Chang'an University

Global Positioning System (GPS) measurements in China indicate that crustal shortening accommodates most of India's penetration into Eurasia. Deformation within the Tibetan Plateau and its margins, the Himalaya, the Altyn Tagh, and the Qilian Shan, absorbs more than 90% of the relative motion between the Indian and Eurasian plates. Internal shortening of the Tibetan plateau itself accounts for more than one-third of the total convergence. However, the Tibetan plateau south of the Kunlun and Ganzi-Mani faults is moving eastward relative to both India and Eurasia. This movement is accommodated through rotation of material around the eastern Syntaxis. The North China and South China blocks, east of the Tibetan Plateau, move coherently east-southeastward at rates of 2 to 8 millimeters per year and 6 to 11 millimeters per year, respectively, with respect to the stable Eurasia.

Holding on to one's strategy is natural and common if the later warrants success and satisfaction. This goes against widespread simulation practices of evolutionary games, where players frequently consider changing their strategy even though their payoffs may be marginally different than those of the other players. Inspired by this observation, we introduce an aspiration-based win-stay-lose-learn strategy updating rule into the spatial prisoner's dilemma game. The rule is simple and intuitive, foreseeing strategy changes only by dissatisfied players, who then attempt to adopt the strategy of one of their nearest neighbors, while the strategies of satisfied players are not subject to change. We find that the proposed win-stay-lose-learn rule promotes the evolution of cooperation, and it does so very robustly and independently of the initial conditions. In fact, we show that even a minute initial fraction of cooperators may be sufficient to eventually secure a highly cooperative final state. In addition to extensive simulation results that support our conclusions, we also present results obtained by means of the pair approximation of the studied game. Our findings continue the success story of related win-stay strategy updating rules, and by doing so reveal new ways of resolving the prisoner's dilemma.

Attributed to the recent COVID-19 pandemic, panic buying is now a frequent occurrence in many countries, leading to stockouts and supply chain disruptions. Consequently, it has received much attention from academics and the retail industry. The aim of this study is to review, identify, and synthesise the psychological causes of panic buying, which is a relatively new and unexplored area in consumer behaviour research. A systematic review of the related literature is conducted. The review suggests that panic buying is influenced by (1) individuals' perception of the threat of the health crisis and scarcity of products; (2) fear of the unknown, which is caused by negative emotions and uncertainty; (3) coping behaviour, which views panic buying as a venue to relieve anxiety and regain control over the crisis; and (4) social psychological factors, which account for the influence of the social network of an individual. This study contributes to the literature by consolidating the scarce and scattered research on the causes of panic buying, drawing greater theoretical insights into each cause and also offers some implications for health professionals, policy makers, and retailers on implementing appropriate policies and strategies to manage panic buying. Recommendations for future research are also provided.

Geopolymer is a new environment-friendly cementitious material, and the development of geopolymer can reduce the carbon dioxide emission caused by the development of cement industry. Geopolymer materials not only have excellent mechanical properties, but also have a series of excellent properties such as fire resistance and corrosion resistance. Most industrial solid waste and waste incineration bottom ash are piled up at will, which not only occupies land resources, but also has a bad impact on the environment. Recycling them can be used as raw materials for preparing geopolymers. Geopolymer materials can effectively adsorb heavy metals, dyes, and other radioactive pollution, which is very beneficial to society's future development. However, due to the excellent properties of geopolymer materials, its application goes beyond that. Some useful information about geopolymer materials was introduced in this paper. The paper included the geopolymerization, the source of raw materials, the types of activators, the preparation methods, and the different application fields of geopolymer materials. The factors affecting the fresh properties and mechanical properties of geopolymer materials were discussed. In this paper, the shortcomings and application limitations of geopolymer materials were summarized, and their progress was summarized to lay a theoretical foundation for the long-term development of geopolymer materials.

