The real-time reverse transcription-polymerase chain reaction (RT-PCR) detection of viral RNA from sputum or nasopharyngeal swab had a relatively low positive rate in the early stage of coronavirus disease 2019 (COVID-19). Meanwhile, the manifestations of COVID-19 as seen through computed tomography (CT) imaging show individual characteristics that differ from those of other types of viral pneumonia such as influenza-A viral pneumonia (IAVP). This study aimed to establish an early screening model to distinguish COVID-19 pneumonia from IAVP and healthy cases through pulmonary CT images using deep learning techniques. A total of 618 CT samples were collected: 219 samples from 110 patients with COVID-19 (mean age 50 years; 63 (57.3%) male patients); 224 samples from 224 patients with IAVP (mean age 61 years; 156 (69.6%) male patients); and 175 samples from 175 healthy cases (mean age 39 years; 97 (55.4%) male patients). All CT samples were contributed from three COVID-19-designated hospitals in Zhejiang Province, China. First, the candidate infection regions were segmented out from the pulmonary CT image set using a 3D deep learning model. These separated images were then categorized into the COVID-19, IAVP, and irrelevant to infection (ITI) groups, together with the corresponding confidence scores, using a location-attention classification model. Finally, the infection type and overall confidence score for each CT case were calculated using the Noisy-OR Bayesian function. The experimental result of the benchmark dataset showed that the overall accuracy rate was 86.7% in terms of all the CT cases taken together. The deep learning models established in this study were effective for the early screening of COVID-19 patients and were demonstrated to be a promising supplementary diagnostic method for frontline clinical doctors.

An outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and its caused coronavirus disease 2019 (COVID-19) have been reported in China since December 2019. More than 16% of patients developed acute respiratory distress syndrome, and the fatality ratio was about 1%–2%. No specific treatment has been reported. Herein, we examined the effects of Favipiravir (FPV) versus Lopinavir (LPV)/ritonavir (RTV) for the treatment of COVID-19. Patients with laboratory-confirmed COVID-19 who received oral FPV (Day 1: 1600 mg twice daily; Days 2–14: 600 mg twice daily) plus interferon (IFN)-α by aerosol inhalation (5 million U twice daily) were included in the FPV arm of this study, whereas patients who were treated with LPV/RTV (Days 1–14: 400 mg/100 mg twice daily) plus IFN-α by aerosol inhalation (5 million U twice daily) were included in the control arm. Changes in chest computed tomography (CT), viral clearance, and drug safety were compared between the two groups. For the 35 patients enrolled in the FPV arm and the 45 patients in the control arm, all baseline characteristics were comparable between the two arms. A shorter viral clearance time was found for the FPV arm versus the control arm (median (interquartile range, IQR), 4 (2.5–9) d versus 11 (8–13) d, P < 0.001). The FPV arm also showed significant improvement in chest imaging compared with the control arm, with an improvement rate of 91.43% versus 62.22% (P = 0.004). After adjustment for potential confounders, the FPV arm also showed a significantly higher improvement rate in chest imaging. Multivariable Cox regression showed that FPV was independently associated with faster viral clearance. In addition, fewer adverse events were found in the FPV arm than in the control arm. In this open-label before-after controlled study, FPV showed better therapeutic responses on COVID-19 in terms of disease progression and viral clearance. These preliminary clinical results provide useful information of treatments for SARS-CoV-2 infection.

Photosynthetic microorganisms are important bioresources for producing desirable and environmentally benign products, and photobioreactors (PBRs) play important roles in these processes. Designing PBRs for photocatalysis is still challenging at present, and most reactors are designed and scaled up using semi-empirical approaches. No appropriate types of PBRs are available for mass cultivation due to the reactors’ high capital and operating costs and short lifespan, which are mainly due to a current lack of deep understanding of the coupling of light, hydrodynamics, mass transfer, and cell growth in efficient reactor design. This review provides a critical overview of the key parameters that influence the performance of the PBRs, including light, mixing, mass transfer, temperature, pH, and capital and operating costs. The lifespan and the costs of cleaning and temperature control are also emphasized for commercial exploitation. Four types of PBRs—tubular, plastic bag, column airlift, and flat-panel airlift reactors are recommended for large-scale operations. In addition, this paper elaborates the modeling of PBRs using the tools of computational fluid dynamics for rational design. It also analyzes the difficulties in the numerical simulation, and presents the prospect for mechanism-based models.

