In this paper, we review the current state-of-the-art techniques used for understanding the inner workings of the brain at a systems level. The neural activity that governs our everyday lives involves an intricate coordination of many processes that can be attributed to a variety of brain regions. On the surface, many of these functions can appear to be controlled by specific anatomical structures; however, in reality, numerous dynamic networks within the brain contribute to its function through an interconnected web of neuronal and synaptic pathways. The brain, in its healthy or pathological state, can therefore be best understood by taking a systems-level approach. While numerous neuroengineering technologies exist, we focus here on three major thrusts in the field of systems neuroengineering: neuroimaging, neural interfacing, and neuromodulation. Neuroimaging enables us to delineate the structural and functional organization of the brain, which is key in understanding how the neural system functions in both normal and disease states. Based on such knowledge, devices can be used either to communicate with the neural system, as in neural interface systems, or to modulate brain activity, as in neuromodulation systems. The consideration of these three fields is key to the development and application of neuro-devices. Feedback-based neuro-devices require the ability to sense neural activity (via a neuroimaging modality) through a neural interface (invasive or noninvasive) and ultimately to select a set of stimulation parameters in order to alter neural function via a neuromodulation modality. Systems neuroengineering refers to the use of engineering tools and technologies to image, decode, and modulate the brain in order to comprehend its functions and to repair its dysfunction. Interactions between these fields will help to shape the future of systems neuroengineering—to develop neurotechniques for enhancing the understanding of whole-brain function and dysfunction, and the management of neurological and mental disorders.
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.
An increased global supply of minerals is essential to meet the needs and expectations of a rapidly rising world population. This implies extraction from greater depths. Autonomous mining systems, developed through sustained R&D by equipment suppliers, reduce miner exposure to hostile work environments and increase safety. This places increased focus on “ground control” and on rock mechanics to define the depth to which minerals may be extracted economically. Although significant efforts have been made since the end of World War II to apply mechanics to mine design, there have been both technological and organizational obstacles. Rock in situ is a more complex engineering material than is typically encountered in most other engineering disciplines. Mining engineering has relied heavily on empirical procedures in design for thousands of years. These are no longer adequate to address the challenges of the 21st century, as mines venture to increasingly greater depths. The development of the synthetic rock mass (SRM) in 2008 provides researchers with the ability to analyze the deformational behavior of rock masses that are anisotropic and discontinuous—attributes that were described as the defining characteristics of in situ rock by Leopold Müller, the president and founder of the International Society for Rock Mechanics (ISRM), in 1966. Recent developments in the numerical modeling of large-scale mining operations (e.g., caving) using the SRM reveal unanticipated deformational behavior of the rock. The application of massive parallelization and cloud computational techniques offers major opportunities: for example, to assess uncertainties in numerical predictions; to establish the mechanics basis for the empirical rules now used in rock engineering and their validity for the prediction of rock mass behavior beyond current experience; and to use the discrete element method (DEM) in the optimization of deep mine design. For the first time, mining—and rock engineering—will have its own mechanics-based “laboratory.” This promises to be a major tool in future planning for effective mining at depth. The paper concludes with a discussion of an opportunity to demonstrate the application of DEM and SRM procedures as a laboratory, by back-analysis of mining methods used over the 80-year history of the Mount Lyell Copper Mine in Tasmania.
In the last five years, China has seen the technological development of intelligent mining and the application of the longwall automation technology developed by the Longwall Automation Steering Committee. This paper summarizes this great achievement, which occurred during the 12th Five-Year Plan (2011–2015), and which included the development of a set of intelligent equipment for hydraulic-powered supports, information transfers, dynamic decision-making, performance coordination, and the achievement of a high level of reliability despite difficult conditions. Within China, the intelligent system of a set of hydraulic-powered supports was completed, with our own intellectual property rights. An intelligent mining model was developed that permitted unmanned operation and single-person inspection on the work face. With these technologies, the number of miners on the work face can now be significantly reduced. Miners are only required to monitor mining machines on the roadway or at the surface control center, since intelligent mining can be applied to extract middle-thick or thick coal seams. As a result, miners’ safety has been improved. Finally, this paper discusses the prospects and challenges of intelligent mining over the next ten years.
