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In this paper, the inertial migration of particles in a periodic straight channel is studied numerically based on the immersed boundary-lattice Boltzmann method in two-dimensional and three-dimensional Poiseuille flow. In the model, on considering the inertial migration of particles, the interaction of a soft particle and a hard particle that are similar in size is studied. We observe an interesting particle interaction phenomenon named the binding focusing state (BFS). That is, when the focused soft particle gets close to the hard particle, both particles will switch their original runways, and then run together on a new equilibrium position. To investigate the characteristics of the BFS, the effects of the soft particle deformability, the Reynolds numbers and the initial particle positions on the BFS are studied in detail. The soft particle deformability and Reynolds numbers can affect the formation of the BFS. Moreover, the streamline and pressure around the particles are analyzed to show the formation laws of the BFS; this is more intuitive and aids in understanding the BFS.

Bioenergy crops provide enormous renewable biomass resources convertible for biofuel production, but lignocellulose recalcitrance fundamentally determines its enzymatic saccharification at high cost and low efficiency. In this study, total 30 diverse Miscanthus lignocellulose substrates were incubated with T. reesei strain to secret lignocellulose-degradation enzymes, and their major wall polymers features (cellulose crystallinity, hemicellulose arabinose and lignin H-monomer) were meanwhile examined with distinct impacts on the enzyme activities. Using characteristic Miscanthus (Msi62) de-lignin residue as inducing substrate with the reesei strain, this study detected that the Msi62-induced enzymes were of consistently higher enhancements on enzymatic saccharification of various lignocellulose residues examined in 17 grassy and woody bioenergy crops, particularly for the hemicellulose hydrolyses, compared to other two reesei-secreted cellulases and three commercial enzymes. Notably, based on SDS-gel protein separation profiling and LC-MS/MS analysis, the Msi62-induced enzymes consist of distinct cellulases (CBHI, BG, EGII) compositions and high-activity xylanases. Therefore, this study has demonstrated an applicable approach to achieve the optimal cellulases and xylanases cocktails that enable for low-costly and high-efficient enzymatic saccharification of diverse lignocellulose sources, providing a potential strategy for large-scale biofuel production in all major bioenergy crops.

Aqueous suppression systems (i.e. fire sprinkler, water mist) have been extensively utilised for compartmental fire suppression due to their significant heat extraction ability. Nevertheless, challenges can be foreseen in suppressing water-reactive chemicals as a violent explosive reaction will be triggered, such as alkali metals (i.e. Na, Li) and alkali metal hydrides (i.e. LiH, LiAlH4). In this study, expandable graphite (EG) is proposed as a potential suppressant against alkaline metal fire due to its advantageous thermal properties and chemical stability. In-house user-defined functions (UDFs) are developed to characterise the particle expansion coupled with the heat and mass transfer process between EG and the fluid mixture. The model is incorporated in the large eddy simulation (LES) framework to study the temporal fire behaviours and the suppression effect of EG against the flame plume. The numerical model was validated by comparison of temperature profiles and expansion rate of EG particles along the suppression event against experimental results. The EG was found to be relatively effective in fire suppression compared to the same amount of natural graphite. Parametric analysis was conducted on a range of EG particle size between 400 µm—1000 µm to investigate the suppression mechanisms and the suppression efficiency of EG particles against metal fires. Within the range of the current study (400 µm—1000 µm), the EG particle diameter of 400 µm has achieved the most effective suppression performance and the suppression time of 2 s. It is observed that the smaller size of EG tends to be effective in fire suppression than the larger sizes.

An ultra-wideband phased array with endfire radiation is proposed for millimeter-wave (mm-wave) applications. This array consists of a linear long slot array (LSA) source and a transverse-electromagnetic (TEM) horn radiator. For each unit, a $\Gamma $ -shaped probe based on a substrate integrated coaxial line (SICL) is adopted to generate a primary slot source. Then, the slot flares in the E-plane with an exponential profile to form a horn unit. The antenna unit is studied with periodical boundaries (PBs) in simulation and operates from 15.4 to 46.4 GHz with active standing wave ratio (SWR) $1\times 16$ finite arrays, Array-1 for 0° scan and Array-2 for 45° scan, are fabricated and tested. Over the operating band of 15.4-46.4 GHz, both arrays obtain good impedance matching in measurements with SWR < 2.3. The measured gain varies from 11.8 to 19.1 dBi for Array-1 and from 10 to 16.9 dBi for Array-2 within 16-40 GHz. Besides, Array-2 exhibits a scan angle of 48° and an average scan loss of 2.4 dB. Moreover, both arrays achieve a radiation efficiency exceeding 73% because of employing the low-loss SICL feed network.

Simultaneous Localization and Mapping (SLAM) systems are proposed to estimate robot poses and reconstruct 3-D map of surrounding environment. Since most of the existing SLAM systems rely on the static-world assumption, to perform them in dynamic environments still remains challenging. In this paper, we propose a novel sparse feature based visual SLAM, named as DP-SLAM, which is based on a moving probability propagation model for dynamic keypoints detection. The probability indicates the likelihood of one keypoint being located on the moving objects. Our approach combines the results of geometry constraints and semantic segmentation to track the dynamic keypoints in a Bayesian probability estimation framework. We integrate our method into the front-end of the ORB-SLAM2 system, which acts as a pre-processing stage to filter out keypoints that are associated with moving objects. Furthermore, we inpaint the frame background that has been occluded by the detected dynamic objects, which benefit some applications such as virtual and augmented reality. Experimental results on the TUM RGB-D dataset and our own sequences demonstrate that our approach can improve performance of state-of-the-art SLAM system in various challenging scenarios.

The rapid economic growth and environmental concerns have led to high demands on paper and paper-based products in terms of variety, quantity, quality, and specialty. Enhancement and functionalization with additives are constantly required. Moving away from traditional petroleum-based additives, researchers have attempted to use “green” nanoadditives by introducing renewable environmentally friendly nanocellulose. This article studies the functions of nanocellulose as bio-additives (enhancer, retention and filtration reagent, and coating aid) in paper and paper products, and overviews the research development of nanocellulose-based additives and their applications in the paper industry for both efficient production and paper functionalization. The review shows that (1) a variety of nanocellulose-based bioadditives have been reported for various applications in paper and paper-based products, while commercially viable developments are to be advanced; (2) nanocellulose was mostly formulated with other polymer and particles as additives to achieve their synergistic effects; (3) major interests have concentrated on the nanocellulose in the specialty papers as representing more value added products and in the efficient utilization of recycled fibers, which remains most attractive and promising for future development. This report shall provide most useful database information for researchers and industries for paper recycling and enhancement, and paper-based products innovation and application.

