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Quantum computing offers significant speedup compared to classical computing, which has led to a growing interest among users in learning and applying quantum computing across various applications. However, quantum circuits, which are fundamental for implementing quantum algorithms, can be challenging for users to understand due to their underlying logic, such as the temporal evolution of quantum states and the effect of quantum amplitudes on the probability of basis quantum states. To fill this research gap, we propose QuantumEyes, an interactive visual analytics system to enhance the interpretability of quantum circuits through both global and local levels. For the global-level analysis, we present three coupled visualizations to delineate the changes of quantum states and the underlying reasons: a Probability Summary View to overview the probability evolution of quantum states; a State Evolution View to enable an in-depth analysis of the influence of quantum gates on the quantum states; a Gate Explanation View to show the individual qubit states and facilitate a better understanding of the effect of quantum gates. For the local-level analysis, we design a novel geometrical visualization dandelion chart to explicitly reveal how the quantum amplitudes affect the probability of the quantum state. We thoroughly evaluated QuantumEyes as well as the novel dandelion chart integrated into it through two case studies on different types of quantum algorithms and in-depth expert interviews with 12 domain experts. The results demonstrate the effectiveness and usability of our approach in enhancing the interpretability of quantum circuits.

Quantum computing is one of the most promising technology advances of the latest years. Qubits are highly sensitive to noise, which can make the output useless. Lately, it has been shown that superconducting qubits are extremely susceptible to external sources of faults, such as ionizing radiation. When adopted in large scale, radiation-induced errors are expected to become a serious challenge for qubits reliability. We propose an evaluation of the impact of transient faults in the execution of quantum circuits on superconducting chips. Inspired by the Architectural and Program Vulnerability Factors, widely used for classical computation, we propose the Quantum Vulnerability Factor (QVF) to measure the impact of qubit corruption on the circuit output. We model faults, and design a fault injector, based on the latest studies on real machines and radiation experiments. We report the finding of more than 388,000,000 fault injections, considering single and double faults, on three algorithms, identifying the faults and qubits that are more likely to impact the output. We give guidelines on how to map the qubits in real devices to reduce the output error and to reduce the probability of having a radiation-induced corruption modifying the output. Finally, we compare simulations with experiments on physical quantum computers.

In this letter, we propose a novel Non-Line-of-Sight (NLOS) identification and error-mitigation method for dynamic object positioning and ultra-wideband (UWB) ranging. By utilizing inverse estimation on known Anchor Points (APs) and improved robust unscented Kalman filter (IRUKF), while fusing Gyroscope and Accelerometer data, the proposed technology identifies and compensates for NLOS occlusions between tag and APs, reducing positioning errors. The approach has been verified through simulation and experiment, with identification precision of 97.02%. After mitigating errors, substantial error reductions of 91.80% and 98.90% were observed in LOS and NLOS situations, respectively. Moreover, the developed IRUKF effectively minimizes mislocalization by 50.48% in harsh scenarios.

Dissolved organic matter (DOM) plays an important role in promoting or suppressing methylmercury (MeHg) production in wetlands. However, in the context of climate warming, the regulatory mechanism of DOM composition and molecular structure changes in permafrost wetland soil on mercury methylation remains unclear. In this study, we analyzed the distribution characteristics of mercury and methylmercury in permafrost wetland soils in the Greater Khingan Mountains (GKM), and elucidated the driving mechanism of mercury methylation by basic physical and chemical properties and DOM spectral characteristics of soils. The results showed that the mean value of total mercury in the permafrost wetlands of the GKM was 111 ng·g-1, the mean value of methylmercury was 5.69 ng·g-1, and the mean percentage of methylmercury was 6.16%. Hg and MeHg concentrations showed different vertical distribution patterns in the four wetlands, with Hg and MeHg concentrations in both scrub and moss wetlands showing a decreasing trend with soil deepening, but herb and forest wetlands did not satisfy this pattern. Soil warming, associated with the decomposition and mineralization of Soil Organic Matter (SOM), induces an elevation in Dissolved Organic Matter (DOM) content, subsequently contributing to increased levels of mercury methylation and an upswing in methylmercury output. Throughout this process, the spectral properties of DOM play a pivotal role in regulating Hg methylation, with the input of plant sources following closely behind. The content of MeHg in the soil is minimally influenced by the mercury content in the soil.

