Day-Ahead Crude Oil Price Forecasting Using a Novel Morphological Component Analysis Based Model

As a typical nonlinear and dynamic system, the crude oil price movement is difficult to predict and its accurate forecasting remains the subject of intense research activity. Recent empirical evidence suggests that the multiscale data characteristics in the price movement are another important stylized fact. The incorporation of mixture of data characteristics in the time scale domain during the modelling process can lead to significant performance improvement. This paper proposes a novel morphological component analysis based hybrid methodology for modeling the multiscale heterogeneous characteristics of the price movement in the crude oil markets. Empirical studies in two representative benchmark crude oil markets reveal the existence of multiscale heterogeneous microdata structure. The significant performance improvement of the proposed algorithm incorporating the heterogeneous data characteristics, against benchmark random walk, ARMA, and SVR models, is also attributed to the innovative methodology proposed to incorporate this important stylized fact during the modelling process. Meanwhile, work in this paper offers additional insights into the heterogeneous market microstructure with economic viable interpretations

Optimization of Deflection of a Big NEO through Impact with a Small One

Using a small near-Earth object (NEO) to impact a larger and potentially threatening NEO has been suggested as an effective method to avert a collision with Earth. This paper develops a procedure for analysis of the technique for specific NEOs. First, an optimization method is used to select a proper small body from the database. Some principles of optimality are achieved with the optimization process. Then, the orbit of the small body is changed to guarantee that it flies toward and impacts the big threatening NEO. Kinetic impact by a spacecraft is chosen as the strategy of deflecting the small body. The efficiency of this method is compared with that of a direct kinetic impact to the big NEO by a spacecraft. Finally, a case study is performed for the deflection of the Apophis NEO, and the efficiency of the method is assessed

A novel multiscale nonlinear ensemble leaning paradigm for carbon price forecasting

In this study, a novel multiscale nonlinear ensemble leaning paradigm incorporating empirical mode decomposition (EMD) and least square support vector machine (LSSVM) with kernel function prototype is proposed for carbon price forecasting. The EMD algorithm is used to decompose the carbon price into simple intrinsic mode functions (IMFs) and one residue, which are identified as the components of high frequency, low frequency and trend by using the Lempel-Ziv complexity algorithm. The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is used to forecast the high frequency IMFs with ARCH effects. The LSSVM model with kernel function prototype is employed to forecast the high frequency IMFs without ARCH effects, the low frequency and trend components. The forecasting values of all the components are aggregated into the ones of original carbon price by the LSSVM with kernel function prototype-based nonlinear ensemble approach. Furthermore, particle swarm optimization is used for model selections of the LSSVM with kernel function prototype. Taking the popular prediction methods as benchmarks, the empirical analysis demonstrates that the proposed model can achieve higher level and directional predictions and higher robustness. The findings show that the proposed model seems an advanced approach for predicting the high nonstationary, nonlinear and irregular carbon price

Efficacy of polyethylene glycol loxenatide versus insulin glargine on glycemic control in patients with type 2 diabetes: a randomized, open-label, parallel-group trial