The overexpression of HIF-1α in solid tumors due to hypoxia is closely related to drug resistance and consequent treatment failure. Herein, we constructed a hypoxia-activated prodrug named as YC-Dox. This prodrug could be activated under hypoxic conditions and undergo self-immolation to release doxorubicin (Dox) and YC-1 hemisuccinate (YCH-1), which could execute chemotherapy and result in HIF-1α downregulation, respectively. This prodrug is capable of specifically releasing Dox and YCH-1 in response to hypoxia, leading to a substantial synergistic potency and a remarkable cytotoxic selectivity (>8-fold) for hypoxic cancer cells over normoxic healthy cells. The in vivo experiments reveal that this prodrug can selectively aim at hypoxic cancer cells and avoid undesired targeting of normal cells, leading to elevated therapeutic efficacy for tumor treatment and minimized adverse effects on normal tissues.

To confirm the robustness of a tunnel, the quality of the rock mass is usually evaluated by several rock mass classification (RMC) systems before its design. Besides, the accuracy of the RMC should be checked through the correlations between different RMC systems. The relationship between the different RMC systems and P-wave velocity indexes is conducive to the interpretation of the rock mass. Here, based on linear or nonlinear regression analysis of 231 samples, we established the RMR–Q, [BQ]–Q, and [BQ]–RMR relationships with R2 = 0.935, 0.732, and 0.759, respectively. Further analysis showed that the grading differences between any two RMC systems yield different characteristics when the rock mass score is at different intervals. To evaluate the relations between RMC indexes and P-wave velocity, we conducted a group study on the different grading differences among RMR, Q, or BQ. Depending on the consistency of the grading results, we arranged the values from large to small as the amplitude of the slope of the curve, and the grading results differed by either one or two levels. Our data demonstrated that the grading differences between the systems are proportional to the discontinuous state of rock mass in the classification. In addition, the classification results of Q, RMR, and BQ showed more significant differences when less consideration is given to the integrity of the rock mass.

Chitosan is a biodegradable, biocompatible, and nontoxic aminopolysaccharide. This review summarizes and discusses the structural modifications, including substitution, grafting copolymerization, cross-linking, and hydrolysis, utilized to improve the physicochemical properties and enhance the bioactivity and functionality of chitosan and related materials. This manuscript also reviews the current progress and potential of chitosan and its derivatives in body-weight management and antihyperlipidemic, antihyperglycemic, antihypertensive, antimicrobial antioxidant, anti-inflammatory, and immunostimulatory activities as well as their ability to interact with gut microbiota. In addition, the potential of chitosan and its derivatives as functional ingredients in food systems, such as film and coating materials, and delivery systems is discussed. This manuscript aims to provide up-to-date information to stimulate future discussion and research to promote the value-added utilization of chitosan in improving the safety, quality, nutritional value and health benefits, and sustainability of our food system while reducing the environmental hazards.

In recent years, human motion recognition, as an important application of the intelligent perception of the Internet of Things, has received extensive attention. Many applications benefit from motion recognition, such as motion monitoring, elderly fall detection, and somatosensory games. Several existing RF-based motion recognition systems are susceptible to multipath effects in complex environments, resulting in lower recognition accuracy and difficulty in extending to other scenarios. To address this challenge, we propose RF-Motion, a device-free commercial off-the-shelf (COTS) RFID-based human motion recognition system that can detect human motion in complex multipath environments such as indoor environments. And when the environment changes, RF-Motion still has high recognition accuracy, even without retraining. In addition, we use data slicing to solve the problem of discontinuity in the time domain of RFID communication and then use the synthetic aperture (SAR) algorithm to obtain the fingerprint feature matrix corresponding to each motion. Finally, the dynamic time warping (DTW) algorithm is used to match the prior motion fingerprint database to complete the motion recognition. Experiments show that RF-Motion can achieve up to 90% accuracy for human motion recognition in an indoor environment, and when the environment changes, it can still reach a minimum accuracy of 87%.

High-performance broadband photodetectors that can operate at UV, visible, and near-infrared wavelengths have been fabricated based on CsPb(Br/I)3 nanocrystal (NC)/CdS-microwire (MW) heterostructures. Under an incident light illumination of 365, 530, and 660 nm, the CsPb(Br/I)3-NC/CdS-MW-heterostructure-based photodetector exhibited a superior photosensitivity and broader spectral response than those of a bare-CdS-MW-based photodetector, which can be attributed to the light-trapping ability of the CsPb(Br/I)3 NCs and charge-transfer efficiency at the CsPb(Br/I)3-NC/CdS-MW-heterojunction interface. The photodetector based on the CsPb(Br/I)3 NC/CdS-MW heterostructure also exhibited a good response to near-infrared light (760 and 810 nm) because the produced heterojunction facilitates the spatial separation of the photogenerated carriers, and the carriers are transferred from the CsPb(Br/I)3 NC part to the CdS MW part through diffusion due to the relatively long diffusion length in the CsPb(Br/I)3 layer. Therefore, the proposed photodetectors are promising for constructing high-performance broadband optoelectronic devices.

As a core step in clustering analysis, distance measurement results can influence clustering accuracy. Existing measurement methods are mostly based on cluster feature information. However, these cluster features may be insufficient and result in losing data information for clusters containing a number of objects. To improve measurement accuracy, we make full use of the distribution characteristics of objects in clusters, i.e., we use descriptive statistics and the Wilcoxon-Mann-Whitney rank sum test in nonparametric statistics to measure distances during clustering. Furthermore, we propose a two-stage clustering algorithm to improve clustering analysis performance. In terms of avoiding preliminarily assuming the number of clusters, with the proposed distance measurement method, the clustering algorithm can discover clusters with arbitrary shapes and improve clustering accuracy. Experiments with multiple datasets compared with other clustering algorithms illustrate the accuracy and efficiency of the proposed clustering algorithm.

