Quantitative Robustness Analysis of Quantum Programs (Extended Version)

Quantum computation is a topic of significant recent interest, with practical advances coming from both research and industry. A major challenge in quantum programming is dealing with errors (quantum noise) during execution. Because quantum resources (e.g., qubits) are scarce, classical error correction techniques applied at the level of the architecture are currently cost-prohibitive. But while this reality means that quantum programs are almost certain to have errors, there as yet exists no principled means to reason about erroneous behavior. This paper attempts to fill this gap by developing a semantics for erroneous quantum while-programs, as well as a logic for reasoning about them. This logic permits proving a property we have identified, called $\epsilon$-robustness, which characterizes possible "distance" between an ideal program and an erroneous one. We have proved the logic sound, and showed its utility on several case studies, notably: (1) analyzing the robustness of noisy versions of the quantum Bernoulli factory (QBF) and quantum walk (QW); (2) demonstrating the (in)effectiveness of different error correction schemes on single-qubit errors; and (3) analyzing the robustness of a fault-tolerant version of QBF.Comment: 34 pages, LaTeX; v2: fixed typo

Bis(4-ammonio-4-methyl­pentan-2-one-κO)dioxalato-κ4 O 1,O 2-copper(II)

The title compound, [Cu(C2O4)2(C6H14NO)2], was synthesized by mixing diacetonamine hydrogen oxalate and copper sulfate in ethanol/water. The mol­ecule is centrosymmetric, so two pairs of equivalent ligands lie trans to each other. The CuII center, located on a position with 2/m site symmetry, is six-coordinated by four O atoms from two oxalate ligands at short distances and the carbonyl O atoms from the 4-amino-4-methyl­pentan-2-one ligands at longer distances. Mol­ecules are linked through inter­molecular N—H⋯O hydrogen bonds between the amino groups and carbonyl O atoms; no intra­molecular hydrogen bonds are formed

Environment-Friendly Construction Materials

Construction materials are the most widely used materials for civil infrastructure in our daily lives. However, from an environmental point of view, they consume a huge amount of natural resources and generate the majority of greenhouse gasses. Therefore, many new and novel technologies for designing environmentally friendly construction materials have been developed recently. This Special Issue, “Environment-Friendly Construction Materials”, has been proposed and organized as a means to present recent developments in the field of construction materials. It covers a wide range of selected topics on construction materials

A prediction model of specific productivity index using least square support vector machine method

In the design of oilfield development plans, specific productivity index plays a vital role. Especially for offshore oilfields, affected by development costs and time limits, there are shortcomings of shorter test time and fewer test sampling points. Therefore, it is very necessary to predict specific productivity index. In this study, a prediction model of the specific productivity index is established by combining the principle of least squares support vector machine (LS-SVM) with the calculation method of the specific productivity index. The model uses logging parameters, crude oil experimental parameters and the specific productivity index of a large number of test well samples as input and output items respectively, and finally predicts the specific productivity index of non-test wells. It reduces the errors caused by short training time, randomness of training results and insufficient learning. A large number of sample data from the Huanghekou Sag in Bohai Oilfield were used to verify the prediction model. Comparing the specific productivity index prediction results of LS-SVM and artificial neural networks (ANNs) with actual well data respectively, the LS-SVM model has a better fitting effect, with an error of only 3.2%, which is 12.1% lower than ANNs. This study can better reflect the impact of different factors on specific productivity index, and it has important guiding significance for the evaluation of offshore oilfield productivity.Cited as: Wu, C., Wang, S., Yuan, J., Li, C., Zhang, Q. A prediction model of specific productivity index using least square support vector machine method. Advances in Geo-Energy Research, 2020, 4(4): 460-467, doi: 10.46690/ager.2020.04.1

Self-excitation and energy recovery of air-core compulsators

As power supplies, compulsators are popular choices for high-end railgun power supplies. In order to increase power and energy density, air-core compulsators are proposed by using composite materials instead of traditional iron-core compulsators. Due to the absence of ferromagnetic material, the flux density in the air-core compulsator can reach to 4–6 T instantaneously, which is much higher than the saturation field strength in traditional iron-core machines. Therefore, self-excitation topology is essential for the air-core compulsator to obtain up to 100-kA field current. This paper carried out research on the key parameters of self-excitation efficiency first, and then focus on the large magnetic energy remained in the inductive field winding after one shot, an implementation scheme and control strategy of energy recovery of air-core compulsator was proposed and analyzed. By controlling the field rectifier working at active inverter state after one discharge process, the magnetic energy stored in the field winding can be converted to rotor kinetic energy again. The simulation results indicate that the energy recovery efficiency can reach to 70% for a reference air-core compulsator. The continuous discharge number of times increased from 3 to 4 during one kinetic energy charging, which means that the delivered energy density increases 33.3%

