With the popularization of the Internet, permeation of sensor networks, emergence of big data, increase in size of the information community, and interlinking and fusion of data and information throughout human society, physical space, and cyberspace, the information environment related to the current development of artificial intelligence (AI) has profoundly changed. AI faces important adjustments, and scientific foundations are confronted with new breakthroughs, as AI enters a new stage: AI 2.0. This paper briefly reviews the 60-year developmental history of AI, analyzes the external environment promoting the formation of AI 2.0 along with changes in goals, and describes both the beginning of the technology and the core idea behind AI 2.0 development. Furthermore, based on combined social demands and the information environment that exists in relation to Chinese development, suggestions on the development of AI 2.0 are given.
Intelligent manufacturing is a general concept that is under continuous development. It can be categorized into three basic paradigms: digital manufacturing, digital-networked manufacturing, and newgeneration intelligent manufacturing. New-generation intelligent manufacturing represents an indepth integration of new-generation artificial intelligence (AI) technology and advanced manufacturing technology. It runs through every link in the full life-cycle of design, production, product, and service. The concept also relates to the optimization and integration of corresponding systems; the continuous improvement of enterprises’ product quality, performance, and service levels; and reduction in resources consumption. New-generation intelligent manufacturing acts as the core driving force of the new industrial revolution and will continue to be the main pathway for the transformation and upgrading of the manufacturing industry in the decades to come. Human-cyber-physical systems (HCPSs) reveal the technological mechanisms of new-generation intelligent manufacturing and can effectively guide related theoretical research and engineering practice. Given the sequential development, cross interaction, and iterative upgrading characteristics of the three basic paradigms of intelligent manufacturing, a technology roadmap for ‘‘parallel promotion and integrated development” should be developed in order to drive forward the intelligent transformation of the manufacturing industry in China.
This paper presents findings from an investigation of the large-scale construction solid waste (CSW) landslide that occurred at a landfill at Shenzhen, Guangdong, China, on December 20, 2015, and which killed 77 people and destroyed 33 houses. The landslide involved 2.73×106 m3 of CSW and affected an area about 1100?m in length and 630?m in maximum width, making it the largest landfill landslide in the world. The investigation of this disaster used a combination of unmanned aerial vehicle surveillance and multistage remote-sensing images to reveal the increasing volume of waste in the landfill and the shifting shape of the landfill slope for nearly two years before the landslide took place, beginning with the creation of the CSW landfill in March, 2014, that resulted in the uncertain conditions of the landfill’s boundaries and the unstable state of the hydrologic performance. As a result, applying conventional stability analysis methods used for natural landslides to this case would be difficult. In order to analyze this disaster, we took a multistage modeling technique to analyze the varied characteristics of the landfill slope’s structure at various stages of CSW dumping and used the non-steady?flow?theory to explain the groundwater seepage problem. The investigation showed that the landfill could be divided into two units based on the moisture in the land: ① a front uint, consisted of the landfill slope, which had low water content; and ② a rear unit, consisted of fresh waste, which had a high water content. This structure caused two effects—surface-water infiltration and consolidation seepage that triggered the landslide in the landfill. Surface-water infiltration induced a gradual increase in pore water pressure head, or piezometric head, in the front slope because the infiltrating position rose as the volume of waste placement increased. Consolidation seepage led to higher excess pore water pressures as the loading of waste increased. We also investigated the post-failure soil dynamics parameters of the landslide deposit using cone penetration, triaxial, and ring-shear tests in order to simulate the characteristics of a flowing slide with a long run-out due to the liquefaction effect. Finally, we conclude the paper with lessons from the tens of catastrophic landslides of municipal solid waste around the world and discuss how to better manage the geotechnical risks of urbanization.
This paper summarizes the development of hydro-projects in China, blended with an international perspective. It expounds major technical progress toward ensuring the safe construction of high dams and river harnessing, and covers the theorization of uneven non-equilibrium sediment transport, inter-basin water diversion, giant hydro-generator units, pumped storage power stations, underground caverns, ecological protection, and so on.