Rationale: Sensitive and accurate imaging of cancer is essential for early diagnosis and appropriate treatment. For generally employed magnetic resonance imaging (MRI) in clinic, comprehending how to enhance the contrast effect of T 1 imaging is crucial for improving the sensitivity of cancer diagnosis. However, there is no study ever to reveal the clear mechanism of how to enhance the effect of T 1 imaging and accurate relationships of influencing factors. Herein, this study aims to figure out key factors that affect the sensitivity of T 1 contrast-enhanced MRI (CE-MRI), thereby to realize sensitive detection of tumors with low dose of CAs. Methods: Manganese oxide (MnO) nanoparticles (NPs) with various sizes and shapes were prepared by thermal decomposition. Factors impacting T 1 CE-MRI were investigated from geometric volume, surface area, crystal face to r 2/r 1 ratio. T 1 CE-MR imaging of liver, hepatic and subcutaneous tumors were conducted with MnO NPs of different shapes. Results: The surface area and occupancy rate of manganese ions have positive impacts on the sensitivity of T 1 CE-MRI, while volume and r 2/r 1 ratio have negative effects. MnO octahedrons have a high r 1 value of 20.07 mM-1s-1 and exhibit an excellent enhanced effect in liver T 1 imaging. ZDS coating facilitates tumor accumulation and cellular uptake, hepatic and subcutaneous tumors could be detected with MnO octahedrons at an ultralow dose of 0.4 mg [Mn]/kg, about 1/10 of clinical dose. Conclusions: This work is the first quantitative study of key factors affecting the sensitivity of T 1 CE-MRI of MnO nanoparticles, which can serve as a guidance for rational design of high-performance positive MRI contrast agents. Moreover, these MnO octahedrons can detect hepatic and subcutaneous tumors with an ultralow dose, hold great potential for sensitive and accurate diagnosis of cancer with lower cost, less dosages and side effects in clinic.

Real-time monitoring of leaf nitrogen (N) content by remote sensing can accurately diagnose crop nutrient status and facilitate precision N management. However, the methods used to estimate of vertically integrated leaf N content do not consider different cropping systems, in which the maize growth stages are not synchronized, resulting in decreased practical value of the results. The purpose of this study was to propose an optimized red-edge absorption area (OREA) index in which the prediction accuracy of vertically integrated leaf N content is improved within spring- and summer-sown maize canopies. The results showed that vertical distributions of N existed regardless of variations in the maize growth stages, that is, the leaf N density of the upper and middle layers was higher than that of the lower layers. These published vegetation indices (VIs) provided relatively good correlations with leaf N density at different layers across all of the datasets. When predicting leaf N density of each leaf layer, an optimal VI is generated, and inconsistent VIs will limit its practical application. To further overcome the drawbacks of the inconsistency of each VI when estimating the leaf N density at different layers, a new OREA index was proposed based on red-edge absorption area parameter. The OREA index showed the highest prediction accuracy with leaf N density for entire canopies (r2 = 0.811, RMSE = 0.374, RE = 13.17%) and canopies without top first leaves (r2 = 0.795, RMSE = 0.269, RE = 15.20%) compared with the other published VIs. It is concluded that the vertically integrated leaf N content under different field experiments can be accurately estimated by the OREA index.

Buildings have a significant impact on global sustainability. During the past decades, a wide variety of studies have been conducted throughout the building lifecycle for improving the building performance. Data-driven approach has been widely adopted owing to less detailed building information required and high computational efficiency for online applications. Recent advances in information technologies and data science have enabled convenient access, storage, and analysis of massive on-site measurements, bringing about a new big-data-driven research paradigm. This paper presents a critical review of data-driven methods, particularly those methods based on larger datasets, for building energy modeling and their practical applications for improving building performances. This paper is organized based on the four essential phases of big-data-driven modeling, i.e., data preprocessing, model development, knowledge post-processing, and practical applications throughout the building lifecycle. Typical data analysis and application methods have been summarized and compared at each stage, based upon which in-depth discussions and future research directions have been presented. This review demonstrates that the insights obtained from big building data can be extremely helpful for enriching the existing knowledge repository regarding building energy modeling. Furthermore, considering the ever-increasing development of smart buildings and IoT-driven smart cities, the big data-driven research paradigm will become an essential supplement to existing scientific research methods in the building sector.

Photocatalytic oxidation desulfurization is presently challenged with low quantum efficiency and high combination rate of electron–hole, and the key to success is to design efficient multicomponent...

A millimeter-wave 4 × 4 magnetoelectric (ME) dipole antenna array using a packaged microstrip line feed network is proposed. This antenna array features a wide impedance bandwidth of more than 50% and small back radiation. Each ME dipole element is excited by an L -shaped probe and each L -shaped probe is connected to the packaged microstrip line through a small hole in the ground plane. The packaged microstrip line forms the whole corporate feed network for the antenna array. The entire antenna structure is realized by multiple printed circuit boards (PCBs), which is low in material cost and easy in fabrication. The results demonstrate that the proposed antenna array enables the impedance bandwidth of 51.5%, high gain up to 20.3 dBi, and stable broadside radiation pattern with small back radiation over the operating frequencies. The array is suitable for the 5G application.

Accurate building energy prediction is vital to develop optimal control strategies to enhance building energy efficiency and energy flexibility. In recent years, the data-driven approach based on machine learning algorithms has been widely adopted for building energy prediction due to the availability of massive data in building automation systems (BASs), which automatically collect and store real-time building operational data. For new buildings and most existing buildings without installing advanced BASs, there is a lack of sufficient data to train data-driven predictive models. Transfer learning is a promising method to develop accurate and reliable data-driven building energy prediction models with limited training data by taking advantage of the rich data/knowledge obtained from other buildings. Few studies focused on the influences of source building datasets, pre-training data volume, and training data volume on the performance of the transfer learning method. The present study aims to develop a transfer learning-based ANN model for one-hour ahead building energy prediction to fill this research gap. Around 400 non-residential buildings’ data from the open-source Building Genome Project are used to test the proposed method. Extensive analysis demonstrates that transfer learning can effectively improve the accuracy of BPNN-based building energy models for information-poor buildings with very limited training data. The most influential building features which influence the effectiveness of transfer learning are found to be building usage and industry. The research outcomes can provide guidance for implementation of transfer learning, especially in selecting appropriate source buildings and datasets for developing accurate building energy prediction models.

