The double Burgers model of fractured rock masses considering creep fracture damage

Creep fracture of rock cracks is responsible for the creep failure of fractured rock masses. To capture creep fracture behaviors of fractured rock, we investigated the time-dependent characteristics of the rock crack propagation. The theoretical analysis shows that, similar to the rock creep process, the creep fracture of rock cracks includes the attenuation and steady creep stages. In addition, we established an equivalent Burgers model for creep fracture of rock cracks by introducing the equivalent stress and proposed a double Burgers model to study creep behaviors of fractured rock masses. Moreover, the proposed double Burgers model was embedding into FLAC3D, using FISH function. The numerical simulations on the specimens, containing ordered and random cracks, show that the creep fracture is responsible for the creep damage of fractured rock masses; moreover, the lateral creep damage is larger than the axial creep damage

Global Control of Tuberculosis: Current Status and Future Prospects

Tuberculosis is a zoonotic disease that is caused by mycobacterium tuberculosis complex and can infect humans, livestock, and wildlife. It spreads primarily through the respiratory tract and was the leading cause of death due to a single infectious disease before the COVID-19 pandemic. TB is a global public health emergency that has reemerged over the past few decades. Substantial efforts are needed to achieve the goals of the End TB Strategy. The World Health Organization has estimated that approximately 9.9 million people worldwide contracted TB in 2020 and that approximately 140,000 of the 10 million new cases of active TB in 2019 were zoonotic TB. During the COVID-19 pandemic, the number of new TB diagnoses and reports decreased sharply, from 7.1 million in 2019 to 5.8 million in 2020, returning to 2012 levels far below the approximately 10 million TB cases in 2020. Simultaneously, the global decrease in the absolute number of TB deaths until 2019 was followed by an increase in 2020 in four of the six WHO regions and most of the 30 high-TB-burden countries. Therefore, extensive immediate actions worldwide are required to restore the health system, and innovations are needed to accelerate progress toward a tuberculosis-free world

Characterization of Colloidal Particles Using Electrical Impedance Spectroscopy in Two-electrode System with Carbon Probe

The colloidal particles have an electrical double layer associated with their surfaces when suspended in an aqueous medium. Under the influence of an alternating electric field, an induced electrical dipole moment can be formed due to the polarization of the electrical double layer. The electrical impedance spectroscopy (EIS) measurement can record the complex impedance, conductivity, relaxation frequency and phase angle caused by the polarization of the electrical double layer. These impedance parameters are in relation to particle characteristics, for example, the particle size. The research about particle size effect on electrical impedance spectra was carried out in a four-electrode system and the result indicated that impedance parameters shows a capability for characterizing the particle size. This paper reports the experimental results from electrical impedance spectroscopy measurements on silica suspensions in a two-electrode system with carbon probe. The main aim is to study the particle size effect on impedance parameters, especially the relaxation frequency and phase angle, to compare the data with those obtained from a four-electrode system with stainless steel electrodes and verify the capability for characterizing colloidal particles in different electrode systems. The particle size effect on the relaxation frequency and impedance phase angle was studied in two different electrode systems and a similar tendency can be observed. It indicates that the capability of impedance parameters for particle characterization is not limited in a four-electrode system, but commonly applicable in different electrode systems

Erratum for “Protective effect of quercetin on bupivacaineinduced neurotoxicity via T-type calcium channel inhibition”

Jin et al Trop J Pharm Res 2017, 16(8): 1827-1833 http://dx.doi.org/10.4314/tjpr.v16i8.11The correct name of the First Author is Zhao as provided above and not Chao earlier published.Citation: Jin Z, Wu H, Tang C, Ke J, Wang Y. Protective effect of quercetin on bupivacaineinduced neurotoxicity via T-type calcium channel inhibition. Trop J Pharm Res 2017; 16(8):1827-1833 Erratum: 2017; 16(9):2051 http://dx.doi.org/10.4314/tjpr.v16i9.

Increasing prevalence of HIV and syphilis but decreasing rate of self-reported unprotected anal intercourse among men who had sex with men in Harbin, China: results of five consecutive surveys from 2006 to 2010

Background To monitor the prevalence of HIV and syphilis as well as behaviours, a sentinel site for men who have sex with men was established in Harbin in 2002. With additional funding, the sentinel surveillance evolved into annual cross-sectional surveys since 2006. Methods Behavioural and serological data collected in five consecutive cross-sectional surveys were analysed. SPSS 13.0 was applied to compare prevalence of HIV and syphilis as well as behavioural variables over time by demographic variables, bivariate and multivariate analysis. Results The prevalence of HIV and syphilis increased from 1.0% in 2006 to 7.5% in 2010 and from 9.2% in 2006 to 22.4% in 2009, respectively, whereas the rate of unprotected anal intercourse decreased from 61.3% in 2006 to 47.1% in 2010. Syphilis positivity and HIV infection are independently associated with each other across years. The rate of unprotected anal sex remains high although it has decreased over the years. Conclusion Findings support an increasing prevalence of HIV and syphilis among men who have sex with men in Harbin. Targeted behavioural intervention and syphilis treatment are urgently needed to prevent the epidemic from growin

EvEval: A Comprehensive Evaluation of Event Semantics for Large Language Models

Events serve as fundamental units of occurrence within various contexts. The processing of event semantics in textual information forms the basis of numerous natural language processing (NLP) applications. Recent studies have begun leveraging large language models (LLMs) to address event semantic processing. However, the extent that LLMs can effectively tackle these challenges remains uncertain. Furthermore, the lack of a comprehensive evaluation framework for event semantic processing poses a significant challenge in evaluating these capabilities. In this paper, we propose an overarching framework for event semantic processing, encompassing understanding, reasoning, and prediction, along with their fine-grained aspects. To comprehensively evaluate the event semantic processing abilities of models, we introduce a novel benchmark called EVEVAL. We collect 8 datasets that cover all aspects of event semantic processing. Extensive experiments are conducted on EVEVAL, leading to several noteworthy findings based on the obtained results

RefBERT: A Two-Stage Pre-trained Framework for Automatic Rename Refactoring

Refactoring is an indispensable practice of improving the quality and maintainability of source code in software evolution. Rename refactoring is the most frequently performed refactoring that suggests a new name for an identifier to enhance readability when the identifier is poorly named. However, most existing works only identify renaming activities between two versions of source code, while few works express concern about how to suggest a new name. In this paper, we study automatic rename refactoring on variable names, which is considered more challenging than other rename refactoring activities. We first point out the connections between rename refactoring and various prevalent learning paradigms and the difference between rename refactoring and general text generation in natural language processing. Based on our observations, we propose RefBERT, a two-stage pre-trained framework for rename refactoring on variable names. RefBERT first predicts the number of sub-tokens in the new name and then generates sub-tokens accordingly. Several techniques, including constrained masked language modeling, contrastive learning, and the bag-of-tokens loss, are incorporated into RefBERT to tailor it for automatic rename refactoring on variable names. Through extensive experiments on our constructed refactoring datasets, we show that the generated variable names of RefBERT are more accurate and meaningful than those produced by the existing method