Method development has always been and will continue to be a core driving force of microbiome science. In this perspective, we argue that in the next decade, method development in microbiome analysis will be driven by three key changes in both ways of thinking and technological platforms: ① a shift from dissecting microbiota structureby sequencing to tracking microbiota state, function, and intercellular interaction via imaging; ② a shift from interrogating a consortium or population of cells to probing individual cells; and ③ a shift from microbiome data analysis to microbiome data science. Some of the recent method-development efforts by Chinese microbiome scientists and their international collaborators that underlie these technological trends are highlighted here. It is our belief that the China Microbiome Initiative has the opportunity to deliver outstanding “Made-in-China” tools to the international research community, by building an ambitious, competitive, and collaborative program at the forefront of method development for microbiome science.
Knee osteoarthritis (OA) is the most common form of arthritis worldwide. The incidence of this disease is rising and its treatment poses an economic burden. Two early targets of knee OA treatment include the predominant symptom of pain, and cartilage damage in the knee joint. Current treatments have been beneficial in treating the disease but none is as effective as total knee arthroplasty (TKA). However, while TKA is an end-stage solution of the disease, it is an invasive and expensive procedure. Therefore, innovative regenerative engineering strategies should be established as these could defer or annul the need for a TKA. Several biomaterial and cell-based therapies are currently in development and have shown early promise in both preclinical and clinical studies. The use of advanced biomaterials and stem cells independently or in conjunction to treat knee OA could potentially reduce pain and regenerate focal articular cartilage damage. In this review, we discuss the pathogenesis of pain and cartilage damage in knee OA and explore novel treatment options currently being studied, along with some of their limitations.
Bionics (the imitation or abstraction of the “inventions of nature) and, to an even greater extent, synthetic biology, will be as relevant to engineering development and industry as the silicon chip was over the last 50 years. Chemical industries already use so-called “white biotechnology” for new processes, new raw materials, and more sustainable use of resources. Synthetic biology is also used for the development of second-generation biofuels and for harvesting the sun's energy with the help of tailor-made microorganisms or biometrically designed catalysts. The market potential for bionics in medicine, engineering processes, and DNA storage is huge. “Moonshot” projects are already aggressively focusing on diseases and new materials, and a US-led competition is currently underway with the aim of creating a thousand new molecules. This article describes a timeline that starts with current projects and then moves on to code engineering projects and their implications, artificial DNA, signaling molecules, and biological circuitry. Beyond these projects, one of the next frontiers in bionics is the design of synthetic metabolisms that include artificial food chains and foods, and the bioengineering of raw materials; all of which will lead to new insights into biological principles. Bioengineering will be an innovation motor just as digitalization is today. This article discusses pertinent examples of bioengineering, particularly the use of alternative carbon-based biofuels and the techniques and perils of cell modification. Big data, analytics, and massive storage are important factors in this next frontier. Although synthetic biology will be as pervasive and transformative in the next 50 years as digitization and the Internet are today, its applications and impacts are still in nascent stages. This article provides a general taxonomy in which the development of bioengineering is classified in five stages (DNA analysis, bio-circuits, minimal genomes, protocells, xenobiology) from the familiar to the unknown, with implications for safety and security, industrial development, and the development of bioengineering and biotechnology as an interdisciplinary field. Ethical issues and the importance of a public debate about the consequences of bionics and synthetic biology are discussed.
The stiffness and nanotopographical characteristics of the extracellular matrix (ECM) influence numerous developmental, physiological, and pathological processes in vivo. These biophysical cues have therefore been applied to modulate almost all aspects of cell behavior, from cell adhesion and spreading to proliferation and differentiation. Delineation of the biophysical modulation of cell behavior is critical to the rational design of new biomaterials, implants, and medical devices. The effects of stiffness and topographical cues on cell behavior have previously been reviewed, respectively; however, the interwoven effects of stiffness and nanotopographical cues on cell behavior have not been well described, despite similarities in phenotypic manifestations. Herein, we first review the effects of substrate stiffness and nanotopography on cell behavior, and then focus on intracellular transmission of the biophysical signals from integrins to nucleus. Attempts are made to connect extracellular regulation of cell behavior with the biophysical cues. We then discuss the challenges in dissecting the biophysical regulation of cell behavior and in translating the mechanistic understanding of these cues to tissue engineering and regenerative medicine.