A wide range of collaborative human-robot applications across diverse domains, encompassing manufacturing, industry, and social interactions, necessitate an autonomous robot to follow its human companion. However, the great degree of flexibility and unpredictability of human mobility makes the integration of multi-sensor measurements for human-following difficult. This paper addresses these challenges by demonstrating the design and implementation of a multi-sensor person-following system. On the one hand, a target tracking control method based on detection of center boxes is proposed. On the other hand, the positioning, navigation and tracking modules are integrated into the robot system. Exhaustive functional verification is conducted on an Ackermann-steering mobile robot, in which an embedded device is the core algorithm board. Notably, we discuss the degradation phenomenon of Light Detection and Ranging (LiDAR) odometry. The positioning error of our system is 2.42%, which is much smaller than other advanced methods. In indoor experiments, our system enables the robot to stably track the target at a distance of approximately 1.5 m. The effectiveness of the entire system is verified, demonstrating its advantage in real-time performance.

With the rapid development of Quantum Machine Learning, quantum neural networks (QNN) have experienced great advancement in the past few years, harnessing the advantages of quantum computing to significantly speed up classical machine learning tasks. Despite their increasing popularity, the quantum neural network is quite counter-intuitive and difficult to understand, due to their unique quantum-specific layers (e.g., data encoding and measurement) in their architecture. It prevents QNN users and researchers from effectively understanding its inner workings and exploring the model training status. To fill the research gap, we propose VIOLET, a novel visual analytics approach to improve the explainability of quantum neural networks. Guided by the design requirements distilled from the interviews with domain experts and the literature survey, we developed three visualization views: the Encoder View unveils the process of converting classical input data into quantum states, the Ansatz View reveals the temporal evolution of quantum states in the training process, and the Feature View displays the features a QNN has learned after the training process. Two novel visual designs, i.e., satellite chart and augmented heatmap, are proposed to visually explain the variational parameters and quantum circuit measurements respectively. We evaluate VIOLET through two case studies and in-depth interviews with 12 domain experts. The results demonstrate the effectiveness and usability of VIOLET in helping QNN users and developers intuitively understand and explore quantum neural networks.

A compound control method based on adaptive high-order fast non-singular terminal sliding mode control (AHONFTSMC) and sliding mode disturbance observer is investigated in this paper, so as to shorten the response time of speed tracking, and improve the perturbation resistance of torque pulsation in permanent magnet synchronous motors. Firstly, considering the contradictory requirement of achieving both rapid response and mitigating severe jitter in traditional non-singular terminal sliding mode control (NFTSMC), an adaptive parameter technique is proposed to enhance the system’s dynamic response. AHONFTSMC not only ensures the system’s good tracking performance, but also effectively reduces the jitter in the control output. Secondly, a nonlinear disturbance observer is established to further improve the system response accuracy, so as to observe the disturbance differentials in the system, and perform feedforward compensation on the controller. The stability and convergence of the designed compound controller are demonstrated through the application of Lyapunov theory. The experimental results demonstrate notable improvements in the motor controlled by AHONFTSMC, especially compared to sliding mode control (SMC) and NFTSMC methods. Specifically, compared to SMC and NFTSMC, the time required to reach the rated speed is reduced by 68.6%, 15.6% respectively. The speed overshoot is reduced by 22 and 3% respectively. Compared to the non-adaptive HONFTSMC method, the time required to reach the rated speed is reduced by 7.3%. Furthermore, AHONFTSMC ensures stable changes in electromagnetic torque, effectively reducing the occurrence of chattering. These findings confirm that the proposed control strategy significantly enhances the accuracy of system control and improves the overall dynamic performance of the system.