Predicting the bike demand can help rebalance the bikes and improve the service quality of a bike-sharing system. A lot of works focus on predicting the bike demand for all the stations, which is unnecessary as the travel cost of rebalance operations increases sharply as the number of stations increases. In this paper, we propose a framework for predicting the hourly bike demand based on the central stations we define. Firstly, we propose Two-Stage Station Clustering Algorithm to assign central stations and common stations into each cluster. Secondly, we propose a hierarchical prediction model to predict the hourly bike demand for every cluster and each central station progressively. Thirdly, we use a well-studied queuing model to determine the target initial inventory for each central station. The most innovative contribution of this paper is proposing the concept of central station, the use of a novel algorithm to cluster the central stations and present a hierarchical model, containing the Time and Weather Similarity Weighted K-Nearest Neighbor Algorithm and a linear model to predict the bike demand for central stations. The experimental results on the New York citi bike system demonstrate that our proposed method is more accurate than other methods in solving existing problems.

Dental caries is a chronic disease with multiple bacterial infections, Streptococcus mutans is the main cariogenic bacteria. Trollius chinensis Bunge is a common folk medicine in the Xinjiang area of China. In this study, we investigated the total flavonoid content and total phenol content in four types of T. chinensis Bunge extracts and the inhibitory effects of these extracts on S. mutans. Agar diffusion method was used to measure the inhibition zone diameters, and the minimum inhibitory concentration and minimum bactericidal concentration were determined by the twofold dilution method. Water extracts from T. chinensis Bunge and ethanol (30, 60, and 90%) extracts at different concentrations could significantly inhibit the growth of S. mutans. Among them, 30% ethanol extract exhibited the best antibacterial and antibiofilms effect. Biofilm research (crystal violet staining and CLSM) showed that 30% ethanol extract of T. chinensis Bunge plays an important role in inhibiting S. mutans growth and the number of biofilms. The results indicate that T. chinensis Bunge extract has good antibacterial and anti-biofilm activity on S. mutans. It has the potential to be developed for the treatment of caries in clinical application.

Carbonation of concrete is a process which absorbs carbon dioxide (CO2). Recycled aggregate concrete (RAC) may own greater potential in CO2 uptake due to the faster carbonation rate than natural aggregate concrete (NAC). A quantitative model was employed to predict the CO2 uptake of RAC in this study. The carbonation of RAC and the specific surface area of recycled coarse aggregates (RCAs) were tested to verify accuracy of the quantitative model. Based on the verified model, results show that the CO2 uptake capacity increases with the increase of RCA replacement percentage. The CO2 uptake amount of 1 m3 C30 RAC within 50 years is 10.6, 13.8, 17.2, and 22.4 kg when the RCA replacement percentage is 30%, 50%, 70%, and 100%, respectively. The CO2 uptake by RCAs is remarkable and reaches 35.8%–64.3% of the total CO2 uptake by RAC when the RCA storage time being 30 days. Considering the fact that the amount of old hardened cement paste in RCAs is limited, there is an upper limit for the CO2 uptake of RCAs.

To study the mechanical responses of large cross-section tunnel reinforced by pretensioned rock bolts and anchor cables, an analytical model is proposed. Considering the interaction between rock mass and bolt-cable support, the strain softening characteristic of rock mass, the elastic-plastic characteristic of bolt-cable support, and the delay effect of installation are considered in the model. To solve the different mechanical cases of tunneling reinforced by bolt-cable support, an analytical approach has been put forward to get the solutions of stress and displacement associated with tunneling. The proposed analytical model is verified by numerical simulation. Moreover, parametric analysis is performed to study the effects of pretension force, cross-section area, length, and supporting density of bolt-cable support on tunnel reinforcement, which can provide references for determining these parameters in tunnel design. Based on the analytical model, a new Ground Response Curve (GRC) considering the reinforcement of bolt-cable support is obtained, which shows the pretension forces and the timely installation are important in bolt-cable support. In addition, the proposed model is applied to the analysis of the Great Wall Station Tunnel, a high-speed railway tunnel with a super large cross-section, which shows that the analytical model of bolt-cable support was a useful tool for preliminary design of large cross-section tunnel.

In this paper, three diiron butane-1,2-dithiolate complexes have been prepared and characterized. Treatment of the starting complex [Fe2(CO)6{µ-SCH2CH(CH2CH3)S}] (1) with a monophosphine ligand tri...

Thousands of items are stored in libraries, archives and supermarkets, it usually takes a long time to find what we want in thousands of items. How to achieve precise localization is particularly important. We present a fine-grained Radio Frequency Identification (RFID) positioning method to help us find the items we want. In order to achieve high-precision positioning of RFID tags, we propose Hyperbolic and Hologram Composite (HHC) localization algorithm, which determines the positioning candidate set by hyperbolic intersection, thereby reducing the positioning delay, and then use the holographic positioning to filter the candidate set. We use multi-frequency holography positioning technology to reduce the influence of the reader’s own inaccurate positioning, and use multipath suppression technology to reduce the interference of multipath in the environment, finally achieves higher precision positioning. The experimental results show that the average calculation time of one positioning of our HHC positioning system is 10.3 ms, and 90% of the positioning error margin is below 4.8 cm, which realizes high-precision and low-delay positioning.

In order to maximize the working potential of a steam ejector, aside from experiment testing of various design parameters, it is also important to understand the involved thermal fluid mixing behaviors. In this study, an ejector refrigeration experimental system was established, and a numerical model was developed to comprehend the complex and non-linear flow characteristic using an ideal gas model which is more consistent with the experimental data compared with the wet steam model. Herein, it was discovered that the choking flow and the occurrence locations of this phenomenon played an essential role in the system efficiency. For the first time, three choke behaviors modes were characterized by means of numerical investigations: “fit-choked flow mode”, “sub-choked flow mode” and “super-choked flow mode”. The relationship between the choke and the normal shock wave was revealed to analyze the two mixing fluids flow conditions. The influence of primary fluid pressure and the back pressure on the ejector performance under three choke flow modes were comprehensively discussed. The simulation results indicated that the choking flow was a critical factor in affecting the performance of the steam ejector, and the performance optimizes at “fit-choked flow mode” when the primary fluid pressure was 0.36 MPa with a remarkable entrainment ratio of 0.525.