Based on systematic experiments on the influence of air entrainment on rock block stability in plunge pools impacted by high-velocity jets, this study presents adaptations of a physically based scour model. The modifications regarding jet aeration are implemented in the Comprehensive Scour Model (CSM), allowing it to reproduce the physical-mechanical processes involved in scour formation concerning the three phases; namely, water, rock, and air. The enhanced method considers the reduction of momentum of an aerated jet as well as the decrease of energy dissipation in the jet diffusive shear layer, both resulting from the entrainment of air bubbles. Block ejection from the rock mass depends on a combination of the aerated time-averaged pressure coefficient and the modified maximum dynamic impulsion coefficient, which was found to be a constant value of 0.2 for high-velocity jets in deep pools. The modified model is applied to the case of the observed scour hole at the Kariba Dam, with good agreement.
Global water security is a severe issue that threatens human health and well-being. Finding sustainable alternative water resources has become a matter of great urgency. For coastal urban areas, desalinated seawater could serve as a freshwater supply. However, since 20%–30% of the water supply is used for flushing waste from the city, seawater with simple treatment could also partly replace the use of freshwater. In this work, the freshwater saving potential and environmental impacts of the urban water system (water-wastewater closed loop) adopting seawater desalination, seawater for toilet flushing (SWTF), or reclaimed water for toilet flushing (RWTF) are compared with those of a conventional freshwater system, through a life-cycle assessment and sensitivity analysis. The potential applications of these processes are also assessed. The results support the environmental sustainability of the SWTF approach, but its potential application depends on the coastal distance and effective population density of a city. Developed coastal cities with an effective population density exceeding 3000 persons·km–2 and located less than 30?km from the seashore (for the main pipe supplying seawater to the city) would benefit from applying SWTF, regardless of other impact parameters. By further applying the sulfate reduction, autotrophic denitrification, and nitrification integrated (SANI) process for wastewater treatment, the maximum distance from the seashore can be extended to 60?km. Considering that most modern urbanized cities fulfill these criteria, the next generation of water supply systems could consist of a freshwater supply coupled with a seawater supply for sustainable urban development.
Additive manufacturing (AM) permits the fabrication of functionally optimized components with high geometrical complexity. The opportunity of using porous infill as an integrated part of the manufacturing process is an example of a unique AM feature. Automated design methods are still incapable of fully exploiting this design freedom. In this work, we show how the so-called coating approach to topology optimization provides a means for designing infill-based components that possess a strongly improved buckling load and, as a result, improved structural stability. The suggested approach thereby addresses an important inadequacy of the standard minimum compliance topology optimization approach, in which buckling is rarely accounted for; rather, a satisfactory buckling load is usually assured through a post-processing step that may lead to sub-optimal components. The present work compares the standard and coating approaches to topology optimization for the MBB beam benchmark case. The optimized structures are additively manufactured using a filamentary technique. This experimental study validates the numerical model used in the coating approach. Depending on the properties of the infill material, the buckling load may be more than four times higher than that of solid structures optimized under the same conditions.
The first author proposed the concept of the cemented material dam (CMD) in 2009. This concept was aimed at building an environmentally friendly dam in a safer and more economical way for both the dam and the area downstream. The concept covers the cemented sand, gravel, and rock dam (CSGRD), the rockfill concrete (RFC) dam (or the cemented rockfill dam, CRD), and the cemented soil dam (CSD). This paper summarizes the concept and principles of the CMD based on studies and practices in projects around the world. It also introduces new developments in the CSGRD, CRD, and CSD.
This study provides a definition for urban big data while exploring its features and applications of China’s city intelligence. The differences between city intelligence in China and the “smart city” concept in other countries are compared to highlight and contrast the unique definition and model for China’s city intelligence in this paper. Furthermore, this paper examines the role of urban big data in city intelligence by showing that it not only serves as the cornerstone of this trend as it also plays a core role in the diffusion of city intelligence technology and serves as an inexhaustible resource for the sustained development of city intelligence. This study also points out the challenges of shaping and developing of China’s urban big data. Considering the supporting and core role that urban big data plays in city intelligence, the study then expounds on the key points of urban big data, including infrastructure support, urban governance, public services, and economic and industrial development. Finally, this study points out that the utility of city intelligence as an ideal policy tool for advancing the goals of China’s urban development. In conclusion, it is imperative that China make full use of its unique advantages—including using the nation’s current state of development and resources, geographical advantages, and good human relations—in subjective and objective conditions to promote the development of city intelligence through the proper application of urban big data.