Molecular imaging (MI) can provide not only structural images using traditional imaging techniques but also functional and molecular information using many newly emerging imaging techniques. Over the past decade, the utilization of nanotechnology in MI has exhibited many significant advantages and provided new opportunities for the imaging of living subjects. It is expected that multimodality nanoparticles (NPs) can lead to precise assessment of tumor biology and the tumor microenvironment. This review addresses topics related to engineered NPs and summarizes the recent applications of these nanoconstructs in cancer optical imaging, ultrasound, photoacoustic imaging, magnetic resonance imaging (MRI), and radionuclide imaging. Key challenges involved in the translation of NPs to the clinic are discussed.
A high-throughput multi-plume pulsed-laser deposition (MPPLD) system has been demonstrated and compared to previous techniques. Whereas most combinatorial pulsed-laser deposition (PLD) systems have focused on achieving thickness uniformity using sequential multilayer deposition and masking followed by post-deposition annealing, MPPLD directly deposits a compositionally varied library of compounds using the directionality of PLD plumes and the resulting spatial variations of deposition rate. This system is more suitable for high-throughput compound thin-film fabrication.
Materials-development projects for advanced ultra-supercritical (A-USC) power plants with steam temperatures of 700 °C and above have been performed in order to achieve high efficiency and low CO2 emissions in Europe, the US, Japan, and recently in China and India as well. These projects involve the replacement of martensitic 9%−12% Cr steels with nickel (Ni)-base alloys for the highest temperature boiler and turbine components in order to provide sufficient creep strength at 700°C and above. To minimize the requirement for expensive Ni-base alloys, martensitic 9%−12% Cr steels can be applied to the next highest temperature components of an A-USC power plant, up to a maximum of 650°C. This paper comprehensively describes the research and development of Ni-base alloys and martensitic 9%−12% Cr steels for thick section boiler and turbine components of A-USC power plants, mainly focusing on the long-term creep-rupture strength of base metal and welded joints, strength loss in welded joints, creep-fatigue properties, and microstructure evolution during exposure at elevated temperatures.
Cellular spheroids serving as three-dimensional (3D) in vitro tissue models have attracted increasing interest for pathological study and drug-screening applications. Various methods, including microwells in particular, have been developed for engineering cellular spheroids. However, these methods usually suffer from either destructive molding operations or cell loss and non-uniform cell distribution among the wells due to two-step molding and cell seeding. We have developed a facile method that utilizes cell-embedded hydrogel arrays as templates for concave well fabrication and in situ MCF-7 cellular spheroid formation on a chip. A custom-built bioprinting system was applied for the fabrication of sacrificial gelatin arrays and sequentially concave wells in a high-throughput, flexible, and controlled manner. The ability to achieve in situ cell seeding for cellular spheroid construction was demonstrated with the advantage of uniform cell seeding and the potential for programmed fabrication of tissue models on chips. The developed method holds great potential for applications in tissue engineering, regenerative medicine, and drug screening.
In 2005, the US passed the Energy Policy Act of 2005 mandating the construction and operation of a high-temperature gas reactor (HTGR) by 2021. This law was passed after a multiyear study by national experts on what future nuclear technologies should be developed. As a result of the Act, the US Congress chose to develop the so-called Next-Generation Nuclear Plant, which was to be an HTGR designed to produce process heat for hydrogen production. Despite high hopes and expectations, the current status is that high temperature reactors have been relegated to completing research programs on advanced fuels, graphite and materials with no plans to build a demonstration plant as required by the US Congress in 2005. There are many reasons behind this diminution of HTGR development, including but not limited to insufficient government funding requirements for research, unrealistically high temperature requirements for the reactor, the delay in the need for a “hydrogen” economy, competition from light water small modular light water reactors, little utility interest in new technologies, very low natural gas prices in the US, and a challenging licensing process in the US for non-water reactors.
