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The great success of numerous community-based open source software (OSS) is based on volunteers continuously submitting contributions, but ensuring sustainability is a persistent challenge in OSS communities. Although the motivations behind and barriers to OSS contributors' joining and retention have been extensively studied, the impacts of, reasons for and solutions to contribution abandonment at the individual level have not been well studied, especially for pull-based development. To bridge this gap, we present an empirical study on pull request abandonment based on a sizable dataset. We manually examine 321 abandoned pull requests on GitHub and then quantify the manual observations by surveying 710 OSS developers. We find that while the lack of integrators' responsiveness, the lack of contributors' time and interest remain the main reasons that deter contributors from participation, limitations during the processes of patch updating and consensus reaching can also cause abandonment. We also show the significant impacts of pull request abandonment on project management and maintenance. Moreover, we elucidate the strategies used by project integrators to cope with abandoned pull requests and highlight the need for a practical handover mechanism. We discuss the actionable suggestions and implications for OSS practitioners and tool builders, which can help to upgrade the infrastructure and optimize the mechanisms of OSS communities.

Background Early identification and treatment of clinical high-risk for psychosis (CHR P) are critical to prevent the onset of psychosis, but there is no objective biomarker for CHR-P diagnosis. Methods Ninety medication naive CHR-P subjects and eighty-six healthy controls (HCs) were recruited. The metabolic profiles of plasma samples were acquired using an untargeted metabolomics approach based on ultra-high-performance liquid chromatography equipped with quadrupole time-of-flight mass spectrometry. The obtained data were further mapped on the Kyoto Encyclopedia of Genes and Genomes for pathway analysis, and an ensemble learning method was applied to identify diagnostic biomarkers. Bayesian linear regression model was then used to explore predicative biomarkers of conversion to psychosis. Receiver-operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic or predicative value of potential biomarkers. Results A total of one hundred and four differential metabolites and forty-eight differential pathways were identified. A panel of five metabolites was found that could effectively discriminate CHR-P from HCs with area under the ROC curve of 1 in the training set (70% of the samples) and 0.997 in the testing set (30% of the samples). The biosynthesis of unsaturated fatty acids pathway perturbed most significantly in CHR-P subjects. Twenty-three CHR-P subjects converted to psychotic disorders during two-year follow-up, and increased 1-stearoyl-2-arachidonoyl-sn-glycerol in plasma was potentially associated with the higher risk of conversion to psychosis. Conclusions These findings demonstrate the alterations of plasma metabolic profiles in CHR-P population, which may deliver valuable biomarkers for early identification and outcome prediction of CHR-P.

Gender differences in the frequency and severity of psychotic symptoms have been widely reported. However, in the screening process for the detection of early psychosis, gender differences were largely overlooked in China. This study investigated gender differences in self-reported psychotic symptoms in a clinical population who initially visited a mental health service. In total, 1931 consecutive new patients were included in the current analysis, with a mean age of 25.3 years, including 852 (44.1%) men and 1079 (55.9%) women, of whom 388 (20.1%) had psychotic disorders and 1543 (79.9%) had non-psychotic disorders. Psychotic symptoms were assessed using the PRIME Screen–Revised (PS-R) questionnaire. The cohort was grouped according to gender, age (adolescents ≤ 21 years, adults > 21 years), and clinical diagnosis. Within the full sample, gender differences in psychotic symptoms were not significant, except that females appeared to have more severe symptoms of disorganized communication than males. However, gender differences began to appear at subgroup levels, after stratification by age and diagnosis. Female adolescents reported more severe psychotic symptoms than male adolescents, especially in the symptom of perceptual abnormalities, which refer to hallucinations. Different patterns and predictors were found to significantly discriminate between psychotic and non-psychotic disorders among age and gender groups. Our study highlights gender differences in the severity, frequency, and pattern of self-reported psychotic symptoms when screening in a first help-seeking population. Therefore, gender differences should be considered during psychotic symptoms screening.

Open Source Software (OSS) community has attracted a large number of distributed developers to work together, e.g. reporting and discussing issues as well as submitting and reviewing code. OSS developers create links among development units (e.g. issues and pull requests in GitHub), share their opinions and promote the resolution of development units. Although previous work has examined the role of links in recommending high-priority tasks and reducing resource waste, the understanding of the actual usage of links in practice is still limited. To address the research gap, we conduct an empirical study based on the 5W1H model and data mining from five popular OSS projects on GitHub. We find that links originating from a PR are more common than the other three types of links, and links are more frequently created in Documentation. We also find that average duration between development units’ create time in a link is half a year. We observed that link behaviors are very complex and the duration of link increases with the complexity of link structure. We also observe that the reasons of link are very different, especially in P–P and I–I. Finally, future works are discussed in conclusion.

This letter studies the problem of air-to-air visual detection of micro unmanned aerial vehicles (UAVs) by monocular cameras. This problem is important for many applications such as vision-based swarming of UAVs, malicious UAV detection, and see-and-avoid systems for UAVs. Although deep learning methods have exhibited superior performance in many object detection tasks, their potential for UAV detection has not been well explored. As the first main contribution of this letter, we present a new dataset, named Det-Fly, which consists of more than 13 000 images of a flying target UAV acquired by another flying UAV. Compared to the existing datasets, the proposed one is more comprehensive in the sense that it covers a wide range of practical scenarios with different background scenes, viewing angles, relative distance, flying altitude, and lightning conditions. The second main contribution of this letter is to present an experimental evaluation of eight representative deep-learning algorithms based on the proposed dataset. To the best of our knowledge, this is the first comprehensive experimental evaluation of deep learning algorithms for the task of visual UAV detection so far. The evaluation results highlight some key challenges in the problem of air-to-air UAV detection and suggest potential ways to develop new algorithms in the future. The dataset is available at https://github.com/Jake-WU/Det-Fly .

