquired with a 70.08 kHz maximum repetition rate and an 821 ns pulse width. The maximum output average power was 153 mW, corresponding to 2.18 μJ single pulse energy and 2.67 W peak power. Although the response wavelength of Mo:BiVO4 is in visible light region, our experimental results demonstrates that a saturable absorption effect for wavelengths much longer than visible light (2 μm wavelength) is still possible due to sub-bandgap absorption. Therefore, we experimentally proved that Mo:BiVO4 nanoparticles are a great candidate for use as an optical modulator of a 2-μm pulsed laser. [ABSTRACT FROM AUTHOR]

Due to increased global carbon dioxide emissions, the greenhouse effect is being aggravated, which has attracted wide attention. China is committed to promoting the low-carbon development of all industries. This paper analyzed the influencing factors of carbon emissions in the Chinese logistics industry, so as to identify the key factors that influence carbon emissions. Based on the carbon emission data of China's logistics industry in 2000–2019, this paper applied the carbon emission coefficients issued by the Intergovernmental Panel on Climate Change. For the first time, the Generalized Divisia Index Method was used to analyze the degree of influence of the factors on carbon emissions. This method considered more variables and their relationships. The results showed that (1) the carbon emissions of the logistics industry were increased by 3.22 times from 2000 to 2018, and showed negative growth for the first time in 2019; (2) the added value of the logistics industry is the most important factor in increasing carbon emissions (with a contribution ratio of 65.45%), energy consumption and practical population size are the main factors in carbon emissions. The promotion of this industry is subjected to decreased per capita carbon emissions, which have a large impact on total carbon emissions; (3) the intensity of carbon output is the most important factor in the reduction of carbon emissions (with a contribution ratio of −29.1%), where the energy carbon intensity and per capita added value are the main influencing factors with regard to the reduction of carbon emissions, while energy intensity has a negative inhibitory effect on carbon emissions, and (4) the influencing factors have negative effects on the cumulative inhibition of carbon emissions in the logistics industry, to an extent that is far less than the integral promotion of carbon emissions. Finally, according to the research conclusions of this paper, it is feasible to make recommendations for the carbon reduction of the logistics industry. [ABSTRACT FROM AUTHOR]

A stable, passively Q-switched Yb:CaGdAlO4 laser based on Mo:BiVO4 saturable absorber was demonstrated. Close observations of the structure and morphology of the nanoparticles by using transmission electron microscope, Raman spectrum and linear absorption were measured. The nonlinear transmission of Mo:BiVO4 was characterized by a 30 ps laser with a central wavelength of 1064 nm and a repetition rate of 10 Hz. The experimental maximum output power of the pulsed laser was 510 mW with a repetition rate of 87 kHz and pulse width of 3.18 μs, corresponding to a peak power of 1.84 W and a single pulse energy of 5.8 μJ. The experimental results indicate that Mo:BiVO4-SA is a great candidate for passively Q-switched lasers in the near infrared region. [ABSTRACT FROM AUTHOR]

The application of artemisinin (ART) in the treatment of malaria has been restricted to a certain degree due to its inherent limitations, such as short half-life, poor solubility, limited bioavailability, and re-crystallization. Electrospun nanofibers loaded with ART provide an excellent solution to these limitations and yield sustained drug release as well as inhibition of drug re-crystallization. In this study, ART-loaded polycaprolactone (PCL)/collagen (Col) nanofibers with different proportions of polymers were prepared. ART-loaded PCL/Col nanofibers were characterized, and further ART anti-crystallization and release behaviors were studied. SEM was used to observe the morphology of PCL/Col nanofibers. X-ray diffraction (XRD) was used to characterize the physical state of ART in ART-loaded PCL/Col nanofibers. Fourier transform infrared spectroscopy (FTIR), water contact angle measurement, weight loss, degree of swelling, and drug release experiments can verify the differences in performance of ART-loaded PCL/Col nanofibers due to different polymer ratios. The release curve was analyzed by kinetics, showing sustained release for up to 48 h, and followed the Fickian release mechanism, which was shown by the diffusion index value obtained from the Korsmeyer-Peppas equation. [ABSTRACT FROM AUTHOR]

