Multi-domain active learning for text classification

Active learning has been proven to be effective in reducing labeling efforts for supervised learning. However, existing active learning work has mainly focused on training models for a single domain. In practical applications, it is common to simultaneously train classifiers for multiple domains. For example, some merchant web sites (like Amazon.com) may need a set of classifiers to predict the sentiment polarity of product reviews collected from various domains (e.g., electronics, books, shoes). Though different domains have their own unique features, they may share some common latent features. If we apply active learning on each domain separately, some data instances selected from different domains may contain duplicate knowledge due to the common features. Therefore, how to choose the data from multipl

Stability Analysis in Determining Safety Drilling Fluid Pressure Windows in Ice Drilling Boreholes

Borehole stability analysis has been well studied in oil and gas exploration when drilling through rock formations. However, a related analysis of ice borehole stability has never been conducted. This paper proposes an innovative method for estimating the drilling fluid pressure window for safe and sustainable ice drilling, which has never been put forward before. First, stress concentration on a vertical ice borehole wall was calculated, based on the common elastic theory. Then, three failure criteria, the Mogi⁻Coulomb, teardrop, and Derradji-Aouat criteria, were used to predict the stability of the ice borehole for an unbroken borehole wall. At the same time, fracture mechanics were used to analyze the stable critical pressure for a fissured wall. Combining with examples, our discussion shows how factors like temperature, strain rate, ice fracture toughness, ice friction coefficient, and fracture/crack length affect the stability of the borehole wall. The results indicate that the three failure criteria have similar critical pressures for unbroken borehole stability and that a fissured borehole could significantly decrease the safety drilling fluid pressure window and reduce the stability of the borehole. The proposed method enriches the theory of borehole stability and allows drillers to adjust the drilling fluid density validly in ice drilling engineering, for potential energy exploration in polar regions

A fast sampling based evolutionary algorithm for million-dimensional multiobjective optimization

Li L, He C, Cheng R, Li H, Pan L, Jin Y. A fast sampling based evolutionary algorithm for million-dimensional multiobjective optimization. Swarm and Evolutionary Computation . 2022;75: 101181.With their complexity and vast search space, large-scale multiobjective optimization problems (LSMOPs) challenge existing multiobjective evolutionary algorithms (MOEAs). Recently, several large-scale multiobjective evolutionary algorithms have been developed to tackle LSMOPs. Unlike conventional MOEAs that concentrate on selection operations in the objective space, large-scale MOEAs emphasize operations in the decision space, such as offspring generation, to tackle the large number of decision variables. Nevertheless, most present large-scale MOEAs experience difficulty effectively and efficiently solving LSMOPs with tens of thousands or more decision variables or exhibit poor versatility in solving different LSMOPs. We propose a fast large-scale MOEA framework with reference-guided offspring generation, named FLEA, aiming at these issues. Generally, FLEA constructs several reference vectors in the decision space to steer the sampling of promising solutions during offspring generation. A parameter is used to allocate computation resources between the convergence and diversity of the offspring population adaptively. Without computationally expensive problem reformulation or decision variable analysis techniques, the proposed method can significantly accelerate the search speed of conventional MOEAs in solving LSMOPs. FLEA is examined on various LSMOPs with up to 1.6 million decision variables, demonstrating its superior effectiveness, efficiency, and versatility in large-scale multiobjective optimization

Conservation and Restoration of Mangroves in Response to Invasion of <i>Spartina alterniflora</i> Based on the MaxEnt Model: A Case Study in China

In China, the invasion of Spartina alterniflora is an important driver for the decrease of mangrove area and ecological service functions related to this habitat. In the past few decades, S. alterniflora clearing and mangrove restoration projects have mainly focused on the areas where it is already changed but ignored the potential distribution areas. This study suggested that implementation of mangrove protection prior to the areas with the threat of S. alterniflora invasion could greatly improve protection efficiency and save costs. Thus, using Maximum Entropy Modeling (MaxEnt), we estimated the potential spatial distribution of both mangroves and S. alterniflora in China, considering the current distribution data, topographical, sediments, sea surface temperature and bioclimatic variables. What’s more, we identified and calculated the potential distributed areas in each province. We aimed to explore (i) the key factors determining the distribution of mangrove and Spartina alterniflora along the coastline and (ii) the hotspots of their competitive occurrence, including S. alterniflora invasion areas and mangroves degradation areas, in order to support mangrove conservation. The model showed that the distance to the coastline and the topography play important roles in the distribution of S. alterniflora, while mangroves were more sensitive to the range of the annual sea surface temperature. Our results furthermore confirm that S. alterniflora has a wider potential distribution area (~10,585 km2) than mangroves (~9124 km2) at the coastline of China; and predict the provinces Zhangzhou, Quanzhou, Zhanjiang, Beihai and Wenzhou as hotspots for the competition between mangroves and S. alterniflora. We propose that priority should be given to the protection or restoration of mangrove plants in those areas which are co-suitable for mangroves and S. alterniflora. In these areas, management measures should be conducted that hinder S. alterniflora invasions or clear existing S. alterniflora plants, firstly. This study provides guidance for the management of native species by preventing biological invasion

The Culprit behind the Mass Death of Mangroves: Egrets or <i>Rats</i> (<i>Rattus losea</i>)?