BackgroundAge effects may be important for improving models for the prediction of conversion to psychosis for individuals in the clinical high risk (CHR) state. This study aimed to explore whether adolescent CHR individuals (ages 9–17 years) differ significantly from adult CHR individuals (ages 18–45 years) in terms of conversion rates and predictors.MethodConsecutive CHR individuals (N = 517) were assessed for demographic and clinical characteristics and followed up for 3 years. Individuals with CHR were classified as adolescent (n = 244) or adult (n = 273) groups. Age-specific prediction models of psychosis were generated separately using Cox regression.ResultsSimilar conversion rates were found between age groups; 52 out of 216 (24.1%) adolescent CHR individuals and 55 out of 219 (25.1%) CHR adults converted to psychosis. The conversion outcome was best predicted by negative symptoms compared to other clinical variables in CHR adolescents (χ2 = 7.410, p = 0.006). In contrast, positive symptoms better predicted conversion in CHR adults (χ2 = 6.585, p = 0.01).ConclusionsAdolescent and adult CHR individuals may require a different approach to early identification and prediction. These results can inform the development of more precise prediction models based on age-specific approaches.

Lumbar disc herniation is a common disorder in adults that is accompanied by lower back and radicular pain. A 32-year-old man visited our clinic with 1-week history of persistent lower back pain and weakness in his right big toe. Magnetic resonance imaging (MRI) of his lumbar spine revealed herniated discs at L3/L4, L5/S1 and L4/L5, where a right-sided intraspinal mass lesion deep to the L4 vertebral body was causing compression of the nerve root. The patient underwent conservative treatment and reported no symptoms referrable to his back or leg 4 months later. Follow-up MRI showed no herniation of the nucleus pulposus at the L4/L5 level or lesion deep to the vertebral body of L4, whereas no changes had occurred to the status of the herniated L3/L4 and L5/S1 discs. The present case and a literature review show that a sequestered lumbar disc herniation can regress within a relatively short timeframe without surgery. The authors emphasise the utility of conservative therapy for patients who do not have a definitive surgical indication.

Smart cities play a vital role in the growth of a nation. In recent years, several countries have made huge investments in developing smart cities to offer sustainable living. However, there are some challenges to overcome in smart city development, such as traffic and transportation management, energy and water distribution and management, air quality and waste management monitoring, etc. The capabilities of the Internet of Things (IoT) and artificial intelligence (AI) can help to achieve some goals of smart cities, and there are proven examples from some cities like Singapore, Copenhagen, etc. However, the adoption of AI and the IoT in developing countries has some challenges. The analysis of challenges hindering the adoption of AI and the IoT are very limited. This study aims to fill this research gap by analyzing the causal relationships among the challenges in smart city development, and contains several parts that conclude the previous scholars’ work, as well as independent research and investigation, such as data collection and analysis based on DEMATEL. In this paper, we have reviewed the literature to extract key challenges for the adoption of AI and the IoT. These helped us to proceed with the investigation and analyze the adoption status. Therefore, using the PRISMA method, 10 challenges were identified from the literature review. Subsequently, determination of the causal inter-relationships among the key challenges based on expert opinions using DEMATEL is performed. This study explored the driving and dependent power of the challenges, and causal relationships between the barriers were established. The results of the study indicated that “lack of infrastructure (C1)”, ”insufficient funds (C2)”, “cybersecurity risks (C3)”, and “lack of trust in AI, IoT” are the causal factors that are slowing down the adoption of AI and IoT in smart city development. The inter-relationships between the various challenges are presented using a network relationship map, cause–effect diagram. The study’s findings can help regulatory bodies, policymakers, and researchers to make better decisions to overcome the challenges for developing sustainable smart cities.

Objective: To analyze the features and patterns of acupoint selection in acupuncture-moxibustion treatment of chronic atrophic gastritis (CAG) by data mining technique. Methods: Relevant clinical studies published before 25 June, 2017 were searched in databases including PubMed, EMBASE, Cochrane library, Chinese Biomedical Literature Database (CBM), China National Knowledge infrastructure (CNKI), and Wanfang Academic Journal Full-text Database (Wanfang). Results: A total of 122 papers were included, involving 69 points. It was found that the top three points on the frequency list were Zusanli (ST 36), Zhongwan (CV 12) and Weishu (BL 21). The points selected were distributed in 11 meridians, in which the Stomach Meridian of Foot Yangming, Conception Vessel, and Bladder Meridian of Foot Taiyang ranked the top and accounted for 74.0% of the total frequency. Of the involved specific points, Five Shu-Transmitting points, crossing points and Back-Shu points ranked the top, accounting for 47.1%. The analysis of association pattern has shown that Zusanli (ST 36) and Zhongwan (CV 12) won the highest support rate in the paired groups; Zusanli (ST 36), Weishu (BL 21) and Zhongwan (CV 12) had the highest support rate among the point groups. The Five Shu-Transmitting points and the Lower He-Sea points had the highest support rate among the specific point groups. Conclusion: The data mining results of the studies on acupuncture-moxibustion for CAG are substantially in line with the acupuncture-moxibustion treatment theories in traditional Chinese medicine. The results can reflect the acupoint selection patterns in treatment of CAG and provide reference for acupuncture-moxibustion treatment of CAG in clinic.

Background: The regulation of angiogenesis in the treatment of cardiovascular diseases has been widely studied and the vascular endothelial growth factor (VEGF) families and VEGF receptor (VEGFR) have been proven to be one of the key regulators. The VEGFR endocytosis has been recently proved to be involved in the regulation of angiogenesis. Our previous study showed that the upregulation of VEGFR endocytosis enhanced angiogenesis in vitro . In this research, we utilized mice with induced hindlimb ischemia, as a model to investigate the role of VEGFR endocytosis in the regulation of angiogenesis in vivo . Our goal was to observe the effect of revascularization with different degrees of VEGFR endocytosis after injecting atypical protein kinase C inhibitor (αPKCi) and dynasore, which could respectively promote and inhibit the VEGFR endocytosis. Methods: We induced the hindlimb ischemia in adult male mice by ligating the hindlimb artery. By directly injecting the ischemic muscles with endothelial progenitor cells (EPCs) alone or EPCs + αPKCi/EPCs + dynasore or control medium (sham group), we divided the mice into four groups and detected lower limb blood flow using a laser Doppler blood perfusion imager. We also measured the immunohistochemistry (IHC) of markers for angiogenesis, such as CD31 and alpha smooth muscle actin (α-SMA) in the ischemic hindlimb tissues. Results: We demonstrated VEGFR endocytosis played an important role in the angiogenesis of the ischemic hindlimb model in vivo . By using atypical PKC inhibitor that increase the VEGFR endocytosis, the angiogenesis in the mice model was promoted. Treatment with EPCs + αPKCi showed greater effects on blood perfusion recovery and increased the α-SMA-positive vessels. Conclusions: The regulation of VEGFR endocytosis represents a valuable method of improving angiogenesis and thus revascularization in ischemic disease model.

