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BackgroundAlthough comorbidity of major depressive disorder (MDD) and chronic pain (CP) has been well-studied, their association with pain catastrophizing is largely elusive. This study aimed to investigate the potential effects of pain catastrophizing in patients with a comorbidity.MethodsIn total, 140 participants were included in this study and divided into three groups according to the Diagnostic and Statistical Manual of Mental Disorders and the International Association for the study of pain (i.e., the comorbidity group: patients with depression with chronic pain, n = 45; depression group: patients with depression without chronic pain, n = 47; and healthy controls: n = 48). The Hamilton Depression Rating Scale (HAMD)-24 and Hamilton Anxiety Rating Scale (HAMA)-14 were used by professional psychiatrists to evaluate the severity of depression and anxiety. Beck Depression Inventory-II (BDI-II) and Beck Anxiety Inventory (BAI) were conducted by patients' self-report to assess the symptom severity. The pain intensity numerical rating scale (PI-NRS) was used to assess the pain intensity. Pain Catastrophizing Scale (PCS) and Pain Anxiety Symptoms Scale (PASS) were used to estimate pain-related negative thinking.ResultsThe results showed that PASS and PCS scores were significantly different among the three groups. Particularly, the scores in the comorbidity group were the highest. The Pearson correlation analysis revealed a positive correlation between PCS (including the patients' helplessness, magnification, rumination, and total scores) and the severity of depression symptoms, anxiety symptoms, and pain intensity (P < 0.05). A stepwise regression analysis further demonstrated that the total PCS score, high monthly income level, and BDI score had positive impacts on PASS (P < 0.05). We also found that the total BDI score, disease course ≥1 year, and pain intensity had positive effects on PCS (P < 0.05), whereas years of education (≤ 12 years) had a negative effect on PCS (P = 0.012). In all, we have clearly demonstrated that PCS and PASS could serve as potentially predictive factors in patients suffering from comorbidity of MDD and CP.ConclusionOur results suggested that the pain-related catastrophic thinking and anxiety were more severe in the comorbidity group than in MDD-only group and healthy group. Pain-related catastrophizing thoughts and anxiety may have potentially effects on the comorbidity of depression and chronic pain.

Literature analysis facilitates researchers to acquire a good understanding of the development of science and technology. The traditional literature analysis focuses largely on the literature metadata such as topics, authors, abstracts, keywords, references, etc., and little attention was paid to the main content of papers. In many scientific domains such as science, computing, engineering, etc., the methods and datasets involved in the scientific papers published in those domains carry important information and are quite useful for domain analysis as well as algorithm and dataset recommendation. In this paper, we propose a novel entity recognition model, called MDER, which is able to effectively extract the method and dataset entities from the main textual content of scientific papers. The model utilizes rule embedding and adopts a parallel structure of CNN and Bi-LSTM with the self-attention mechanism. We evaluate the proposed model on datasets which are constructed from the published papers of four research areas in computer science, i.e., NLP, CV, Data Mining and AI. The experimental results demonstrate that our model performs well in all the four areas and it features a good learning capacity for cross-area learning and recognition. We also conduct experiments to evaluate the effectiveness of different building modules within our model which indicate that the importance of different building modules in collectively contributing to the good entity recognition performance as a whole. The data augmentation experiments on our model demonstrated that data augmentation positively contributes to model training, making our model much more robust in dealing with the scenarios where only small number of training samples are available. We finally apply our model on PAKDD papers published from 2009-2019 to mine insightful results from scientific papers published in a longer time span.

The booming online e-commerce platforms demand highly accurate approaches to segment queries that carry the product requirements of consumers. Recent works have shown that the supervised methods, especially those based on deep learning, are attractive for achieving better performance on the problem of query segmentation. However, the lack of labeled data is still a big challenge for training a deep segmentation network, and the problem of Out-of-Vocabulary (OOV) also adversely impacts the performance of query segmentation. Different from query segmentation task in an open domain, e-commerce scenario can provide external documents that are closely related to these queries. Thus, to deal with the two challenges, we employ the idea of distant supervision and design a novel method to find contexts in external documents and extract features from these contexts. In this work, we propose a BiLSTM-CRF based model with an attention module to encode external features, such that external contexts information, which can be utilized naturally and effectively to help query segmentation. Experiments on two datasets show the effectiveness of our approach compared with several kinds of baselines.

Numerous studies have been made to control the climate conditions in underground mines, but most of them are concentrated on a single roadway with through airflow, and those on the simulation of heat problems in developing roadway with local ventilation are very few. Therefore it is necessary to establish a proper method to simulate the thermal environmental conditions within the working face with auxiliary ventilation for controlling the thermal climate there effectively.As a basic study on control of the thermal environment in working face with auxiliary ventilation, the CFD method is employed to simulate the transfer of heat and moisture between rock surface and the airflow at a heading face with forcing auxiliary ventilation. Based on the simulation results, the distribution of local heat-transfer coefficient and its relationship with airflow rate, variation of heat transfer flux from the strata to the ventilation air with time and wetness fraction, the distribution of rock temperature and its variation with time elapse are discussed in the paper.The study provides an important basis for establishing a proper method to predict the thermal environment conditions on locally ventilated working places.

In order to promote the efficiency of gas drainage in gob, numerical simulation based on basic theories of computational fluid mechanics is thus implemented towards gob during drainage through pipe in upper corner by means of Fluent. According to related results, the negative drainage pressure produces a significant impact on drainage effect, gas distribution in upper corner and air leakage in gob. As the negative drainage pressure reaches a certain level, volume of drained gas is basically steady. With increasing negative drainage pressure, gas concentration in upper corner is decreasing and scope of air leakage in gob keeps expanding and even the exception that air flows in working face move to gob emerges nearby upper corner.

This paper focuses on how to use class-modular single-hidden-layer perceptrons (MLPs) with sigmoid activation functions (SAFs) to solve the multi-class learning problems, and pays special attention to the unbalanced data sets. Our solutions are as follows. (A) An n-class learning problem first decomposes into n two-class problems (B) A single-output MLP is responsible for solving a two-class problem, separating its represented class with all the other classes, and trained only by the samples from the represented class and some neighboring ones. (C) The samples from the minority classes or in the thin regions are virtually reinforced (D)The generalization region of an MLP is localized. The proposed method is verified effective by the experimental result of letter recognition.