Exploration practices have proven that over mature shale gas exhibits a feature of carbon isotope reversal. The geochemical statistics indicate that the wetness (C2-C5/C1-C5) of shale gas with carbon isotope reversal is less than 1.8%. In addition, the magnitude of carbon isotope reversal (δ13C1- δ13C2) for the over mature shale gas presents a parabolic variation with decreasing wetness. δ13C1-δ13C2 increases with decreasing wetness within a wetness range of 0.9% ~1.8% and then decreases with decreasing wetness at wetness < 0.9%. The CH4 cracking experiment demonstrates that CH4 polymerization occurring in the early stage of CH4 cracking is an important factor involved in isotope reversal of over mature shale gas. Moreover, δ13C1- δ13C2 decreases with an increase in experimental temperature prior to CH4 substantial cracking. The values of δ13C1 and δ13C2 tend to equalize during CH4 substantial cracking. The δ13C1-δ13C2 of mud gas present at different depths during shale gas drilling in Sichuan Basin increases initially, then decreases with further increase in the depth and finally tends to zero, with only a trace hydrocarbon gas being detectable. Statistical data suggests that the shale gas production in Sichuan Basin decreases with the decreasing δ13C1-δ13C2 value and wetness. Thus, δ13C1-δ13C2 and wetness could potentially serve as useful criteria to screen CH4 cracking degree and to determine the largest depth of natural gas exploration. Great care should be taken during shale gas exploration in deeper layers, with wetness and δ13C1-δ13C2 less than 0.2% and 1%, respectively, since very low wetness (<0.2%) and δ13C1-δ13C2 (<1%) might be indicative of CH4 substantial cracking in a geological setting.

Initiatives to safeguard athletes from interpersonal violence (IV) are rapidly growing. In Belgium, the knowledge on the magnitude of IV in sport is based on one retrospective prevalence study from 2016 (n=2.043 adults), who participated in organized sport before 18 years. Data on victimization rates in current youth sport populations are lacking. This study aimed to investigate the magnitude of IV in a sample of 769 athletes (13-21 years old), using the Violence Towards Athletes Questionnaire (VTAQ). All types of IV are prevalent in this sample, ranging from 27% (sexual violence) to 79% (psychological violence and neglect). Boys reported significantly more physical violence, while girls reported significantly more sexual violence. IV perpetrated by peer athletes was reported to the same degree as IV perpetrated by a coach (70%), while IV perpetrated by a parent in the context of sport was somewhat less common, but still prevalent (48%). These findings, including factors associated with elevated exposure rates, can serve as a baseline measurement to monitor and evaluate current and future safeguarding interventions in Belgian sport.

Historical analysis of Australian bushfire data spanning 170 years addresses whether the strength of recent fire events has been exacerbated by human-induced climate change. The question of “cause” looks at the characteristics of a wider range of natural hazards. Fire characteristics are compared with earthquake hazard characteristics: (1) energy – termed “magnitude”; (2) severity – termed “intensity”; and (3) resultant damage to people and structures – termed “impact”.Published global, Northern and Southern hemisphere temperature data are shown to vary consistently in phase over 170 years, but vary in amplitude with statistical significance. CO₂ levels north and south of the Equator have tracked quite consistently. Thus, Southern Hemisphere bushfire magnitude and intensity is compared with the Southern Hemisphere climate record, rather than a global data set.28 major bushfires and associated droughts since 1850 show neither apparent drought extent, nor area burned, nor bushfire intensity, correlates with changes in Southern Hemisphere climate. Average rainfall from 1900 shows a wetter, rather than drier trend. Cyclone energy shows no significant trend with climate. Planet-wide “greening”, through CO₂ fertilisation, is an insignificant contributor to bushfire magnitude. Combustion theory shows recorded “global warming” could have had no significant influence on bushfire magnitude or intensity. Any increase in Australian bushfire impact, as judged by lives lost, similarly, shows no correlation with bushfire magnitude, nor indeed, any observed Southern Hemisphere global warming.Thus, bushfire magnitude seems much more likely driven by fuel load and any anomalous bushfire intensity is likely driven by anomalous ground level fuel load. The evidence suggests that any CO₂ emissions reduction will have no impact on future bushfire “severity.