Abstract The study aims to examine the potential relationship between public health expenditures, logistics performance indices, renewable energy, and ecological sustainability in Association of Southeast Asian Nations member countries. The study used secondary data, which downloaded from the World Bank website and tested for hypotheses using the structural equation modeling. The results show that the use of renewable energy in logistics operations will improve environmental and economic performance to reduce emissions, whereas environmental performance is negatively correlated with public health expenditures, indicating that greater environmental sustainability can improve human health and economic growth. The results also show that increased public health spending and poor environmental performance undermine economic growth in low efficiency and low labor productivity, thus reducing the speed of economic activity. On the other hand, the use of renewable energy in logistics cannot only improve the sustainability of the environment but also create a better national image and provide better export opportunities in environmentally friendly countries to promote sustainable economic growth. The outcomes of this study will help the policy/decision makers to make the proper planning to their investments for achieving sustainable economic growth.

Smart manufacturing is the core idea of the fourth industrial evolution. For a smart manufacturing shop floor, real-time monitoring, simulation and prediction of manufacturing operations are vital to improve the production efficiency and flexibility. In this paper, the Cyber-Physical System (CPS) and Digital Twin technologies are introduced to build the interconnection and interoperability of a physical shop floor and corresponding cybershop floor. A Digital Twin-based Cyber-Physical Production System (DT-CPPS) is further established, and the configuring mechanism, operating mechanism and real-time data-driven operations control of DT-CPPS are discussed in detail. It is expected that DT-CPPS will provide the basis for shop floors to march towards smart manufacturing.

Abstract All‐inorganic double‐metal perovskite materials have recently gained much attention due to their three dimensionality (3D) and non‐toxic nature to replace lead‐based perovskite materials. Among all those double perovskite materials, theoretical works have demonstrated that Cs 2 AgBiBr 6 shows high stability and possesses a suitable band gap for solar‐cell applications. However, the film‐forming ability of Cs 2 AgBiBr 6 is found to be the utmost challenge hindering its development in thin‐film solar‐cell devices. In this work, a high‐quality Cs 2 AgBiBr 6 film with ultra‐smooth morphology, micro‐sized grains, and high crystallinity is realized via anti‐solvent dropping technology and post‐annealing at high temperature. After optimization, the first example of an inverted planar heterojunction solar‐cell device based on Cs 2 AgBiBr 6 exhibits a power conversion efficiency of 2.23 % with V OC =1.01 V, J SC =3.19 mA/cm 2 , and FF=69.2 %. Besides, the device shows no hysteresis and a high stability.

Abstract Thermal barrier coatings (TBCs) can effectively protect the alloy substrate of hot components in aeroengines or land-based gas turbines by the thermal insulation and corrosion/erosion resistance of the ceramic top coat. However, the continuous pursuit of a higher operating temperature leads to degradation, delamination, and premature failure of the top coat. Both new ceramic materials and new coating structures must be developed to meet the demand for future advanced TBC systems. In this paper, the latest progress of some new ceramic materials is first reviewed. Then, a comprehensive spalling mechanism of the ceramic top coat is summarized to understand the dependence of lifetime on various factors such as oxidation scale growth, ceramic sintering, erosion, and calcium-magnesium-aluminium-silicate (CMAS) molten salt corrosion. Finally, new structural design methods for high-performance TBCs are discussed from the perspectives of lamellar, columnar, and nanostructure inclusions. The latest developments of ceramic top coat will be presented in terms of material selection, structural design, and failure mechanism, and the comprehensive guidance will be provided for the development of next-generation advanced TBCs with higher temperature resistance, better thermal insulation, and longer lifetime.

Abstract Amid rising environmental concerns, Industry 4.0 and blockchain technology (BCT) are transforming circular economy (CE) practices and prevailing business models. Recognize the same; this study examines the role of blockchain technology in circular CE practices and their impact on eco‐environmental performance, which influences organizational performance. The study collects data from 404 enterprises located in Chinese and Pakistani territories, involved in cross‐border supply chain operations. Both countries' sample has great relevance due to the China Pakistan Economic Corridor (CPEC), which possesses several positive fallouts in terms of technology spillovers across firms. Using the partial least squares structural equation modeling (PLS‐SEM) modeling framework, this study provides three key findings. First, BCT significantly improves the circular economy practices (circular procurement, circular design, recycling, and remanufacturing). Second, CE practices help improve firms' environmental performance and stimulate their financial performance. Third, higher eco‐environmental performance significantly boosts organizational performance. This study sets out the foundations for participating countries/firms that simultaneously achieve financial and sustainable goals by integrating blockchain technology in circular economy practices.