With the development of the fourier ptychography in microscopy imaging, more and more researchers consider applying fourier ptychography in long-distance imaging. However being different from microscopic imaging, long distance fourier ptychographic imaging will face more challenges, one of which is the source image quality. In this manuscript, the influence of the source image quality on camera scanning FP imaging will be investigated and simulated. In the first part, the calibration aims to solve the influence of unenenly illumination. After that, a poisson-gaussian mixed noise model based denoising is uesd and could effectively suppresses the noise through parameter estimation. Finally, Phase correalation regisitrtion has to be used to correct the mismatch between adjacent images caused by camera scanning mode. The simulation results demonstrate the effectiveness of the preprocessing methods and could make FP more robust.The signal to noise ratio could reach to 40.5dB while obtained 5×improvement in resolution.

Prediction of developers’ programming behaviors is an effective way to improve their development efficiency and optimize the organization of project modules and files. However, little research exists investigating on this direction. In order to address this knowledge gap, we proposed a NLP-based approach to predict the programming behaviors in OSS (Open Source Software) communities. The proposed approach i) embeds the historical programming behavior data of a project into a multi-dimensional vector space to capture the potential laws in the data, ii) forms an eigenvector matrix reflecting the semantic relationship of the development behavior data, and iii) predicts the next programming behavior of a specific developer based on the eigenvector matrix. Our experiments on five OSS projects show that the prediction accuracy rate of the proposed prediction approach can reach up to about 50%, indicating that it can summarize the development behavior data law and effectively predict the programming behavior of developers. Our work can provide valuable assistance for developers’ programming and projects’ maintenance in practice.

Rationale: Nitrous oxide (N2O), commonly known as “laughing gas,” is being increasingly abused by young people as a recreational drug; this can subsequently result in myelopathy and peripheral neuropathy, however, in China, few cases of neurologic deterioration by N2O abuse have been reported. Patient concerns: Herein, we present 2 patients who developed progressive limb weakness, numbness, and ataxia. Both of them had recreationally inhaled N2O intermittently for a long time. Diagnosis: Subacute combined degeneration (SCD) based on myelopathy and polyneuropathy after N2O abuse. Interventions: The 2 patients were treated with cessation of N2O inhalation, methylcobalamin capsule 500 μg tid (ter in die, which means 3 times a day), and compound vitamin B 1 tablet tid p.o.(per os, which means taken orally) for 1 month. Outcomes: The symptoms of altered sensation and the patients’ gait improved significantly. Lessons: The 2 cases raise awareness of the important mechanisms of N2O neurotoxicity, and clinicians should be made fully aware of such substance-related diseases. The incidence of N2O -induced neurotoxicity is insufficiently recognized and should be considered as an important cause of SCD, especially in adolescents with undifferentiated weakness and abnormal sensation; this is essential because serious complications such as irreversible paralysis can result from the absence of early diagnosis and treatment.

Aim A growing number of studies suggest a role of neuroinflammation and oxidative stress in the pathophysiology of psychosis. Sulforaphane (SFN), a natural compound extracted from cruciferous vegetables, has shown anti-inflammatory and anti-oxidative effects which imply a potential effect on decreasing the risk of psychosis. However, there is no study testing the efficacy of SFN for this purpose. It's necessary to evaluate its efficacy on individuals at clinical high risk (CHR) for psychosis. Methods This is a randomized, double-blind, placebo-controlled, multi-centre trial. A total of 300 CHR subjects will be identified in the course of face-to-face interviews using the Structured Interview for Prodromal Syndromes. All participants will be randomly allocated to SFN group (n = 150) or placebo group (n = 150). The study duration includes an intervention for 52 consecutive weeks, and additional 1-year follow-up. Results The primary outcome is 2-year conversion rate of psychosis. Secondary outcomes include 1-year conversion rate of psychosis, the severity and duration of prodromal symptoms, predictive risk of psychosis conversion, neurocognitive functioning and peripheral blood biomarkers of inflammation, oxidative stress and metabolism. Safety monitoring will be performed using scales for side effect, serious adverse events recording, and laboratory tests. Conclusion This trial is expected to clarify the efficacy of SFN in improving prodromal symptoms, and its role in decreasing the risk and conversion rate of psychosis among CHR subjects. The results will also provide solid evidence about the efficacy and safety of SFN in CHR population. Potential challenges and their solutions in performing the present trial are discussed.

The most widely adopted approach for layout capacitance extraction is pattern matching, where a layout is partitioned into small sections and matched against a pre-characterized pattern library. The capacitance formula associated with the matched pattern is used to compute the capacitance. The pattern matching approach can meet the speed and capacity requirements for large ASICs, but the pattern library and associated formula must be manually created by experienced engineers through a tedious process. For each new process node or any change to the technology profile, this process has to be repeated, causing a heavy burden on the EDA vendors and delays in PDK releases by the foundries. In this paper, we propose an automatic approach that significantly reduces the effort and time compared with the manual approach for the pattern matching method for capacitance extraction. Our approach consists of the following steps: 1) generate sample geometries that cover the problem space, 2) for each sample geometry, call a field solver to compute the capacitance value, 3) use unsupervised learning with the field solver values and foundry values as guidance to cluster geometries into patterns and derive the capacitance formula, and 4) use supervised learning to train a neural network that will be used to classify any layout geometry into a pattern in the library, which will be used at the extraction time. Experiment results show the proposed approach achieves satisfactory accuracy well above the foundry requirements and runs as fast as traditional pattern matching software. Although our approach is only demonstrated for 2D, it is the first framework using the automatic procedure and machine learning in capacitance extraction. As the interconnect structures and foundry requirements become more complex, our approach may be attractive to EDA vendors and foundries.

