A permeability model for the hydraulic fracture filled with proppant packs under combined effect of compaction and embedment

The authors acknowledge the financial support from Science Foundation of China University of Petroleum, Beijing (No. 2462014YJRC060 and No.2462014YJRC059)Peer reviewedPostprin

Modeling nitrogen loadings from agricultural soils in southwest China with modified DNDC

Degradation of water quality has been widely observed in China, and loadings of nitrogen (N) and other nutrients from agricultural systems play a key role in the water contamination. Process‐based biogeochemical models have been applied to quantify nutrient loading from nonpoint sources at the watershed scale. However, this effort is often hindered by the fact that few existing biogeochemical models of nutrient cycling are able to simulate the two‐dimensional soil hydrology. To overcome this challenge, we launched a new attempt to incorporate two fundamental hydrologic features, the Soil Conservation Service curve and the Modified Universal Soil Loss Equation functions, into a biogeochemistry model, Denitrification‐Decomposition (DNDC). These two features have been widely utilized to quantify surface runoff and soil erosion in a suite of hydrologic models. We incorporated these features in the DNDC model to allow the biogeochemical and hydrologic processes to exchange data at a daily time step. By including the new features, DNDC gained the additional ability to simulate both horizontal and vertical movements of water and nutrients. The revised DNDC was tested against data sets observed in a small watershed dominated by farmlands in a mountainous area of southwest China. The modeled surface runoff flow, subsurface drainage flow, sediment yield, and N loading were in agreement with observations. To further observe the behaviors of the new model, we conducted a sensitivity test with varied climate, soil, and management conditions. The results indicated that precipitation was the most sensitive factor determining the rate of N loading from the tested site. A Monte Carlo test was conducted to quantify the potential uncertainty derived by variations in four selected input parameters. This study demonstrates that it is feasible and effective to use enhanced biogeochemical models such as DNDC for quantifying N loadings by incorporating basic hydrological features into the model framework

MOPRD: A multidisciplinary open peer review dataset

Open peer review is a growing trend in academic publications. Public access to peer review data can benefit both the academic and publishing communities. It also serves as a great support to studies on review comment generation and further to the realization of automated scholarly paper review. However, most of the existing peer review datasets do not provide data that cover the whole peer review process. Apart from this, their data are not diversified enough as they are mainly collected from the field of computer science. These two drawbacks of the currently available peer review datasets need to be addressed to unlock more opportunities for related studies. In response to this problem, we construct MOPRD, a multidisciplinary open peer review dataset. This dataset consists of paper metadata, multiple version manuscripts, review comments, meta-reviews, author's rebuttal letters, and editorial decisions. Moreover, we design a modular guided review comment generation method based on MOPRD. Experiments show that our method delivers better performance indicated by both automatic metrics and human evaluation. We also explore other potential applications of MOPRD, including meta-review generation, editorial decision prediction, author rebuttal generation, and scientometric analysis. MOPRD is a strong endorsement for further studies in peer review-related research and other applications

Automated scholarly paper review: Technologies and challenges

Peer review is a widely accepted mechanism for research evaluation, playing a pivotal role in scholarly publishing. However, criticisms have long been leveled on this mechanism, mostly because of its inefficiency and subjectivity. Recent years have seen the application of artificial intelligence (AI) in assisting the peer review process. Nonetheless, with the involvement of humans, such limitations remain inevitable. In this review paper, we propose the concept and pipeline of automated scholarly paper review (ASPR) and review the relevant literature and technologies of achieving a full-scale computerized review process. On the basis of the review and discussion, we conclude that there is already corresponding research and implementation at each stage of ASPR. We further look into the challenges in ASPR with the existing technologies. The major difficulties lie in imperfect document parsing and representation, inadequate data, defective human-computer interaction and flawed deep logical reasoning. Moreover, we discuss the possible moral & ethical issues and point out the future directions of ASPR. In the foreseeable future, ASPR and peer review will coexist in a reinforcing manner before ASPR is able to fully undertake the reviewing workload from humans

Predicting taxonomic and functional structure of microbial communities in acid mine drainage.

