Search

Ambipolar carbon nanotube based field-effect transistors (AP-CNFETs) exhibit unique electrical characteristics, such as tri-state operation and bi-directionality, enabling systems with complex and reconfigurable computing. In this paper, AP-CNFETs are used to design a mixed-signal machine learning logistic regression classifier. The classifier is designed in SPICE with feature size of 15 nm and operates at 250 MHz. The system is demonstrated in SPICE based on MNIST digit dataset, yielding 90% accuracy and no accuracy degradation as compared with the classification of this dataset in Python. The system also exhibits lower power consumption and smaller physical size as compared with the state-of-the-art CMOS and memristor based mixed-signal classifiers.

Gene therapy employs exogenous nucleic acids to treat genetic diseases and disorders. The insufficient cytosolic delivery of genetic materials remains a major hurdle for effective gene therapy. To address this challenge, we have designed and synthesized various cationic protein nanocapsules that can efficiently condense nucleic acids via self-assembly. Through systematically investigating the gene transfection efficiency of these nanocapsules as delivery vectors, we find that nanocapsules, which were synthesized with hydrolyzable polymers containing tertiary amine groups, afford the highest transfection efficiency (∼ 80%), resulting in stable protein expression for over four days. The mechanistic study reveals that tertiary amine groups facilitate the endosomal escape of the nucleic acid-nanocapsule complexes after their cell internalization via endocytosis. The subsequent hydrolysis of the polymers triggers the cytosolic release of the nucleic acids, thereby prompting gene expression. Our results not only provide a new class of gene delivery vectors but also detail the parameters for future vector design.

At present, medical images have played a more and more important role in clinical treatment. Lung images provide an important reference for doctors to make a diagnosis. Especially for surgical patients, a tumor can be accurately removed based on the full cognition about its size, position, and quantity. Therefore, computer-aided diagnosis for the analysis and treatment of a lot of lung tumor images is very important. Aiming at complexity and self-adaption of image segmentation in lung tumors, this article proposed a parallel deep learning algorithm with hybrid attention mechanism for image segmentation. First, lung parenchyma was extracted via preprocessing images. Then, images were input into hybrid attention mechanism and densely connected convolutional networks (DenseNet) module, respectively, where hybrid attention mechanism consisted of a spatial attention mechanism and a channel attention mechanism. Finally, four feasible solutions were proposed for the verification through changing the convolution quantity of dense block in DenseNet. The network structure with the better performance was achieved. The experimental results prove the parallel deep learning algorithm with hybrid attention mechanism performed well in image segmentation of lung tumors, and its accuracy can reach 94.61%.

PurposeMeasuring the performance of public participation is conducive to improving participation systems. However, such measurement, particularly in urban regeneration projects, is difficult because of the complex indicators and multiple stakeholders involved. The purpose of this paper is to measure the public participation level in urban regeneration projects in China.Design/methodology/approachThis study adopts a perception difference-based method to measure the public participation level in urban regeneration projects in China. Specifically, an indicator system consisting of 12 indicators from three categories was first purposed. A perception difference-based method that integrates ANOVA test and Tukey test were then developed. The method was validated using five represented projects, and the results are interpreted based on a proposed measurement matrix.FindingsRegardless of the type of indicator, the perception of the government aligns with the perception of private sector professions, however, deviates from the perception of citizens. By taking the mean score and the significance level among stakeholders of perception as two dimensions, different patterns of issues in the current participation practice in urban regeneration are manifested.Research limitations/implicationsTheoretically, the proposed indicator system and perception difference-based method combined to provide a holistic view of public participation, which is verified to provide a better measurement. Practically, the authors’ methodology helps in revealing issues in current participation practice and further leading to designing coping strategies. Nonetheless, the proposed method requires further validation in participation practices in China and other countries.Originality/valueBy considering the perception mean and the significance level as two dimensions, a public participation measurement matrix is proposed. The performance in different indicators are classified into four stages accordingly, namely idling, starting, running-in and accelerating.

The development of beyond-CMOS technologies with alternative basis logic functions necessitates the introduction of novel design automation techniques. In particular, recently proposed computing systems based on memristors and bilayer avalanche spin-diodes both provide asymmetric functions as basis logic gates - the implication and inverted-input AND, respectively. This article therefore proposes a method by which Karnaugh maps can be directly applied to systems with asymmetric basis logic functions. A set of identities is defined for these memristor and spintronic logic functions, enabling the formal demonstration of the Karnaugh map method and an explanation of the proposed technique. This method thus, enables the direct minimization of spintronic and memristive logic circuits without translation to conventional Boolean algebra, facilitating the further development of these novel computing paradigms. Preliminary analyses demonstrate that this Karnaugh map minimization approach can provide a 28 percent reduction in step count as compared to previous manual optimization.

Matrix completion has been widely used in image recovery and recommendation. The conventional matrix completion models based on multi-layer perceptron (MLP) only has local constraints on the observation data so that the completed matrix contains a lot of noise. Therefore, this paper proposes a novel matrix completion model based on the MLP integrating kernel regularization (MCKR). The proposed model uses the MLP to extract feature, which can automatically extract interaction feature of missing data and observational data. Moreover, through a non-linear mapping of low-dimensional latent subspace and schatten p-norm, a novel kernel regularization is derived to provide a global constraint for reducing the noise of the completed matrix. Finally, the validity and practicability of the proposed model is verified on three publicly available datasets. Compared with baselines including matrix factorization (MF), Logistic matrix factorization (LMF), expertise matrix factorization (EMF) and multi-layer perceptron (MLP), the proposed model achieves state-of-the-art result.

Obtaining excellent mechanical properties of spur bevel gear has been widely emphasized, owing to its indispensable effect on momentum. However, the conventional long-time and high-temperature heat treatment can cause microstructure coarsening, which dramatically decreases the mechanical properties. Since the normalizing process can significantly modulate the mechanical properties of spur bevel gears by adjusting their microstructure and crystallography behaviors, in the present study, the normalizing before or after carburizing heat treatment processes (NBCP and NACP, respectively) were proposed. The effect of heat treatment processes on the microstructure and crystallography of gear specimens was investigated. The results show that both NBAP and NACP had a strong effect on microstructure refinement and hardness improvement compared to the conventional process. NBCP raised the tendency for AlN precipitation, which could retard the microstructure coarsening during carburizing. Furthermore, NACP directly promoted the precipitation of globular Cr-rich M3C carbides which achieved the strongest pinning effect and brought about extremely fine microstructure. For crystallographic analysis, the 24 martensite variants in all gear specimens held Kurdjumov–Sachs orientation relationship to parent austenite. The orientations of martensite variants in gear specimens applying conventional process and NBCP were distributed regularly. However, the orientations of martensite variants in NACP gear specimen did not follow the strict rule for variant selection inside prior austenite grains owing to the distortive effect of diffuse M3C carbides on the matrix, and the adjacent martensite variants possessed less sharing of {110} habit plane compared to NBCP and conventional process.