Blind image deblurring is a long-standing and challenging inverse problem in image processing. In this paper, we propose a new spatial-scale-regularized approach to estimate a blur kernel (BK) from a single motion blurred image by regularizing the spatial scale sizes of image edges. Furthermore, by applying shock filter into the proposed model, our method is able to recover sharp large-scale edges for accurate BK estimation. Finally, we propose an efficient optimization strategy which can solve the proposed model efficiently. Extensive experiments compared with state-of-the-art blind motion deblurring methods demonstrate the effectiveness of the proposed method in terms of subjective vision, deconvolution error ratio (DER), peak signal-to-noise ratio (PSNR), self-similarity measure (SSIM), and sum of squared differences error (SSDE).

GuiSen Wu,1 RanPiao Gan,1 ZhiXing Li,1 LiHua Xu,1 XiaoChen Tang,1 YanYan Wei,1 YeGang Hu,1 HuiRu Cui,1 HuiJun Li,2 YingYing Tang,1 Li Hui,3 XiaoHua Liu,1 ChunBo Li,1 JiJun Wang,1,4,5 TianHong Zhang1 1Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Key Laboratory of Psychotic Disorders, Shanghai 200030, People’s Republic of China; 2Florida a & M University, Department of Psychology, Tallahassee, FL 32307, USA; 3Institute of Mental Health, The Affiliated Guangji Hospital of Soochow University, Soochow University, Suzhou 215137, Jiangsu, People’s Republic of China; 4Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai, People’s Republic of China; 5Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, People’s Republic of ChinaCorrespondence: TianHong Zhang; JiJun WangShanghai Key Laboratory of Psychotic Disorders (No.13dz2260500), Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, 600 Wanping Nan Road, Shanghai 200030, People’s Republic of ChinaTel +86-21-34773065Fax +86-21-64387986Email jijunwang27@163.comBackground: The clinical high-risk (CHR) state is identified as a critical period for early prevention and intervention during the development of psychosis and early treatment may reduce the risk of conversion to psychosis. However, it remains controversial whether antipsychotics are effective in CHR populations. Limited previous randomised controlled trials of antipsychotic treatment of CHR individuals indicated possible short-term efficacy on psychotic symptoms with unclear long-term effects. To answer this question, it is necessary to establish a high-quality real-world cohort study with large sample size to explore the effectiveness and safety of antipsychotics in CHR individuals.Methods: We plan to consecutively recruit 600 CHR individuals from Shanghai Mental Health Centre in the ongoing SHARP-2 (ShangHai At Risk for Psychosis-Phase 2) project between 2019 and 2022. At baseline, participants will be assessed by the Structured Interview for Prodromal Syndromes, the MATRICS Consensus Cognitive Battery, demographic information, and clinical medication history. They will be followed up in a naturalistic way in which the research team will not prescribe antipsychotics or provide pharmacological consultation. First, CHR participants and their families will be trained to record their medication daily and self-evaluate symptoms through smart-phone application-based assessment and report their information weekly. Second, telephone calls will be arranged monthly so that the researchers are informed about the participants’ symptoms, medications and daily functions. Third, face-to-face interviews will be conducted annually for repeating assessment of baseline. The primary outcomes will include conversion to psychosis and functional outcome (scored with less than 60 in the Global Assessment of Function) at the end of the follow-up period.Conclusion: The current study will improve our knowledge on the effectiveness and safety of the use of antipsychotics at the prodromal phase, and will eventually facilitate optimisation of individualised interventions for psychosis prevention and treatment.Keywords: clinical high-risk, early interventions, antipsychotics, subgroup

ObjectiveTo investigate the effect of electroacupuncture (EA) on endothelial dysfunction related to high fat diet (HFD)-induced insulin resistance through the phosphatidylinositol 3-kinase (PI3K)-protein kinase B (Akt) signalling pathway.MethodsTwenty-four male Sprague-Dawley rats were fed a regular diet (Control group, n=8) or a HFD (n=16) for 12 weeks to induce an insulin resistance model. HFD-fed rats were divided into two groups that remained untreated (HFD group, n=8) or received electroacupuncture (HFD+EA group, n=8). EA was applied at PC6, ST36, SP6 and BL23. At the end of the experiment, fasting blood glucose (FBG), serum insulin (FINS), serum C-peptide (C-P) and homeostatic model assessment of insulin resistance (HOMA-IR) indices were determined. Pancreatic islet samples were subjected to histopathological examination. The thoracic aorta was immunostained with anti-rat insulin receptor substrate (IRS)-1, Akt and endothelial nitric oxide synthase (eNOS) antibodies. mRNA and protein expression of IRS-1, PI3K, Akt2 and eNOS in the vascular endothelium were determined by real-time PCR and Western blot analysis, respectively.ResultsThe bodyweight increase of the HFD+EA group was smaller than that of the untreated HFD group. Compared with the HFD group, the levels of FBG, FINS, C-P and HOMA-IR in the HFD+EA group decreased significantly (PConclusionEA may improve insulin resistance and attenuate endothelial dysfunction, and therefore could play a potential role in the prevention or treatment of diabetic complications and cardiovascular disease through the PI3K/Akt signalling pathway.

Internet-scale open source software (OSS) production in various communities generates abundant reusable resources for software developers. However, finding the desired and mature software with keyword queries from a considerable number of candidates, especially for the fresher, is a significant challenge because current search services often fail to understand the semantics of user queries. In this paper, we construct a software term database (STDB) by analyzing tagging data in Stack Overflow and propose a correlation-based software search (CBSS) approach that performs correlation retrieval based on the term relevance obtained from STDB. In addition, we design a novel ranking method to optimize the initial retrieval result. We explore four research questions in four experiments, respectively, to evaluate the effectiveness of the STDB and investigate the performance of the CBSS. The experiment results show that the proposed CBSS can effectively respond to keyword-based software searches and significantly outperforms other existing search services at finding mature software.