The rapid development of additive manufacturing and advances in shape memory materials have fueled the progress of four-dimensional (4D) printing. With the right external stimulus, the need for human interaction, sensors, and batteries will be eliminated, and by using additive manufacturing, more complex devices and parts can be produced. With the current understanding of shape memory mechanisms and with improved design for additive manufacturing, reversibility in 4D printing has recently been proven to be feasible. Conventional one-way 4D printing requires human interaction in the programming (or shape-setting) phase, but reversible 4D printing, or two-way 4D printing, will fully eliminate the need for human interference, as the programming stage is replaced with another stimulus. This allows reversible 4D printed parts to be fully dependent on external stimuli; parts can also be potentially reused after every recovery, or even used in continuous cycles—an aspect that carries industrial appeal. This paper presents a review on the mechanisms of shape memory materials that have led to 4D printing, current findings regarding 4D printing in alloys and polymers, and their respective limitations. The reversibility of shape memory materials and their feasibility to be fabricated using three-dimensional (3D) printing are summarized and critically analyzed. For reversible 4D printing, the methods of 3D printing, mechanisms used for actuation, and strategies to achieve reversibility are also highlighted. Finally, prospective future research directions in reversible 4D printing are suggested.

An intelligent manufacturing system is a composite intelligent system comprising humans, cyber systems, and physical systems with the aim of achieving specific manufacturing goals at an optimized level. This kind of intelligent system is called a human–cyber–physical system (HCPS). In terms of technology, HCPSs can both reveal technological principles and form the technological architecture for intelligent manufacturing. It can be concluded that the essence of intelligent manufacturing is to design, construct, and apply HCPSs in various cases and at different levels. With advances in information technology, intelligent manufacturing has passed through the stages of digital manufacturing and digital-networked manufacturing, and is evolving toward new-generation intelligent manufacturing (NGIM). NGIM is characterized by the in-depth integration of new-generation artificial intelligence (AI) technology (i.e., enabling technology) with advanced manufacturing technology (i.e., root technology); it is the core driving force of the new industrial revolution. In this study, the evolutionary footprint of intelligent manufacturing is reviewed from the perspective of HCPSs, and the implications, characteristics, technical frame, and key technologies of HCPSs for NGIM are then discussed in depth. Finally, an outlook of the major challenges of HCPSs for NGIM is proposed.

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.

In ethnopharmacology, and especially in traditional Chinese medicine, medicinal plants have been used for thousands of years. Similarly, agricultural plants have been used throughout the history of mankind. The recent development of the genetic engineering of plants to produce plants with desirable features adds a new and growing dimension to humanity’s usage of plants. The biotechnology of plants has come of age and a plethora of bioengineering applications in this context have been delineated during the past few decades. Callus cultures and suspension cell cultures offer a wide range of usages in pharmacology and pharmacy (including Chinese medicine), as well as in agriculture and horticulture. This review provides a timely overview of the advancements that have been made with callus cultures in these scientific fields. Genetically modified callus cultures by gene technological techniques can be used for the synthesis of bioactive secondary metabolites and for the generation of plants with improved resistance against salt, draft, diseases, and pests. Although the full potential of callus plant culture technology has not yet been exploited, the time has come to develop and market more callus culture-based products.

