QASMTrans: A QASM based Quantum Transpiler Framework for NISQ Devices

The success of a quantum algorithm hinges on the ability to orchestrate a successful application induction. Detrimental overheads in mapping general quantum circuits to physically implementable routines can be the deciding factor between a successful and erroneous circuit induction. In QASMTrans, we focus on the problem of rapid circuit transpilation. Transpilation plays a crucial role in converting high-level, machine-agnostic circuits into machine-specific circuits constrained by physical topology and supported gate sets. The efficiency of transpilation continues to be a substantial bottleneck, especially when dealing with larger circuits requiring high degrees of inter-qubit interaction. QASMTrans is a high-performance C++ quantum transpiler framework that demonstrates up to 369X speedups compared to the commonly used Qiskit transpiler. We observe speedups on large dense circuits such as uccsd_n24 and qft_n320 which require O(10^6) gates. QASMTrans successfully transpiles the aforementioned circuits in 69s and 31s, whilst Qiskit exceeded an hour of transpilation time. With QASMTrans providing transpiled circuits in a fraction of the time of prior transpilers, potential design space exploration, and heuristic-based transpiler design becomes substantially more tractable. QASMTrans is released at http://github.com/pnnl/qasmtrans

The relationship between health belief and sleep quality of Chinese college students: The mediating role of physical activity and moderating effect of mobile phone addiction

BackgroundPoor sleep quality has become a common health problem encountered by college students.MethodsHealth belief scale (HBS), physical activity rating scale (PARS-3), mobile phone addiction tendency scale (MPATS) and Pittsburgh sleep quality index (PSQI) were adopted to analyze the data collected from survey questionnaires, which were filled out by 1,019 college students (including 429 males and 590 females) from five comprehensive colleges and universities from March 2022 to April 2022. The data collected from survey questionnaires were analyzed using SPSS and its macro-program PROCESS.Results(1) Health belief, physical activity, mobile phone addiction and sleep quality are significantly associated with each other (P < 0.01); (2) physical activity plays a mediating role between health belief and sleep quality, and the mediating effects account for 14.77%; (3) mobile phone addiction can significantly moderate the effect size of health belief (β = 0.062, p < 0.05) and physical activity (β = 0.073, P < 0.05) on sleep quality, and significantly moderate the effect size of health belief on physical activity (β = −0.112, p < 0.001).ConclusionThe health belief of college students can significantly improve their sleep quality; college students’ health belief can not only improve their sleep quality directly, but also improve their sleep quality through physical activity; mobile phone addiction can significantly moderate the effect size of health belief on sleep quality, the effect size of health belief on physical activity, and the effect size of physical activity on sleep quality

Validation of Deep Learning techniques for quality augmentation in diffusion MRI for clinical studies

The objective of this study is to evaluate the efficacy of deep learning (DL) techniques in improving the quality of diffusion MRI (dMRI) data in clinical applications. The study aims to determine whether the use of artificial intelligence (AI) methods in medical images may result in the loss of critical clinical information and/or the appearance of false information. To assess this, the focus was on the angular resolution of dMRI and a clinical trial was conducted on migraine, specifically between episodic and chronic migraine patients. The number of gradient directions had an impact on white matter analysis results, with statistically significant differences between groups being drastically reduced when using 21 gradient directions instead of the original 61. Fourteen teams from different institutions were tasked to use DL to enhance three diffusion metrics (FA, AD and MD) calculated from data acquired with 21 gradient directions and a b-value of 1000 s/mm2. The goal was to produce results that were comparable to those calculated from 61 gradient directions. The results were evaluated using both standard image quality metrics and Tract-Based Spatial Statistics (TBSS) to compare episodic and chronic migraine patients. The study results suggest that while most DL techniques improved the ability to detect statistical differences between groups, they also led to an increase in false positive. The results showed that there was a constant growth rate of false positives linearly proportional to the new true positives, which highlights the risk of generalization of AI-based tasks when assessing diverse clinical cohorts and training using data from a single group. The methods also showed divergent performance when replicating the original distribution of the data and some exhibited significant bias. In conclusion, extreme caution should be exercised when using AI methods for harmonization or synthesis in clinical studies when processing heterogeneous data in clinical studies, as important information may be altered, even when global metrics such as structural similarity or peak signal-to-noise ratio appear to suggest otherwise

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Probabilistic load margin assessment considering forecast error of wind power generation

The increasing integration of wind power in power systems necessitates the probabilistic assessment of various uncertain factors. In operational planning, modeling short-term scale uncertainties, i.e., wind power forecast errors, plays an important role. In this paper, according to the different forecast values, the corresponding probability distributions of wind power forecast errors are developed using a data-driven manner. Then, the polynomial chaos expansion surrogate is developed to facilitate the probabilistic load margin assessment considering wind power forecast errors. The effectiveness of the forecast error model is verified using the historical data of realistic wind power plants. The results show that the probability distributions of forecast errors vary with the level of forecast values. Moreover, the performance of the polynomial chaos expansion surrogate in estimating probabilistic load margin is validated in the IEEE 30-bus system. The results demonstrate that the versatile forecast error distributions significantly impact the characteristics of load margin. Moreover, the polynomial chaos expansion surrogate can accelerate the load margin assessment compared to the Monter Carlo simulation while retaining the same accuracy