Code reviews in pull-based model are open to community users on GitHub. Various participants are taking part in the review discussions and the review topics are not only about the improvement of code contributions but also about project evolution and social interaction. A comprehensive understanding of the review topics in pull-based model would be useful to better organize the code review process and optimize review tasks such as reviewer recommendation and pull-request prioritization. In this paper, we first conduct a qualitative study on three popular open-source software projects hosted on GitHub and construct a fine-grained two-level taxonomy covering four level-1 categories (code correctness, pull-request decision-making, project management, and social interaction) and 11 level-2 subcategories (e.g., defect detecting, reviewer assigning, contribution encouraging). Second, we conduct preliminary quantitative analysis on a large set of review comments that were labeled by TSHC (a two-stage hybrid classification algorithm), which is able to automatically classify review comments by combining rule-based and machine-learning techniques. Through the quantitative study, we explore the typical review patterns. We find that the three projects present similar comments distribution on each subcategory. Pull-requests submitted by inexperienced contributors tend to contain potential issues even though they have passed the tests. Furthermore, external contributors are more likely to break project conventions in their early contributions.

Planetary gearbox is widely used in rotating machinery and prone to damage and failure due to heavy load and complex operating conditions. Furthermore, due to heavy background noise and complicated transmission path, the fault characteristics imbedded in the acquired signals is very weak and difficult to be extracted. Therefore, it is a challenge task for extracting fault characteristics of planetary gearboxes. To tackle this task, this paper proposes an adaptive stochastic resonance method for weak fault characteristic extraction of planetary gearbox, where a chaos ant colony algorithm characterized global optimization ability is employed to achieve adaptive matching between potential parameters and thereby overcoming the local optimization shortcoming of traditional stochastic resonance. Simulation and planetary gearbox experiments with a missing tooth and broken tooth validate the effectiveness of the proposed method, respectively. The results show that the proposed method is not only able to extract weak fault characteristics but also superior to singular value decomposition method.

This paper mainly uses CFD method to simulate the ward internal environment, including contaminant distribution, velocity field and temperature filed. Using Gambit to establish a standard ward physical model. Based on the model, using Fluent to calculate, then get the ward’s comprehensive flow field. On this basis, analysis the influence of personnel’s thermal comfort and health, to optimize the ward air distribution form, adjustment of inlet and outlet position put to provide theory basis.

Summary Background Coronary heart disease is rapidly increasing in developing countries, but access to cardiac rehabilitation and secondary prevention remains low. In this study, we aimed to assess the effectiveness of a smartphone-based cardiac rehabilitation and secondary prevention programme delivered via the social media platform WeChat (SMART-CR/SP). Methods In this parallel-group, single-blind, randomised controlled trial, we recruited patients aged 18 years or older with coronary heart disease who had received percutaneous coronary interventions from a large tertiary hospital in Shanghai, China. Participants were randomly assigned (1:1) by block randomisation to either a 2-month intensive programme followed by a 4-month step-down phase of SMART-CR/SP or to usual care. In the SMART-CR/SP group, participants received comprehensive cardiac rehabilitation and secondary prevention via WeChat. The usual care group received standard outpatient cardiology follow-up but without formal cardiac rehabilitation and secondary prevention. Assessments were done at baseline, 2 months, 6 months, and 12 months. The primary outcome was change in functional capacity from baseline, measured by 6-min walk distance, at 2 months and 6 months. Analysis was by intention to treat. Research personnel involved in assessments were blinded to group allocation. Adverse-event analysis was based on percentage of patients who discontinued the study owing to adverse events. SMART-CR/SP programme-related safety issues were also recorded. This study was registered with the Chinese Clinical Trial Registry, number ChiCTR-INR-16009598. Findings Between Nov 17, 2016, and March 18, 2017, 312 patients (mean age 60·5 years [SD 9·2]), of whom 58 (19%) were female and 254 (81%) were male, were recruited and subsequently randomly assigned to SMART-CR/SP (n=156) or usual care (n=156). The improvement in 6-min walk distance at 2 months was significantly greater in the SMART-CR/SP group (from 489·2 m [99·4] at baseline to 539·1 m [68·0]) than in the control group (from 485·0 m [93·5] at baseline to 517·8 m [74.6]), with an adjusted mean difference of 20·64 m (95% CI 7·50–33·77; p=0·034). This improvement was maintained at 6 months (mean 6-min walk distance 543·4 m [67·5] in the SMART-CR/SP group vs 523·5 m [60·2] in the control group), with a mean between-group difference of 22·29 m (8·19–36·38; p=0·027). No adverse events or SMART-CR/SP programme-related safety issues were reported by participants during the study. Interpretation SMART-CR/SP was found to be a cardiac rehabilitation and secondary prevention service model with high efficacy and accessibility and to be easy to use. These results justify the implementation of similar models of care on a broader scale. Funding Curtin University.

and purpose Acupuncture has gained increasing attention in the treatment of insulin resistance (IR). This study systematically reviews the efficacy of acupuncture on clinical IR outcomes.Cochrane Central Register of Controlled Trials, Embase, Medline (via OVID), China National Knowledge Infrastructure (CNKI), Wan Fang and China Science and Technology Journal Database (VIP) were searched to collect randomized controlled trials (RCTs) of patients with IR treated by acupuncture. Meta-analysis was performed by RevMan 5.3.With acupuncture, the homeostasis model assessment of insulin resistance (homa-IR) significantly decreased (mean difference (MD) = -1.04, 95% confidence interval (CI) -1.37 to -0.71; P 0.00001), as did fasting blood glucose (FBG) (MD = -0.56, 95% CI -0.88 to -0.25; P = 0.0005), 2 h postprandial blood glucose (2hPG) (MD = -0.91, 95% CI -1.62 to -0.20; P = 0.01), and fasting insulin (FINS) (MD = -3.23, 95% CI -4.14 to -2; P 0.00001). Meanwhile, the insulin sensitivity index (ISI) (MD = 0.36, 95% CI 0.18 to 0.53; P 0.0001) increased, and fewer adverse events occurred.Acupuncture may improve homa-IR, ISI, FBG, 2hPG and FINS with fewer adverse events than other treatments, making it a viable treatment for IR.