In order to build a ceramic component by inkjet printing, the object must be fabricated through the interaction and solidification of drops, typically in the range of 10−100 pL. In order to achieve this goal, stable ceramic inks must be developed. These inks should satisfy specific rheological conditions that can be illustrated within a parameter space defined by the Reynolds and Weber numbers. Printed drops initially deform on impact with a surface by dynamic dissipative processes, but then spread to an equilibrium shape defined by capillarity. We can identify the processes by which these drops interact to form linear features during printing, but there is a poorer level of understanding as to how 2D and 3D structures form. The stability of 2D sheets of ink appears to be possible over a more limited range of process conditions that is seen with the formation of lines. In most cases, the ink solidifies through evaporation and there is a need to control the drying process to eliminate the: “coffee ring” defect. Despite these uncertainties, there have been a large number of reports on the successful use of inkjet printing for the manufacture of small ceramic components from a number of different ceramics. This technique offers good prospects as a future manufacturing technique. This review identifies potential areas for future research to improve our understanding of this manufacturing method.

With the rapid developments of artificial intelligence (AI) and deep learning (DL) techniques, it is critical
to ensure the security and robustness of the deployed algorithms. Recently, the security vulnerability of
DL algorithms to adversarial samples has been widely recognized. The fabricated samples can lead to various
misbehaviors of the DL models while being perceived as benign by humans. Successful implementations
of adversarial attacks in real physical-world scenarios further demonstrate their practicality.
Hence, adversarial attack and defense techniques have attracted increasing attention from both machine
learning and security communities and have become a hot research topic in recent years. In this paper,
we first introduce the theoretical foundations, algorithms, and applications of adversarial attack techniques.
We then describe a few research efforts on the defense techniques, which cover the broad frontier
in the field. Several open problems and challenges are subsequently discussed, which we hope will provoke
further research efforts in this critical area.

Additive manufacturing (AM), also known as 3D printing, is gaining increasing attention from academia and industry due to the unique advantages it has in comparison with traditional subtractive manufacturing. However, AM processing parameters are difficult to tune, since they can exert a huge impact on the printed microstructure and on the performance of the subsequent products. It is a difficult task to build a process–structure–property–performance (PSPP) relationship for AM using traditional numerical and analytical models. Today, the machine learning (ML) method has been demonstrated to be a valid way to perform complex pattern recognition and regression analysis without an explicit need to construct and solve the underlying physical models. Among ML algorithms, the neural network (NN) is the most widely used model due to the large dataset that is currently available, strong computational power, and sophisticated algorithm architecture. This paper overviews the progress of applying the NN algorithm to several aspects of the AM whole chain, including model design, in situ monitoring, and quality evaluation. Current challenges in applying NNs to AM and potential solutions for these problems are then outlined. Finally, future trends are proposed in order to provide an overall discussion of this interdisciplinary area.

After the first concrete was poured on December 9, 2012 at the Shidao Bay site in Rongcheng, Shandong Province, China, the construction of the reactor building for the world’s first high-temperature gas-cooled reactor pebble-bed module (HTR-PM) demonstration power plant was completed in June, 2015. Installation of the main equipment then began, and the power plant is currently progressing well toward connecting to the grid at the end of 2017. The thermal power of a single HTR-PM reactor module is 250 MW_th, the helium temperatures at the reactor core inlet/outlet are 250/750 °C, and a steam of 13.25 MPa/567 °C is produced at the steam generator outlet. Two HTR-PM reactor modules are connected to a steam turbine to form a 210 MW_e nuclear power plant. Due to China’s industrial capability, we were able to overcome great difficulties, manufacture first-of-a-kind equipment, and realize series major technological innovations. We have achieved successful results in many aspects, including planning and implementing R&D, establishing an industrial partnership, manufacturing equipment, fuel production, licensing, site preparation, and balancing safety and economics; these obtained experiences may also be referenced by the global nuclear community.

Disc cutter consumption is a critical problem that influences work performance during shield tunneling processes and directly affects the cutter change decision. This study proposes a new model to estimate the disc cutter life (H_f) by integrating a group method of data handling (GMDH)-type neural network (NN) with a genetic algorithm (GA). The efficiency and effectiveness of the GMDH network structure are optimized by the GA, which enables each neuron to search for its optimum connections set from the previous layer. With the proposed model, monitoring data including the shield performance database, disc cutter consumption, geological conditions, and operational parameters can be analyzed. To verify the performance of the proposed model, a case study in China is presented and a database is adopted to illustrate the excellence of the hybrid model. The results indicate that the hybrid model predicts disc cutter life with high accuracy. The sensitivity analysis reveals that the penetration rate (PR) has a significant influence on disc cutter life. The results of this study can be beneficial in both the planning and construction stages of shield tunneling.