Due to the non-uniform dynamic recrystallization (DRX) and deformation behavior during hot forging process, the spur bevel gears tend to receive heterogeneous microstructure which greatly affects their mechanical properties and service life. Therefore, the aim of this study is to investigate the evolution of microstructure and texture of hot forged 20CrMnTiH steel spur bevel gear utilizing both simulation and experiment method. In the present study, a sound finite element model of hot forging of a 20CrMnTiH steel spur bevel gear was developed under the Deform-3D software. The experimental analysis was conducted by employing scanning electron microscope, electron backscatter diffraction and transmission electron microscope. The simulation results indicated that the grain size, effective strain and DRX volume fraction were inhomogeneous from the surface region to the core region of gear tooth. The experimental analysis revealed that the surface region of gear tooth possessed the strongest γ-fiber and lowest dislocation density. However, both subsurface and core regions had a combination of moderate α- and γ-fibers, and large amount of dislocations pinned by precipitates can be observed in these two regions. Furthermore, the microhardness exhibited parabola distribution through the gear tooth, and the subsurface region possessed the largest microhardness value.

Amorphous-nanocrystalline (a-nc) boron carbide thin films were prepared by chemical vapor deposition (CVD) by using ortho-carborane as a single-source precursor for inertial confinement fusion (ICF) application. The effects of deposition temperature (Tdep) and total pressure (Ptot) on chemical composition, microstructure, stoichiometry and morphology of the boron carbide films were investigated. The TEM results show that the structure of the film is mainly composed of amorphous boron carbide with dispersive nano-grains, which will be able to improve the mechanical properties of the film with relatively low roughness. The hardness of the (a-nc) boron carbide film obtained in this study reached 20.6 GPa, and roughness of 3.21 nm. The deposited films sized 0.2–1.9 μm in thickness with B/C atomic ratio ranged from 0.14 to 3.29. The deposition rate decreased with increasing deposition temperature and Ptot, while B/C ratio increased.

With the advancement of Internet of Things technology, we are able to collect massive people's trajectory data from various GPS services. These large amounts of trajectory records enable us to better understand human mobility patterns. Meanwhile, we are able to extract social relationships based on these digital records to provide personalized recommendation services, such as points of interests (POI) recommendation and friend recommendation. In this paper, we propose to recommend friends for taxi drivers based on vehicular trajectory records. For this purpose, we propose to construct a vehicular ad hoc network based on co-occurrence phenomenon. Furthermore, we take advantages of the network representation learning technique on the vehicular ad hoc network for learning driver vectors. Finally, potential friends are recommended based on the similarity of driver vectors. Extensive experimental results on two real-world datasets demonstrate that our proposed method has the best performance on friend recommendation compared with several state-of-the-art methods. To the best of our knowledge, this is the first attempt to recommend friends for taxi drivers based on vehicular ad hoc network representation learning.

Avionics systems software usually has the characteristics of complex structure, wide function coverage, high reliability, and high safety. These characteristics have led to the increasing scale and complexity of software systems, and the increasing variety of research & development (R&D) personnel. This further intensify the knowledge-intensive trend of software development, and make the software requirements elicitation activities more complicated. In the knowledge engineering community, an ontology is an explicit specification of a conceptualization. This paper uses the ontology method to construct a requirement knowledge framework and requirement knowledge is expressed as a clear, complete, and consistent hierarchical ontology concept and association, which is more conducive to knowledge sharing and reuse as well as reflects multiple viewpoints of stakeholders. This paper builds a requirements knowledge multi-ontology framework which is divided into GO (generalization ontology), TO (task ontology), DO (domain ontology), and AO (application ontology). And it decomposes the GO into structure ontology and action ontology in the framework. It makes up for the deficiency of undifferentiated knowledge representation of the GO. Then, this paper proposes and integrates the concept of software requirement error pattern into the multi-ontology framework in a consistent form. In addition, this paper evaluates the quality of the constructed ontology and the evaluation results show that the constructed ontology in this paper has a high quality. Finally, this paper facilitates the requirements elicitation based on the multi-ontology framework to avoid errors and improve the quality and reliability of software products.

Orthogonal defect classification (ODC) is a multi-dimensional measurement system with both qualitative and quantitative characteristics. And it is currently widely used in the software industry. However, its high level of abstraction leads to limited semantic information. Therefore, it seems to have a limited role in the process of software engineering of software-intensive systems (SISs). To solve this problem, this paper first analyzes software error lifetime from the perspective of knowledge-based software engineering and proposes an error generation model. Then, the paper proposes the concepts of software error pattern (SEP) and software requirements error pattern (SREP) based on the ODC. Then, according to an error generation mechanism, four types of software-hardware integrated error pattern (SHIEP) in the requirement stage, which is a sub-category of SREP, and corresponding ontology representation are given, focusing on “scenario”, “error manifestation” and “solution”. Finally, this paper takes a certain type of airborne radar software system as an example, uses protege to edit the SHIEPs and instances, and further introduces the application of software FMEA based on the above work. The results show that the prior information based on the SHIEPs is helpful to discover potential failures and failure modes that may adversely affect the function or performance of SISs. Therefore, the proposed SHIEP is of great significance for improving the quality of software development and verification.

The effects of Ti on the microstructures, macrohardness and compressive properties of the as-cast Al0.5CrFeNi2.1Mn0.8Tix HEAs were investigated. The results showed that the microstructures of as-cast Al0.5CrFeNi2.1Mn0.8Tix alloys was changed from FCC phase to a mixture of FCC and BCC phases, then to a mixture of BCC phase and Ti-containing intermetallic compound as the increasing of Ti content. Chrysanthemum-like eutectic microstructure was obtained in the Al0.5CrFeNi2.1Mn0.8Ti0.5 and Al0.5CrFeNi2.1Mn0.8Ti alloys. The area of flower core was composed of BCC2 phase, and eutectic microstructure was achieved in the petal area which contained BCC1 phase and BCC2 phase. Moreover, the macrohardness of the as-cast alloys increased with the increasing of Ti, and the Al0.5CrFeNi2.1Mn0.8Ti0.5 alloy showed excellent comprehensive compressive properties.