Human identification based on motion capture data has received signification attentions for its wide applications in authentication and surveillance systems. The optical motion capture system (OMCS) can dynamically capture the high-precision three-dimensional locations of optical trackers that are implemented on a human body, but its potential in applications on gait recognition has not been studied in existing works. On the other hand, a typical OMCS can only support one player one time, which limits its capability and efficiency. In this paper, our goals are investigating the performance of OMCS-based gait recognition performance, and realizing gait recognition in OMCS such that it can support multiple players at the same time. We develop a gait recognition method based on decision fusion, and it includes the following four steps: feature extraction, unreliable feature calibration, classification of single motion frame, and decision fusion of multiple motion frame. We use kernel extreme learning machine (KELM) for single motion classification, and in particular we propose a reliability weighted sum (RWS) decision fusion method to combine the fuzzy decisions of the motion frames. We demonstrate the performance of the proposed method by using walking gait data collected from 76 participants, and results show that KELM significantly outperforms support vector machine (SVM) and random forest in the single motion frame classification task, and demonstrate that the proposed RWS decision fusion rule can achieve better fusion accuracy compared with conventional fusion rules. Our results also show that, with 10 motion trackers that are implemented on lower body locations, the proposed method can achieve 100% validation accuracy with less than 50 gait motion frames. [ABSTRACT FROM AUTHOR]

In order to solve the environmental problems caused by the increasing private car use in China, such as transport energy consumption, traffic congestion, and air pollution, many policy measures including car purchase taxes, restrictions on car use in the city center, and incentives to promote electric vehicles have been developed. By taking Hangzhou, a low-carbon metropolitan city in China, as an illustrative example, green transport policies have been proactively implemented in order to turn the metropolitan city into an ecologically livable city. However, citizens' acceptance of comprehensive green transport policies has seldom been studied and explored, which is actually quite valuable information for implementing and assessing the effectiveness of green transport policies. This study presents a new integrated framework by extending the value belief norm (VBN) theory in order to explore the internal factors for predicting citizens' acceptance of comprehensive green transport policies and other pro-environmental behaviors in the transport field. A survey on car use reduction was conducted among citizens in Hangzhou and a quantitative analysis was performed using a structural equation model (SEM) method. Results show that personal norms can successfully predict citizens' acceptance of pull policies for reducing car use, while is less capable of predicting that of push ones. The theoretical implications of different pro-environmental behaviors are explained. This analysis may inspire policy makers to implement appropriate policies to encourage the public to use low-carbon transport in daily life. [ABSTRACT FROM AUTHOR]

It remains uncertain as to whether people who support waste classification end up transforming such environmental initiation into reality. Thus, to investigate the intention and actual behavior of Chinese residents on waste classification and the influencing factors, this study integrated the theory of planned behavior (TPB) and norm activation model (NAM), and extended them by adding external information factors, namely information publicity type and information quality. A questionnaire survey was conducted in mainland China, and the primary data from 349 individuals were analyzed by partial least squares structural equation modeling (PLS-SEM) to verify the model. The conclusions confirmed that personal norm was a major predictor of residents' waste classification intention, and there exists a gap between Chinese residents' waste classification intentions and actual behaviors. Furthermore, strategies such as moral education and information publicity are important in policy implementation. These findings are helpful for Chinese policymakers in promoting and planning waste classification, and also provide experiences to other countries for combating similar waste problems in their metropolises. [ABSTRACT FROM AUTHOR]

The rapidly increasing use of unmanned aerial vehicles pose a significant challenge to no-fly zone management. The vehicle state in flight should be available for the whole mission, enabling an alert to be issued to the relevant users and entities at an appropriate time and location before intrusion into a no-fly zone. In addition to spatial databases and other control mechanisms, the navigation system used must have the required accuracy, integrity, continuity, and availability. In this paper, the accuracy and integrity requirements, and the positioning system for no-fly zone unmanned aerial vehicle management are specified. The proposed positioning system integrates global navigation satellite systems (GNSS) and inertial navigation system (INS) in the measurement domain. An integrity monitoring layer is incorporated for fault detection and exclusion as well as real-time horizontal protection level computation functions. Experimental results show that the algorithm proposed is capable of delivering accuracy and integrity requirements for unmanned aerial vehicle (UAV) no-fly zone management. [ABSTRACT FROM AUTHOR]

Personnel performance is important for the high-technology industry to ensure its core competitive advantages are present. Therefore, predicting personnel performance is an important research area in human resource management (HRM). In this paper, to improve prediction performance, we propose a novel framework for personnel performance prediction to help decision-makers to forecast future personnel performance and recruit the best suitable talents. Firstly, a hybrid convolutional recurrent neural network (CRNN) model based on self-attention mechanism is presented, which can automatically learn discriminative features and capture global contextual information from personnel performance data. Moreover, we treat the prediction problem as a classification task. Then, the k-nearest neighbor (KNN) classifier was used to predict personnel performance. The proposed framework is applied to a real case of personnel performance prediction. The experimental results demonstrate that the presented approach achieves significant performance improvement for personnel performance compared to existing methods. [ABSTRACT FROM AUTHOR]