In modern transportation, pavement is one of the most important civil infrastructures for the movement of vehicles and pedestrians. Pavement service quality and service life are of great importance for civil engineers as they directly affect the regular service for the users. Therefore, monitoring the health status of pavement before irreversible damage occurs is essential for timely maintenance, which in turn ensures public transportation safety. Many pavement damages can be detected and analyzed by monitoring the structure dynamic responses and evaluating road surface conditions. Advanced technologies can be employed for the collection and analysis of such data, including various intrusive sensing techniques, image processing techniques, and machine learning methods. This review summarizes the state-of-the-art of these three technologies in pavement engineering in recent years and suggests possible developments for future pavement monitoring and analysis based on these approaches.

Three-dimensional (3D) bioprinting is a rapidly growing technology that has been widely used in tissue engineering, disease studies, and drug screening. It provides the unprecedented capacity of depositing various types of biomaterials, cells, and biomolecules in a layer-by-layer fashion, with precisely controlled spatial distribution. This technology is expected to address the organ-shortage issue in the future. In this review, we first introduce three categories of 3D bioprinting strategies: inkjet-based printing (IBP), extrusion-based printing (EBP), and light-based printing (LBP). Biomaterials and cells, which are normally referred to as “bioinks,” are then discussed. We also systematically describe the recent advancements of 3D bioprinting in fabricating cell-laden artificial tissues and organs with solid or hollow structures, including cartilage, bone, skin, muscle, vascular network, and so on. The development of organs-on-chips utilizing 3D bioprinting technology for drug discovery and toxicity testing is reviewed as well. Finally, the main challenges in current studies and an outlook of the future research of 3D bioprinting are discussed.

Trillions of microbes have evolved with and continue to live on and within human beings. A variety of environmental factors can affect intestinal microbial imbalance, which has a close relationship with human health and disease. Here, we focus on the interactions between the human microbiota and the host in order to provide an overview of the microbial role in basic biological processes and in the development and progression of major human diseases such as infectious diseases, liver diseases, gastrointestinal cancers, metabolic diseases, respiratory diseases, mental or psychological diseases, and autoimmune diseases. We also review important advances in techniques associated with microbial research, such as DNA sequencing, metabonomics, and proteomics combined with computation-based bioinformatics. Current research on the human microbiota has become much more sophisticated and more comprehensive. Therefore, we propose that research should focus on the host-microbe interaction and on cause-effect mechanisms, which could pave the way to an understanding of the role of gut microbiota in health and disease. and provide new therapeutic targets and treatment approaches in clinical practice.

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.

Due to growing concerns regarding climate change and environmental protection, smart power generation has become essential for the economical and safe operation of both conventional thermal power plants and sustainable energy. Traditional first-principle model-based methods are becoming insufficient when faced with the ever-growing system scale and its various uncertainties. The burgeoning era of machine learning (ML) and data-driven control (DDC) techniques promises an improved alternative to these outdated methods. This paper reviews typical applications of ML and DDC at the level of monitoring, control, optimization, and fault detection of power generation systems, with a particular focus on uncovering how these methods can function in evaluating, counteracting, or withstanding the effects of the associated uncertainties. A holistic view is provided on the control techniques of smart power generation, from the regulation level to the planning level. The benefits of ML and DDC techniques are accordingly interpreted in terms of visibility, maneuverability, flexibility, profitability, and safety (abbreviated as the ″5-TYs”), respectively. Finally, an outlook on future research and applications is presented.