Morphological features have shown promising performances for hyperspectral image (HSIs) classification, as they can efficiently extract the multilevel spatial information of HSIs. However, the objects in the scenes are always with different sizes and shapes, making it difficult to excavate spatial information of important structures completely by the existing morphological methods. To address this problem, we propose a novel two-stage framework based on morphology and superpixel. Specifically, we propose self-dual saliency profiles (SPs) based on a saliency measure considering the grayscale contrast within objects and edge information. SPs are hierarchical features that characterize spatial information for salient objects whose saliency index is the significant local maxima. The SPs are constructed based on a two-step algorithm. First, all salient objects of different shapes in the scene are preserved, and the undesired spatial details are discarded by attribute filters based on the saliency measure. Second, the morphological feature is generated based on the organization structure of salient objects in the scene, which provides hierarchical spatial features of the image. Then, the superpixel segmentation is performed on each of the extracted SPs on the basis of the spatial information of salient objects. And, two types of superpixel-based features are extracted from SPs to exploit the information in SPs. The extracted innersuperpixel and intersuperpixel features are fused with spectral information to produce the classification map. The experiments conducted on three HSIs show that, the proposed approach significantly outperforms the other state-of-the-art methods.

The domain-wall (DW)-magnetic tunnel junction (MTJ) device implements universal Boolean logic in a manner that is naturally compact and cascadable. However, an evaluation of the energy efficiency of this emerging technology for standard logic applications is still lacking. In this article, we use a previously developed compact model to construct and benchmark a 32-bit adder entirely from DW-MTJ devices that communicates with DW-MTJ registers. The results of this large-scale design and simulation indicate that while the energy cost of systems driven by spin-transfer torque (STT) DW motion is significantly higher than previously predicted, the same concept using spin-orbit torque (SOT) switching benefits from an improvement in the energy per operation by multiple orders of magnitude, attaining competitive energy values relative to a comparable CMOS subprocessor component. This result clarifies the path toward practical implementations of an all-magnetic processor system.

Summary There is high risk of fetal neurodevelopmental defects in pregestational diabetes mellitus (PGDM). However, the effective mechanism of hyperglycemia-induced neurodevelopmental negative effects, including neural stem cell self-renewal and differentiation, still remains obscure. Neuropoietic cytokines have been shown to play a vital part during nervous system development and in the coordination of neurons and gliocytes. Nuclear factor (erythroid-derived 2)-like 2 (Nrf2) dysfunction might be related to a reduction of self-protective response in brain malformation induced by hyperglycemia. We therefore evaluated the role of Nrf2 and neuropoietic cytokines in fetal neurodevelopmental defects induced by PGDM and determined the mechanisms involved. Our data reveal that PGDM dramatically impairs the developmental switch of neural stem cells from neurogenesis to gliogenesis, principally under the cooperative mediation of neuropoietic cytokine CNTF and Nrf2 antioxidative signaling. This indicates that CNTF and Nrf2 could be potentially used in the prevention or therapy of neurodevelopmental defects of PGDM offspring., Graphical Abstract, Highlights • Hyperglycemia affects self-renewal ability of neural progenitor cells • Hyperglycemia twists the developmental switch from neurogenesis into gliogenesis • CNTF regulates hyperglycemia-induced imbalance between neurogenesis and gliogenesis • CNTF and Nrf2 co-ordinately regulate neural development through p-STAT3, Molecular Neuroscience; Developmental Neuroscience; Diabetology

The microstructures, compression and corrosion behaviors of the as-cast AlxCrFeMn0.8Ni2.1 high-entropy alloys (0 ≤ x ≤ 2.3) were investigated in this paper. It was found that the crystal structure changed from initial dual FCC structure to mixed FCC and BCC structure, then to BCC structure as the increasing of Al content. Al0.8CrFeMn0.8Ni2.1 alloy exhibited a typical spinodal structure consisting of alternating two phases microstructure. Moreover, sunflower-like microstructure was obtained in the as-cast AlxCrFeMn0.8Ni2.1 alloys (1.0 ≤ x ≤ 2.0). With the increasing of Al, the macrohardness increased while the plasticity decreased in the alloys. The addition of an appropriate amount of Al could improve the compressive fracture strength of the alloys. In addition, the corrosion resistance deteriorated slightly with the increasing of Al in 1 mol/L NaCl solution.

For the pollution of radioactive materials, it is of great importance to develop efficient adsorbents for radioactive iodine adsorption in aqueous solution. In this work, a simple and green strategy was developed to construct hydrophobic surface on natural cotton fibers (n-CF) based on organic-soluble carbon dots (OCDs) for the first time. The results demonstrated the successful constructed hydrophobic n-CF@OCDs expressed excellent stability and selectivity for iodine (I2) adsorption in water. The maximum adsorption capacity for I2 on n-CF@OCDs is calculated to be 190.1 mg g−1, which is about 6.8 times higher than that of n-CF (28.1 mg g−1), this highly I2 adsorption efficiency should be attributed to the hydrophobic properties of adsorbent. The adsorption mechanism was also discussed in this work. In addition, the adsorbed I2 could be desorbed easily with a simple reductive process at ambient conditions, which can lead to not only the restore of I2 but also the recycling of adsorbent, illustrating their good practicability. Furthermore, this universal strategy can also be used for construction of hydrophobic surface on various natural biomaterials, demonstrating its potential application in constructing of hydrophobic surface and used for the adsorption and removal of nonpolar pollutions or radioactive waste in aqueous solutions.

Building damage assessment is a critical task following major hurricane events. Use of remotely sensed data to support building damage assessment is a logical choice considering the difficulty of gaining ground access to the impacted areas immediately after hurricane events. However, a remote sensing based damage assessment approach is often only capable of detecting severely damaged buildings. In this study, an airborne LiDAR based approach is proposed to assess multi-level hurricane damage at the community scale. In the proposed approach, building clusters are first extracted using a density-based algorithm. A novel cluster matching algorithm is proposed to robustly match post-event and pre-event building clusters. Multiple features including roof area and volume, roof orientation, and roof shape are computed as building damage indicators. A hierarchical determination process is then employed to identify the extent of damage to each building object. The results of this study suggest that our proposed approach is capable of 1) recognizing building objects, 2) extracting damage features, and 3) characterizing the extent of damage to individual building properties.

