The Research on Dynamic Rules of Crack Extension during Hydraulic Fracturing for Oil Shale In-Situ Exploitation

AbstractIt is a tough problem of low permeability for in-situ exploiting oil shale, while improving low permeability by hydraulic fracturing can generate permeable belts, and this is vital importance for oil exploitation. According to the layer property of oil shale, making full use of cohesive element to simulate, it established mathematical models for hydraulic fracturing and its fracturing rules, then conducted 3D numerical simulation. We can get: the shape of fractures is oval, and fractures extend along different directions are different, due to anisotropic property of oil shale and geostatic stress influenced, shown as from fig.9 to fig.10; the leak-off flow rate of fracturing fluid rises, reduces, and tends to a fixed value shown in fig.11; fracture opening is dependant on the volume and injection velocity of fluid injection and the rules of damage evolution and fracturing opening refer to fig.5, fig.6 and fig.13

An intervention trial targeting methadone maintenance treatment providers to improve clients' treatment retention in China.

BackgroundService providers including doctors, nurses, and other healthcare professionals play an essential role in methadone maintenance treatment (MMT). This study evaluated the impact of an intervention targeting MMT providers on their clients' treatment retention.MethodsThis study was conducted in 68 MMT clinics in five provinces of China with 36 clients randomly selected from each clinic. The clinics were randomized to intervention or control condition. The MMT CARE intervention started with group sessions to enhance providers' communication skills. The trained providers were encouraged to conduct individual sessions with clients to promote treatment engagement. The outcomes, which include client retention (main outcome) and their reception of provider-delivered individual sessions (process outcome), were measured over a 24-month period.ResultsSignificantly fewer intervention clients dropped out from MMT than control clients during the study period (31% vs. 41%; p < 0.0001). Dropout hazard was significantly lower in the intervention condition compared to the control condition (HR = 0.71, 95% CI: 0.57, 0.89). More intervention clients had individual sessions than control clients (93% vs. 70%; p < 0.0001). Having individual sessions was associated with a significantly lower dropout hazard (HR = 0.30, 95% CI: 0.23, 0.40). The intervention clients had a significantly lower dropout hazard than the control clients if they started the individual sessions during the first six months (HR = 0.68, 95% CI: 0.51, 0.90).ConclusionsThe MMT CARE intervention focusing on provider capacity building has demonstrated efficacy in reducing clients' treatment dropout. This study sheds light on MMT service improvement in China and other global community-based harm reduction programs

Self-Evolved Diverse Data Sampling for Efficient Instruction Tuning

Enhancing the instruction-following ability of Large Language Models (LLMs) primarily demands substantial instruction-tuning datasets. However, the sheer volume of these imposes a considerable computational burden and annotation cost. To investigate a label-efficient instruction tuning method that allows the model itself to actively sample subsets that are equally or even more effective, we introduce a self-evolving mechanism DiverseEvol. In this process, a model iteratively augments its training subset to refine its own performance, without requiring any intervention from humans or more advanced LLMs. The key to our data sampling technique lies in the enhancement of diversity in the chosen subsets, as the model selects new data points most distinct from any existing ones according to its current embedding space. Extensive experiments across three datasets and benchmarks demonstrate the effectiveness of DiverseEvol. Our models, trained on less than 8% of the original dataset, maintain or improve performance compared with finetuning on full data. We also provide empirical evidence to analyze the importance of diversity in instruction data and the iterative scheme as opposed to one-time sampling. Our code is publicly available at https://github.com/OFA-Sys/DiverseEvol.git

Bayesian Lasso-mixed quantile regression

In this paper, we discuss the regularization in linear-mixed quantile regression. A hierarchical Bayesian model is used to shrink the fixed and random effects towards the common population values by introducing an l1 penalty in the mixed quantile regression check function. A Gibbs sampler is developed to simulate the parameters from the posterior distributions. Through simulation studies and analysis of an age-related macular degeneration (ARMD) data, we assess the performance of the proposed method. The simulation studies and the ARMD data analysis indicate that the proposed method performs well in comparison with the other approaches. © 2012 Taylor & Francis

Do maternal health problems influence child's worrying status? Evidence from the British Cohort Study

Conventional methods apply symmetric prior distributions such as a normal distribution or a Laplace distribution for regression coefficients, which may be suitable for median regression and exhibit no robustness to outliers. This work develops a quantile regression on linear panel data model without heterogeneity from a Bayesian point of view, i.e. upon a location-scale mixture representation of the asymmetric Laplace error distribution, and provides how the posterior distribution is summarized using Markov chain Monte Carlo methods. Applying this approach to the 1970 British Cohort Study (BCS) data, it finds that a different maternal health problem has different influence on child's worrying status at different quantiles. In addition, applying stochastic search variable selection for maternal health problems to the 1970 BCS data, it finds that maternal nervous breakdown, among the 25 maternal health problems, contributes most to influence the child's worrying status

