This paper examines if using ultra high frequency (UHF, e.g., tick-by-tick) data could improve the accuracy of beta forecasts compared with using only moderately high frequency (MHF, minute-level) data. We propose a n...
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This paper examines if using ultra high frequency (UHF, e.g., tick-by-tick) data could improve the accuracy of beta forecasts compared with using only moderately high frequency (MHF, minute-level) data. We propose a novel two-step paired t-test for performance evaluation. Our test exploits the cross-sectional variations in the beta forecasts and avoids the issues associated with the traditional approach which requires choosing a proxy for the true beta. Our tests provide strong evidence that using UHF data generally yields more accurate beta forecasts than using MHF data. Furthermore, we show that the UHF estimator consistently belongs to the group of best risk-hedging performers for portfolios constructed based on both industrial classifications and size and book-to-market ratios. However, we also find that using UHF data of a coarser scale (e.g., 5 or 15 s) leads to reduced benefits compared with using tick-by-tick data. Our conclusions hold when different UHF estimators and sample periods are used.
Zwitterionic materials with cationic and anionic moieties in the same chain, being electrically neutral, have excellent hydrophilicity, stability, biocompatibility, and outstanding anti-biofouling performance. Because...
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Zwitterionic materials with cationic and anionic moieties in the same chain, being electrically neutral, have excellent hydrophilicity, stability, biocompatibility, and outstanding anti-biofouling performance. Because of their unique properties, zwitterionic materials are widely applied to membrane separation, drug delivery, surface coating, etc. However, what is the root of their unique properties? It is necessary to study the structure-property relationships of zwitterionic compounds to guide the design and development of zwitterionic materials. Modeling and simulation methods are considered to be efficient technologies for understanding advanced materials in principle. This Review systematically summarizes the computational exploration of zwitterionic materials in recent years. First, the classes of zwitterionic materials are summarized. Second, the different scale simulation methods are introduced briefly. To reveal the structure-property relationships of zwitterionic materials, multiscale modeling and simulation studies at different spatial and temporal scales are summarized. The study results indicated that the strong electrostatic interaction between zwitterions with water molecules promotes formation of a stable hydration layer, namely, superhydrophilicity, leading to the excellent anti-fouling properties. Finally, we offer our viewpoint on the development and application of simulation techniques on zwitterionic materials exploration in the future. This work establishes a bridge from atomic and molecular scales to mesoscopic and macroscopic scales and helps to provide an in-depth understanding of the structure-property relationships of zwitterionic materials.
Bio-conversion of lignocellulosic biomass to bioethanol fuel is a highly desirable yet challenging objective because of the low catalytic activity and high cost of beta-glucosidase (BGL). Recently, ZIF-8, an emerging ...
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Bio-conversion of lignocellulosic biomass to bioethanol fuel is a highly desirable yet challenging objective because of the low catalytic activity and high cost of beta-glucosidase (BGL). Recently, ZIF-8, an emerging organic porous material, has been proposed as a promising candidate for enzyme immobilization to improve associated activity and stability. However, the underlying interaction mechanism of binding BGL on the ZIF-8 surface is yet to be clarified. Here, the adsorption of BGL onto ZIF-8 is explored for the first time by molecular dynamics simulations. The results show that BGL adsorbs on the ZIF-8 surface with a "back-on" orientation. The adsorption free energy analysis shows that the adsorption process is enthalpy driven. In addition, the electrostatic interaction between negatively charged residues and Zn2+ on the surface of ZIF-8 is found to play a decisive role in surface binding, which accounts for 98% of the total interaction energy. The secondary structure of BGL is not affected despite the strong adsorption, suggesting the good biocompatibility of ZIF-8. This study not only provides a reliable theoretical insight into understanding the interaction mechanism between BGL and ZIF-8, but also helps the rational design of ZIF-8-based materials for bio-related applications. Electrostatic interactions account for 98% of the total energy for beta-glucosidase binding onto ZIF-8.
Traditional description methods for describing rock traces heavily rely on the operator's knowledge and expertise. For the characterization of hard rock pillar traces in complex underground mines, this subjective ...
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Traditional description methods for describing rock traces heavily rely on the operator's knowledge and expertise. For the characterization of hard rock pillar traces in complex underground mines, this subjective evaluation strategy not only limits the generalization of the description method, but also causes local trace omissions, increasing the potential for quantization error. To that end, 19,780 trace images with a resolution of 1000x1000\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1000 \times 1000$$\end{document} pixels were collected to train and test the DeepLabV3 + model. This paper introduces a novel description method termed the intersection grid description (IGD) to describe and quantify traces predicted by the DeepLabV3 + model. In addition, a novel performance index, namely precision efficiency covalent (PEC), is established to evaluate the trace description accuracy. The results indicate that the proposed method can effectively captures the similarity and integrity of hard rock pillar traces. Furthermore, trace features (length, angle, density, and intensity) quantified by the proposed method closely match actual survey results. Overall, the proposed IGD method with high performance in characterization of traces on hard rock pillar surfaces suggests its potential for further application in trace investigation of other rocks in the complex environments.