Systems for ambient assisted living (AAL) that integrate service robots with sensor networks and user monitoring can help elderly people with their daily activities, allowing them to stay in their homes and live active lives for as long as possible. In this paper, we outline the AAL system currently developed in the European project Robot-Era, and describe the engineering aspects and the service-oriented software architecture of the domestic robot, a service robot with advanced manipulation capabilities. Based on the robot operating system (ROS) middleware, our software integrates a large set of advanced algorithms for navigation, perception, and manipulation. In tests with real end users, the performance and acceptability of the platform are evaluated.
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.
Fischer-Tropsch synthesis (FTS) is an increasingly important approach for producing liquid fuels and chemicals via syngas—that is, synthesis gas, a mixture of carbon monoxide and hydrogen—generated from coal, natural gas, or biomass. In FTS, dispersed transition metal nanoparticles are used to catalyze the reactions underlying the formation of carbon-carbon bonds. Catalytic activity and selectivity are strongly correlated with the electronic and geometric structure of the nanoparticles, which depend on the particle size, morphology, and crystallographic phase of the nanoparticles. In this article, we review recent works dealing with the aspects of bulk and surface sensitivity of the FTS reaction. Understanding the different catalytic behavior in more detail as a function of these parameters may guide the design of more active, selective, and stable FTS catalysts.
This paper reviews the recent achievements made by our team in the mitigation of rockburst risk. It includes the development of neural network modeling on rockburst risk assessment for deep gold mines in South Africa, an intelligent microseismicity monitoring system and sensors, an understanding of the rockburst evolution process using laboratory and in situ tests and monitoring, the establishment of a quantitative warning method for the location and intensities of different types of rockburst, and the development of measures for the dynamic control of rockburst. The mitigation of rockburst at the Hongtoushan copper mine is presented as an illustrative example.
The emergence of multidrug-resistant bacteria creates an urgent need for alternative antibiotics with new mechanisms of action. In this study, we synthesized a novel type of antimicrobial agent, Acr3-NLS, by conjugating hydrophobic acridines to the N-terminus of a nuclear localization sequence (NLS), a short cationic peptide. To further improve the antimicrobial activity of our agent, dimeric (Acr3-NLS)2 was simultaneously synthesized by joining two monomeric Acr3-NLS together via a disulfide linker. Our results show that Acr3-NLS and especially (Acr3-NLS)2 display significant antimicrobial activity against gram-negative and gram-positive bacteria compared to that of the NLS. Subsequently, the results derived from the study on the mechanism of action demonstrate that Acr3-NLS and (Acr3-NLS)2 can kill bacteria by membrane disruption and DNA binding. The double targets–cell membrane and intracellular DNA–will reduce the risk of bacteria developing resistance to Acr3-NLS and (Acr3-NLS)2. Overall, this study provides a novel strategy to design highly effective antimicrobial agents with a dual mode of action for infection treatment.
This paper reviews the development history of alkali element doping on Cu(In,Ga)Se2 (CIGS) solar cells and summarizes important achievements that have been made in this field. The influences of incorporation strategies on CIGS absorbers and device performances are also reviewed. By analyzing CIGS surface structure and electronic property variation induced by alkali fluoride (NaF and KF) post-deposition treatment (PDT), we discuss and interpret the following issues: ① The delamination of CIGS thin films induced by Na incorporation facilitates CuInSe2 formation and inhibits Ga during low-temperature co-evaporation processes. ② The mechanisms of carrier density increase due to defect passivation by Na at grain boundaries and the surface. ③ A thinner buffer layer improves the short-circuit current without open-circuit voltage loss. This is attributed not only to better buffer layer coverage in the early stage of the chemical bath deposition process, but also to higher donor defect (CdCu+) density, which is transferred from the acceptor defect (VCu−) and strengthens the buried homojunction. ④ The KF-PDT-induced lower valence band maximum at the absorber surface reduces the recombination at the absorber/buffer interface, which improves the open-circuit voltage and the fill factor of solar cells.