A fractional slot multiphase air-core compulsator with concentrated winding

Compulsator is a specially designed generator capa¬ble of delivering high current pulses to a low-impedance load, such as the electromagnetic railgun. In order to increase the tip speed of the rotor, advanced composite materials have been used in the recent compulsator prototype, which is mentioned as air core instead of the traditional iron core. For typical air-core compulsators, there are no slots and steel teeth to place the armature windings due to the nonmachinability of composite materials. Therefore, concentric windings in racetrack style are often adopted instead of traditional lap winding in most cases, since it is more convenient to be fixed by composite materials. However, overlap occurs at the end winding part for multiphase compulsators, which are not easy to be formed during the manufacture process. In this paper, a fractional slot multiphase air-core compulsator with concentrated windings is proposed and analyzed. The main advantage of fractional slot configuration is that it can offer a concentrated winding structure under certain conditions, which means each coil only spans one “tooth,” and will not cause any intersection between each phase at the end winding. Two referenced fractional slot air-core compulsators with two phases, six poles, and four “slots” or eight “slots” (q = 1/3 and q = 2/3, q is the “slot” per pole per phase) are analyzed and compared with the performance of a traditional integral slot machine. The results indicated that the output voltage and self-excitation performance of a fractional slot compulsator can reach the same level with an integral slot one, and the discharging performance can reach an acceptable level. Thus, the fractional slot multiphase concept can be further used to improve the manufacture process of the winding in the future

A knowledge‐enhanced deep reinforcement learning‐based shape optimizer for aerodynamic mitigation of wind‐sensitive structures

Structural shape optimization plays an important role in the design of wind‐sensitive structures. The numerical evaluation of aerodynamic performance for each shape search and update during the optimization process typically involves significant computational costs. Accordingly, an effective shape optimization algorithm is needed. In this study, the reinforcement learning (RL) method with deep neural network (DNN)‐based policy is utilized for the first time as a shape optimization scheme for aerodynamic mitigation of wind‐sensitive structures. In addition, “tacit” domain knowledge is leveraged to enhance the training efficiency. Both the specific direct‐domain knowledge and general cross‐domain knowledge are incorporated into the deep RL‐based aerodynamic shape optimizer via the transfer‐learning and meta‐learning techniques, respectively, to reduce the required datasets for learning an effective RL policy. Numerical examples for aerodynamic shape optimization of a tall building are used to demonstrate that the proposed knowledge‐enhanced deep RL‐based shape optimizer outperforms both gradient‐based and gradient‐free optimization algorithms

Influence of encapsulated sunflower oil on the mechanical and self-healing properties of dense-graded asphalt mixtures

This paper re-evaluates the effect of sunflower oil capsules on the mechanical and self-healing properties of dense-graded asphalt mixtures. Different percentages of capsules (0.50wt.%, 0.75wt.% and 1.00wt.%) were mixed into dense asphalt. The influence of capsules on the properties of asphalt such as density, indirect tensile strength, particle loss, fatigue life, and self-healing, has been investigated. The distribution and integrity of the capsules has been also evaluated by means of CT Scans. It has been proven that capsules can survive the mixing and compaction process of asphalt mixture, do not decrease its mechanical properties and they rupture and release the oil under a high compression loading. Higher capsule content in the mixture resulted in higher oil release ratios. Furthermore, the oil released from the capsules significantly increased the self-healing capability of mixtures. Results from previous research were validated, where it had been found that 0.5% of capsules is the optimal content to obtain good mechanical performance, without affecting the rheological properties of dense-graded asphalt mixtures

Fatigue Properties of Layered Double Hydroxides Modified Asphalt and Its Mixture

This study investigated the influence of layered double hydroxides (LDHs) on the fatigue properties of asphalt mixture. In this paper, different aging levels (thin film oven test (TFOT) and ultraviolet radiation aging (UV aging for short)) of bitumen modified with various mass ratios of the LDHs were investigated. The TFOT and UV aging process were used to simulate short-term field thermal-oxidative aging and long-term field light UV aging of bitumen, respectively. The influences of LDHs on the fatigue properties of LDHs were evaluated by dynamic shear rheometer (DSR) and indirect tensile fatigue test. Results indicated that the introduction of LDHs could change the fatigue properties of bitumen under a stress control mode. The mixture with modified bitumen showed better fatigue resistance than the mixture with base bitumen. The results illustrated that the LDHs would be alternative modifiers used in the bitumen to improve the lifetime of asphalt pavements

Literature Explorer: effective retrieval of scientific documents through nonparametric thematic topic detection

© 2020 The Authors. Published by Springer. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1007/s00371-019-01721-7Scientific researchers are facing a rapidly growing volume of literatures nowadays. While these publications offer rich and valuable information, the scale of the datasets makes it difficult for the researchers to manage and search for desired information efficiently. Literature Explorer is a new interactive visual analytics suite that facilitates the access to desired scientific literatures through mining and interactive visualisation. We propose a novel topic mining method that is able to uncover “thematic topics” from a scientific corpus. These thematic topics have an explicit semantic association to the research themes that are commonly used by human researchers in scientific fields, and hence are human interpretable. They also contribute to effective document retrieval. The visual analytics suite consists of a set of visual components that are closely coupled with the underlying thematic topic detection to support interactive document retrieval. The visual components are adequately integrated under the design rationale and goals. Evaluation results are given in both objective measurements and subjective terms through expert assessments. Comparisons are also made against the outcomes from the traditional topic modelling methods.This research is supported by the European Commission with project Dr Inventor (No 611383), MyHealthAvatar (No 60929), and by the UK Engineering and Physical Sciences Research Council with project MyLifeHub (EP/L023830/1).Published onlin