BACKGROUND: During the synthesis of isopropyl alcohol (IPA) by hydration of propylene, diisopropyl ether (DIPE) is usually produced as a by‐product. Because IPA and DIPE can form a binary homogeneous azeotrope with the minimum boiling temperature at atmospheric pressure, it is difficult to separate the binary mixture by simple distillation. RESULT: For separating the azeotrope IPA + DIPE, side‐stream extractive distillation column (SSED) and extractive dividing wall column (EDWC) processes with three entrainers [2‐methoxyethanol (2‐MEA), 2‐ethoxyethanol and propylene glycol ethyl ether (PGEE)] were simulated and optimized based on the minimal total annual cost (TAC). Moreover, CO₂ emissions of the six sequences were calculated and the impact on the environment evaluated. CONCLUSIONS: The sequence of SSED with PGEE as entrainer can reduce TAC by ≤34.48%, but EDWC with PGEE can offer TAC reduction of ≤52.46%. The results demonstrate that the EDWC sequence with PGEE as entrainer is favourable for separating the binary azeotrope IPA + DIPE. © 2020 Society of Chemical Industry

Various studies suggest that the performances of TMDs are largely thickness dependent. In this paper, we develop a chemical vapor deposition method to synthesis monolayer and bilayer MoS2 flakes with a solution precursor. The MoS2 phototransistors were prepared to investigate their optoelectronic performance. The MoS2 photodetectors exhibit high detectivity of 2.44 × 1011 and a fast response/recovery time of 97 ms/291 ms. The photoresponsivity of bilayer MoS2 flakes was found up to 7160 A W−1. Our research will pave a pathway to control the layer numbers of other TMDs nanostructures, expand the application of high performance 2D materials.

The fire event in a tunnel creates a rapid spread of heat and smoke flows in a long and confined space, which not only endangers human life but also challenges the fire-evacuation and firefighting strategies. A quick and accurate identification for the location and size of the original fire source is of great scientific and practical value in guiding fire rescue and fighting the tunnel fire. Nevertheless, it is a big challenge to acquire fire-source information in an actual tunnel fire event. In this study, the framework of artificial intelligence (AI) and big data is applied to predict the fire source in a numerical model of the tunnel. A big tunnel fire database of numerical simulations, with varying fire locations, fire sizes, and ventilation conditions, is constructed. Temporally varied temperatures measured by multiple sensor devices are used to train a long-short term memory recurrent neural network. Results demonstrate that the location and size of the tunnel fire and the ventilation wind speed can be predicted by the trained model with an accuracy of 90%. Sensitivity analysis is also carried out to optimize the database configuration and spatial–temporal arrangement of sensors in order to achieve a fast and reliable fire prediction. This work addresses the possibility of AI-based detection and prediction of fire source and hazard, thus, providing scientifically based guidance for smart-firefighting technologies and paving the way for future emergency-response tactics in a smart city.

The crystal structures of the antimicrobial drug tinidazole [TNZ; systematic name: 1-(2-ethylsulfonylethyl)-2-methyl-5-nitroimidazole, C8H13N3O4S] and the 1:1 cocrystal of TNZ with the naturally occurring compound vanillic acid (VA; systematic name: 4-hydroxy-3-methoxybenzoic acid, C8H8O4), namely, the TNZ–VA cocrystal, were determined by single-crystal X-ray analysis at 100 K. The supramolecular structure of the TNZ–VA cocrystal is composed of a carboxylic acid dimer and an O—H...N(heterocycle) synthon in the form of layers made up of O—H...N and O—H...O hydrogen bonds. The layers are joined via C—H...O hydrogen bonds, π–π stacking and C—H...π interactions. The energy framework analysis, together with interaction energy calculations using the DLPNO-CCSD(T) method, indicates that the TNZ–VA cocrystal inherits strong interactions from the TNZ and VA crystals, which accounts for the enhanced thermal stability and reduced dissolution rate. To the best of our knowledge, this is the first example of a cocrystal containing TNZ.

In this study, the authors demonstrate the potential of hard-decision algorithm in industrial 5G NR channel coding. This study aims to show different forward error correction algorithms that can be expected in the context of cloud radio access network. Based on low density parity check (LDPC) min-sum (MS) algorithm, an improved hard-decision algorithm is proposed. The decoding process and performances are shown and compared with MS algorithm. To identify the advantage of this proposal, a test bench is launched in Cisco server using X86 chipset. Its results show a good trade-off between latency and performance.

Specific chemical reactions only happen in the tumor region and produce abundant special chemicals to in situ trigger a train of biological and pathological effects that may enable tumor-specific curative effects to treat cancer without causing serious side effects on normal cells or organs. Chemodynamic therapy (CDT) is a rising tactic for cancer therapy, which induces cancer cell death via a localized Fenton reaction. However, the tumor therapeutic effect is limited by the efficiency of the chemical reaction and relies heavily on the catalyst. Here, we constructed hollow porous carbon coated FeS2 (HPFeS2@C)-based nanocatalysts for triple-enhanced CDT. Tannic acid was encapsulated in HPFeS2@C for reducing Fe3+ to Fe2+, which had a better catalytic activity to accelerate the Fenton reaction. Afterward, glucose oxidase (GOx) in nanocatalysts could consume glucose in the tumor microenvironment and in situ synchronously produce H2O2, which could improve Fenton reaction efficiency. Meanwhile, the consumption of glucose could lead to the starvation effect for cancer starvation therapy. The photothermal effects of HPFeS2@C could generate heat, which further sped up the Fenton process and implemented synergetic photothermal therapy/starvation therapy/CDT. The biodistribution of nanoparticles was investigated by multimodal magnetic resonance, ultrasound, and photoacoustic imaging. These nanocatalysts could trigger the catalytic Fenton reaction at a high degree, which might provide a good paradigm for nanocatalytic tumor therapy.