The Made in China 2025 initiative will require full automation in all sectors, from customers to production. This will result in great challenges to manufacturing systems in all sectors. In the future of manufacturing, all devices and systems should have sensing and basic intelligence capabilities for control and adaptation. In this study, after discussing multiscale dynamics of the modern manufacturing system, a five-layer functional structure is proposed for uncertainties processing. Multiscale dynamics include: multi-time scale, space-time scale, and multi-level dynamics. Control action will differ at different scales, with more design being required at both fast and slow time scales. More quantitative action is required in low-level operations, while more qualitative action is needed regarding high-level supervision. Intelligent manufacturing systems should have the capabilities of flexibility, adaptability, and intelligence. These capabilities will require the control action to be distributed and integrated with different approaches, including smart sensing, optimal design, and intelligent learning. Finally, a typical jet dispensing system is taken as a real-world example for multiscale modeling and control.
Additive manufacturing and 3D printing technology have been developing rapidly in the last 30 years, and indicate great potential for future development. The promising future of this technology makes its impact on traditional industry unpredictable. 3D printing will propel the revolution of fabrication modes forward, and bring in a new era for customized fabrication by realizing the five “any”s: use of almost any material to fabricate any part, in any quantity and any location, for any industrial field. Innovations in material, design, and fabrication processes will be inspired by the merging of 3D-printing technology and processes with traditional manufacturing processes. Finally, 3D printing will become as valuable for manufacturing industries as equivalent and subtractive manufacturing processes.
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.
A growing number of?three-dimensional (3D)-print-ing processes have been applied to tissue engineering. This paper presents a state-of-the-art study of 3D-printing technologies?for tissue-engineering applications, with particular focus on the development of a computer-aided scaffold design system; the direct 3D printing of functionally graded scaffolds; the modeling of selective laser sintering (SLS) and fused deposition modeling (FDM) processes; the indirect additive manufacturing of scaffolds, with both micro and macro features; the development of a bioreactor; and 3D/4D bioprinting. Technological limitations will be discussed so as to highlight the possibility of future improvements for new 3D-printing methodologies for tissue engineering.
In this paper, progresses of color maintenance, also known as color shift, in solid-state lighting (SSL) systems are thoroughly reviewed. First, color shift is introduced and a few examples are given from different real-life industrial conditions. Different degradation mechanisms in different parts of the system are also explained. Different materials used as lenses/encapsulants in light-emitting diode (LED)-based products are introduced and their contributions to color shift are discussed. Efforts put into standardization, characterizing, and predicting lumen maintenance are also briefly reviewed in this paper.
Electron beam selective melting (EBSM) is an additive manufacturing technique that directly fabricates three-dimensional parts in a layerwise fashion by using an electron beam to scan and melt metal powder. In recent years, EBSM has been successfully used in the additive manufacturing of a variety of materials. Previous research focused on the EBSM process of a single material. In this study, a novel EBSM process capable of building a gradient structure with dual metal materials was developed, and a powder-supplying method based on vibration was put forward. Two different powders can be supplied individually and then mixed. Two materials were used in this study: Ti6Al4V powder and Ti47Al2Cr2Nb powder. Ti6Al4V has excellent strength and plasticity at room temperature, while Ti47Al2Cr2Nb has excellent performance at high temperature, but is very brittle. A Ti6Al4V/Ti47Al2Cr2Nb gradient material was successfully fabricated by the developed system. The microstructures and chemical compositions were characterized by optical microscopy, scanning microscopy, and electron microprobe analysis. Results showed that the interface thickness was about 300 μm. The interface was free of cracks, and the chemical compositions exhibited a staircase-like change within the interface.
Rockburst is an important phenomenon that has affected many deep underground mines around the world. An understanding of this phenomenon is relevant to the management of such events, which can lead to saving both costs and lives. Laboratory experiments are one way to obtain a deeper and better understanding of the mechanisms of rockburst. In a previous study by these authors, a database of rockburst laboratory tests was created; in addition, with the use of data mining (DM) techniques, models to predict rockburst maximum stress and rockburst risk indexes were developed. In this paper, we focus on the analysis of a database of in situ cases of rockburst in order to build influence diagrams, list the factors that interact in the occurrence of rockburst, and understand the relationships between these factors. The in situ rockburst database was further analyzed using different DM techniques ranging from artificial neural networks (ANNs) to naive Bayesian classifiers. The aim was to predict the type of rockburst—that is, the rockburst level—based on geologic and construction characteristics of the mine or tunnel. Conclusions are drawn at the end of the paper.