Quantum computing has attracted considerable public attention due to its exponential speedup over classical computing. Despite its advantages, today's quantum computers intrinsically suffer from noise and are error-prone. To guarantee the high fidelity of the execution result of a quantum algorithm, it is crucial to inform users of the noises of the used quantum computer and the compiled physical circuits. However, an intuitive and systematic way to make users aware of the quantum computing noise is still missing. In this paper, we fill the gap by proposing a novel visualization approach to achieve noise-aware quantum computing. It provides a holistic picture of the noise of quantum computing through multiple interactively coordinated views: a Computer Evolution View with a circuit-like design overviews the temporal evolution of the noises of different quantum computers, a Circuit Filtering View facilitates quick filtering of multiple compiled physical circuits for the same quantum algorithm, and a Circuit Comparison View with a coupled bar chart enables detailed comparison of the filtered compiled circuits. We extensively evaluate the performance of VACSEN through two case studies on quantum algorithms of different scales and in-depth interviews with 12 quantum computing users. The results demonstrate the effectiveness and usability of VACSEN in achieving noise-aware quantum computing.

This work aims to improve the low accuracy caused by interference information during real-time weld surface detection. First, a weld surface dataset with 7580 pictures containing eight types of defects was established. Second, an improved detection model named Yolo-MSAPF was designed based on Yolo-v5 model and verified by the self-established database. Finally, a real-time detection system was built to analyze the performance of the detection model in an industrial environment. The design principle of the Yolo-MSAPF is to eliminate interference information but enhance necessary features in each scale as much as possible by multiscale alignment fusion (MSAF) with parallel features filtering (PFF) modules (i.e., MSAPF strategy). For the MSAF, not only the accuracy but the richness of fused features is guaranteed by aligning features at one level to fuse all other scales. After that, the fused features in each scale were individually filtered out in parallel spatial and channel in the PFF module. The results show that rate for the images with missing detection for eight types of defects sharply drops from 21.47% to 1.68% when the MSAPF strategy is used. Moreover, it is worth noting that the mAP@0.5 of the Yolo-MSAPF model reaches 95.3%, which is similar to the Yolo-v7 model, while the number of parameters is reduced by 30.1%, compared with the baseline model. Additionally, the great ability to screen out unqualified weld is also verified in the industrial environment. Soon, code and dataset will be available at https://github.com/Luckycat518/Yolo-MSAPF.

Background: Frailty is common in Parkinson’s disease (PD) and increases vulnerability to adverse outcomes. Early detection of this syndrome aids in early intervention. Aims: To objectively identify frailty at an early stage during routine motor tasks in PD patients using a Kinect-based system. Methods: PD patients were recruited and assessed with the Fried criteria to determine their frailty status. Each participant was recorded performing the Movement Disorder Society-Sponsored Revision of the Unified Parkinson’s Disease Rating Scale part III (MDS-UPDRS III) extremity tasks with a Kinect-based system. Statistically significant kinematic parameters were selected to discriminate the pre-frail from the non-frail group. Results: Of the fifty-two participants, twenty were non-frail and thirty-two were pre-frail. Decreased frequency in finger tapping (P= 0.005), hand grasping (P= 0.002), toe tapping (P= 0.002), and leg agility (P= 0.019) alongside reduced hand grasping speed (P= 0.030), lifting (P< 0.001) and falling speed (P< 0.001) in leg agility were observed in the pre-frail group. Amplitude in leg agility (P= 0.048) and amplitude decrement rate (P= 0.046) in hand grasping showed marginally significant differences between two groups. Moderate discriminative values were found in frequency and speed of the extremity tasks to identify pre-frailty with sensitivity, specificity, and area under the curve (AUC) in the range of 45.00–85.00%, 68.75–100%, and 0.701–0.836, respectively. The combination of frequency and speed in extremity tasks showed moderate to high discriminatory ability, with AUC of 0.775 (95% CI 0.637–0.913, P< 0.001) for upper limb tasks and 0.909 (95% CI 0.832–0.987, P< 0.001) for lower limb tasks. When combining these features in both upper and lower limb tasks, the AUC increased to 0.942 (95% CI 0.886–0.999, P< 0.001). Conclusions: Our findings demonstrated the promise of utilizing Kinect-based kinematic data from MDS-UPDRS III tasks as early indicators of frailty in PD patients.