The cross-view multimedia are widely existed and attract many attentions in recent years. Nevertheless, it is noted that the phenomenon, that data in different classes from same view are more similar than that in same class from different views, is usually presented for cross-view multimedia data. The intrinsic imperfection leads disappointing performance for cross-view multimedia recognition or classification. To solve this problem, in this paper, we propose a novel discriminative learning framework with low-rank constraint, which can be applied for view-invariant low-dimensional subspace learning. The advantages of our framework include three aspects. Firstly, to unlock the latent class structure and view structure, a self-expressed model by dual low-rank constraints are presented, which can separate the two manifold structures in the learned subspace. Secondly, two effective discriminative graphs are constructed to guide the affinity relationship of data in the above two low-dimensional projected subspaces respectively. Finally, the joint semantic consensus constraint is designed to be integrated into the learning framework, which can explore the shared and view-specific information for enforcing the view-invariant character in semantic space. Experimental results on several public cross-view multimedia datasets demonstrate that our proposed method outperforms existing excellent subspace learning approaches.

Recent advances in evolutionary algorithms based on membrane computing have shown that the mechanism of membrane computing is an effective way to solve optimization problems. In this work, we propose a new evolutionary multi-objective algorithm that uses membrane systems to solve multi-objective optimization problems. Based on the mechanism of living cell structure and function, the algorithm introduces three factors, including membrane structure, multiset and reaction rules. The membrane structure of the proposed algorithm is inspired by the structure of the membrane system, which has multiple layers and nested structures in the skin membrane. Two special symbol-objects are designed to improve the search efficiency of the algorithm. In addition, some reaction rules are used to evolve the symbol-objects of multiset in the inner region of the membrane. In addition, the proposed method combines external archive to maintain the diversity of non-dominated solutions and enhance the search capabilities of the solutions. Our proposed method is compared to five state-of-the-art multi-objective heuristic algorithms. For comparison, six different criteria were used: the quality of the resulting approximate set, the diversity of candidate solutions, and the rate of convergence to the Pareto front. Experimental results show that the proposed method is competitive in performance in qualitative and quantitative measurements of selected test functions. Therefore, the algorithm is feasible and effective for solving multi-objective optimization problems.

A silicon nitride horizontal slot waveguide with silicon dioxide cladding is designed to achieve flat negative dispersion ( $\text {nm}\cdot \text {km}$ )) over an octave-spanning bandwidth (782 to 2100 nm). Simulations show that, when a 10-fs pulse with 1-W peak power is input into a 5-cm long waveguide, the pulse width becomes 448 times wider and its peak power is reduced to $2.23\times 10^{-3}$ W. After passing through another ideal positive dispersion element, we find that the output pulse peak power can be recompressed close to 1 W in the case of different input pulse widths. Moreover, for the input pulse with a much higher peak power, the pulse width of the output pulse can be compressed to a much narrower level because of the spectral broadening introduced by optical nonlinearity. A 100-fs input pulse with 10-kW peak power can be compressed to 12.5 fs with a peak power of 63 kW. Considering the time-domain broadening and the peak power reduction, such a slot waveguide with a flat negative dispersion could be used as a time stretcher for 10-fs level pulse in on-chip chirped pulse amplification (CPA) system.

Molecular probes featuring promising capabilities including specific targeting, high signal-to-noise ratio, and in situ visualization of deep tissues are in great demand for tumor diagnosis and therapy. 19F magnetic resonance imaging (MRI) techniques incorporating stimuli-responsive probes are anticipated to be highly beneficial for specific detection and imaging of tumors because of negligible background and deep tissue penetration. Herein, we report a cascaded multiresponsive self-assembled nanoprobe, which enables sequential redox-triggered and near-infrared (NIR) irradiation-induced 19F MR signal activation/amplification for sensing and imaging. Specifically, we designed and synthesized a cascaded multiresponsive 19F-bearing nanoprobe based on the self-assembly of amphiphilic redox-responsive 19F-containing polymers and NIR-absorbing indocyanine green (ICG) molecules. It could realize the activation of 19F signals in the reducing tumor microenvironment and subsequent signal amplification via the photothermal process. This stepwise two-stage activation/amplification of 19F signals was validated by 19F NMR and MRI both in vitro and in vivo. The multiresponsive 19F nanoprobes capable of cascaded 19F signal activation/amplification and photothermal effect exertion can provide accurate sensing and imaging of tumors.

Controlling the interlayer coupling in van der Waals (vdW) homo- or heterostructures by twist angle between two adjacent layers is a powerful approach to manipulate their electronic properties. Her...

The drill and blast method is widely used in constructing tunnels in rock. Unfortunately, blasting vibration can damage newly performed shotcrete layers which are major support structures to stabilize surrounding structures. Therefore, investigation of the influence of blasting on shotcrete and determining reasonable distance between blasting work face and shotcrete position is of great importance. In this paper, a large-span tunnel excavated by drill and blast method acting as a high-speed railway station has been investigated. Blast vibration in the tunnel was recorded using microseismic monitoring technique. Empirical prediction equations for peak particle velocity (PPV) were obtained through regression analysis based on the obtained monitoring data. The attenuation law of tensile stress imposed on shotcrete layer due to blasting and bond strength of shotcrete-rock interface was also investigated. Minimum safety distance between shotcrete and blasting positions was calculated based on bond failure criterion. Evolution law considering different factors including blasting charge, rock mass class, and setting time of shotcrete was also obtained, which could be applied to determine blast charge shotcrete arrangements for tunnel constructions in future. The obtained results showed that the safety of shotcrete could be ensured and shotcrete falling off the rock could be prevented under current blast constructions.

WS2, as a promising alternative of noble metal based electrocatalysts, has the advantages of abundant resource and low cost, attracting more and more attention in hydrogen evolution reaction (HER). However, the poor conductivity, and serous stacking and aggregation commonly existed in WS2 nanostructures have greatly limited its application in practical hydrogen production. Here, novel binder-free and free-standing N-enriched carbon foam@WS2 nanoflakes (NE-CF@WS2 NFs) was successfully prepared via a facile, one-step thermal evaporation process, which could directly be used as the working electrode for HER in water splitting, displaying excellent electrocatalytic activity with a low overpotential of 153 mV at the current density of −10 mA cm−2 and a small Tafel slope of 58.7 mV dec−1, and favorable electrochemical stability and durability. Our study also indicates that the outstanding HER performance of the present catalyst could mainly be attributed to the fast electron transfer at the catalyst/electrolyte interface and the enhanced number of active sites originating from the interconnected 3D porous framework structure with high conductivity. In addition, the present environmentally friendly synthesis strategy might also be employed to prepare other interconnected 3D porous, free-standing and binder-free nanocomposites for various applications, such as Li-ion batteries, Na-ion batteries, supercapacitors and water electrolysis.

