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Accurate short-term load forecasting (STLF) is of positive significance to the effective management of power companies and the stable operation of society. In spite of many studies conducted in this field, there are few to consider the inherent disadvantages of an individual module, which results in sub-optimal forecasting accuracy. Therefore, by integrating data preprocessing module and optimization module into support vector regression (SVR) forecasting module, this paper successfully presents a novel model (AHO-Lasso-SVR). The data preprocessing module, which is comprised of feature construction and Lasso feature selection, is used to construct and select meaningful features. An adaptive hybrid optimization (AHO) algorithm is proposed by introducing two strategies on the basis of standard particle swarm optimization (PSO). The AHO algorithm inevitably increases the computational complexity of model learning, thus, this paper proposes a subsampling technology to improve the optimization efficiency of the algorithm based on the sparsity of SVR. The proposed model is used to forecast the load at 48 points in the next day. To verify the properties of the proposed model, power load data from New South Wales, Australia are adopted as a case study. The results reveal that our model positively exceeds all comparison models in terms of forecasting accuracy and stability. [ABSTRACT FROM AUTHOR]

Quantum detector tomography is a fundamental technique for calibrating quantum devices and performing quantum engineering tasks. In this paper, a novel quantum detector tomography method is proposed. First, a series of different probe states are used to generate measurement data. Then, using constrained linear regression estimation, a stage-1 estimation of the detector is obtained. Finally, the positive semidefinite requirement is added to guarantee a physical stage-2 estimation. This Two-stage Estimation (TSE) method has computational complexity $O(nd^{2}M)$ , where $n$ is the number of $d$ -dimensional detector matrices and $M$ is the number of different probe states. An error upper bound is established, and optimization on the coherent probe states is investigated. We perform simulation and a quantum optical experiment to testify the effectiveness of the TSE method. [ABSTRACT FROM AUTHOR]

Background: To facilitate informed consent, consent forms should use language below the grade eight level. Research Ethics Boards (REBs) provide consent form templates to facilitate this goal. Templates with inappropriate language could promote consent forms that participants find difficult to understand. However, a linguistic analysis of templates is lacking. Methods: We reviewed the websites of 124 REBs for their templates. These included English language medical school REBs in Australia/New Zealand (n = 23), Canada (n = 14), South Africa (n = 8), the United Kingdom (n = 34), and a geographically-stratified sample from the United States (n = 45). Template language was analyzed using Coh-Metrix linguistic software (v.3.0, Memphis, USA). We evaluated the proportion of REBs with five key linguistic outcomes at or below grade eight. Additionally, we compared quantitative readability to the REBs’ own readability standards. To determine if the template’s country of origin or the presence of a local REB readability standard influenced the linguistic variables, we used a MANOVA model. Results: Of the REBs who provided templates, 0/94 (0%, 95% CI = 0–3.9%) provided templates with all linguistic variables at or below the grade eight level. Relaxing the standard to a grade 12 level did not increase this proportion. Further, only 2/22 (9.1%, 95% CI = 2.5–27.8) REBs met their own readability standard. The country of origin (DF = 20, 177.5, F = 1.97, p = 0.01), but not the presence of an REB-specific standard (DF = 5, 84, F = 0.73, p = 0.60), influenced the linguistic variables. Conclusions: Inappropriate language in templates is an international problem. Templates use words that are long, abstract, and unfamiliar. This could undermine the validity of participant informed consent. REBs should set a policy of screening templates with linguistic software. [ABSTRACT FROM AUTHOR]

• Renewable energy supply capacity planning problem considering electrical energy storage on a country scale. • Parallel investigation and comparison of heuristic model with commercial Gurobi optimisation. • Benchmark of two configurations with 168 h on 25 × 30 and 2400 h on 8 × 10 grid with six renewable technologies. When deciding on a country's future energy policy, it is essential to accurately estimate the future generation mix and technology costs, as well as future generation site locations in electricity power grids. This estimate needs to allow for changing levels of demand and increasing levels of renewable energy supply, with both having high fluctuations geographically and temporally. This objective leads to high-dimensional mathematical models with high computational complexity that cannot be solved analytically. Various simplifications and heuristics are proposed globally, however, these need to be anchored in terms of an indication of their likely performance. We offer a novel parallel investigation of a near-optimal and heuristic optimisation approach on a country scale. The Linear Programming (LP) optimisation problem finds the global minimum for the chosen set of input variables, this however comes at the cost of limitations regarding model size due to complexity. The alternative heuristic model limits connectivity of model parameters but rather than aggregating variables over space or time, it maintains a high resolution to allow for more granular estimates. We compare the quality and performance of a heuristic and LP optimisation method, using two configurations for the example of Australia and find relevant solutions for both. The solution space requires substantial simplification for the LP to be solvable and leads to overly optimistic capacity factors, installed capacity and cost, due to spatial aggregation. The heuristic approach has a significant performance advantage requiring only 3% of the near-optimal runtime and a fraction of calculation iterations. [ABSTRACT FROM AUTHOR]

This paper demonstrates how the data-association technique known as the probabilistic multi-hypothesis tracker (PMHT) can be applied to the feature-based simultaneous localization and map building (SLAM) problem. The main advantage of PMHT over other conventional data-association techniques is that it has low computational complexity, while still providing good performance. Low complexity is a particularly desirable feature for the SLAM problem where the estimators used may already be costly to implement. The paper also proposes an estimation approach based on generalized expectation-maximization iterations of the PMHT SLAM problem, which is able to achieve low computation complexity at the expense of local convergence. The performance of the PMHT SLAM algorithm is compared with other approaches, and its output is demonstrated on a benchmark data set recorded in Victoria Park, Sydney, Australia. [ABSTRACT FROM AUTHOR]