Artificial intelligence (AI) and deep learning technologies hold promise for identifying effective drugs for human diseases, including pain. Here, we present an interpretable deep-learning-based ligand image- and receptor’s three-dimensional (3D)-structure-aware framework to predict compound-protein interactions (LISA-CPI). LISA-CPI integrates an unsupervised deep-learning-based molecular image representation (ImageMol) of ligands and an advanced AlphaFold2-based algorithm (Evoformer). We demonstrated that LISA-CPI achieved ∼20% improvement in the average mean absolute error (MAE) compared to state-of-the-art models on experimental CPIs connecting 104,969 ligands and 33 G-protein-coupled receptors (GPCRs). Using LISA-CPI, we prioritized potential repurposable drugs (e.g., methylergometrine) and identified candidate gut-microbiota-derived metabolites (e.g., citicoline) for potential treatment of pain via specifically targeting human GPCRs. In summary, we presented that the integration of molecular image and protein 3D structural representations using a deep learning framework offers a powerful computational drug discovery tool for treating pain and other complex diseases if broadly applied.

Background: The association between the frailty index (FI) and the risk of chronic lung diseases (CLDs) remains unexplored, warranting further research. Methods and materials: This study investigated the relationship between FI and CLD risk using data from the China Health and Retirement Longitudinal Study (CHARLS) and English Longitudinal Study of Ageing (ELSA), comprising a combined sample of 9642 individuals. Propensity score weighting was used to ensure similar distribution of covariates across FI groups. The Wilcoxon rank-sum test was used to analyze differences in FI scores between groups with and without CLD. Kaplan–Meier curves and Cox regression analysis were employed to explore the association between frailty status and CLD incidence, with sensitivity analyses conducted for validation. Results: Higher FI scores were significantly associated with increased CLD risk in both cohorts (p< .05). Kaplan–Meier survival and Cox regression analyses indicated that frail individuals have a significantly elevated risk of CLD compared to robust individuals, particularly in certain subgroups (e.g., female) within the CHARLS cohort (p< .05). The ELSA cohort yielded similar results (p< .05), affirming FI as a strong predictor of CLD. Additional risk factors identified included age, smoking, and unmarried status (p< .05). Frail individuals consistently exhibited the highest risk in both cohorts (CHARLS HR = 1.54, p= .003; ELSA HR = 6.64, p< .001). The sensitivity analysis did not substantially alter the significant associations. Conclusion: These findings emphasize the critical role of frailty in the development of CLD, suggesting that targeted interventions could reduce CLD risk.