The excavation of large-span triple tunnels using drilling and blasting method inevitably causes complicated load transfer effects and induces potentially damaging ground vibrations. In this study, the structural responses (including the surrounding rock pressure, normal-contact pressure between the primary and secondary linings, internal forces in the secondary lining) and the seismic responses (including peak particle velocity and corner frequency), are systematically recorded. It is found that the first-excavated left tunnel is influenced heavily by the excavation of the last-excavated middle tunnel, whereas it is hardly affected by the excavation of the second-excavated right tunnel. The load carried by the primary lining is approximately three times as that carried by the secondary lining. The middle tunnel was in the least desirable state due to the formation of the large Protodyakonov’s equilibrium arch (PEA). Based on timely feedback of the comprehensive monitoring system, a series of vibration-reducing techniques were applied and effectively guaranteed safety during blasting construction. By referring to Chinese codes, the minimum safety factor of the secondary lining is 1.3; the maximum PPV (0.15 cm/s) is lower than the allowable value; and the corner frequency (40-140 Hz) will not cause resonant vibration of the Great Wall.

Highly active free-standing and flexible MoS2/rGO nanofilms is mass-produced via ball-milling the bulk layered materials and further hydrazine-reducing process. In the well assembled structure, the loose few layered MoS2 nanosheets and rGO make the composites with enhanced active reaction sites. The synergetic effect between MoS2 nanosheets and rGO could also improve the electronic conductivity and flexibility of the obtained electrode films. The flexible MoS2/rGO nanofilms films are fabricated as symmetric supercapacitor, which deliver a specific capacitance of 365 F g−1 at current density of 0.5 A g−1 and exhibits a high energy density of 89.0 Wh·kg−1 at the power density of 16.7 kW kg−1.

Multifunctional nanoplatforms featuring promising properties including excellent loading efficiency, real-time monitoring, and improved cargo bioavailability and bioselectivity are in great demand by the biomedical research community. During the development of such nanoplatforms, stimuli-responsive nanoparticles (NPs) as a smart nanoplatform have recently received extensive attention. Herein, we report small-sized octapod-shaped hollow porous manganese(II) oxide (HPMO) NPs as a stimuli-responsive T1-activatable nanoplatform for tumor-specific cargo delivery and real-time monitoring. The HPMO NPs functionalized by zwitterionic dopamine sulfonate (ZDS) can act as a versatile platform to load organic dyes or chemotherapeutic drugs with high loading efficiency. The obtained Cargo@HPMO would decompose into paramagnetic Mn2+ ions and subsequently release cargoes in mild acidic conditions, especially in tumor microenvironment and lysosome. The released Mn2+ can enhance T1 magnetic resonance signal for real-time monitoring of the cargo delivery in vivo. This octapod-shaped Cargo@HPMO can act as a smart and versatile nanoplatform with pH-responsive multimodal imaging and site-specific drug delivery for the development of accurate diagnosis and effective therapy for cancer.

Heart failure (HF) is a disease that is harmful to human health. Recent advances in machine learning yielded new techniques to train deep neural networks, which resulted in highly successful applications in many pattern recognition tasks such as object detection and speech recognition. To improve the diagnostic accuracy of HF staging, this study evaluates the performance of deep learning-based models on combined features for its categorization. We proposed a novel deep convolutional neural network-Recurrent neural network (CNN-RNN) model for automatic staging of heart failure diseases in real-time and dynamically. We employed the data segmentation and data augmentation pre-processing dataset to make the classification performance of the proposed architecture better. Specifically, this paper use convolutional neural network (CNN) as a feature extractor instead of training the entire network to extract the characteristics of the electrocardiogram (ECG) signals and form a feature set. We combine the above feature set with other clinical features, feed the combined features to RNN for classification, and finally obtain 5 classification results. Experiments shows that the CNN-RNN model proposed in this paper achieved an accuracy of 97.6%, the sensitivity of 96.3%, specificity of 97.4% and proportion of 97.1% for two seconds of ECG segments. We obtained an accuracy, sensitivity, specificity and proportion of 96.2%, 96.9%, 95.7%, and 94.3% respectively for five seconds of ECG duration. The model can be used as an aid to help clinicians confirm their diagnosis.

A dual linearly polarized endfire antenna, excited by an E-plane 180° 3 dB hybrid coupler, is reported for millimeter-wave applications. The radiator is constructed by two stacked open-ended substrate integrated waveguides (SIWs). A slope-shaped slot is employed to the common metallic layer of the two SIWs, operating as a polarizer. Experimentally, the proposed antenna achieves an impedance bandwidth (SWR < 2) from 32 to 40 GHz for both the vertical and horizontal polarizations. The port isolation keeps above 14.5 dB over almost the whole operating band. Besides, the proposed antenna is compact and obtains symmetrical radiation patterns, therefore being suitable for array designs.

The cap effects on the wave forces acting on the piles underneath the cap are important for the estimation of the wave loads on the elevated pile-cap foundations of sea-crossing bridges. Full scale 3-dimensional numerical simulations are carried out for large square caps with piles placed at different locations underneath. The influences of wave height, wave length, cap dimension and submerged depth on the cap effects are investigated. The analytical method is introduced and its solutions are compared with the numerical simulation results. The results show that the wave forces on piles are influenced by the free surface runup and rundown in front of the cap, and the negative force on the pile near the front edge of the cap can be remarkably larger than the positive force with large wave height. The cap coefficient K c a p decreases with the increase of the relative cap dimension. The cap shows force-increasing effect on piles when the relative cap dimension is smaller, i.e. K c a p > 1 , which should be highly concerned in the sea-crossing bridge design.