In this work, nanoporous Ni3S2 film (Ni3S2/Ni) is in situ synthesized by direct sulfurization of Ni foam under a mild hydrothermal process. Surprisingly, it is found out that the obtained Ni3S2/Ni exhibits outstanding HER activity and excellent stability in acidic electrolyte. The structure and nature of the Ni3S2/Ni are analyzed with X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS) and field emission scanning electron microscope (FE-SEM). On Ni3S2/Ni, the onset potential is only −6.23 mV (vs. RHE) while the large exchange current density is 790 µA cm−2 and the Tafel slope is 62.47 mV dec−1. The experimental results demonstrate the potential of Ni3S2/Ni for its replacement of Pt-based catalysts.

Compared to hot forming, cold forming can manufacture components with higher accuracy and better mechanical properties. However, for some light-weight and high-strength materials, such as 7xxx series aluminum alloy, their room-temperature plasticity is poor, which limits the production of complex structural components of these materials. In this study, the microstructure evolution of annealed 7075 aluminum alloy and its influence on room-temperature plasticity were investigated. Firstly, the annealing experiments with different annealing temperature and soaking time were conducted, and the annealed microstructure evolution was analyzed. Secondly, the room-temperature tensile tests were conducted to evaluate the room-temperature plasticity. The relationship between annealed microstructure and plastic deformation behavior was studied. The new effect mechanisms of annealed microstructure on room-temperature plasticity was found. The results indicated that the annealed grains with high Schmid factor (˃0.4) adjacent to coarse granular-shaped secondary particles exhibited easy multiple slip system activation and smoothly transformed strain through the slip bands, which promoted the grain fragmentation. Concurrently, the secondary particles underwent rupture and separation. Owing to the low strain localization and multiple slip systems of the grain fragments, they can coordinately deform around the prior particle-matrix interfaces or between the separate particle pieces, which led to good room-temperature plasticity.

Prediction of temperature field is a critical step in the fire resistance design of concrete filled steel tubes (CFST). Theoretically, this step is to solve a transient heat conduction problem defined in a composite medium, of which analytical solutions are either very complex or not available in an explicit form. Though the problem can be solved satisfactorily by various numerical methods, it is not easy and convenient for engineers to implement the methods in practical design or include them in design codes. It has been shown recently, instead of calculating the distribution of temperature, the average temperature over the cross section of a CFST can be used in the calculation of its fire resistance. Based on the average temperature, this paper aims at providing a unified analytical solution for calculating fire resistance of circular and equilateral polygonal CFST columns with or without protection. A simplified method for calculating average temperature over a CFST cross section with or without protective coating is developed and validated, which can take the effect of cross sectional shape and material thermal properties into consideration. The simple temperature calculation method is incorporated into the unified method to calculate the fire resistance of CFST columns with or without protective coating. Comparisons are made between the calculation results using the new equations with those from other existing methods and experiments, which suggest that the newly developed unified method can predict the average temperature and the fire resistance f CFST columns satisfactorily.

In this study, oxalic acid showed better performance than inorganic acid in the indium leaching system from waste liquid crystal displays. The In3+ leaching efficiency with the powder dosage of 50 ...

In recent years, advanced geospatial technologies have been playing an increasingly important role in supporting critical decision makings in disaster response. One rising challenge to effectively use the growing volume of geospatial data sets is to rapidly process the data and to extract useful information. Unprocessed data are intangible and non-consumable, and often create the so-called “data-rich-but-information-poor” situation. To address this issue, this study proposed a Data Envelopment Analysis (DEA) based information salience framework to prioritize the sequence of the information processing tasks. The proposed model integrates the DEA efficiency score with a linguistic group decision process. For the input variables, computational complexity and intensity are selected to measure the difficulty in information processing. For the outputs, the performance of each processing tasks is evaluated based on the experts’ judgment on how the processing tasks satisfy the needs of decision makers. These needs are characterized by four classic disaster functions. A unique element of our proposed framework is that cone constraints are added to the DEA model based on the experts’ evaluation of the importance of the four disaster functions to model the dynamic information need. The proposed model was validated with a Hurricane Sandy based case study. The results indicate that the proposed framework is capable of prioritizing geospatial data processing tasks in a systematic manner and accelerating information extraction from disaster related geospatial data sets.

Memristors are being explored as a potential technology to replace CMOS for logic-in-memory systems that exploit the memristive non-volatility. Memristors are two-terminal, non-volatile device that exhibit a variable resistance that is dependent on the applied voltage history of the device, providing the capability to store and process information within the same structure. The ability of memristors to perform logic has been previously demonstrated, but previous analyses of memristor logic efficiency have not included the overhead CMOS circuitry that is required to control memristor logic operations. In this paper, the required overhead CMOS circuitry for implementing logic with memristors is evaluated for standard logic gates and a one-bit full adder to enable an analysis of the overall system efficiency. The results show that the number of CMOS devices in the overhead circuitry can be upwards of 50 times that of a conventional CMOS implementation, and that the power-delay product of the memristor logic with overhead circuitry is roughly one billion times greater than for conventional CMOS circuits. These results enable the conclusion that the overhead circuit requirements for stateful memristor logic threaten to negate any efficiency improvements that are achieved by the memristors themselves.

A nanohybrid-based ratiometric phosphorescent assay composed of water-soluble carbon dots (CDs) and water-dispersible lanthanide-doped inorganic nanoparticles CaTiO3:Pr3+@SiO2 is constructed. The as-prepared CDs and CaTiO3:Pr3+@SiO2 nanoparticles were systematically characterized by transmission electron microscopy (TEM), scanning electron microscopy, dynamic light scattering, X-ray diffraction and X-ray photoelectron spectroscopy (XPS). Under a single excitation wavelength at 345 nm, a dual-emission of CDs at 446 nm and CaTiO3:Pr3+@SiO2 nanoparticles at 615 nm are detected, and the long luminescent lifetimes of the two phosphorescent emissions were confirmed by 3-D time-resolved phosphorescence emission spectra. The excellent phosphorescence performance of CDs and CaTiO3:Pr3+@SiO2 nanoparticles is utilized for the development of a ratiometric phosphorescent platform. In the presence of Hg2+, the phosphorescent emission of CDs was dramatically quenched, and that of CaTiO3:Pr3+@SiO2 nanoparticles nearly unchanged. Taking advantage of this, an effective ratiometric phosphorescent sensor unaffected by sensor concentration, background fluorescence and environmental conditions is constructed. Furthermore, the successful application of the nanohybrid phosphorescent system in water and biological samples lays the foundation for building a reliable approach to monitor heavy metal ions in complex environmental and biological samples.