The emerging prototype for a Smart City is one of an urban environment with a new generation of innovative services for transportation, energy distribution, healthcare, environmental monitoring, business, commerce, emergency response, and social activities. Enabling the technology for such a setting requires a viewpoint of Smart Cities as cyber-physical systems (CPSs) that include new software platforms and strict requirements for mobility, security, safety, privacy, and the processing of massive amounts of information. This paper identifies some key defining characteristics of a Smart City, discusses some lessons learned from viewing them as CPSs, and outlines some fundamental research issues that remain largely open.
Given the significant requirements for transforming and promoting the process industry, we present the major limitations of current petrochemical enterprises, including limitations in decision-making, production operation, efficiency and security, information integration, and so forth. To promote a vision of the process industry with efficient, green, and smart production, modern information technology should be utilized throughout the entire optimization process for production, management, and marketing. To focus on smart equipment in manufacturing processes, as well as on the adaptive intelligent optimization of the manufacturing process, operating mode, and supply chain management, we put forward several key scientific problems in engineering in a demand-driven and application-oriented manner, namely: ① intelligent sensing and integration of all process information, including production and management information; ② collaborative decision-making in the supply chain, industry chain, and value chain, driven by knowledge; ③ cooperative control and optimization of plant-wide production processes via human-cyber-physical interaction; and ④life-cycle assessments for safety and environmental footprint monitoring, in addition to tracing analysis and risk control. In order to solve these limitations and core scientific problems, we further present fundamental theories and key technologies for smart and optimal manufacturing in the process industry. Although this paper discusses the process industry in China, the conclusions in this paper can be extended to the process industry around the world.
Coal is the dominant primary energy source in China and the major source of greenhouse gases and air pollutants. To facilitate the use of coal in an environmentally satisfactory and economically viable way, clean coal technologies (CCTs) are necessary. This paper presents a review of recent research and development of four kinds of CCTs: coal power generation; coal conversion; pollution control; and carbon capture, utilization, and storage. It also outlines future perspectives on directions for technology research and development (R&D). This review shows that China has made remarkable progress in the R&D of CCTs, and that a number of CCTs have now entered into the commercialization stage.
Various technologies have recently been developed for high-speed railways, in order to boost commercial speeds from 300 km·h−1to 400 km·h−1. Among these technologies, this paper introduces the 400 km·h−1 class current collection performance evaluation methods that have been developed and demonstrated by Korea. Specifically, this paper reports details of the video-based monitoring techniques that have been adopted to inspect the stability of overhead contact line (OCL) components at 400 km·h−1 without direct contact with any components of the power supply system. Unlike conventional OCL monitoring systems, which detect contact wire positions using either laser sensors or line cameras, the developed system measures parameters in the active state by video data. According to experimental results that were obtained at a field-test site established at a commercial line, it is claimed that the proposed measurement system is capable of effectively measuring OCL parameters.
The rise of big data has led to new demands for machine learning (ML) systems to learn complex models, with millions to billions of parameters, that promise adequate capacity to digest massive datasets and offer powerful predictive analytics (such as high-dimensional latent features, intermediate representations, and decision functions) thereupon. In order to run ML algorithms at such scales, on a distributed cluster with tens to thousands of machines, it is often the case that significant engineering efforts are required—and one might fairly ask whether such engineering truly falls within the domain of ML research. Taking the view that “big” ML systems can benefit greatly from ML-rooted statistical and algorithmic insights—and that ML researchers should therefore not shy away from such systems design—we discuss a series of principles and strategies distilled from our recent efforts on industrial-scale ML solutions. These principles and strategies span a continuum from application, to engineering, and to theoretical research and development of big ML systems and architectures, with the goal of understanding how to make them efficient, generally applicable, and supported with convergence and scaling guarantees. They concern four key questions that traditionally receive little attention in ML research: How can an ML program be distributed over a cluster? How can ML computation be bridged with inter-machine communication? How can such communication be performed? What should be communicated between machines? By exposing underlying statistical and algorithmic characteristics unique to ML programs but not typically seen in traditional computer programs, and by dissecting successful cases to reveal how we have harnessed these principles to design and develop both high-performance distributed ML software as well as general-purpose ML frameworks, we present opportunities for ML researchers and practitioners to further shape and enlarge the area that lies between ML and systems.