Corona viruses cause extensive SARS epidemics via super spread events (SSE). Due to variation in infection risk and heterogeneity of reproduction numbers specific distinction between SSE’s and typical case events is essential. SARS transmissions unveil a complex scenario in which SSE’s are shaped by multiple factors. Specific screening strategies for infection emergence within potential super spreading groups will help to efficiently control the SARS-2 pandemic and alleviate the partially effective general restriction measures.

Effective discharge, which represents the flow, or range of flows, that transport the most sediment over long term, was determined based on the mean daily flow discharge and mean daily suspended sediment discharge recorded between 1994 and 2014 at four gauging stations along the Trotuș River. This study proposes an efficient method for the estimation of effective discharge based on observed values of the suspended sediment load. By employing this method the suspended sediment load is no longer either under- or overestimated as in the cases when the assessment is based on sediment rating curves. The assessment on effective discharge was performed at two distinct levels: for the entire data series during the investigated time spans and, subsequently, for flows less than the bankfull discharge. The effectiveness curves of the suspended sediment transport characteristics revealed highly multimodal characteristics with many peaks, indicating ample ranges for the effective discharges. The main effective discharge corresponded to large flood events, which are typical for the upper end of the discharge range, whereas the secondary effective discharges corresponded to sub-bankfull flows, which are more frequent. The changes that occurred in the channel bed are reflected by the temporal variations in the effective discharge.

The traditional sequent peak algorithm (SPA) was used to assess the reservoir volume (VR) for comparison with deficit volume, DT, (subscript T representing the return period) obtained from the drought magnitude (DM) based method with draft level set at the mean annual flow on 15 rivers across Canada. At an annual scale, the SPA based estimates were found to be larger with an average of nearly 70% compared to DM based estimates. To ramp up DM based estimates to be in parity with SPA based values, the analysis was carried out through the counting and the analytical procedures involving only the annual SHI (standardized hydrological index, i.e. standardized values of annual flows) sequences. It was found that MA2 or MA3 (moving average of 2 or 3 consecutive values) of SHI sequences were required to match the counted values of DT to VR. Further, the inclusion of mean, as well as the variance of the drought intensity in the analytical procedure, with aforesaid smoothing led DT comparable to VR. The distinctive point in the DM based method is that no assumption is necessary such as the reservoir being full at the beginning of the analysis - as is the case with SPA.

Deep neural networks (DNNs) have gained prominence in addressing regression problems, offering versatile architectural designs that cater to various applications. In the field of earthquake engineering, seismic response prediction is a critical area of study. Simplified models such as single-degree-of-freedom (SDOF) and multi-degree-of-freedom (MDOF) systems have traditionally provided valuable insights into structural behavior, known for their computational efficiency facilitating faster simulations. However, these models have notable limitations in capturing the nuanced nonlinear behavior of structures and the spatial variability of ground motions. This study focuses on leveraging ambient vibration (AV) measurements of buildings, combined with earthquake (EQ) time-history data, to create a predictive model using a neural network (NN) in image format. The primary objective is to predict a specific building's earthquake response accurately. The training dataset consists of 1,197 MDOF 2D shear models, generating a total of 32,319 training samples. To evaluate the performance of the proposed model, termed MLPER (Machine Learning based Prediction of building structures' Earthquake Response), several metrics are employed. These include mean absolute percentage error (MAPE) and mean deviation angle (MDA) for comparisons in the time domain. Additionally, we assess magnitude-squared coherence values and phase differences (Δφ) for comparisons in the frequency domain. This study underscores the potential of MLPER as a reliable tool for predicting building earthquake response, addressing the limitations of simplified models. By integrating AV measurements and EQ time-history data into a neural network framework, MLPER offers a promising avenue for enhancing our understanding of structural behavior during seismic events, ultimately contributing to improved earthquake resilience in building design and engineering.