The uniform refinement mechanisms and methods of deformed mixed and coarse grains inside a solution-treatment Ni-based superalloy during two-stage annealing treatment have been investigated. The two-stage heat treatment experiments include an aging annealing treatment (AT) and a subsequent recrystallization annealing treatment (RT). The object of AT is to precipitate some δ phases and consume part of storage energy to inhibit the grain growth during RT, while the RT is to refine mixed and coarse grains by recrystallization. It can be found that the recrystallization grains will quickly grow up to a large size when the AT time is too low or the RT temperature is too high, while the deformed coarse grains cannot be eliminated when the AT time is too long or the RT temperature is too low. In addition, the mixed microstructure composed of some abnormal coarse recrystallization grains (ACRGs) and a large number of fine grains can be observed in the annealed specimen when the AT time is 3 h and RT temperature is 980 ℃. The phenomenon attributes to the uneven distribution of δ phase resulted from the heterogeneous deformation energy when the AT time is too short. In the regions with a large number of δ phases, the recrystallization nucleation rate is promoted and the growth of grains is limited, which results in fine grains. However, in the regions with few δ phases, the recrystallization grains around grain boundaries can easily grow up, and the new recrystallization nucleus is difficult to form inside grain, which leads to ACRGs. Thus, in order to obtain uniform and fine annealed microstructure, it is a prerequisite to precipitate even-distributed δ phase by choosing a suitable AT time, such as 12 h. Moreover, a relative high RT temperature is also needed to promote the recrystallization nucleation around δ phase. The optimal annealing parameters range for uniformly refining mixed crystal can be summarized as: 900 ℃×12 h + 990 ℃×(40−60 min) and 900 ℃×12 h + 1000 ℃×(10−15 min). [ABSTRACT FROM AUTHOR]

Long non-coding RNAs (lncRNAs) are an emerging class of regulators in cancer. A lncRNA, MCM3AP-AS1, has been demonstrated as a versatile mediator in many cancers, except papillary thyroid cancer. The aim of this study is to investigate the role and mechanism of MCM3AP-AS1 in papillary thyroid cancer. Quantitative real-time PCR was used to assess the level of MCM3AP-AS1 and miR-211-5p in papillary thyroid cancer tissues and cells. Western blot was used to detect E-cadherin and secreted protein acidic and cysteine rich (SPARC) protein levels. CCK-8, scratch wound assay, and transwell assay were used to evaluate papillary thyroid cancer cell proliferation, migration, and invasion, respectively. BLAST alignment and luciferase assay were used to explore the interaction among MCM3AP-AS1, mi/r-211, and SPARC. In papillary thyroid cancer, MCM3AP-AS1 was upregulated, while miR-211 was downregulated. MCM3AP-AS1 overexpression promoted papillary thyroid cancer proliferation, migration, and invasion. Further, MCM3AP-AS1 was shown to be negatively correlated with miR-211-5p. We next validated that miR-211-5p overexpression could reverse the promoting role of MCM3AP-AS1 in papillary thyroid cancer, whereby SPARC plays an important regulating role. In vivo, we confirmed the anti-tumor role of MCM3AP-AS1 silencing and the close relation among MCM3AP-AS1, miR-211-5p, and SPARC. MCM3AP-AS1 promotes papillary thyroid cancer by regulating the MCM3AP-AS1/miR-211-5p/SPARC axis, which could potentially be a therapeutic target in papillary thyroid cancer.

In authors' previous work [Mater. Charact. 141 (2018) 212–222], it was found that the heterogeneous deformed microstructures can be replaced by the relatively homogeneous recrystallized grains through an annealing treatment. However, there are still some relatively large recrystallized grains. To find the reasons for the formation of large grains, some new annealing treatment tests were done, and the cellular automation (CA) simulations were carried out in the present work. The experimental results showed that the microstructural evolution during annealing treatment is significantly affected by the content of δ phase. So, the effects of δ phase on the nucleation and growth of grains are carefully considered in the CA model to accurately simulate the microstructural evolution behavior. By the CA simulation, it is found that the dislocation density rapidly decreases due to the nucleation of static recrystallization (SRX) and the growth of dynamc recrystallization (DRX) nuclei at the early stage of annealing. The high initial dislocation density can provide the high velocity for the growth of DRX nuclei, which is responsible for the formation of coarse grains. However, the growth rate of SRX nuclei is relatively small due to the low dislocation density and pinning effects of δ phase. [ABSTRACT FROM AUTHOR]