HTsort: Enabling Fast and Accurate Spike Sorting on Multi-Electrode Arrays

Spike sorting is used to classify the spikes (action potentials acquired by physiological electrodes), aiming to identify their respective firing units. Now it has been developed to classify the spikes recorded by multi-electrode arrays (MEAs), with the improvement of micro-electrode technology. However, how to improve classification accuracy and maintain low time complexity simultaneously becomes a difficulty. A fast and accurate spike sorting approach named HTsort is proposed for high-density multi-electrode arrays in this paper. Several improvements have been introduced to the traditional pipeline that is composed of threshold detection and clustering method. First, the divide-and-conquer method is employed to utilize electrode spatial information to achieve pre-clustering. Second, the clustering method HDBSCAN (hierarchical density-based spatial clustering of applications with noise) is used to classify spikes and detect overlapping events (multiple spikes firing simultaneously). Third, the template merging method is used to merge redundant exported templates according to the template similarity and the spatial distribution of electrodes. Finally, the template matching method is used to resolve overlapping events. Our approach is validated on simulation data constructed by ourselves and publicly available data and compared to other state-of-the-art spike sorters. We found that the proposed HTsort has a more favorable trade-off between accuracy and time consumption. Compared with MountainSort and SpykingCircus, the time consumption is reduced by at least 40% when the number of electrodes is 64 and below. Compared with HerdingSpikes, the classification accuracy can typically improve by more than 10%. Meanwhile, HTsort exhibits stronger robustness against background noise than other sorters. Our more sophisticated spike sorter would facilitate neurophysiologists to complete spike sorting more quickly and accurately

Simultaneous removal of ammonia and phosphate by electro-oxidation and electrocoagulation using RuO2–IrO2/Ti and microscale zero-valent iron composite electrode

Electro-oxidation using RuO2–IrO2/Ti plate anode and electrocoagulation using iron plate anode were widely applied to remove ammonia and phosphate in an aquatic environment, respectively. In this work, we designed magnetically bound ZVI microparticles on RuO2–IrO2/Ti plate as a composite electrode for the simultaneous removal of ammonia and phosphate from aqueous solution via combined EO and EC (EO/EC) processes. We present a series of experiments to study such simultaneous removal under an electric field via the EO/EC process. In the electrochemical unit, mZVI-RuO2-IrO2/Ti, mZVI-graphite, and RuO2–IrO2/Ti electrodes were used as anodes. The influence of applied voltage, initial pH, zero-valent iron dosage, reaction temperature, and organic compounds on the EO/EC process was also examined. Ammonia and phosphate could be completely removed at an applied voltage of 10 V, pH of 7, zero-valent iron dosage of 0.1 g, and reaction temperature of 35 °C using mZVI-RuO2-IrO2/Ti anode when influent ammonia and phosphate concentrations is 200 and 100 mg L−1. Ammonia degradation was consistent with pseudo-zero-order kinetic model. The characterization was analyzed by scanning electron microscope-energy dispersive spectrometer (SEM-EDS), X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS). Hence, the mZVI-RuO2-IrO2/Ti electrode can be used for efficient simultaneous removal of ammonia and phosphate

Statistical methods for body mass index: a selective review

Obesity rates have been increasing over recent decades, causing significant concern among policy makers. Excess body fat, commonly measured by body mass index, is a major risk factor for several common disorders including diabetes and cardiovascular disease, placing a substantial burden on health care systems. To guide effective public health action, we need to understand the complex system of intercorrelated influences on body mass index. This paper, based on all eligible articles searched from Global health, Medline and Web of Science databases, reviews both classical and modern statistical methods for body mass index analysis. We give a description of each of these methods, exploring the classification, links and differences between them and the reasons for choosing one over the others in different settings. We aim to provide a key resource and statistical library for researchers in public health and medicine to deal with obesity and body mass index data analysis.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work has been supported in part by the National Institute for Health Research Method Grant (NIHR RMOFS-2013-03-09) and the National Natural Science Foundation of China (Grant No. 71490725, 11261048, 11371322)

Treatment of landfill leachate using magnetically attracted zero-valent iron powder electrode in an electric field

This study combined electro-oxidation (EO) and electrocoagulation (EC) process (EO/EC) to treat landfill leachate by using RuO2-IrO2/Ti plate and microscale zero-valent iron powder composite anode. EO was achieved by direct oxidation and indirect oxidation on RuO2-IrO2/Ti plate, whereas EC was achieved using iron powder to lose electrons and produce coagulants in situ. The influences of variables including type of anode material, applied voltage, zero-valent iron dosage, interelectrode gap, and reaction temperature on EO/EC were evaluated. Results showed that at an applied voltage of 10 V, zero-valent iron dosage of 0.2 g, interelectrode gap of 1 cm, and non-temperature-controlled mode, the removal efficiencies were 72.5% for total organic carbon (TOC), 98.5% for ammonia, and 98.6% for total phosphorus (TP). Some heavy metals and hardness were also removed. Further analysis indicated that the removal of TOC, ammonia, and TP followed pseudo-first order, pseudo-zero order, and pseudo-second order kinetic models, respectively. Other characteristics were examined by scanning electron microscopy–energy dispersive spectrometry, X-ray diffraction, and X-ray photoelectron spectroscopy. Overall, our results showed that EO/EC can be used to efficiently remove organic matter, ammonia, TP, and heavy metals from landfill leachate