The stability of pillars in underground mining is key to safety and efficient operations. Predicting pillar stability is a complex task, influenced by dynamic and nonlinear factors, which necessitates predictive metho...
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The stability of pillars in underground mining is key to safety and efficient operations. Predicting pillar stability is a complex task, influenced by dynamic and nonlinear factors, which necessitates predictive methods with higher accuracy and practicality. This study introduces two innovative stability assessment methods-the hard-rock pillar stability index (HPSI) and a hybrid gene-expression programming (HGEP) model-developed using 195 pillar cases from multiple hard-rock mines. The HPSI provides a quantitative scoring indicator for stability, while HGEP offers an explicit discriminant function to capture complex nonlinear interactions. Validation results demonstrate the proposed models' superiority over traditional approaches. The HGEP model achieved an accuracy improvement of approximately 15%, reaching 88.4%, and both models exhibited strong generalization performance on external datasets. Additionally, SHapley Additive exPlanations (SHAP) analysis revealed the critical influence of geometric and stress parameters, enhancing the models' transparency and interpretability. Beyond technical contributions, the outcomes of this study hold significant practical relevance. The HPSI and HGEP models provide reliable and interpretable tools for assessing hard-rock pillar stability, supporting safer and more efficient underground mining operations.
BackgroundThe need for anterior cervical discectomy and fusion (ACDF) for cervical degenerative disc disease (CDDD) will probably grow dramatically in the geriatric population. However, ACDF with self-locking standalo...
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BackgroundThe need for anterior cervical discectomy and fusion (ACDF) for cervical degenerative disc disease (CDDD) will probably grow dramatically in the geriatric population. However, ACDF with self-locking standalone cages in patients over 80 years has not yet been investigated. This study aimed to assess the clinical and radiographic results in patients over 80 years treated by ACDF with self-locking standalone cages. MethodsBetween January 2018 and December 2019, patients with CDDD treated with ACDF were retrospectively stratified into two groups: the older group (>= 80 years) and the younger group (< 65 years). The data collected included the demographics, preoperative comorbidities, intraoperative parameters, length of hospital stay, complications, clinical scores, and radiological parameters. ResultsA total of 123 patients were included in the study. The mean follow-up duration was 28.3 +/- 2.4 months. The hospital stay was 5.3 +/- 0.6 days and 3.8 +/- 0.4 days, respectively, for the older and younger groups. Postoperative complication rate was found higher in the older group than that of the young group without significance. All the patient-reported outcome parameters had significant improvement at the final follow-up. The two groups had no significant differences in terms of the excellent and reasonable rates, fusion rate, and the C2-C7 Cobb angle. ConclusionsAlthough a slightly higher incidence of complications, poorer recovery rate, and more extended hospital stay were found, without significant differences, satisfactory clinical and radiographic results were obtained in the older patients. The self-locking standalone cage is a safe and viable option for patients over 80 years who suffer from *** of evidence Level IV. ConclusionsAlthough a slightly higher incidence of complications, poorer recovery rate, and more extended hospital stay were found, without significant differences, satisfactory clinical and radiographic results were obtained in the
The energy gaps, spin-orbit coupling (SOC), and admixture coefficients over a series of the configurations are evaluated by the SA-CASSCF/6-31G, SA-CASSCF/6-31G*, SA-CASSCF/ANO-RCC-VDZP, and MS-CASPT2/ANO-RCC-VDZP to ...
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The energy gaps, spin-orbit coupling (SOC), and admixture coefficients over a series of the configurations are evaluated by the SA-CASSCF/6-31G, SA-CASSCF/6-31G*, SA-CASSCF/ANO-RCC-VDZP, and MS-CASPT2/ANO-RCC-VDZP to reveal the extent of the inaccuracy of the SA-CASSCF. By comparing the mean absolute errors for the energy gaps and the admixture coefficient magnitudes (ACMs) measured between the SA-CASSCF/6-31G, SA-CASSCF/6-31G*, or SA-CASSCF/ANO-RCC-VDZP and the MS-CASPT2/ANO-RCC-VDZP, the SA-CASSCF/6-31G is selected as the electronic structure method in the nonadiabatic molecular dynamics simulation. The major components of the ACMs of the SA-CASSCF/6-31G and MS-CASPT2/ANO-RCC-VDZP are identified and compared;we find that the ACMs are underestimated by the SA-CASSCF/6-31G, which is verified by the reasonable triplet quantum yield simulated by the trajectory surface hopping and the calibrated SA-CASSCF/6-31G. The magnitude of the singlet-triplet mixing positively correlates to the hopping probability between the mixed singlet and triplet states, which is confirmed by the computed S-T transition probability.