Background: Coronary heart disease (CHD) is rapidly increasing in developing countries, but access to cardiac rehabilitation and secondary prevention (CR/SP) remains low. We assessed the effectiveness of SMARTphone and WeChat-based CR/SP (SMART-CR/SP) in the first randomised controlled trial (RCT) of a dedicated social media-based CR/SP program for patients with CHD. Methods: In this parallel-group, single-blind, RCT, CHD patients from a large tertiary hospital in Shanghai, China were randomised (1:1) to an eight-week 'intensive', followed by a 16-week 'step down' program of SMART-CR/SP, or usual care. Assessments were conducted at baseline, eight and 24 weeks. The primary outcome was functional capacity measured by six-minute walk test distance (6MWTD). Secondary outcomes included CHD knowledge, and risk factor control. Analysis was by intention-to-treat. Findings: Between November 17, 2016 and March 18, 2017, 312 patients (mean age, 60.5 [SD 9.2] years, 81.4% male) were randomised to SMART-CR/SP (n=156) or usual care (n=156). The mean increase in 6MWTD was greater at both eight (46.5 vs. 20.9 m; 95% CI 9.6 to 41.5; p=0.002) and 24 weeks (54.2 vs. 26.2 m; 95% CI 10.9 to 44.9; p=0.001) in the SMART-CR/SP group versus controls. The SMART-CR/SP group had a better knowledge of CHD score at eight (17.9 vs. 13.8; 95% CI 2.84 to 5.44; p

This paper proposes an optimization process based on a parametric platform for building climate responsive design. Taking residential buildings in six typical American cities as examples, it proposes thermal environment comfort (Discomfort Hour, DH), building energy demand (BED) and building global cost (GC) as the objective functions for optimization. The design variables concern building orientation, envelope components, and window types, etc. The optimal solution is provided from two different perspectives of the public sector (energy saving optimal) and private households (cost-optimal) respectively. By comparing the optimization results with the performance indicators of the reference buildings in various cities, the outcome can give the precious indications to rebuild the U.S. residential buildings with a view to energy-efficiency and cost optimality depending on the location.

Li–O2 batteries with higher theoretical specific capacity are a promising energy storage system. Designing an efficient cathode catalyst can alleviate the problem that derives from the sluggish dynamic process. In this study, morphologies of MnCo2O4 are easily tuned from square blocks to nanowire arrays by simple adjusting mole numbers of NH4F under hydrothermal conditions. The MnCo2O4 nanowire arrays on carbon cloth with a large specific surface area of 194.9 m2 g−1 are synthesized by 9 mmol NH4F, which as a cathode catalyst for Li–O2 batteries can maintain for 167 cycles with a cutoff capacity to 500 mA h g−1 at 340 mA g−1 (0.2 mA cm−2). And the MnCo2O4 nanowire array electrodes show the discharge capacities of 8364, 7544 and 5887 mA h g−1 at 200, 400 and 800 mA g−1, respectively, which show good rate performance.

With the improvement of technologies, people’s demand for intelligent devices of indoor and outdoor living environments keeps increasing. However, the traditional control system only adjusts living parameters mechanically, which cannot better meet the requirements of human comfort intelligently. This article proposes a building intelligent thermal comfort control system based on the Internet of Things and intelligent artificial intelligence. Through the literature review, various algorithms and prediction methods are analyzed and compared. The system can automatically complete a series of operations through IoT hardware devices which are located at multiple locations in the building with key modules. The code is developed and debugged by Python to establish a model for energy consumption prediction with environmental factors such as temperature, humidity, radiant temperature, and air velocity on thermal comfort indicators. By using the simulation experiments, 1700 data sets are used for training. Then, the output PMV predicted values are compared with the real figure. The results show that the performance of this system is superior to traditional control on energy-saving and comfort.

In GitHub, the pull-based development model enables community contributors to collaborate in a more efficient way. However, the distributed and parallel characteristics of this model pose a potential risk for developers to submit duplicate pull-requests (PRs), which increase the extra cost of project maintenance. To facilitate the further studies to better understand and solve the issues introduced by duplicate PRs, we construct a large dataset of historical duplicate PRs extracted from 26 popular open source projects in GitHub by using a semi-automatic approach. Furthermore, we present some preliminary applications to illustrate how further researches can be conducted based on this dataset.

Cerebral small vessel disease (CSVD) describes a syndrome of neuroimaging, pathological, and associated clinical features caused by small intracranial vascular lesions, which commonly coexists with large artery atherosclerosis (LAA) and has been identified as a major cause of motor impairment over time. In this review, we aim to summarize the relationship between CSVD and LAA, and discuss the effects of CSVD on the clinical outcome of patients with ischemic stroke caused by LAA.We searched and scanned all the literature with the keyword cerebral small vessel disease, cerebral microbleeds, white matter hyperintensity, silent lacunar infarcts, enlarged perivascular spaces and stroke in the database of Pubmed and MEDLINE (from inception to 1 February 2015).Various imaging phenotypes of CSVDs have different influences on the clinical outcome of patients with LAA stroke. The total brain burden of CSVD can represent the cumulative effects of different MRI features of CSVD, which has predictive value for recurrent stroke after ischemic stroke. Better quantification of the total brain burden of CSVD may help stratify patients more effectively in clinical practice.CSVD can significantly influence the clinical outcome of stroke in patients with cerebral infarction caused by LAA, and the methods to capture whole brain CSVD burden cross the spectrum of changes in CSVD imaging features may be useful for patient stratification in clinical trials. Further studies should be performed to validate the relationship between CSVD related brain damage and LAA stroke.