Sustainable development is a powerful impetus to achieve the coordination between economic advancement and environmental protection. Therefore, the promotion and adoption of green consumption has attracted increasing attention from academics. As an important tool for multivariate data analyses, structural equation modelling has been extensively used in purchase intention for green products. However, most previous research has recognized green products as a general class when studying their purchase intention, which focused little attention on comparing purchase intention for different kinds of green products. This study extends and tests the theory of planned behavior (TPB) and includes another cognitive construct, that is, environmental concern, to explore purchase intention for different kinds of green products. First, the effect of TPB constructs was examined on purchase intention for utilitarian green products and hedonic green products. Second, a cognitive factor, that is, environmental concern, was used to test its different effects on these two kinds of green products. Third, the indirect effect of environmental concern on purchase intention was measured for these two kinds of green products. The study showed that extended TPB constructs, that is, attitude, perceived behavior control, and environmental concern positively and significantly affected purchase intention for both utilitarian green products and hedonic green products, while subjective norm only significantly and positively affected purchase intention for utilitarian green products. Moreover, the cognitive factor, that is, environmental concern, had a higher effect on purchase intention for utilitarian green products than purchase intention for hedonic green products, and it also had a similar indirect effect on both kinds of green products. This study provides an insight to policymakers, entrepreneurs, and marketers into how to increase purchase intention for different kinds of green products. [ABSTRACT FROM AUTHOR]

As part of the increasing efforts toward the prevention and control of motor vehicle pollution, the Chinese government has practiced a range of policies to stimulate the purchase and use of battery electric vehicles (BEVs). Zhejiang Province, a key province in China, has proactively implemented and monitored an environmental protection plan. This study aims to contribute toward streamlining marketing and planning activities to introduce strategic policies that stimulate the purchase and use of BEVs. This study considers the nature of human behavior by extending the theory of planned behavior model to identify its predictors, as well as its non-linear relationship with customers' purchase intention. To better understand the predictors, a substantial literature review was given to validate the hypotheses. A quantitative study using 382 surveys completed by customers in Zhejiang Province was conducted by integrating a structural equation model (SEM) and a neural network (NN). The initial analysis results from the SEM revealed five factors that have impacted the customers' purchase intention of BEVs. In the second phase, the normalized importance among those five significant predictors was ranked using the NN. The findings have provided theoretical implications to scholars and academics, and managerial implications to enterprises, and are also helpful for decision makers to implement appropriate policies to promote the purchase intention of BEVs, thereby improving the air quality. [ABSTRACT FROM AUTHOR]

Selecting optimal suppliers in fuzzy environments has become a major challenge for enterprises. Reputation plays an important role in the process of supplier selection because of its fuzziness, dynamicity, and transitivity. In this study, we first present a novel intuitionistic fuzzy sets (IFS)-hyperlink-induced topic search (HITS) method that combines the intuitionistic fuzzy set with the hyperlink-induced topic search (HITS) algorithm to extend the ability of processing fuzzy information in order to obtain post-propagated reputation values of suppliers. Then, we employ the dynamic intuitionistic fuzzy weighted average operator to gain dynamic reputation values and other evaluation attribute values. After that, intuitionistic fuzzy entropy weight method is adopted to acquire more accurate weights for each evaluation attribute. Finally, we employ the Vlsekriterijumska Optimizacija I Kompromisno Resenje method to acquire comprehensive evaluation values of candidate supplier to select optimal suppliers. Two groups of experiments for supplier selection are given to explain feasibility and practicality of the proposed method. [ABSTRACT FROM AUTHOR]

Research front detection and topic evolution has for a long time been an important direction for research in the informetrics field. However, most previous studies either simply use a citation count for scientific document clustering or assume that each scientific document has the same importance in detecting the clustering theme in a cluster. In this study, utilizing the topological structure and the PageRank algorithm, we propose a new research front detection and topic evolution approach based on graph theory. This approach is made up of three stages: (1) Setting a time window with appropriate length according to the accuracy of scientific documents clustering results and the time delay of a scientific document to be cited, dividing scientific documents into several time windows according to their years of publication, calculating similarities between them according to their topological structure, and clustering them in each time window based on the fast greedy algorithm; (2) combining the PageRank algorithm and keywords' frequency to detect the clustering theme, which assumes that the more important a scientific document in the cluster is, the greater the possibility that it is cited by the other documents in the same cluster; and (3) reconstructing the cluster graph where nodes represent clusters and edges' strengths represent the similarities between different clusters, then detecting research front and identifying topic evolution based on the reconstructed cluster graph. To evaluate the performance of our proposed approach, the scientific documents related to data mining and covered by Science Citation Index Expanded (SCI-EXPANDED) or Social Science Citation Index (SSCI) in Web of Science are collected as a case study. The experiment's results show that the proposed approach can obtain reasonable clustering results, and it is effective for research front detection and topic evolution. [ABSTRACT FROM AUTHOR]