Intelligent vehicle detection and counting are becoming increasingly important in the field of highway management. However, due to the different sizes of vehicles, their detection remains a challenge that directly affects the accuracy of vehicle counts. To address this issue, this paper proposes a vision-based vehicle detection and counting system. A new high definition highway vehicle dataset with a total of 57,290 annotated instances in 11,129 images is published in this study. Compared with the existing public datasets, the proposed dataset contains annotated tiny objects in the image, which provides the complete data foundation for vehicle detection based on deep learning. In the proposed vehicle detection and counting system, the highway road surface in the image is first extracted and divided into a remote area and a proximal area by a newly proposed segmentation method; the method is crucial for improving vehicle detection. Then, the above two areas are placed into the YOLOv3 network to detect the type and location of the vehicle. Finally, the vehicle trajectories are obtained by the ORB algorithm, which can be used to judge the driving direction of the vehicle and obtain the number of different vehicles. Several highway surveillance videos based on different scenes are used to verify the proposed methods. The experimental results verify that using the proposed segmentation method can provide higher detection accuracy, especially for the detection of small vehicle objects. Moreover, the novel strategy described in this article performs notably well in judging driving direction and counting vehicles. This paper has general practical significance for the management and control of highway scenes.

Abstract The objective of this research is to examine the role of economic growth, technology innovation, and renewable energy in reducing transport sector CO 2 emission in China by using the annual data of 1990–2018. An application of the QARDL approach discloses that economic growth, technology innovation, and renewable energy significantly influence CO 2 emission in the transportation sector in China. Both renewable energy consumption and innovation show a negative impact on emissions of CO 2 related to transport. It depicts that due to the increase in renewable energy and innovation, the CO 2 emission in the transport sector is likely to decrease; however, an increase in the GDP of a country will upsurge the emission of CO 2 in the transportation sector. However, China should make new policies to introduce innovation in the transportation sector to minimize the emission of CO 2 .

This article presents a delay-suppressed sliding-mode observer (SMO) to observe the real-time rotor position of a permanent magnet synchronous machine (PMSM) controlled by vector control algorithms. First, in order to solve the low-pass filter (LPF) delay problem existing in the traditional signum function-based SMO, a brand new hyperbolic function is initially selected as the switching function. Because a hyperbolic function with a proper boundary layer is capable of reducing the chattering phenomenon of an SMO, it is not necessary to reemploy LPFs to eliminate the adverse impacts of chattering on the position estimation accuracy. In order to ensure the reachability and stability of the hyperbolic-function-based SMO, the observer gain is calculated by the means of a Lyapunov function in this article. Second, to solve the problem of calculation delay caused by digital computation, a current precompensation scheme based on dual-sampling strategy in one switching period is proposed. After compensating the calculation delay, the accuracy of position estimation as well as the motor control performance can be improved. Finally, the proposed SMOs with and without delay compensation are verified by both simulation and experiments that are conducted on a three-phase 1.5-kW PMSM drive prototype.

Abstract Artificial olfaction, i.e., e‐nose, plays a critical function in robotics by mimicking the human olfactory organ that can recognize different smells that correlate with a range of fields, including environment monitoring, disease diagnosis, public security affairs, agricultural production, food industry, etc. The advances in the artificial olfaction (electronic nose) technology and its applications are concisely reviewed herein. Three main elements are investigated and presented, with an emphasis on the emerging sensors and algorithm of the artificial neural network in the relevant fields. The first element is the diverse applications of e‐nose in medical care, food industry, environment monitoring, public security affairs, and agricultural production. The second element is the investigation of the sensors in e‐nose and representative and promising advances, which is the building block of e‐nose through mimicking the olfactory receptors. The third element is the introduction to the algorithm of the artificial neural network to serve in the recognition of the pattern of odors (i.e., their chemical profiles). Promises and challenges of the separately reviewed parts and the combined parts are presented and discussed. Ideas regarding further orientation and development of the e‐nose system are also considered.