Crude oil leakage from tankers, offshore platforms, drilling rigs and wells, causing severe pollution to the environment has led to irreversible damage to ocean habitat and inhabitants. It has become one of the greatest global environmental concerns which has recently attracted major public awareness. In addition, the contamination of sea and inhabitants. It has significantly harmed the fishing and seafood industry, and even raises health and life issues for millions of human beings. Until now, there is still no viable and practical method to effectively reduce the damage from crude oil spill. This has attracted numerous researchers’ attention. For developing an environmentally friendly and cost-effective polymer absorbent for oil spill cleaning. Recently, among all the efforts, it is proven that biomass aerogel can be used as an outstanding absorbent for oil–water separation, which is a feasible solution for tackling the crude oil issue. In this article, a comprehensive review on the current state-of-art for biomass-based aerogels utilised in the field of oil/water separation is provided. This includes the preparation procedures, fabrication processes, and the categorisation of various types of aerogels. Additionally, the future direction and technological advancement will be discussed in detail.

Breast cancer is one of the most common malignant tumors in women, which seriously affect women's physical and mental health and even threat to life. At present, mammography is an important criterion for doctors to diagnose breast cancer. However, due to the complex structure of mammogram images, it is relatively difficult for doctors to identify breast cancer features. At present, deep learning is the most mainstream image classification algorithm. Therefore, this study proposes an improved DenseNet neural network model, also known as the DenseNet-II neural network model, for the effective and accurate classification of benign and malignant mammography images. Firstly, the mammogram images are preprocessed. Image normalization avoids interference from light, while the adoption of data enhancement prevents over-fitting cause by small data set. Secondly, the DenseNet neural network model is improved, and a new DenseNet-II neural network model is invented, which is to replace the first convolutional layer of the DenseNet neural network model with the Inception structure. Finally, the pre-processed mammogram datasets are input into AlexNet, VGGNet, GoogLeNet, DenseNet network model and DenseNet-II neural network model, and the experimental results are analyzed and compared. According to the 10-fold cross validation method, the results show that the DenseNet-II neural network model has better classification performance than other network models. The average accuracy of the model reaches 94.55%, which improves the accuracy of the benign and malignant classification of mammogram images. At the same time, it also proves that the model has good generalization and robustness.

The Qulong and Xiongcun porphyry deposits in the Gangdese Copper (Molybdenum) Metallogenic Belt (GCMB) are both giant porphyry deposits. They were located in the eastern and the central GCMB (about 400 km apart), and were generated in the Miocene and the Jurassic, respectively. In order to reveal why the two asynchronous deposits could occur in the same metallogenic belt, the in-situ zircon U-Pb, zircon and apatite fission track (ZFT and AFT), and apatite (U-Th)/He (AHe) dating were conducted. Their associated exhumation amounts and histories were compared, and finally the prospecting significance was evaluated. AHe ages of the Qulong deposit indicate it underwent an amount of at least 1.3–2.8 km exhumation since 13.8–13.1 Ma. However, AFT data and modeling results reveal that the Xiongcun deposit experienced two stages of rapid cooling at 20–13 Ma and 13–8 Ma, respectively. The temperature decreases from 160 °C to 40 °C after ∼20 Ma and the corresponding exhumation amount is about 4–6 km. Integrated the regional thermochronological data, exhumation amounts of 4–6 km and

As a severe tire failure, tire blowout during driving can significantly threaten vehicle stability and road safety. Tire blowout models were developed in the literature to conclude that a vehicle always deviates to the tire blowout side. However, this conclusion is proved to be inaccurate in this paper, since one important factor was largely ignored in the existing tire blowout models. Toe angle, as a basic and widely applied setup on ground vehicles, can provide preset and symmetric lateral tire forces for normal driving. However, when tire blowout occurs, different toe angle setups can impact vehicle motions in different ways. For the first time, the toe angle is explicitly considered and integrated into a tire blowout model in this paper. For different tire blowout locations, driving maneuvers, and drivetrain configurations, the impacts of different toe angle setups on the variations of tire friction forces and vehicle motions are analyzed. The developed tire blowout model with toe angles is validated through both high-fidelity carsim simulation results and experimental results of a scaled test vehicle. Both simulation and experimental results show that a vehicle may not deviate to the tire blowout side, depending on the toe angle setups and driving maneuvers. Moreover, the experimental results also validate that the proposed tire blowout model can accurately evaluate the tire blowout impacts on vehicle dynamics.

Background Accurately segment the tumor region of MRI images is important for brain tumor diagnosis and radiotherapy planning. At present, manual segmentation is wildly adopted in clinical and there is a strong need for an automatic and objective system to alleviate the workload of radiologists. Methods We propose a parallel multi-scale feature fusing architecture to generate rich feature representation for accurate brain tumor segmentation. It comprises two parts: (1) Feature Extraction Network (FEN) for brain tumor feature extraction at different levels and (2) Multi-scale Feature Fusing Network (MSFFN) for merge all different scale features in a parallel manner. In addition, we use two hybrid loss functions to optimize the proposed network for the class imbalance issue. Results We validate our method on BRATS 2015, with 0.86, 0.73 and 0.61 in Dice for the three tumor regions (complete, core and enhancing), and the model parameter size is only 6.3 MB. Without any post-processing operations, our method still outperforms published state-of-the-arts methods on the segmentation results of complete tumor regions and obtains competitive performance in another two regions. Conclusions The proposed parallel structure can effectively fuse multi-level features to generate rich feature representation for high-resolution results. Moreover, the hybrid loss functions can alleviate the class imbalance issue and guide the training process. The proposed method can be used in other medical segmentation tasks.

It is an essential capability of indoor mobile robots to avoid various kinds of obstacles. Recently, multimodal deep reinforcement learning (DRL) methods have demonstrated great capability for learning control policies in robotics by using different sensors. However, due to the complexity of indoor environment and the heterogeneity of different sensor modalities, it remains an open challenge to obtain reliable and robust multimodal information for obstacle avoidance. In this work, we propose a novel multimodal DRL method with auxiliary task (MDRLAT) for obstacle avoidance of indoor mobile robot. In MDRLAT, a powerful bilinear fusion module is proposed to fully capture the complementary information from two-dimensional (2D) laser range findings and depth images, and the generated multimodal representation is subsequently fed into dueling double deep Q-network to output control commands for mobile robot. In addition, an auxiliary task of velocity estimation is introduced to further improve representation learning in DRL. Experimental results show that MDRLAT achieves remarkable performance in terms of average accumulated reward, convergence speed, and success rate. Moreover, experiments in both virtual and real-world testing environments further demonstrate the outstanding generalization capability of our method.