Mangroves play a crucial role in maintaining coastal ecological balance. This study focused on the impact of branch-breaking behavior on the mortality of Rhizophora stylosa in the Guangxi Shankou Mangrove Reserve. However, we found mangrove mortality in areas devoid of egret habitation, prompting a reevaluation of our research hypothesis. Further investigation suggested that nesting behavior was the primary cause of mangrove mortality. A comparison of the data from areas with egrets (Egretta garzetta, Ardea intermedia) and lesser rice-field rats (Rattus losea) activity indicated significant mechanical damage caused by rats to mangroves as the main cause of mortality. Additionally, we found that the biological characteristics of R. stylosa, particularly its stunted growth and recovery abilities after branch breaking, were key factors affecting its survival. These findings imply that rat-induced mortality may not occur in other less susceptible mangrove species. The results contradict assumptions regarding the impact of egret behavior and highlight the importance of the biological characteristics of R. stylosa. This offers fresh insights into mangrove conservation and management, emphasizing the need for ongoing observation and hypotheses verification. Future studies should explore the influence of lesser rice-field rats’ activity and the intrinsic characteristics of R. stylosa on the ecosystem’s long-term stability

DataSheet_1_Prognostic value of receptor tyrosine kinases in malignant melanoma patients: A systematic review and meta-analysis of immunohistochemistry.doc

BackgroundSubstantial evidence suggests that receptor tyrosine kinases (RTKs) are overexpressed in tumors; however, few studies have focused on the prognostic value of RTKs in melanoma.ObjectivesThe objective of this study is to evaluate the association between overexpression of RTKs and survival in melanoma patients based on immunohistochemistry (IHC) analysis.MethodsOur review is registered on PROSPERO (http://www.crd.york.ac.uk/PROSPERO), registration number CRD42021261460. Seven databases were searched, and data were extracted. We used IHC to measure the association between overexpression of RTKs and overall survival (OS), disease-free survival (DFS), progression-free survival (PFS), and clinicopathology in melanoma patients. Pooled analysis was conducted to assess the differences between Hazard Ratios along with 95% confidence intervals.ResultsOf 5,508 publications examined following the database search, 23 publications were included in this study, which included data from a total of 2,072 patients. Vascular endothelial growth factor receptor 2 (VEGF-R2) overexpression was associated with worse OS and DFS in melanoma. Furthermore, there was an association between OS and the expression of several RTKs, including epidermal growth factor receptor (EGFR), mesenchymal-epithelial transition factor (MET), vascular endothelial growth factor receptor 1 (VEGF-R1), and insulin-like growth factor 1 receptor (IGF-1R). There were no significant correlations between EGFR overexpression and worse DFS or PFS. EGFR overexpression was associated with worse OS cutaneous and nasal melanoma, but not uveal melanoma. However, MET overexpression was related to worse OS in both cutaneous and uveal melanoma. Furthermore, EGFR overexpression was associated with a worse OS in Europe compared to other geographic areas. Moreover, EGFR and MET overexpression showed significant prognostic value in patients with the cut-off “≥10% staining”.ConclusionsOur findings build concrete evidence that overexpression of RTKs is associated with poor prognosis and clinicopathology in melanoma, highlighting RTK expression has the potential to inform individualized combination therapies and accurate prognostic evaluation.</p

Continual expansion of Spartina alterniflora in the temperate and subtropical coastal zones of China during 1985–2020

Biological invasions are considerably altering ecosystem structure and functions, especially in coastal ecosystems that are subject to intensive anthropogenic disturbances. Spartina alterniflora has been recognized as the most serious invasive species in coastal China, which has received considerable attention from the government and the public. There is urgent need to control this invasive species at regional and national scales, but such efforts were impeded by lack of time-series data of Spartina spread. Here, we assessed the pixel- and phenology-based algorithm for mapping Spartina saltmarshes, and applied this algorithm to generate annual Spartina saltmarsh maps (30-m spatial resolution) from 1985 to 2020 by using time series Landsat 5/7/8 images. The resulting maps suggest that Spartina has been expanding since 1990 in coastal China, with three noticeable phases (rapid, moderate, and rapid). Along the latitudinal gradient, Spartina exhibited a longer invasion history and more frequent changes at low latitudes. Although human interventions caused the decline of Spartina in certain areas, rapid natural spread was primarily responsible for its extensive and continual invasion. These results provide insights for efficiently managing this invasive species, enhancing the conservation of coastal wetlands, and promoting the sustainability of coastal wetlands