Spintronic three-terminal magnetic-tunnel-junction (3T-MTJ) devices have gained considerable interest in the field of neuromorphic computing. Previously, these devices required external circuitry to implement the leaking functionality that leaky integrate-and-fire (LIF) neurons should display. However, the use of external circuitry results in decreased device efficiency. We previously demonstrated lateral inhibition with a 3T-MTJ neuron that intrinsically performs the leaking, integrating, and firing functions; however, it required the fabrication of a complex multilayer structure. In this paper, we introduce an anisotropy gradient to implement a single-layer intrinsically leaking 3T-MTJ LIF neuron without the use of any external circuitry. This provides the leaking functionality with no hardware cost and reduced fabrication complexity, which increases the device, circuit, system, and cost efficiency.

A highly sensitive and selective luminescent sensor for the detection of methyl parathion (MP) pesticide was described in this study. The target molecule HL was synthesized by modifying the structure of 4-hydroxybenzlidene imidazolinone (HBI) with nitrogen-containing heterocyclic 1,10-phenanthroline. In the presence of Eu3+, a HL–Eu3+ complex was formed which could emit strong red fluorescence due to the removal of coordinated water molecules and an intramolecular energy transfer from HL to Eu3+. Addition of MP into the strongly fluorescent solution of HL–Eu3+ induced quenching of the complex's fluorescence, and this quenching behavior occurred because of the competition coordination of MP and HL for Eu3+. A calibration curve was developed that related the extent of fluorescence quenching to MP concentration, making the HL–Eu3+ system a sensitive and selective fluorescent sensor for MP. Under the experimental conditions, the detection limit for MP was down to 95 nM based on LOD = 3σ/S. Moreover, the fluorescence assay developed here allowed the detection of MP in two different types of real samples including pond water and pear juice, and satisfactory results demonstrate that this fluorescent sensor based on HL–Eu3+ has potential application in environment and food analysis.

In this study, bimetallic lanthanide coordination polymer nanoparticles (Tb-GMP-Eu CPNs) were developed for sensing alkaline phosphatase (ALP) by utilizing guanine monophosphate (GMP) as the bridge ligand to self-assemble with Tb3+ and Eu3+ in Tris–HCl buffer solution. In this polymer framework, an efficient energy transfer from Tb3+ to Eu3+ occurred because the emission energy of Tb3+ matched well with the absorption energy of Eu3+. As a result, the synthesized Tb-GMP-Eu CPNs exhibited the characteristic red emission of Eu3+ when excited at 290 nm. When ALP was introduced into the Tb-GMP-Eu CPNs system, it would destruct the combination between Tb and GMP by a dephosphorylation process, resulting in interruption of energy transfer from Tb3+ to Eu3+ and therefore the fluorescence quenching of Eu3+. Based on these facts, a fluorescence assay based on bimetallic lanthanide coordination polymer nanoparticles for ALP detection was developed. Such a Tb-GMP-Eu CPNs sensor exhibited sensitive response to ALP with a detection limit down to 0.004 U L−1. Moreover, the successful application of ALP inhibitor evaluation and determination of ALP in serum samples have been demonstrated. Therefore, the proposed method has great potential application for ALP detection in biological and clinical diagnosis.

Orthogonal defect classification is a multi-dimensional measurement system with both qualitative and quantitative features, which is widely used in the software industry. However, the level of abstraction is high, which leads to limited semantic information. Therefore, it has a limited effect in the process of software engineering of complex software systems. To solve this problem, this paper first analyses the software error lifetime from the perspective of knowledge-based software engineering. Furthermore, based on the orthogonal defect classification, the concept of software error pattern is proposed, and its constituent elements and value sets are given. Moreover construct the software error pattern ontology in the requirements analysis phase, focusing on the elements such as “scenario”, “error-manifestation” and “solution”. The example verification part takes unmanned aerial vehicle flight control and management system software as an example to carry out the software requirements error pattern ontology representation and conducts software verification activities based on this and measures the development quality. The results show that the software error pattern can effectively guide the verification of complex software systems and measure the development quality. Therefore, the proposal of software error pattern is of great significance to improve the quality of software development and verification.

Regarding the continuous development of high-speed trains and the increase of running speeds, the aerodynamic design of high-speed trains has become significantly important, while reduction of drag and noise comprises a significant challenge in order to optimize aerodynamic design of high-speed trains. The design form factor of a high-speed train is highly influenced by aerodynamic aspects including aerodynamic drag, lift force, and noise. With the high-speed train as the object, the paper aims to take bionic concept as the entry point, selecting the hummingbird as the bionic prototype and extracting bionic elements to establish a bionic train model. Then, the finite volume method was used for numerical simulation and analysis of the aerodynamic performance and aerodynamic noise of the bionic high-speed train. Computational results prove that drag and noise of the bionic head type were lower than those of the original train; drag of the head train of the bionic model was reduced by 2.21% in comparison with the original model, while the whole-train drag was reduced by 3.53%, indicating that drag reduction effects are available and implying that the bionic head type could reduce drag and noise. Noise sources of the bionic train are mainly located at positions with easy airflow separation and violent turbulence motion. Large turbulence energy is in bogie areas and mainly exists at the leeward side of the bogie area. Obviously, the bogie area is the major noise source of the train. Aerodynamic noise of the bionic train in far-field comprises a wide-frequency range. Noises were concentrated within 613 Hz~3150 Hz. When the bionic high-speed train ran at 350 km/h, through comparative analysis of total noise levels at observed points of the high-speed train, it is found that this position with the maximum noise level was 25 m away from the head train nose tip, with the maximum value of 88.4 dB (A). When the bionic train ran at 600 km/h, the maximum sound pressure level at the longitudinal point was 99.7 dB (A) and the average noise level was 96.6 dB (A). When the running speed increased from 350 km/h to 600 km/h, the maximum noise level increased by 11.3 dB (A) and the average noise level increased by 11.6 dB (A). Computation results of aerodynamic noise at the point which is 7.5 m away from the rail center show that the maximum aerodynamic noise level existed at the first-end bogie of the head train, while the noise level was larger at the position closer to the ground.