Objective: This trial aimed to evaluate the glycemic control of polyethylene glycol loxenatide measured with continuous glucose monitoring (CGM) in patients with type 2 diabetes mellitus (T2DM), with the hypothesis that participants given PEG-Loxe would spend more time in time-in-range (TIR) than participants were given insulin glargine after 24 weeks of treatment.Methods: This 24-week, randomized, open-label, parallel-group study was conducted in the Department of Endocrine and Metabolic Diseases, Longhu Hospital, Shantou, China. Participants with T2DM, who were ≥45 years of age, HbA1c of 7.0%–11.0%, and treated at least 3 months with metformin were randomized (1:1) to receive PEG-Loxe or insulin glargine. The primary endpoint was TIR (blood glucose range: 3.9–10.0 mmol/L) during the last 2 weeks of treatment (weeks 22–24).Results: From March 2020 to April 2022, a total of 107 participants with T2DM were screened, of whom 78 were enrolled into the trial (n = 39 per group). At the end of treatment (weeks 22–24), participants given PEG-Loxe had a greater proportion of time in TIR compared with participants given insulin glargine [estimated treatment difference (ETD) of 13.4% (95% CI, 6.8 to 20.0, p < 0.001)]. The tight TIR (3.9–7.8 mmol/L) was greater with PEG-Loxe versus insulin glargine, with an ETD of 15.6% (95% CI, 8.9 to 22.4, p < 0.001). The time above range (TAR) was significantly lower with PEG-Loxe versus insulin glargine [ETD for level 1: −10.5% (95% CI: −14.9 to −6.0), p < 0.001; ETD for level 2: −4.7% (95% CI: −7.9 to −1.5), p = 0.004]. The time below range (TBR) was similar between the two groups. The mean glucose was lower with PEG-Loxe versus insulin glargine, with an ETD of −1.2 mmol/L (95% CI, −1.9 to −0.5, p = 0.001). The SD of CGM glucose levels was 1.88 mmol/L for PEG-Loxe and 2.22 mmol/L for insulin glargine [ETD -0.34 mmol/L (95% CI: −0.55 to −0.12), p = 0.002], with a similar CV between the two groups.Conclusion: The addition of once-weekly GLP-1RA PEG-Loxe to metformin was superior to insulin glargine in improving glycemic control and glycemic variability evaluated by CGM in middle-aged and elderly patients with T2DM

Association between fasting blood glucose and thyroid stimulating hormones and suicidal tendency and disease severity in patients with major depressive disorder

Thyroid dysfunction and diabetes are reported to be associated with depression. However, their role in the suicide risk in patients with major depressive disorder (MDD) is unclear. The purpose of this study was to investigate and compare thyroid dysfunction and diabetes between suicide attempters and non-suicide attempters in a large sample of first-episode drug-naïve (FEND) MDD patients. A descriptive study was conducted on 1279 Chinese outpatients with a diagnosis of first-episode MDD. Their sociodemographic information, blood levels of thyroid hormones, glucose, lipids and body mass index (BMI) parameters were collected. The positive subscales of the positive and negative syndrome scale (PANSS), Hamilton Anxiety Rating Scale (HAMA), Hamilton Depression Rating Scale (HAMD) were measured for psychotic, anxiety and depressive symptoms. Our results showed that compared with non-suicide attempters (P<0.01), suicide attempters had statistically higher scores on HAMD, HAMA and PANSS psychotic symptoms, as well as higher thyroid stimulating hormone (TSH) serum levels, glucose, anti-thyroglobulin (A-TG), anti-thyroid peroxidase (A-TPO), total cholesterol (TC), triglycerides (TG), low density lipoprotein cholesterol (LDL-C), systolic blood pressure and diastolic blood pressure (all with P<0.001). These results revealed that TSH, A-TG, A-TPO, TC, TG and LDL-C may be promising biomarkers of suicide risk in MDD, implying the importance of regular assessment of blood glucose level and thyroid function parameters for suicide prevention, along with possible treatment for impaired thyroid function and diabetes for the suicide intervention in MDD patients. Such patients with abnormal blood sugar and TSH must undergo thorough screening for suicidal ideation

Pharmacoeconomic analysis (CER) of Dulaglutide and Liraglutide in the treatment of patients with type 2 diabetes

AimTo evaluate the treatment effect Fand pharmacoeconomic value of Dugaglutide in women with type 2 diabetes.MethodsWomen (n=96) with type 2 diabetes recruited from June 2019 to December 2021 were randomized into two equal groups. The control group was treated with Liraglutide, and the observation group was treated with Dulaglutide, both for 24 weeks. The blood glucose levels, biochemical index, insulin resistance index (HOMA-IR), cost-effect ratio (CER), and drug safety were determined and compared between the two groups.ResultsBlood glucose levels, the biochemical index, and HOMA-IR were lower in both groups after the treatment (P &lt; 0.05), and there was no statistical difference in the blood glucose levels, biochemical index and HOMA-IR between the two groups (P &gt; 0.05). The CER levels did not differ statistically between the two groups (P &gt; 0.05). Both the cost and the incidence of drug side effects during solution injection were lower in the observation group than in the control group after 24 weeks of treatment (P &lt; 0.05).ConclusionBoth Dulaglutide and Liraglutide can reduce blood glucose levels, improve biochemical index, and HOMA-IR levels in women with type 2 diabetes. Dulaglutide is more cost-effective and safe.Clinical trial registrationhttps://www.chictr.org.cn/index.aspx, identifier ChiCTR1900026514

Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering

This publication is the Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering from July 6-8, 2022. The EG-ICE International Workshop on Intelligent Computing in Engineering brings together international experts working on the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolution of challenges such as supporting multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways. &nbsp

Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering

This publication is the Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering from July 6-8, 2022. The EG-ICE International Workshop on Intelligent Computing in Engineering brings together international experts working on the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolution of challenges such as supporting multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways. &nbsp

Dye-sensitized photocathodes: Materials and interfacial photodynamics

he performance of a tandem DSPEC cell is hampered by the low efficiency of the photocathode. Various strategies have been proposed in order to enhance the performance. The primary limitation is that the chemical reaction occurs significantly slower than charge recombination between the NiO and the dye-catalyst assembly. Therefore, it is important to further develop fundamental insight, and based on that adjust the photocathode design. In Chapter 2, the experimental details including sample preparation and characterization are described. In addition, the principles behind femtosecond transient absorption and time-resolved photoluminescence spectroscopy used for mechanistic studies are discussed. In Chapter 3, the effect of Cu-doping of NiO on the photodynamics and performance is explored. It turns out that Cu-doping can slow down surface charge recombination and enhance the photocurrent of the P1 dye-sensitized photocathode. This work highlights that the nature of the NiO surface plays an important role in improving the efficiency. In Chapter 4, the photodynamics of P1-sensitized NiO in different working environments is studied. Both fast hole injection as well as fast charge recombination are observed when the photocathode is exposed to an aqueous or humid environment. We assign this to a dual role of surface OH- on the NiO, accelerating both light-induced hole injection and recombination, highlighting the importance of balancing the quantity of surface OH-. In Chapter 5, the photodynamics of P1-sensitized NiO in aqueous electrolyte under different external potentials is investigated by time-resolved photoluminescence and femtosecond transient absorption. At more negative potential, charge recombination is slowed down, analogous to literature.91 However, hole injection appears to also be slowed down instead of being promoted. We assign this to compositional changes (H+ and OH‑) in the electrochemical double layer induced by the external bias, which is in agreement with the dual function of OH- as described in Chapter 4. In the research described in Chapter 6, myristic acid with a long hydrophobic alkyl chain and a carboxylic acid anchoring group is co-adsorbed on the NiO surface with the aim to decrease the fast charge recombination between de P1 dye and NiO. In addition to control over the surface hydroxylation, the long alkyl chains also provide an apolar environment to the P1 dye molecules and inhibit the molecular twisting after photoexcitation, which appears to drive H2 evolution from water without additional catalyst. In Chapter 7, the preparation and analysis of a film of a new p-type material, CuBO2, is described. The properties of the obtained CuBO2 film are sensitive to the precursor solution and annealing conditions. The CuBO2-based photocathode shows significantly slow charge recombination compared with the NiO-based analogue

A Star-Identification Algorithm Based on Global Multi-Triangle Voting

A star-identification algorithm aimed at identifying imaged stars in a “lost in space” scene, named the global multi-triangle voting algorithm (GMTV), is presented in this paper. There are two core parts included in the proposed algorithm: in the initial match part, triangle feature units are treated as vote units to find the initial match relationship via matching vote units and counting the vote number of each catalog star. During this step, the principal component analysis (PCA) method is implemented to reduce feature dimensions, and a two-dimension lookup table and fuzzy match strategy are utilized to promote database searching efficiency and noise tolerance. After acquiring the initial match results, a verification part is implemented to filter potential errors from initial candidates by the largest cluster method and output the final identification results. The proposed algorithm achieves a 98.6% identification rate with 2.0 pixels position noise and exhibits more robustness to position noise, magnitude noise, and false stars of different levels than the two reference algorithms used in simulations. In addition, our algorithm’s real-time performance is better than reference algorithms, but it requires a larger database