Loess soil deposits are widely distributed in arid and semi-arid regions and constitute about 10% of land area of the world. These soils typically have a loose honeycomb-type meta-stable structure that is susceptible to a large reduction in total volume or collapse upon wetting. Collapse characteristics contribute to various problems to infrastructures that are constructed on loess soils. For this reason, collapse triggering mechanism for loess soils has been of significant interest for researchers and practitioners all over the world. This paper aims at providing a state-of-the-art review on collapse mechanism with special reference to loess soil deposits. The collapse mechanism studies are summarized under three different categories, i.e. traditional approaches, microstructure approach, and soil mechanics-based approaches. The traditional and microstructure approaches for interpreting the collapse behavior are comprehensively summarized and critically reviewed based on the experimental results from the literature. The soil mechanics-based approaches proposed based on the experimental results of both compacted soils and natural loess soils are reviewed highlighting their strengths and limitations for estimating the collapse behavior. Simpler soil mechanics-based approaches with less parameters or parameters that are easy-to-determine from conventional tests are suggested for future research to better understand the collapse behavior of natural loess soils. Such studies would be more valuable for use in conventional geotechnical engineering practice applications.

Highly contaminated groundwater can affect the human health and constrain the economic development of a country. For this study 105 groundwater samples were collected in rock-dominant semi-arid (RDSA) region, India and analyzed for hydrochemical parameters including major ions, fluoride, and nitrate. The human health risks due to groundwater fluoride and nitrate contamination were also assessed. The analysis reveals that most of the groundwater samples are alkaline in nature. Hydrochemical types of groundwater in the study area are mainly Ca∙Mg–HCO3 and Na–HCO3 types and a few samples belong to Ca∙Mg–Cl and Na–Cl types in the RDSA region. Fluoride concentration ranges from 0.5 to 3.5 mg/L, and predominantly eastern part of the study region has higher concentrations of fluoride with comparison to the western part. About 49% of the groundwater samples have fluoride concentration above 1.5 mg/L in groundwater, indicating a high health risk to residents. Eventually, water-rock interactions are the main processes to elevate the fluoride concentration in eastern part of the study area. Nitrate concentration ranges from 12 to 212 mg/L, and northern and southern parts of the study region have much higher nitrate concentrations than the central region of the study area, where nitrate concentration is below 45 mg/L. In terms of nitrate, 55% and 17% of the groundwater samples are under high risk and very high risk categories, respectively, which are not suitable for drinking purposes in the study region. Fertilizer application for crop yields could be one of the reasons for elevated nitrate concentration. The total hazard index for adults ranges from 0.87 to 7.08, and for children 1.17 to 9.57, which suggests that children are at higher health risk than adults in the study region. Therefore, contaminants filters and rainwater harvesting are suggested as measures to reduce the health risk in the area.

Death from Covid-19 of Health Workers in China This report describes the deaths from Covid-19 of 23 health care workers, including physicians, surgeons, a nurse, and an electrocardiography technici...

Multiple Object Tracking (MOT) plays an important role in solving many fundamental problems in video analysis and computer vision. Most MOT methods employ two steps: Object Detection and Data Association. The first step detects objects of interest in every frame of a video, and the second establishes correspondence between the detected objects in different frames to obtain their tracks. Object detection has made tremendous progress in the last few years due to deep learning. However, data association for tracking still relies on hand crafted constraints such as appearance, motion, spatial proximity, grouping etc. to compute affinities between the objects in different frames. In this paper, we harness the power of deep learning for data association in tracking by jointly modeling object appearances and their affinities between different frames in an end-to-end fashion. The proposed Deep Affinity Network (DAN) learns compact, yet comprehensive features of pre-detected objects at several levels of abstraction, and performs exhaustive pairing permutations of those features in any two frames to infer object affinities. DAN also accounts for multiple objects appearing and disappearing between video frames. We exploit the resulting efficient affinity computations to associate objects in the current frame deep into the previous frames for reliable on-line tracking. Our technique is evaluated on popular multiple object tracking challenges MOT15, MOT17 and UA-DETRAC. Comprehensive benchmarking under twelve evaluation metrics demonstrates that our approach is among the best performing techniques on the leader board for these challenges. The open source implementation of our work is available at https://github.com/shijieS/SST.git.