The p-type Ge doped Fe0.4Co3.6Sb12-xGex skutterudites with multi-scaled impurity dots (500 nm-2 mm) were successfully prepared by using melt-quenching (MQ) and subsequent spark plasma sintering (SPS) technique. Compared with traditional method, the new technology significantly shortened the processing time from several days to less than 24 hours. The phase of impurity dots was demonstrated to be CoSb through analysis of X-ray diffraction (XRD) and energy-dispersive spectrum (EDS). Impurity dots were induced by Ge substitution of Sb in the non-equilibrium synthesized process. Due to the abandonment of the long reaction of annealing crystallization, a few of Ge atoms would fail to substitute Sb site of skutterudite in this non-equilibrium synthesized process, leading to that the multi-scaled impurity dots randomly distributed in the matrix of skutterudite Fe0.4Co3.6Sb12-xGex. The combination of multi-scaled impurity dots scattering long wavelength heat-carrying phonons and the point defect scattering short and middle wavelength heat-carrying phonons dramatically made the 22.2% reduction of lattice thermal conductivity. As a result, compared with unsubstituted sample of Fe0.4Co3.6Sb12, the maximum ZT value was increased by 30.5%. Thus, the two marked features of this new synthesis process, the shortened preparation time and the enhanced thermoelectric performance, would make a promising commercial application in the future.

With the world development of high-speed railways and increasing speeds, aerodynamic forces and moments acting on trains have been increased further, making trains stay at a “floated” state. Under a strong crosswind, the aerodynamic performance of a train on the embankment is greatly deteriorated; lift force and horizontal force borne by trains will be increased quickly; trains may suffer derailing or overturning more easily compared with the flat ground; train derailing will take place when the case is serious. All of these phenomena have brought risks to people’s life and properties. Hence, the paper establishes an aerodynamic model about a high-speed train passing an air barrier, computes aerodynamic forces and moments, and analyzes pulsating pressures on the train surface as well as those of unsteady flow fields around the train. Computational results indicate that when the train passed the embankment air barrier, the head wave of air pressure full wave is more than the tail wave; the absolute value of negative wave is more than that of the positive wave, which is more obvious in the head train. When the train is passing the air barrier, pressure pulsation values at head train points are more than those at other points, while pressure changes most violently at the train bottom, and pressure values close to the air barrier are more than those points far from the air barrier. Pressure values at the cross section 1 were larger than those of other points. Pressure values at measurement points of the tail train ranked the second place, with the maximum negative pressure of 1253 Pa. Pressure change amplitudes and maximum negative pressure on the train surface are increased quickly, while pressure peak values on the high-speed train surface are in direct ratio to the running speed. With the increased speed of the high-speed train, when it is running in the embankment air barrier, the aerodynamic force and moment borne by each train body are increased sharply, while the head train suffers the most obvious influences of aerodynamic effects.

A simple, rapid, and ultrasensitive fluorescence strategy for adenosine triphosphate (ATP) detection was developed by using a FAM (carboxyfluorescein) labeled DNA (FAM-DNA). In this strategy, highly fluorescent FAM-DNA was used as a probe, and nanoceria (CeO2 NPs) acted as an efficient quencher. FAM-DNA attached to the surface of nanoceria through the coordination between the phosphate group of DNA and NP surface, which induced complete quenching in the FAM-DNA fluorescence due to a photo induced electron transfer (PET) process. It was found that ATP can readily displace adsorbed DNA from nanoceria surface because of the stronger coordination ability of ATP with nanoceria, and the nanoceria-based competitive binding resulted in over 7-fold fluorescence enhancement. Over a wide range from 0.1nM to 1.5μM, a good linear relationship between the fluorescence intensity and the concentration of ATP was obtained and the detection limit was estimated to be as low as 54pM. This method was successfully used to analyze ATP in a single drop of blood and human urine.

Lithium-air batteries are considered to be a potential alternative to lithium-ion batteries for transportation applications, owing to their high theoretical specific energy. So far, however, such systems have been largely restricted to pure oxygen environments (lithium-oxygen batteries) and have a limited cycle life owing to side reactions involving the cathode, anode and electrolyte. In the presence of nitrogen, carbon dioxide and water vapour, these side reactions can become even more complex. Moreover, because of the need to store oxygen, the volumetric energy densities of lithium-oxygen systems may be too small for practical applications. Here we report a system comprising a lithium carbonate-based protected anode, a molybdenum disulfide cathode and an ionic liquid/dimethyl sulfoxide electrolyte that operates as a lithium-air battery in a simulated air atmosphere with a long cycle life of up to 700 cycles. We perform computational studies to provide insight into the operation of the system in this environment. This demonstration of a lithium-oxygen battery with a long cycle life in an air-like atmosphere is an important step towards the development of this field beyond lithium-ion technology, with a possibility to obtain much higher specific energy densities than for conventional lithium-ion batteries.

The detection of nitroaromatic explosives is important due to their strong explosive force and harmfulness in the environment, and for homeland security and public safety. Herein, a phosphorescence CaTiO3:Pr3+@SiO2 nanosensor was successfully developed for the sensitive and selective detection of 2,4,6-trinitrophenol (TNP). The red-emitting phosphorescent chemosensor CaTiO3:Pr3+@SiO2 possessed a long enough phosphorescence lifetime of 0.016 s. The TNP induced phosphorescence quenching process of CaTiO3:Pr3+@SiO2 presented a wide linearity with TNP concentrations ranging from 0.5 to 100 μM, and the detection limit was found to be 20.6 nM. Furthermore, this sensor displayed a high selectivity toward TNP over other structurally similar compounds like 2,4,6-trinitrotoluene (TNT), 2,4-dinitrotoluene (DNT), 4-nitrotoluene (NT), 2,4-dinitrophenol (DNP), 4-nitrophenol (NP) and phenol. This sensing system was successfully used in the test of TNP concentrations in water samples, and showed great potential for practical application.

We introduce a real-time quantitative analytical method for universal silver ions (Ag(I)) and silver nanoparticles (AgNPs) analysis based on a portable nanozyme catalysis-powered portable mini-drainage device. The device is composed of three main glass containers with different specifications. The catalase mimic of ascorbic acid-coated platinum nanoparticles (AA-PtNPs) was used to provide the pumping power to drain water by catalyzing a gas-generation reaction, and the inhibition effect of Ag(I) on the catalytic activity of AA-PtNPs is adopted to connect the target detection event with the mini-drainage device. Experimental results reveal that the mass of the overflowed water is inversely proportional to the concentration of Ag(I) and AgNPs so that their quantitation can be accomplished via real-time weighing of the overflowed water. The importance is that without requiring advanced instruments, this device can quantify Ag(I) and AgNPs with a limit of detection (LOD) of 2.0 nM for Ag(I), and 3.8 pM for AgNPs within 30 min, respectively. The reliability and accuracy are comparable with the inductively coupled plasma optical emission spectrometer (ICP-OES). All these appealing features provide us a remarkable insight into the design of versatile portable devices with potential applications in in-situ environmental monitoring under remote areas and resource-limited settings.