This article provides in-depth insights into the necessary technologies for automated driving in future cities. State of science is reflected from different perspectives such as in-car computing and data management, road side infrastructure, and cloud solutions. Especially the challenges for the application of HD maps as core technology for automated driving are depicted in this article.
Our next generation of industry—Industry 4.0—holds the promise of increased flexibility in manufacturing, along with mass customization, better quality, and improved productivity. It thus enables companies to cope with the challenges of producing increasingly individualized products with a short lead-time to market and higher quality. Intelligent manufacturing plays an important role in Industry 4.0. Typical resources are converted into intelligent objects so that they are able to sense, act, and behave within a smart environment. In order to fully understand intelligent manufacturing in the context of Industry 4.0, this paper provides a comprehensive review of associated topics such as intelligent manufacturing, Internet of Things (IoT)-enabled manufacturing, and cloud manufacturing. Similarities and differences in these topics are highlighted based on our analysis. We also review key technologies such as the IoT, cyber-physical systems (CPSs), cloud computing, big data analytics (BDA), and information and communications technology (ICT) that are used to enable intelligent manufacturing. Next, we describe worldwide movements in intelligent manufacturing, including governmental strategic plans from different countries and strategic plans from major international companies in the European Union, United States, Japan, and China. Finally, we present current challenges and future research directions. The concepts discussed in this paper will spark new ideas in the effort to realize the much-anticipated Fourth Industrial Revolution.
Municipal wastewater treatment has long been known as a high-cost and energy-intensive process that destroys most of the energy-containing molecules by spending energy and that leaves little energy and few nutrients available for reuse. Over the past few years, some wastewater treatment plants have tried to revamp themselves as “resource factories,” enabled by new technologies and the upgrading of old technologies. In particular, there is an renewed interest in anaerobic biotechnologies, which can convert organic matter into usable energy and preserve nutrients for potential reuse. However, considerable technological and economic limitations still exist. Here, we provide an overview of recent advances in several cutting-edge anaerobic biotechnologies for wastewater treatment, including enhanced side-stream anaerobic sludge digestion, anaerobic membrane bioreactors, and microbial electrochemical systems, and discuss future challenges and opportunities for their applications. This review is intended to provide useful information to guide the future design and optimization of municipal wastewater treatment processes.
Tissue engineering is a relatively new but rapidly developing field in the medical sciences. Noncoding RNAs (ncRNAs) are functional RNA molecules without a protein-coding function; they can regulate cellular behavior and change the biological milieu of the tissue. The application of ncRNAs in tissue engineering is starting to attract increasing attention as a means of resolving a large number of unmet healthcare needs, although ncRNA-based approaches have not yet entered clinical practice. In-depth research on the regulation and delivery of ncRNAs may improve their application in tissue engineering. The aim of this review is: to outline essential ncRNAs that are related to tissue engineering for the repair and regeneration of nerve, skin, liver, vascular system, and muscle tissue; to discuss their regulation and delivery; and to anticipate their potential therapeutic applications.
To date, the Three Gorges Project is the largest hydro junction in the world. It is the key project for the integrated water resource management and development of the Changjiang River. The technology of the project, with its huge scale and comprehensive benefits, is extremely complicated, and the design difficulty is greater than that of any other hydro project in the world. A series of new design theories and methods have been proposed and applied in the design and research process. Many key technological problems regarding hydraulic structures have been overcome, such as a gravity dam with multi-layer large discharge orifices, a hydropower station of giant generating units, and a giant continual multi-step ship lock with a high water head.
In recent years, risk analysis techniques have proved to be a useful tool to inform dam safety management. This paper summarizes the outcomes of three themes related to dam risk analysis discussed in the Benchmark Workshops organized by the International Commission on Large Dams Technical Committee on “Computational Aspects of Analysis and Design of Dams.” In the 2011 Benchmark Workshop, estimation of the probability of failure of a gravity dam for the sliding failure mode was discussed. Next, in 2013, the discussion focused on the computational challenges of the estimation of consequences in dam risk analysis. Finally, in 2015, the probability of sliding and overtopping in an embankment was analyzed. These Benchmark Workshops have allowed a complete review of numerical aspects for dam risk analysis, showing that risk analysis methods are a very useful tool to analyze the risk of dam systems, including downstream consequence assessments and the uncertainty of structural models.