The accurate monitoring of state of charge (SOC) and state of health (SOH) is critical for the reliable management of lithium-ion battery (LIB) systems. In this paper, online model identification is scrutinized to realize high modeling accuracy and robustness, and a model-based joint estimator is further proposed to estimate the SOC and SOH of an LIB concurrently. Specifically, an adaptive forgetting recursive least squares (AF-RLS) method is exploited to optimize the estimation’s alertness and numerical stability so as to achieve an accurate online adaption of model parameters. Leveraging the online adapted battery model, a joint estimator is proposed by combining an open-circuit voltage (OCV) observer with a low-order state observer to co-estimate the SOC and capacity of an LIB. Simulation and experimental studies are performed to verify the feasibility of the proposed data–model fusion method. The proposed method is shown to effectively track the variation of model parameters by using the onboard measured current and voltage data. The SOC and capacity can be further estimated in real time with fast convergence, high stability, and high accuracy. [ABSTRACT FROM AUTHOR]

Due to the specificity, high efficiency, and gentleness of enzyme catalysis, the industrial utilization of enzymes has attracted more and more attention. Immobilized enzymes can be recovered/recycled easily compared to their free forms. The primary benefit of immobilization is protection of the enzymes from harsh environmental conditions (e.g., elevated temperatures, extreme pH values, etc.). In this paper, catalase was successfully immobilized in a poly(aryl ether sulfone) carrier (PAES-C) with tunable pore structure as well as carboxylic acid side chains. Moreover, immobilization factors like temperature, time, and free-enzyme dosage were optimized to maximize the value of the carrier and enzyme. Compared with free enzyme, the immobilized-enzyme exhibited higher enzymatic activity (188.75 U g -1 , at 30 °C and pH 7) and better thermal stability (at 60 °C). The adsorption capacity of enzyme protein per unit mass carrier was 4.685 mg. Hydrogen peroxide decomposition carried out in a continuous-flow reactor was selected as a model reaction to investigate the performance of immobilized catalase. Immobilized-enzymes showed a higher conversion rate (90% at 8 mL/min, 1 h and 0.2 g) compared to intermittent operation. In addition, PAES-C has been synthesized using dichlorodiphenyl sulfone and the renewable resource bisphenolic acid, which meets the requirements of green chemistry. These results suggest that PAES-C as a carrier for immobilized catalase could improve the catalytic activity and stability of catalase, simplify the separation of enzymes, and exhibit good stability and reusability.

Because of the high carrier concentration, copper telluride (Cu 2 Te) has a relatively low Seebeck coefficient and high thermal conductivity, which are not good for its thermoelectric performance. To simultaneously optimize carrier concentration, lower thermal conductivity and improve the stability, BiCuTeO, an oxygen containing compound with lower carrier concentration, is in situ formed in Cu 2 Te by a method of combining self-propagating high-temperature synthesis (SHS) with spark plasma sintering (SPS). With the incorporation of BiCuTeO, the carrier concentration decreased from 8.1 × 10 20 to 3.8 × 10 20 cm -3 , bringing the increase of power factor from ~1.91 to ~2.97 μW cm -1 K -2 at normal temperature. At the same time, thermal conductivity reduced from 2.61 to 1.48 W m -1 K -1 at 623 K. Consequently, (Cu 2 Te) 0.95 -(BiCuTeO) 0.05 composite sample reached a relatively high ZT value of 0.13 at 723 K, which is 41% higher than that of Cu 2 Te.

The emissive states of carbon dots have been tuned by controlling the charge transfer process. The carbon dots couple with molecules, which are made of a benzene ring and different heteroatom substituents, through amino-carboxylic bonds that are generally identified as charge transfer promoters at the interface. New ways of radiative recombination are created due to the transfer of photo-excited electrons from carbon dots to the lowest unoccupied molecular orbital (LUMO) of the grafted molecules. By variation of the molecular orbital energy levels via heteroatom substituents in the benzene ring, the different optical properties and emission colors of the carbon dots were presented. This work opens up new opportunities for the application of carbon dots since different heteroatom substituents could lead to many possibilities for conjugation with drugs and biomolecules.