In this study, amorphous silicon nitride fibers were prepared through the nitridation of cured polycarbosilane fibers. It was observed that their composition and properties can be controlled by adjusting the flow of NH 3 during the nitridation process. Based on their compositional and structural stability, the samples could be divided into two classes: stable fibers and unstable fibers. As indicated by the electron spin resonance spectra, the amount of the residual free radicals in the unstable fibers was significantly higher than that in the stable fibers. Because of the reactions of the radicals with the moisture in the air, the oxygen content of the unstable fibers increased day by day until the residual radicals were exhausted. Thus, to develop silicon nitride fibers with both low oxygen content and low carbon content, the amount of NH 3 used should be optimized to eliminate the free radicals. These results suggest that it is possible to tailor the nitridation conditions for preparing high-purity silicon nitride materials so that they exhibit desirable properties, such as compositional and structural stability, good mechanical properties, and high electrical resistivity.

In this study, silica coatings with different thickness were prepared on silicon nitride fibers by a continuous dip-coating method. The effects of the coatings on the mechanical properties of the silicon nitride fibers were investigated. The SiO 2 coatings with uniform thickness were prepared from a sol solution with a concentration of 0.75 wt% and then heat-treated at 400 °C, and the strength of the fibers was improved by the treated coating. The tensile strength of a coated fiber was approximately 26% higher than that of an uncoated fiber because the thin coating healed the surface defects. Our study also confirmed that the size of sol particles must match that of the flaws on the fiber surface before these flaws could be effectively repaired. Finally, a probable mechanism will be proposed here to explain this effect. The present results demonstrate that the strength of silicon nitride fibers can be enhanced by coating them through the sol–gel process, and the findings are expected to provide guidelines for repairing strength-limiting flaws in other fibers.

Bogies are an important component of high-speed trains. The level of mechanical performance of bogies has a major influence on the safety and reliability of high-speed train. Therefore, conducting fault diagnoses on bogies with big data is very important. Fault mechanisms of bogies are very complex, and feature signals are nonobvious. For these reasons, fault information of bogies cannot be effectively extracted using the traditional signal processing method. Therefore, this paper adopted the deep neural network to recognize faults in bogies. The deep neural network offers numerous benefits in this context. Using deep neural networks, fault information in a signal spectrum can be extracted in a selfadaptive method. This technique is free of dependence on extensive signal processing knowledge and diagnostic experience. Compared with the traditional intelligent diagnosis method, the deep neural network can obtain a higher diagnostic accuracy. Additionally, the deep neural network does not depend on the sample size, and it can obtain high diagnostic accuracy even when the sample size is relatively small. It also achieves very high diagnostic accuracy applied to high-speed trains with different speeds and different faults, which shows that the method is extensively applicable. Furthermore, the recognition accuracy rate of the deep neural network under normal conditions can reach 100%. This method provides a new paradigm for fault diagnosis of the high-speed train with big data and plays an important role in this field.

A novel method has been presented to modify glassy carbon electrode (GCE) with graphene oxide (GO) nanocomposite without introducing any electrode binder such as chitosan and Nafion. First, modify GCE with RuO2 nanoparticles which have been dispersed in cetyltrimethyl ammonium bromide (CTAB) aqueous solution. Then, highly adhesive RuO2/CTAB/GO nanocomposite membrane formed on GCE by immersing RuO2/CTAB modified GCE in GO suspension. CTAB plays significant roles not only in the preparation of the nanocomposite but also in the immobilization of nanocomposite on GCE surface. First, CTAB was used as the dispersant of RuO2 nanoparticles. Second, CTAB acted as the molecular linker to bind RuO2 nanoparticles on graphene sheets. Third, CTAB formed CTAB/GO nanocomposite which is highly adhesive on the surface of electrodes such as GCE and ITO (indium tin oxide). The obtained RuO2/CTAB/GO/GCE shows excellent electrocatalytic ability towards the oxidation of hydrazine. The oxidation of hydrazine on RuO2/CTAB...

Amorphous silicon nitride fibers were characterized with respect to their chemical and phase compositions. The high-temperature behavior of the fibers was investigated in N2 and Ar at temperatures ranging from 1350 to 1600 °C through tensile tests, pore volume analysis, elemental analysis, weight loss, X-ray diffraction, and scanning electron microscopy to elucidate the effects of the atmosphere on the fiber degradation mechanism. The fibers demonstrated better high-temperature stability in N2 instead of Ar. In particular, the fibers heat-treated in N2 at 1450 °C retained 43% of their original strength (0.64 GPa), while the fiber strength was completely lost in Ar at the same temperature. The primary reason for the strength degradation of the amorphous fibers was the expanding nanopores before the formation of the large Si3N4 grains. However, a catastrophic decrease in strength was due to the drastic thermal decomposition of SiNxOy and crystallization of Si3N4 grains, influenced by N2 pressure. Thermodynamic considerations were additionally applied to estimate the decomposition and carbothermal decomposition of crystalline Si3N4, thereby explaining the significant discrepancy of crystallization transformation at 1600 °C under different atmospheres.

This study investigated the effect of sintering temperature on the microstructure and mechanical properties of dental zirconia-toughened alumina (ZTA) machinable ceramics. Six groups of gelcast ZTA ceramic samples sintered at temperatures between 1100 °C and 1450 °C were prepared. The microstructure was investigated by mercury intrusion porosimetry (MIP), X-ray diffraction (XRD), and scanning electron microscopy (SEM) techniques. The mechanical properties were characterized by flexural strength, fracture toughness, Vickers hardness, and machinability. Overall, with increasing temperature, the relative density, flexural strength, fracture toughness, and Vickers hardness values increased and more tetragonal ZrO2 transformed into monoclinic ZrO2; on the other hand, the porosity and pore size decreased. Significantly lower brittleness indexes were observed in groups sintered below 1300 °C, and the lowest values were observed at 1200 °C. The highest flexural strength and fracture toughness of ceramics reached 348.27 MPa and 5.23 MPa m1/2 when sintered at 1450 °C, respectively. By considering the various properties of gelcast ZTA that varied with the sintering temperature, the optimal temperature for excellent machinability was determined to be approximately 1200–1250 °C, and in this range, a low brittleness index and moderate strength of 0.74–1.19 µm−1/2 and 46.89–120.15 MPa, respectively, were realized.