Soft errors are becoming an important issue in computing systems. Near-threshold voltage (NTV), reduced circuit sizes, high performance computing (HPC), and high altitude computing all present interesting challenges in this area. Much of the existing literature has focused on hardware techniques to mitigate and measure soft errors at the hardware level. Instead, in this paper, we explore the soft error susceptibility of three common sorting algorithms at the software layer. We focus on the comparison operator and use our software fault injection tool to place faults with fine precision during the execution of these algorithms. We explore how the algorithm susceptibilities vary based on input and bit position and relate these faults back to the source code to study how algorithmic decisions impact the reliability of the codes. Finally, we look at the question of the number of fault injections required for statistical significance. Using standard practice equations used in hardware fault injection experiments, we calculate the number of injections that should be required to achieve confidence in our results. Then we show, empirically, that more fault injections are required before we gain confidence in our experiments.

A reference optimality criterion has been proposed by Xu and Tang based on the expected Kullback–Leibler divergence between the posterior and the prior distributions of the parameters of interest. In this paper, we use the reference optimality criterion to plan simple step-stress accelerated life tests for log-location-scale distributions. Monte Carlo algorithms are developed to find the optimal plans. Effects of the priors on the optimal plans are also investigated. Results indicate that the optimal Bayesian plans are robust to the choice of the priors.

The turbo diesel SCR system has been researched and analyzed in this paper. By using software of CATIA, three-dimensional physical model of SCR system has been established, and with software of AVL-FIRE, the boundary conditions have been set, simulated and optimized. In the process of SCR system optimizing, it mainly optimized the pray angle. Compare the effects of processing NOx to obtain batter optimization results. At last the optimization results are compared by bench test, and the experimental results are quite consistent with simulation.

Calculation of the critical condition for the initiation of DRX is of considerable interest for the modeling of industrial processes but it strongly depends on the chemical composition of the material, the grain size prior to deformation, and the deformation conditions ( T and ε ). This paper was therefore to show an approach that allows for accurate but convenient identification of the occurrence of DRX. For a Fe-Cr-Ni super stainless steel, the results show that there is a linear relationship between σc and lnZ ( σc=457.26-12.52lnZ). In addition, within the entire range of temperature and strain rate studied, it can be found that the values of both σc and σp or εc and εp keep similar variation trends, respectively. Finally, it was found that the critical ratios of both σc/σp and εc/εp remain fairly constant (≈0.92 and ≈0.47, respectively).

In constant strain rate tests, the occurrence of dynamic recrystallization (DRX) is traditionally identified from the presence of stress peaks in flow curves. However, not all materials display well-defined peaks when tested under these conditions. It is shown that the onset of DRX can also be identified from the inflection point on the strain hardening rate (θ=dσ/dε)versus flow stress (σ) curve. In this paper, the hot compression curves can be described by an equation that fits the experimental θ-σ data from zero to the peak stress. An appropriate third order equation was fitted to the strain hardening data. The results show that the critical stress at initiation σc=-B/3A where A and B are coefficients of the third order equation. It is evident that this value depends on the deformation conditions. The stress–strain curve was then normalized with respect to the peak stress, leading to a normalized value of the critical stress (uc) equal to uc=σc/σp=-B'/3A'. Here A'and B'are coefficients of the normalized third order equation. This value is constant and independent of the deformation conditions.

In order to improve the performance of the diesel engine, the original engine fuel injection advance angle is optimized, and a new advance angle of fuel injection is proposed in this paper. By numerical calculation with simulation of software FIRE, the effect of different combustion chamber structures on the cylinder pressure, temperature, accumulated heat release and the parameters such as NOx mass fraction was analyzed. From the simulation results, the optimized fuel injection advance angle had greatly improved the diesel combustion and emission performance. Finally, via experimental verification, the engine with optimized fuel injection advance angle has better dynamic performance, as well as less emission than original machine.