The most promising strategies in tissue engineering involve the integration of a triad of biomaterials, living cells, and biologically active molecules to engineer synthetic environments that closely mimic the healing milieu present in human tissues, and that stimulate tissue repair and regeneration. To be clinically effective, these environments must replicate, as closely as possible, the main characteristics of the native extracellular matrix (ECM) on a cellular and subcellular scale. Photo-fabrication techniques have already been used to generate 3D environments with precise architectures and heterogeneous composition, through a multi-layer procedure involving the selective photocrosslinking reaction of a light-sensitive prepolymer. Cells and therapeutic molecules can be included in the initial hydrogel precursor solution, and processed into 3D constructs. Recently, photo-fabrication has also been explored to dynamically modulate hydrogel features in real time, providing enhanced control of cell fate and delivery of bioactive compounds. This paper focuses on the use of 3D photo-fabrication techniques to produce advanced constructs for tissue regeneration and drug delivery applications. State-of-the-art photo-fabrication techniques are described, with emphasis on the operating principles and biofabrication strategies to create spatially controlled patterns of cells and bioactive factors. Considering its fast processing, spatiotemporal control, high resolution, and accuracy, photo-fabrication is assuming a critical role in the design of sophisticated 3D constructs. This technology is capable of providing appropriate environments for tissue regeneration, and regulating the spatiotemporal delivery of therapeutics.

The synthesis of fluorescent nanomaterials has received considerable attention due to the great potential of these materials for a wide range of applications, from chemical sensing through bioimaging to optoelectronics. Herein, we report a facile and scalable approach to prepare fluorescent carbon dots (FCDs) via a one-pot reaction of citric acid with ethylenediamine at 150 °C under ambient air pressure. The resultant FCDs possess an optical bandgap of 3.4 eV and exhibit strong excitation-wavelength-independent blue emission (λEm= 450 nm) under either one- or two-photon excitation. Owing to their low cytotoxicity and long fluorescence lifetime, these FCDs were successfully used as internalized fluorescent probes in human cancer cell lines (HeLa cells) for two-photon excited imaging of cells by fluorescence lifetime imaging microscopy with high-contrast resolution. They were also homogenously mixed with commercial inks and used to draw fluorescent patterns on normal papers and on many other substrates (e.g., certain flexible plastic films, textiles, and clothes). Thus, these nanomaterials are promising for use in solid-state fluorescent sensing, security labeling, and wearable optoelectronics.

Evaluation of the efficacy of traditional Chinese medicines (TCMs) is an important prerequisite for discovering effective substances, lead compounds, and quality markers (Q markers). At present, there is an urgent need to develop a biological language that can act as abridge for the scientific elaboration of the efficacy of TCMs, and to further highlight the significant value of TCM. Chinese medicinal syndromes and formulae are two essential parts of TCM that directly relate to its efficacy. Syndromes and formulae have been taken as the research objects. The serum pharmacochemistry of the TCM approach with metabolomics were integrated to establish an innovative chinmedomics strategy, which is able to explore syndrome biomarkers and evaluate TCM efficacy in order to discover effective substances from TCMs. A great deal of concrete work in chinmedomics has already performed to bridge the gap between Chinese and Western medicine, and to provide a powerful approach to enhance the scientific value of TCM theory and clinical practice. This article summarizes the application of chinmedomics in identifying the candidate biomarkers of a syndrome and revealing the efficacy of the related formula. We also highlight the discovery of lead compounds and Q markers from TCMs.

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.

Energy production based on fossil fuel reserves is largely responsible for carbon emissions, and hence global warming. The planet needs concerted action to reduce fossil fuel usage and to implement carbon mitigation measures. Ocean energy has huge potential, but there are major interdisciplinary problems to be overcome regarding technology, cost reduction, investment, environmental impact, governance, and so forth. This article briefly reviews ocean energy production from offshore wind, tidal stream, ocean current, tidal range, wave, thermal, salinity gradients, and biomass sources. Future areas of research and development are outlined that could make exploitation of the marine renewable energy (MRE) seascape a viable proposition; these areas include energy storage, advanced materials, robotics, and informatics. The article concludes with a sustainability perspective on the MRE seascape encompassing ethics, legislation, the regulatory environment, governance and consenting, economic, social, and environmental constraints. A new generation of engineers is needed with the ingenuity and spirit of adventure to meet the global challenge posed by MRE.

