Intelligent Optimized Combined Model Based on GARCH and SVM for Forecasting Electricity Price of New South Wales, Australia

Daily electricity price forecasting plays an essential role in electrical power system operation and planning. The accuracy of forecasting electricity price can ensure that consumers minimize their electricity costs and make producers maximize their profits and avoid volatility. However, the fluctuation of electricity price depends on other commodities and there is a very complicated randomization in its evolution process. Therefore, in recent years, although large number of forecasting methods have been proposed and researched in this domain, it is very difficult to forecast electricity price with only one traditional model for different behaviors of electricity price. In this paper, we propose an optimized combined forecasting model by ant colony optimization algorithm (ACO) based on the generalized autoregressive conditional heteroskedasticity (GARCH) model and support vector machine (SVM) to improve the forecasting accuracy. First, both GARCH model and SVM are developed to forecast short-term electricity price of New South Wales in Australia. Then, ACO algorithm is applied to determine the weight coefficients. Finally, the forecasting errors by three models are analyzed and compared. The experiment results demonstrate that the combined model makes accuracy higher than the single models

6,7,8,14,15,16-Hexaphenyldibenzo[c,gh]naphtho[3,2,1,8-pqra]tetraphene-5,13-dione dichloromethane monosolvate

The main mol­ecule of the title compound, C66H38O2·CH2Cl2, is centrosymmetric, the asymmetric unit is composed of two half-mol­ecules, located on inversion centers, and a mol­ecule of dichloro­methane. The large π-conjugated fused polycyclic system including eight six-membered rings is nearly planar, with r.m.s. deviations of 0.2114 and 0.2081 Å in the two independent mol­ecules

Valence band offset of InN/BaTiO3 heterojunction measured by X-ray photoelectron spectroscopy

X-ray photoelectron spectroscopy has been used to measure the valence band offset of the InN/BaTiO(3 )heterojunction. It is found that a type-I band alignment forms at the interface. The valence band offset (VBO) and conduction band offset (CBO) are determined to be 2.25 ± 0.09 and 0.15 ± 0.09 eV, respectively. The experimental VBO data is well consistent with the value that comes from transitivity rule. The accurate determination of VBO and CBO is important for use of semiconductor/ferrroelectric heterojunction multifunctional devices

Low Loss and Magnetic Field-tuned Superconducting THz Metamaterial

Superconducting terahertz (THz) metamaterial (MM) made from superconducting Nb film has been investigated using a continuous-wave THz spectroscopy with a superconducting split-coil magnet. The obtained quality factors of the resonant modes at 132 GHz and 450 GHz are about three times as large as those calculated for a metal THz MM operating at 1 K, which indicates that superconducting THz MM is a very nice candidate to achieve low loss performance. In addition, the magnetic field-tuning on superconducting THz MM is also demonstrated, which offer an alternative tuning method apart from the existed electric, optical and thermal tuning on THz MM

Influence of terrestrial and marine air mass on the constituents and intermixing of bioaerosols over a coastal atmosphere

Coastal environments provide an ideal setting for investigating the intermixing processes between terrestrial and marine aerosols. In this study, fine particulate matter (PM2.5) samples categorized into terrestrial, marine, and mixed air masses were collected from a coastal location in northern China. The chemical and biological constituents, including water-soluble ions (WSIs), metallic elements, and bacterial and fungal aerosols, were investigated from January to March 2018, encompassing both the winter heating and spring dust seasons. Terrestrial air masses constituted 59.94 % of the total air masses throughout the sampling period, with a significant increase during severe haze pollution (up to 90 %). These air masses exhibited a higher concentration of PM2.5 (240 µg m−3) and carried more water-soluble ions and metal elements. The terrestrial air mass also contained a larger number of animal parasites or symbionts, as well as human pathogens from anthropogenic emissions, such as Staphylococcus, Deinococcus, Sphingomonas, Lactobacillus, Cladosporium, and Malassezia. Conversely, a significant quantity of saprophytic bacteria such as hydrocarbon-degrading and gut bacteria from the genera Comamonas, Streptococcus, Novosphingobium, and Aerococcus and the saprophytic fungus Aspergillus were the most abundant species in the marine air mass samples. The mixed air mass elucidates the intermixing process of terrestrial and marine sources, a result of microorganisms originating from both anthropogenic and terrestrial emissions, which includes pathogenic microorganisms from hospitals and sewage treatment plants, and a multitude of soil bacteria. A stronger correlation was noted between microorganisms and continental elements in both terrestrial and mixed air mass samples, specifically K+, Mg2+, and Ca2+ derived from soil dust. Marine air masses exhibited a significant correlation with sea salt ions, specifically Na+. In the mixed air mass sample, a fusion of marine and terrestrial microorganisms is characterized by alterations in the ratio of pathogenic to saprophytic microorganisms when compared to samples derived from either terrestrial or marine sources. This study on the constituents and amalgamation of bioaerosols over the coastal atmosphere encompassing distinct air masses is crucial to understand the transport, intermixing processes, and health implications of terrestrial and marine air masses.</p

Intelligent Optimized Combined Model Based on GARCH and SVM for Forecasting Electricity Price of New South Wales, Australia

Daily electricity price forecasting plays an essential role in electrical power system operation and planning. The accuracy of forecasting electricity price can ensure that consumers minimize their electricity costs and make producers maximize their profits and avoid volatility. However, the fluctuation of electricity price depends on other commodities and there is a very complicated randomization in its evolution process. Therefore, in recent years, although large number of forecasting methods have been proposed and researched in this domain, it is very difficult to forecast electricity price with only one traditional model for different behaviors of electricity price. In this paper, we propose an optimized combined forecasting model by ant colony optimization algorithm (ACO) based on the generalized autoregressive conditional heteroskedasticity (GARCH) model and support vector machine (SVM) to improve the forecasting accuracy. First, both GARCH model and SVM are developed to forecast short-term electricity price of New South Wales in Australia. Then, ACO algorithm is applied to determine the weight coefficients. Finally, the forecasting errors by three models are analyzed and compared. The experiment results demonstrate that the combined model makes accuracy higher than the single models

Prognostic value of pretreatment Controlling Nutritional Status score in esophageal cancer: a meta-analysis

Background and purpose: The association between the pretreatment Controlling Nutritional Status (CONUT) score and the prognosis of esophageal cancer patients remains unclear. The aim of this meta-analysis was to further elucidate the prognostic role of the pretreatment CONUT score in esophageal cancer based on current evidence.Methods: The PubMed, Embase, Web of Science and CNKI databases were searched up to 27 September 2022. The primary and secondary outcomes were overall survival (OS) and progression-free survival (PFS)/cancer-specific survival (CSS), and the hazard ratio (HR) and 95% confidence interval (CI) were pooled for analysis.Results: A total of 11 retrospective studies involving 3,783 participants were included. The pooled results demonstrated that a higher pretreatment CONUT score was significantly related to poor OS (HR = 1.82, 95% CI: 1.31–2.54, p &lt; 0.001), and subgroup analysis stratified by pathological type showed similar results. In addition, the pretreatment CONUT score was associated with poor PFS (HR = 1.19, 95% CI: 1.10–1.28, p &lt; 0.001) and CSS (HR = 2.67, 95% CI: 1.77–4.02, p &lt; 0.001).Conclusion: The pretreatment CONUT score was predictive of worse prognosis in esophageal cancer, and patients with a higher CONUT score showed worse survival