Today, the sensors are used in the different fields, especially in the automobiles and Aeronautics and Astronautics industry. Speed sensor in the car is very useful . The engine speed sensor and speed sensor are used to collect the movement of the car, which Can intelligently judge drivers true intentions of the operation, in accordance with a set of systems, intelligently solve the problem that driver can not reasonably Use the clutch. Through intelligent systems, controlling the clutch can effectively reduce the fuel consumption of the car.The system enables more intelligent operation of the vehicle, improved shift quality of the car, to improve vehicle handling and stability. Operating system with high intelligent and saving fuel is easy to operate. The operating system can effectively prevent the occurrence of car flameout. The operating system can adapt to all kinds of driver to use, especially for beginners and handicapped people to bring the gospel.

This article describes a system device capable of displaying a car outside sounds, the system device sound signals collected by the sound sensor , combined with the single-chip analytical processing Finally, LED lights, buzzer and other electronic actuator smart car external speaker sound source direction and strength of the drivers are able to accurately determine the rear of the vehicle the location and intentions, in order to take more appropriate driving route and speed, so that the operation of the vehicle more intelligent, is conducive to maintaining the smooth flow of traffic, more effective in reducing traffic accidents, to further improve automotive safety system, greatly improves the safety performance of vehicles.

Abstract: The Bayesian estimators for the unknown parameters of the bivariate Marshall–Olkin exponential distribution under noninformative priors have been considered and several reference priors have been derived. A class of priors is found by matching the coverage probability of one-side Bayesian credible intervals with the corresponding frequentist coverage probabilities. It is noted that some of the reference priors are also matching priors and the posterior distributions based on the reference priors and matching priors are proper. Closed forms of Bayesian estimators are obtained with respect to the quadratic loss function. Gibbs sampling is utilized to obtain the credible intervals and coverage probabilities of parameters. Comparisons in the efficiency of the maximum likelihood estimators and Bayesian estimators under different reference priors and matching priors for various sample sizes have been done by Monte Carlo simulations. A real data set is analyzed for illustrative purpose. [Copyright &y& Elsevier]

Response and decision-making support in expressway emergency management and rescue greatly affects some key factors like emergency rescue plan, emergency duration time, effect range, economy and time loss. Experts system is a kind of intelligent program, using knowledge and reasoning to solve the complex problem that only experts can work out, it is a popular and efficient decision-support means, has some practical features that manual way cannot compare with and offers a modem scientific measure in incident management. Expert system can provide high-efficiency, rapid, exact and powerful assistance in decision support. In order to deal with various possible emergencies promptly and efficiently and therefore ensure the safety and unblocked state of the expressway, develop the expert system has become a trend of emergency rescue and management. In expert system,The knowledge representation is the key basic issues has the important influence to construction of the knowledge base and the reasoning of the expert system. So there has the practical significance to further research on knowledge representation of expressway accident management system, According to features of different knowledge, in exsiting expert systems different knowledge representation methods were adopted such as frame, production rule, procedure, first-order predicate logic, etc. This paper focus on the ontology’s application in the field of expressway emergency rescue and propose a expressway event managemnet expert system based on ontology and rule reasoning.

Design principle of SCR control system for modern diesel engine is based on MAP which is obtained from experiments. The data of urea injection is obtained from MAP, the many experiments with different revolutions and loads are necessary. MAP from experiments is not accurate, cannot be used for all the range exactly in case of engine running conditions. Therefore, it is very difficult to design PID controller based on MAP. Moreover, the robust performance can not be achieved. To overcome these problems, the control scheme based on artificial neural network (ANN) is proposed. This controller has good robust performance and rapid adjustment. From experiments, the system using this controller can achieve good performance.