In the modern power system, fast and accurate simulation tools are needed for transient analysis such as electromagnetic transients program (EMT). In our previous paper, we proposed a computational framework to model, convert, and accelerate the grid-level EMT simulations based on full custom VLSI (very large scale integration) techniques that runs orders of magnitudes faster than software or FPGA approaches. However, due to the complexity of exciters, power system stabilizers and governors, these components were omitted and the corresponding values are assumed to be constant. In this paper, we propose an efficient VLSI architecture for exciters, power system stabilizers and governors, and implement the architecture using the 45nm process. Circuit simulation results show that the proposed VLSI circuits can solve the system significantly faster than real time without sacrificing accuracy.

Most of the previous emotion classifications are based on binary or ternary classifications, and the final emotion classification results contain only one type of emotion. There is little research on multi-emotional coexistence, which has certain limitations on the restoration of human’s true emotions. Aiming at these deficiencies, this paper proposes a Bidirectional Long-Short Term Memory Multiple Classifiers (BLSTM-MC) model to study the five classification problems in code-switching text, and obtains text contextual relations through BLSTM-MC model. It fully considers the relationship between different emotions in a single post, at the same time, the Attention mechanism is introduced to find the importance of different features and predict all emotions expressed by each post. The model achieved third place in all submissions in the conference NLP&&CC_task1 2018.

Document clustering refers to grouping similar documents together automatically. Labels of the clusters, usually edited manually, are helpful for users to quickly grasp the major meaning of the grouped documents. Therefore, high quality labels are desired in many user-facing applications. However, assigning the labels manually is time consuming and tedious. In this paper a hybrid approach is proposed to automate the labeling process. First, linguistic knowledge are used to ensure candidate labels' readability and information quantity by exploring the dependencies between words. Second, a statistical generative model is proposed to select representative labels. It scores a label w.r.t. a cluster by estimating how likely the cluster is generated by the label. The proposed approach is evaluated on two data sets in both English and Chinese. Experimental results show that the proposed approach produces high quality labels and outperforms existing state-of-art methods on both manual and automatic evaluations.

In order to achieve the goal to test the dynamic performance for a new hydraulic cylinder product in an old test system, an electro-hydraulic servo position control system is redesigned and modified, which is control by fuzzy PID controller. Compared with the traditional PID controller, the fuzzy PID controller has better precision and faster response speed when tracking step signal, sinusoidal signal and simulated movement, that is, the error calculated by the root mean square is smaller and the response time is shorter. The result show that fuzzy PID Control has higher accuracy and better stability and well achieve the goal.

Ischemic stroke is a serious cerebrovascular disease that leads to death and/or severe disability, and extensive research has been directed in search of appropriate treatment measures. One such potential treatment is the use of cerebral ischemic post-conditioning (IPostC), and its neuroprotective effects and controllability have been widely confirmed and validated. Numerous studies have indicated that cerebral IPostC can suppress the inflammatory reaction in ischemia reperfusion injury, improve cerebral circulation, reduce infarct volume, and promote both neurogenesis and angiogenesis. The neuroprotective mechanisms underlying IPostC have been systematically studied, but still need a comprehensive summary and a more exhaustive understanding. Continuing research has proposed further applications of IPostC, including remote ischemic post-conditioning, pharmacologic ischemic post-conditioning, and delayed ischemic post-conditioning. However, most studies of IPostC are performed on animal models, and clinical studies are scarce. Therefore, future studies will need to focus on increasing our understanding of neuroprotective mechanisms underlying cerebral IPostC as well as accelerating its clinical translation.

The Internet-scale open source software (OSS) production in various communities are generating abundant reusable resources for software developers. However, how to retrieve and reuse the desired and mature software from huge amounts of candidates is a great challenge: there are usually big gaps between the user application contexts (that often used as queries) and the OSS key words (that often used to match the queries). In this paper, we define the scenario-based query problem for OSS retrieval, and then we propose a novel approach to reformulate the raw query by leveraging the crowd wisdom from millions of developers to improve the retrieval results. We build a software-specific domain lexical database based on the knowledge in open source communities, by which we can expand and optimize the input queries. The experiment results show that, our approach can reformulate the initial query effectively and outperforms other existing search engines significantly at finding mature software.

The global open source software resources have become an Internet-scale repository, which provide abundant resources for software reuse. However, how to locate the desired resource efficiently and accurately from such large amounts is quite a challenge problem. Most of recent works barely focus on the semantic similarity by analyzing the software itself, which often result in inappropriate ranking. In this paper, we propose Dolphin, a global OSS search engine, which leverage the crowd discussions around OSS across communities for optimizing software ranking. Dolphin employs the crowd wisdom as an important factor and combines it with the semantic similarity to optimize search results ranking. We implement an OSS search engine based on the proposed approach, which provides online service continuously. Extensive experiments and user studies suggest the effectiveness of our approach.

With the development of E-Commerce, it's harder and harder for consumers to find the product they want, and the recommendation systems are applied more and more widely. A recommendation system includes user model, the recommended model and recommendation algorithm. The improvement considered in this paper mainly refer to the recommendation algorithm. Limited resource, data valid time and cold start problems are not well considered in existing E-Commerce recommendation system. According to the problems descripted above, a algorithm based on limited resource and a solution to cold start problem are proposed.

Most traditional network anomalies and attacks detection systems tend to employ supervised strategies, which require labeled training dataset that is arduous and expensive to obtain and fails to detect unknown attacks jeopardizing the system security and reliability. In this paper we present a new unsupervised approach based on abnormality weights rendering and subspace clustering techniques to detect network anomalies without previously labeled traffic or signatures. In order to examine capability of the proposed approach, we conducted several experiments on both real network traffic and KDD99 dataset. Its performance was compared against previously used network anomaly detection methods. The results show that our approach is better than the other detection techniques.