Computer vision techniques, in conjunction with acquisition through remote cameras and unmanned aerial vehicles (UAVs), offer promising non-contact solutions to civil infrastructure condition assessment. The ultimate goal of such a system is to automatically and robustly convert the image or video data into actionable information. This paper provides an overview of recent advances in computer vision techniques as they apply to the problem of civil infrastructure condition assessment. In particular, relevant research in the fields of computer vision, machine learning, and structural engineering are presented. The work reviewed is classified into two types: inspection applications and monitoring applications. The inspection applications reviewed include identifying context such as structural components, characterizing local and global visible damage, and detecting changes from a reference image. The monitoring applications discussed include static measurement of strain and displacement, as well as dynamic measurement of displacement for modal analysis. Subsequently, some of the key challenges that persist towards the goal of automated vision-based civil infrastructure and monitoring are presented. The paper concludes with ongoing work aimed at addressing some of these stated challenges.

Medical models, or “phantoms,” have been widely used for medical training and for doctor-patient interactions. They are increasingly used for surgical planning, medical computational models, algorithm verification and validation, and medical devices development. Such new applications demand high-fidelity, patient-specific, tissue-mimicking medical phantoms that can not only closely emulate the geometric structures of human organs, but also possess the properties and functions of the organ structure. With the rapid advancement of three-dimensional (3D) printing and 3D bioprinting technologies, many researchers have explored the use of these additive manufacturing techniques to fabricate functional medical phantoms for various applications. This paper reviews the applications of these 3D printing and 3D bioprinting technologies for the fabrication of functional medical phantoms and bio-structures. This review specifically discusses the state of the art along with new developments and trends in 3D printed functional medical phantoms (i.e., tissue-mimicking medical phantoms, radiologically relevant medical phantoms, and physiological medical phantoms) and 3D bio-printed structures (i.e., hybrid scaffolding materials, convertible scaffolds, and integrated sensors) for regenerated tissues and organs.

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.

Concentrated solar power (CSP) plants with thermal energy storage (TES) system are emerging as one kind of the most promising power plants in the future renewable energy system, since they can supply dispatchable and low-cost electricity with abundant but intermittent solar energy. In order to significantly reduce the levelized cost of electricity (LCOE) of the present commercial CSP plants, the next generation CSP technology with higher process temperature and energy efficiency is being developed. The TES system in the next generation CSP plants works with new TES materials at higher temperatures (> 565 °C) compared to that with the commercial nitrate salt mixtures. This paper reviews recent progress in research and development of the next generation CSP and TES technology. Emphasis is given on the advanced TES technology based on molten chloride salt mixtures such as MgCl₂/NaCl/KCl which has similar thermo-physical properties as the commercial nitrate salt mixtures, higher thermal stability (> 800 °C), and lower costs (< 0.35 USD∙kg⁻¹). Recent progress in the selection/optimization of chloride salts, determination of molten chloride salt properties, and corrosion control of construction materials (e.g., alloys) in molten chlorides is reviewed.

Cadaverine, a natural polyamine with multiple bioactivities that is widely distributed in prokaryotes and eukaryotes, is becoming an important industrial chemical. Cadaverine exhibits broad prospects for various applications, especially as an important monomer for bio-based polyamides. Cadaverine-based polyamide PA 5X has broad application prospects owing to its environmentally friendly characteristics and exceptional performance in water absorption and dimensional stability. In this review, we summarize recent findings on the biosynthesis, metabolism, and physiological function of cadaverine in bacteria, with a focus on the regulatory mechanism of cadaverine synthesis in Escherichia coli (E. coli). We also describe recent developments in bacterial production of cadaverine by direct fermentation and whole-cell bioconversion, and recent approaches for the separation and purification of cadaverine. In addition, we present an overview of the application of cadaverine in the synthesis of completely bio-based polyamides. Finally, we provide an outlook and suggest future developments to advance the production of cadaverine from renewable resources.