Organic solar cells with inverted planar heterojunction structure based on subphthalocyanine and C60 were fabricated using several kinds of materials as cathode buffer layer (CBL), including tris-8-hydroxy-quinolinato aluminum (Alq3), bathophenanthroline (Bphen), bathocuproine, 2,3,8,9,14,15-hexakis-dodecyl-sulfanyl-5,6,11,12,17,18-hexaazatrinaphthylene (HATNA), and an inorganic compound of Cs2CO3. The influence of the lowest unoccupied molecular orbital level and the electron mobility of organic CBL on the solar cells performance was compared. The results showed that Alq3, Bphen, and HATNA could significantly improve the device performance. The highest efficiency was obtained from device with annealed HATNA as CBL and increased for more than 7 times compared with device without CBL. Furthermore, the simulation results with space charge-limited current theory indicated that the Schottky barrier at the organic/electrode interface in inverted OSC structure was reduced for 27% by inserting HATNA CBL.

In this paper, a 3D-FE model in process of tube bending is built under the ABAQUS/Explicit environment based on the solution of several key techniques, such as contact boundary condition, material properties definition and meshing technology, etc. The results show that both friction coefficients and bending angles have an important influence on the wall thickness of tubes. The variation trends of outside and inside wall thickness are found out by using finite element simulation. The achievements of this study may provide some advice for experiments and manufactures.

We investigate the formation of a heteronuclear tetramer A3B by an Efimov-resonance-assisted stimulated Raman adiabatic passage scheme, which is considered a viable means of creating the homonuclear tetramer A4 [H. Jing and Y. Jiang, Phys. Rev. A 77, 065601 (2008)]. The atom-molecule dark-state solution for the system is derived, and the adiabatic conversion from atoms to heteronuclear tetramers is studied via the concept of adiabatic fidelity. In addition, the effects of external field parameters (including Rabi pulse strength, width, and single photon detuning) on the conversion are discussed. [ABSTRACT FROM AUTHOR]

The ground states of a harmonically trapped spin-1 Bose–Einstein condensate (BEC) with SU(3) spin-orbit coupling (SOC) affected by the external rotation are investigated. The two-dimensional density, phase and magnetization distributions of the ground states under different parameter conditions are calculated numerically. It is found that a new kind of ground-states with clover-type structure in the density distribution of the condensate is induced by the external rotation. The vortex with one or several cores appears in the three parts of the structure once the angular frequency of the rotation is increased and passed over a critical value. The distribution of the vortex cores is also divided into three groups since all the holes in the density distribution are inlaid into the three parts. The total number of the holes is increased with the angular frequency of the rotation, the strengths of the SOC and the atomic density-density interaction. Together with the density distribution, the phase and magnetization distributions demonstrate the nontrivial topological characteristics of the states. Furthermore, when the rotation is closed abruptly, the states with vortex cores will rotate periodically in the coordinate space very long time without obvious decay. [ABSTRACT FROM AUTHOR]

Conventional coronary heart disease (CHD) detection methods are expensive, rely much on doctors’ subjective experience, and some of them have side effects. In order to obtain rapid, high-precision, low-cost, non-invasive detection results, several methods in machine learning were attempted for CHD detection in this paper. The paper adopted multiple evaluation criteria to measure features, combined with heuristic search strategy and seven common classification algorithms to verify the validity and the importance of feature selection (FS) in the Z-Alizadeh Sani CHD dataset. On this basis, a novelty algorithm integrating multiple FS methods into the ensemble algorithm (ensemble algorithm based on multiple feature selection, EA-MFS) was further proposed. The algorithm adopted Bagging approach to increase data diversity, used the aforementioned MFS methods for functional perturbation, employed major voting method to carry out the decision results, and performed selective integration in terms of the difference of base classifiers in the ensemble process. Compared with the single FS method, the EA-MFS algorithm could comprehensively describe the relationship of features, enhance the classification effect, and displayed better robustness. That meant the EA-MFS algorithm could reduce the dependence on dataset and strengthen the stability of the algorithm, all of which were of great significance for the clinical application of machine learning algorithm in coronary heart disease detection.