The Tetrahedral Constellation Gravitational Wave Observatory(TEGO)[1]represents a significant leap forward in gravitational wave detection,advancing our understanding of gravitational *** introducing a fourth spacecra...
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The Tetrahedral Constellation Gravitational Wave Observatory(TEGO)[1]represents a significant leap forward in gravitational wave detection,advancing our understanding of gravitational *** introducing a fourth spacecraft to form a tetrahedral configuration,TEGO enhances traditional triangular setups,enabling the simultaneous sensitivity to all six gravitational wave polarization modes,including those not predicted by general relativity.
The development of heterogeneous catalysts with high activity is highly desired for hydrogenation reactions, where the catalyst support plays a crucial role in enhancing the activity of hydrogenation catalysts. In thi...
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The development of heterogeneous catalysts with high activity is highly desired for hydrogenation reactions, where the catalyst support plays a crucial role in enhancing the activity of hydrogenation catalysts. In this study, a novel hollow graphene nanosphere (HGS) with open pores was synthesized via chemical vapor deposition (CVD) using Mg0.91Fe0.09O and Mg0.9Mn0.1O solid-solution catalysts for the first time. The growth mechanism of HGS was elucidated through detailed characterization of the catalyst and HGS. It was found that the in situ formation of solid solutions during the CVD process is the key to HGS growth. The unique pore structures and high surface area of HGS made it an excellent catalyst support. A Ru/HGS catalyst was prepared via impregnation and evaluated for the hydrogenation of 1-methylindole (NMID). Ru/HGS exhibits superior catalytic performance (TOF = 75.6 min-1), surpassing commercial Ru/Al2O3 (TOF = 41.8 min-1). The activation energy for NMID hydrogenation using Ru/HGS was determined to be 41.7 kJ/mol, significantly lower than the 80.7 kJ/ mol for Ru/Al2O3. The catalyst characterization analysis and DFT calculations reveal that Ru nanoparticles on HGS were smaller, better dispersed, and formed stronger interactions with the support, enhancing NMID adsorption and hydrogen activation, particularly on the Ru(101) surface, which explained the superior catalytic performance of Ru/HGS over Ru/Al2O3. Furthermore, the Ru/HGS catalyst demonstrated broad applicability and high efficiency in hydrogenating various aromatics and N-heterocyclic compounds, including benzene, toluene, quinoline, pyridine, N-ethylcarbazole and indole derivatives. This study introduces a new strategy for designing advanced catalysts with enhanced efficiency for hydrogenation.
To solve the problem of identifying the mental health status of college students, this study investigated the psychological conditions of students in a certain department of a university in Hubei Province through a qu...
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To solve the problem of identifying the mental health status of college students, this study investigated the psychological conditions of students in a certain department of a university in Hubei Province through a questionnaire survey using the SCL - 90 scale. It combined machine learning algorithms to analyze the applicability of the model and explore the differences between students with healthy and sub - healthy mental states. Data (including basic information) of 500 students were randomly collected. A self - compiled questionnaire was used in combination with on - site scoring by psychological teachers to classify the mental states of the 500 students into healthy and sub - healthy states. Questionnaire data were analyzed through decision tree, support vector machine, random forest, and XGBOOST algorithms to quickly identify the healthy and sub - healthy states and to mine the behavioral characteristics that have a certain correlation with the mental health status of students. The data information of 500 students was modeled respectively, and the classification effects of the models were evaluated through accuracy, precision, recall, F1 - score, and AUC. The results showed that among the four methods, the random forest had the best classification effect, with an R2 score of 0.8891, which was higher than the R2 score of 0.8393 for the decision tree, the R2 score of 0.8840 for the support vector machine, and the R2 score of 0.8618 for the XGBOOST algorithm. Considering the advantages of the random forest in terms of classification performance, modeling time, interpretability, feature selection, and simplicity, we recommend using the random forest model to assist in the diagnosis of mental health status classification. The experimental results on the SCL - 90 scale survey and the student basic information dataset show that the proposed model has high accuracy and can converge quickly, enabling more effective and accurate prediction of students' mental health stat
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