Firstly, the paper established a finite element model for a steel motor shell and computed related modals, vibration velocities, stress and strain respectively. Computational results show that the flange and end shield of the motor shell had the maximum vibration velocities and strain because these locations lacked the reinforcing ribs, while the maximum stress was mainly at joints between different structures. Secondly, the steel material was replaced by the aluminum alloy. Mechanical parameters of the motor shell were recomputed and compared with those of the steel structure. Results show that modal frequency on each order increased, which is good for avoiding the structural resonance. In addition, the maximum stress of the structure decreased by 4.4 MPa, and the maximum strain decreased by 0.27 mm, which could effectively improve the fatigue characteristics of the motor shell under long-term excitation. Then, the boundary element method was used to compute radiation noises of the motor shell in far field, where the radiation noise presented an obvious directivity. Finally, the paper proposed a GA-BP neural network model to predict the radiation noise of the motor and compared the prediction results with the boundary element. In the whole analyzed frequency band, the maximum difference between the neural network prediction and the real values did not exceed 5 dB, indicating that it is feasible to predict radiation noises of the motor by the neural network. Additionally, experiments were also conducted and compared with two kinds of numerical methods. Methods proposed in this paper provide some references for realizing the rapid noise reduction and light weight of motors.

With the development of science and technology and the extension of online users, e-commerce platforms gradually and effectively gather social resources such as manpower, technology, production, and capital. More and more traditional enterprises combine online and offline business as their operation and trade methods. E-commerce, as a new channel, has become one of the main transaction modes in the society. Under the background of reform of the supply side, and with the high-speed development of E-commerce, we will promote stock adjustment through incremental reform and optimize the structure of investment and financing in the process of increasing investment of e-commerce enterprises. High-quality products and diverse trading experience are the new development pattern in e-commerce. There are different profit maximization strategies for e-commerce enterprises when they face different economic situations. Besides, cross-border business is an advised way to expand business when the enterprise is under bigger profit. Via all these methods, e-commerce enterprise can help to optimize the structure of industries, circulation, and consumption and promote resource integration and optimization, and people’s living standards will be further improved. Finally, the e-commerce enterprise profit maximization will come true.

In comparison to the conventional complementary pull-up and pull-down logic structure, the pass transistor logic (PTL) family reduces the number of transistors required to perform logic functions, thereby reducing both area and power consumption. However, this logic family requires inter-stage inverters to ensure signal integrity in cascaded logic circuits, and inverters must be used to provide each logical input signal in its complementary form. These inverters and complementary signals increase the device count and significantly degrade overall system efficiency. Dual-gate ambipolar field-effect transistors natively provide a single-transistor XNOR operation and permit highly-efficient and compact circuits due to their ambipolar capabilities. Similar to PTL, logic circuits based on ambipolar field-effect transistors require complementary signals. Therefore, numerous inverters are required, with significant energy and area costs. Ambipolar field-effect transistors are a natural match for PTL, as hybrid ambipolar-PTL circuits can simultaneously use these inverters to satisfy their necessity in both PTL and ambipolar circuits. We therefore propose a new hybrid ambipolar-PTL logic family that exploits the compact logic of PTL and the ambipolar capabilities of ambipolar field-effect transistors. Novel hybrid ambipolar-PTL circuits were designed and simulated in SPICE, demonstrating strong signal integrity along with the efficiency advantages of using the required inverters to simultaneously satisfy the requirements of PTL and ambipolar circuits. In comparison to the ambipolar field-effect transistors in the conventional CMOS logic structure, our hybrid full adder circuit can reduce propagation delay by 47%, energy consumption by 88%, energy-delay product by a factor of 9, and area-energy-delay product by a factor of 20.

The objective of this work is to research impacts of oxidized (OCS), acetylated (ACS) and cross-linked (CLCS) corn starch on gelatinization properties of rice flour and their gel structure during storage at 4 °C for 1 and 7 days. OCS led to significant increase of pasting temperature and solubility in blend flour but decrease of pasting viscosity and swelling power compared with rice flour, while ACS showed an opposite effect in pasting temperature and viscosity. CLCS remarkably raised pasting temperature and viscosity of blend flour and insignificant increase as increasing concentration, and the mixture showed relatively lower swelling power and solubility than rice flour. During cold storage, OCS and ACS substantially inhibited the reorganization of gel network structure and showed high stability in rheology and texture properties. CLCS accelerated the formation of gel network structure, and the mixture gel appeared to stronger rigid structure and denser microstructure than those of rice gel. Formation distinctions among these three gels were found in rheology properties, texture properties, water migration, FTIR spectra, and microstructure of SEM. Overall, CLCS improved the rice stability in heat-processing, and OCS and ACS reduced the change of rice gel quality during cold storage.

In this paper, we study the approximate message passing (AMP) detection approach for beam indexed spatial modulation (BISM) in mmWave massive MIMO system. The spatial modulation (SM) technologies provide flexibility in adjusting the system parameters to achieve spectral efficiency or energy efficiency. This paper presents a BISM scheme based on the concept of SM and beamforming technologies. The AMP detection is studied for BISM with the symbol vector composed of modulation symbols and null symbols. Our results show that BISM has better BER performance than MIMO-BF, and the AMP detection is feasible to detect BISM signal. We provide a decision rule for BISM to choose the most suitable detection methods at different situations.

The dual active bridge (DAB) DC-DC converter not only results in greater backflow-power, but also produces greater current stress on the switch tube under the control of single-phase-shift. Two kinds of dual-phase-shift control methods are compared in detailed by the transmission power, backflow power and current stress in this paper. Current stress of the DPS was smaller under this strategy compared with the EPS. The strategy can not only make the requirements of switching devices lower, reduce the loss, but also improve the efficiency of converter. The advantages and disadvantages of them under optimal control are analyzed in terms of backflow power and current stress. The power characteristics of different phase-shift control methods are presented by establishing mathematical models. The analysis of the characteristics of the transfer function lay a foundation through the small signal modeling.

Although many researches have investigated the effects of starch characteristics on its functional properties to evaluate rice quality, few studies were carried out the correlations between starch molecular structure and rice processing properties. In this study, eight varieties of japonica rice with similar content of protein and lipid and less variant content of amylose (12.12-18.19%) were elaborately selected. The result showed that crystalline and pasting parameters were mainly reliant on the differences in chain length distribution of amylopectin. Amylose content and chain length distribution, especially for average exterior chain length (ECL) and interior chain length (ICL) and their entanglement, showed significant correlation on rheological parameters. Swelling power of rice flour showed negative correlation with amylose content. Furthermore, in temperature-reduction process when the temperature is lower gelatinization temperature of amylopectin, the variation of loss modulus was affected by amylopectin. In addition, the PCA plot clearly revealed the interrelationship of all parameters.