A novel methodology for the fault diagnosis of rolling bearing in strong background noise, based on sensitive intrinsic mode functions (IMFs) selection of ensemble empirical mode decomposition (EEMD) and adaptive stochastic resonance, is proposed. The original vibration signal is decomposed into a group of IMFs and a residual trend item by EEMD. Constructing weighted kurtosis index difference spectrum (WKIDS) to adaptively select sensitive IMFs, this method can overcome the shortcomings of the existing methods such as subjective choice or need to determine a threshold using the correlation coefficient. To further reduce noise and enhance weak characteristics, the adaptive stochastic resonance is employed to amplify each sensitive IMF. Then, the ensemble average is used to eliminate the stochastic noise. The simulation and rolling element bearing experiment with an inner fault are performed to validate the proposed method. The results show that the proposed method not only overcomes the difficulty of choosing sensitive IMFs, but also, combined with adaptive stochastic resonance, can better enhance the weak fault characteristics. Moreover, the proposed method is better than EEMD and adaptive stochastic resonance of each sensitive IMF, demonstrating the feasibility of the proposed method in highly noisy environments.

Peer-to-Peer (P2P) technology has recently become a tremendously attractive solution to offload servers in large-scale Video-on-Demand (VoD) applications by utilizing the upload capability of participating peers. However, the alleviation of server load may not be very effective in the multi-channel P2P VoD systems due to the intra-channel and inter-channel upload bandwidth imbalances. In this paper, we evaluate the impact of these bandwidth imbalances on the system performance through two analytically models: a queuing network model which describes the whole system, and a stage-based model which describes a single channel. Our models capture several aspects of peer behavior, such as participating in the system, sojourning in a channel, downloading and uploading the content, wandering around channels and leaving the system. We apply our models to three potential P2P VoD designs: Naive bandwidth Allocation Design (NAD), Independent-channel Chunk-aware bandwidth Allocation design (ICA) and Cross-channel Chunk-aware bandwidth Allocation design (CCA). By developing an asymptotic theory to provide theoretical results, we analytically show that ICA can perform better than NAD, and CCA can perform best of the three designs in terms of server load. We also present optimization problems and simple heuristic strategies for ICA and CCA. Our analytical results are validated by extensive simulations.

Text representation is a necessary procedure for text categorization tasks. Currently, bag of words (BOW) is the most widely used text representation method but it suffers from two drawbacks. First, the quantity of words is huge; second, it is not feasible to calculate the relationship between words. Semantic analysis (SA) techniques help BOW overcome these two drawbacks by interpreting words and documents in a space of concepts. However, existing SA techniques are not designed for text categorization and often incur huge computing cost. This paper proposes a concise semantic analysis (CSA) technique for text categorization tasks. CSA extracts a few concepts from category labels and then implements concise interpretation on words and documents. These concepts are small in quantity and great in generality and tightly related to the category labels. Therefore, CSA preserves necessary information for classifiers with very low computing cost. To evaluate CSA, experiments on three data sets (Reuters-21578, 20-NewsGroup and Tancorp) were conducted and the results show that CSA reaches a comparable micro- and macro-F"1 performance with BOW, if not better one. Experiments also show that CSA helps dimension sensitive learning algorithms such as k-nearest neighbor (kNN) to eliminate the ''Curse of Dimensionality'' and as a result reaches a comparable performance with support vector machine (SVM) in text categorization applications. In addition, CSA is language independent and performs equally well both in Chinese and English.

Rationale: Although anaphylaxis caused by jellyfish stings is common in coastal areas, an allergic shock caused by cooked jellyfish ingestion has never been reported in China. In this paper, we report a case of allergic shock being caused by ingestion of cooked salt-preserved jellyfish shortly after being stung by a live jellyfish. Patient concerns: A 26-year-old Chinese man presented with dizziness, pruritus, dyspnea, hypotension, and tachycardia after eating cooked salted jellyfish. The patient had been stung twice by jellyfish half a year ago. Diagnoses: Allergic shock caused by ingestion of cooked jellyfish. Interventions: The patient was treated with phenergan (25 mg, intramuscular injection), 250 mL normal saline (NS) and 10 mg dexamethasone (intravenous drip), 500 mL NS and 0.4 g cimetidine (intravenous drip), and 500 mL NS for rapid fluid infusion (intravenous drip). Outcomes: After the treatment, the main clinical symptoms of the patient improved quickly. Five days later, the patient's urticaria had dissipated. Lessons: A history of jellyfish contact or sting might be an important allergic factor for individuals who consume any kind of jellyfish.

This paper presents the linguistic analysis infrastructure developed within the XLike project. The main goal of the implemented tools is to provide a set of functionalities supporting the XLike main objectives: Enabling cross-lingual services for publishers, media monitoring or developing new business intelligence applications. The services cover seven major and minor languages: English, German, Spanish, Chinese, Catalan, Slovenian, and Croatian. These analyzers are provided as web services following a lightweigth SOA architecture approach, and they are publically accessible and shared through META-SHARE. 1

Social context understanding is a fundamental problem on social analysis. Social contexts are usually short, informal and incomplete and these characteristics make methods for formal texts give poor performance on social contexts. However, we discover part of relations between importance words in formal texts are helpful to understand social contexts. We propose a method that extracts semantic chunks using these relations to express social contexts. A semantic chunk is a phrase which is meaningful and significant expression describing the fist of given texts. We exploit semantic chunks by utilizing knowledge learned from semantically parsed corpora and knowledge base. Experimental results on Chinese and English data sets demonstrate that our approach improves the performance significantly.

News articles express information by concentrating on named entities like who, when, and where in news. Whereas, extracting the relationships among entities, words and topics through a large amount of news articles is nontrivial. Topic modeling like Latent Dirichlet Allocation has been applied a lot to mine hidden topics in text analysis, which have achieved considerable performance. However, it cannot explicitly show relationship between words and entities. In this paper, we propose a generative model, Entity-Centered Topic Model(ECTM) to summarize the correlation among entities, words and topics by taking entity topic as a mixture of word topics. Experiments on real news data sets show our model of a lower perplexity and better in clustering of entities than state-of-the-art entity topic model(CorrLDA2). We also present analysis for results of ECTM and further compare it with CorrLDA2.