Uncertain power flow calculation and global sensitivity analysis considering parametric probability-boxes

Probabilistic power flow is one of the fundamental tools for assessing the impacts of uncertainties on the operating states of power systems. However, this analysis requires sufficient historical data to obtain precise probability distributions of input variables, which may not be met in practical engineering problems. In this paper, input variables with insufficient data are represented by parametric probability boxes (p-boxes), i.e., probability distributions with imprecise parameters. In order to facilitate the uncertain power flow calculation with p-boxes, a polynomial chaos expansion-based method is developed. Moreover, the interval-valued Borgonovo index is proposed for global sensitivity analysis and to identify the input variables that have critical impacts on systems. The simulations in IEEE 14-bus and 118-bus systems verify the accuracy and efficiency of the proposed method by comparing it with the conventional double-loop sampling method

Power coupling and grid-connected support control of the PV/ESS power generation system with virtual inertia

Under virtual synchronous control, the photovoltaic energy storage grid-connected system can realize synchronous grid connection. However, the power coupling relationship between units needs to be analyzed to reduce additional operation risks and improve the support performance of virtual synchronous control. In this paper, the definition of virtual inertia of the energy storage device is described, and the power coupling relationship between the virtual synchronous generator and synchronous generator is analyzed by establishing a small-signal model of photovoltaic energy storage grid-connected power generation system. The influence of the power coupling control coefficient on the static and transient stability of the grid-connected system is analyzed by the extended equal area rule and eigenvalue method. Based on this, a power coupling control strategy is proposed to improve inertia and damping support performance. Finally, a grid-connected power generation simulation system with a high proportion of photovoltaic energy storage is built to test and verify. The results demonstrate that the proposed control can reduce the risk of power oscillation induced by virtual inertia and improve the grid-connected system’s frequency support and power oscillation suppression ability

Peroxymonosulfate activation for efficient sulfamethoxazole degradation by Fe\u3csub\u3e3\u3c/sub\u3eO\u3csub\u3e4\u3c/sub\u3e/Β-FeOOH nanocomposites:Coexistence of radical and non-radical reactions

\u3cp\u3eEnvironmental friendly magnetic Fe\u3csub\u3e3\u3c/sub\u3eO\u3csub\u3e4\u3c/sub\u3e/β-FeOOH nanocomposites with low cost were prepared via a simple one pot method and their physiochemical properties were investigated. The Fe\u3csub\u3e3\u3c/sub\u3eO\u3csub\u3e4\u3c/sub\u3e/β-FeOOH nanocomposites efficiently catalyzed the activation of peroxymonosulfate (PMS) for sulfamethoxazole (SMX) degradation and can be easily recovered through magnetic separation. The effects of catalyst dosage, PMS dosage, temperature and pH were evaluated. The catalyst showed great stability and reusability based on the successive degradation cycles. The reactive oxygen species (ROS) including sulfate radical (SO\u3csub\u3e4\u3c/sub\u3e \u3csup\u3e−[rad]\u3c/sup\u3e), hydroxyl radical ([rad]OH) and singlet oxygen (\u3csup\u3e1\u3c/sup\u3eO\u3csub\u3e2\u3c/sub\u3e) were generated in the Fe\u3csub\u3e3\u3c/sub\u3eO\u3csub\u3e4\u3c/sub\u3e/β-FeOOH/PMS system, while both of SO\u3csub\u3e4\u3c/sub\u3e \u3csup\u3e−[rad]\u3c/sup\u3e and \u3csup\u3e1\u3c/sup\u3eO\u3csub\u3e2\u3c/sub\u3e were dominantly attributed to the SMX degradation. The special tunnel-type structure and surface oxygen vacancies of β-FeOOH may be responsible for the high catalytic activity towards PMS to degrade SMX. At last, the catalytic mechanism of PMS on the surface of catalysts were proposed.\u3c/p\u3

A Study of Two-level Inventory Allocation for E-commerce Pre-sales Based on Two Return Methods

In recent years, e-commerce shopping festivals based on the presale model have become increasingly popular. Under the pre-sale model, some e-commerce companies have used inventory front-loading methods to give consumers higher time satisfaction for alleviating the pressure caused by order piling. At the same time a new return method has been generated, but existing studies have only considered the traditional return method. In this paper, a dualobjective decision model based on NSGA-II algorithm is developed for maximizing enterprise profit and maximizing consumer time satisfaction under the premise of considering both return methods, and the optimal two-level inventory allocation decision scheme is calculated for e-commerce enterprises. The results show that the existence of two return methods will have a more significant impact on enterprise profit, and therefore is more practical for e-commerce enterprises to make two-level inventory allocation decisions