Periodic road crack monitoring is an essential procedure for effective pavement management. Highly efficient and accurate crack measurements are key research topics in both academia and industry. Automatic methods gradually replaced traditional manual surveys for more reliable evaluation outputs and better efficiency, whereas the devices are not available to all functional classes of pavements and different departments considering the high cost versus the limited budget. Recently, the widespread use of smartphones and digital cameras made it possible to collect pavement surface crack images at an affordable price in easier ways. However, the qualities of these crack images are diversely influenced by the noises from pavement background, roadways, and so forth. Thus, traditional methods usually fail to extract accurate crack information from pavement images. Therefore, this research proposes a state-of-the-art pixelwise crack detection architecture called CrackU-net, which is featured by its utilization of advanced deep convolutional neural network technology. CrackU-net achieved pixelwise crack detection through convolution, pooling, transpose convolution, and concatenation operations, forming the “U”-shaped model architecture. The model is trained and validated by 3,000 pavement crack images, in which 2,400 for training and 600 for validating, using the Adam algorithm. CrackU-net has the performance of loss = 0.025, accuracy = 0.9901, precision = 0.9856, recall = 0.9798, and F-measure = 0.9842 with learning rate of 10−2. Meanwhile, the false-positive crack detection problem is avoided in CrackU-net. Therefore, CrackU-net outperforms both traditional approaches and fully convolutional network (FCN) and U-net for pixelwise crack detections.

The Energy Internet has become a hot topic for the integration of sustainable energies. However, as a result, there are numerous sustainable energy forms and participants, the system is extremely complex, and some key issues are difficult to overcome, such as the control and management of distributed sustainable energy forms. On the other hand, blockchain technology consists of distributed data storage, peer-to-peer transmission, a consensus mechanism, encryption algorithms, and smart contracts. Applying the technical advantages of the blockchain to the Energy Internet can solve many of the problems that hinder its development. The purpose of this paper is to review the development of blockchain and the Energy Internet, and provide some references for the possible applications of blockchain technology to the Energy Internet. Firstly, the definition and characteristics of blockchain and the Energy Internet are introduced in detail. Secondly, the compatibility of the two is analyzed. Then, several application scenarios of blockchain in the Energy Internet are put forward. Finally, the challenges that still exist when applying the current blockchain technology to the Energy Internet are analyzed.

This research was carried out to delineate the spatial distribution of shallow groundwater quality in Yan'an City, an important cultural city on the Chinese Loess Plateau, and to estimate the health risks induced by nitrate exposure in drinking water in this city. For this study, 36 shallow groundwater samples were collected from shallow pumping wells/dug wells. The entropy-weighted water quality index (EWQI) was used to evaluate the overall groundwater quality, and the spatial distribution of the groundwater quality was discussed. The potential health hazards due to nitrate intake through drinking water were also assessed. The results of the study demonstrate that the main hydrochemical facies are governed by natural processes and anthropogenic activities. Over half of the groundwater samples require treatment of different degrees before being consumed. TH, TDS, SO42–, and NO3– are the most prevalent contaminants affecting the groundwater quality in this area. Poor-quality water is prevalent in the region along the main river. Residents in almost half of the study area face noncarcinogenic health risks with children being the more susceptible group to groundwater nitrate contamination. Reducing the concentration of NO3– in drinking water by regulating excessive fertilizer application and issuing regulations supervising the domestic sewage are important to reduce the health risks. Integrated management of groundwater and surface water as well as rainwater is recommended for the groundwater quality management.