Predicting the dynamics of community composition and functional attributes responding to environmental changes is an essential goal in community ecology but remains a major challenge, particularly in microbial ecology. Here, by targeting a model system with low species richness, we explore the spatial distribution of taxonomic and functional structure of 40 acid mine drainage (AMD) microbial communities across Southeast China profiled by 16S ribosomal RNA pyrosequencing and a comprehensive microarray (GeoChip). Similar environmentally dependent patterns of dominant microbial lineages and key functional genes were observed regardless of the large-scale geographical isolation. Functional and phylogenetic β-diversities were significantly correlated, whereas functional metabolic potentials were strongly influenced by environmental conditions and community taxonomic structure. Using advanced modeling approaches based on artificial neural networks, we successfully predicted the taxonomic and functional dynamics with significantly higher prediction accuracies of metabolic potentials (average Bray-Curtis similarity 87.8) as compared with relative microbial abundances (similarity 66.8), implying that natural AMD microbial assemblages may be better predicted at the functional genes level rather than at taxonomic level. Furthermore, relative metabolic potentials of genes involved in many key ecological functions (for example, nitrogen and phosphate utilization, metals resistance and stress response) were extrapolated to increase under more acidic and metal-rich conditions, indicating a critical strategy of stress adaptation in these extraordinary communities. Collectively, our findings indicate that natural selection rather than geographic distance has a more crucial role in shaping the taxonomic and functional patterns of AMD microbial community that readily predicted by modeling methods and suggest that the model-based approach is essential to better understand natural acidophilic microbial communities

Impact of Triage: A Study of Mozilla and Gnome

Abstract—Triage is of great interest in software projects because it has the potential to reduce developer effort by involving a broader base of non-developer contributors to filter and augment reported issues. Using issue tracking data and interviews with experienced contributors we investigate ways to quantify the impact of triagers on reducing the number of issues developers need to resolve in two OSS projects: Mozilla and Gnome. We find the primary impact of triagers to involve issue filtering, filling missing information, and determining the relevant product. While triagers were good at filtering invalid issues and as accurate as developers in filling in missing issue attributes, they had more difficulty accurately pinpointing the relevant product. We expect that this work will highlight the importance of issue triage in software projects and will help design further studies on understanding and improving triage practices. I

High-power, electrically-driven continuous-wave 1.55-μm Si-based multi-quantum well lasers with a wide operating temperature range grown on wafer-scale InP-on-Si (100) heterogeneous substrate

A reliable, efficient and electrically-pumped Si-based laser is considered as the main challenge to achieve the integration of all key building blocks with silicon photonics. Despite the impressive advances that have been made in developing 1.3-μm Si-based quantum dot (QD) lasers, extending the wavelength window to the widely used 1.55-μm telecommunication region remains difficult. In this study, we develop a novel photonic integration method of epitaxial growth of III-V on a wafer-scale InP-on-Si (100) (InPOS) heterogeneous substrate fabricated by the ion-cutting technique to realize integrated lasers on Si substrate. This ion-cutting plus epitaxial growth approach decouples the correlated root causes of many detrimental dislocations during heteroepitaxial growth, namely lattice and domain mismatches. Using this approach, we achieved state-of-the-art performance of the electrically-pumped, continuous-wave (CW) 1.55-µm Si-based laser with a room-temperature threshold current density of 0.65 kA/cm−2, and output power exceeding 155 mW per facet without facet coating in CW mode. CW lasing at 120 °C and pulsed lasing at over 130 °C were achieved. This generic approach is also applied to other material systems to provide better performance and more functionalities for photonics and microelectronics

A regression approach to ROC surface, with applications to Alzheimer’s disease

We consider the estimation of three-dimensional ROC surfaces for continuous tests given covariates. Three way ROC analysis is important in our motivating example where patients with Alzheimer’s disease are usually classified into three categories and should receive different category-specific medical treatment. There has been no discussion on how covariates affect the three way ROC analysis. We propose a regression framework induced from the relationship between test results and covariates. We consider several practical cases and the corresponding inference procedures. Simulations are conducted to validate our methodology. The application on the motivating example illustrates clearly the age and sex effects on the accuracy for Mini-Mental State Examination of Alzheimer’s disease