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In the research of mining software repositories, we need to label a large amount of data to construct a predictive model. The correctness of the labels will affect the performance of a model substantially. However, limited studies have been performed to investigate the impact of mislabeled instances on a predictive model. To bridge the gap, in this work, we perform a case study on the security bug report (SBR) prediction. We found five publicly available datasets for SBR prediction contains many mislabeled instances, which lead to the poor performance of SBR prediction models of recent studies (e.g., the work of Peters et al. and Shu et al.). Furthermore, it might mislead the research direction of SBR prediction. In this paper, we first improve the label correctness of these five datasets by manually analyzing each bug report, and we find 749 SBRs, which are originally mislabeled as Non-SBRs (NSBRs). We then evaluate the impacts of datasets label correctness by comparing the performance of the classification models on both the noisy (i.e., before our correction) and the clean (i.e., after our correction) datasets. The results show that the cleaned datasets result in improvement in the performance of classification models. The performance of the approaches proposed by Peters et al. and Shu et al. on the clean datasets is much better than on the noisy datasets. Furthermore, with the clean datasets, the simple text classification models could significantly outperform the security keywords-matrix-based approaches applied by Peters et al. and Shu et al.

The release note is one of the most important software artifacts that serves as a bridge for communication among stakeholders. Release notes contain a set of crucial information, such as descriptions of enhancements, improvements, potential issues, development, evolution, testing, and maintenance of projects throughout the whole development lifestyle. A comprehensive understanding of what makes a good release note and how to write one for different stakeholders would be highly beneficial. However, in practice, the release note is often neglected by stakeholders and has not to date been systematically investigated by researchers. In this paper, we conduct a mixed methods study to investigate the use of release notes in practice. We first conducted a large-scale empirical study of 32,425 release notes from 1,000 GitHub projects to understand current contents and information found in real-world release notes. We then performed interviews with 15 practitioners and an online survey with 314 respondents to investigate how key stakeholders perceive release notes. From the analysis of these data, we summarized eight categories of information that are normally documented in release notes in GitHub projects. We found that stakeholders consider that well-formed release notes have a positive impact on software development, such as software evolution. We concluded 28 statements grouped into eight topics based on stakeholders' opinions. There exist significant discrepancies between different stakeholders on how release notes should be written and used. Our study provides new insights on release notes and facilitates stakeholders to better take advantage of them during software development.

Objective: To evaluate the effect of traditional Chinese medicine (TCM)-characteristic rehabilitation program on levels of pain and swelling after total knee arthroplasty. Methods: A total of 72 in-patients who recovered from total knee arthroplasty in our department were selected as the study subjects. They were divided into two groups according to a random number table: the control group (n = 36) with routine nursing and the treatment group (n = 36) with TCM rehabilitation nursing. The pain scores and joint swelling were statistically analyzed. Results: The pain scores in the treatment group were lower than those in the control group, and the difference was statistically significant (P < 0.05). Conclusion: The implementation of a TCM rehabilitation program for patients after total knee arthroplasty can effectively and quickly reduce the pain and swelling of the affected limb, promote the local blood circulation, and promote the quality of life of patients after rehabilitation.

Cross-project defect prediction (CPDP), aiming to apply defect prediction models built on source projects to a target project, has been an active research topic. A variety of supervised CPDP methods and some simple unsupervised CPDP methods have been proposed. In a recent study, Zhou et al. found that simple unsupervised CPDP methods (i.e., ManualDown and ManualUp) have a prediction performance comparable or even superior to complex supervised CPDP methods. Therefore, they suggested that the ManualDown should be treated as the baseline when considering non-effort-aware performance measures (NPMs) and the ManualUp should be treated as the baseline when considering effort-aware performance measures (EPMs) in future CPDP studies. However, in that work, these unsupervised methods are only compared with existing supervised CPDP methods in terms of one or two NPMs and the prediction results of baselines are directly collected from the primary literature. Besides, the comparison has not considered other recently proposed EPMs, which consider context switches and developer fatigue due to initial false alarms. These limitations may not give a holistic comparison between the supervised methods and unsupervised methods. In this paper, we aim to revisit Zhou et al.'s study. To the best of our knowledge, we are the first to make a comparison between the existing supervised CPDP methods and the unsupervised methods proposed by Zhou et al. in the same experimental setting, considering both NPMs and EPMs. We also propose an improved supervised CPDP method EASC and make a further comparison between this method and the unsupervised methods. According to the results on 82 projects in terms of 12 performance measures, we find that when considering NPMs, EASC can achieve similar results with the unsupervised method ManualDown without statistically significant difference in most cases. However, when considering EPMs, our proposed supervised method EASC can statistically significantly outperform the unsupervised method ManualUp with a large improvement in terms of Cliff's delta in most cases. Therefore, the supervised CPDP methods are more promising than the unsupervised method in practical application scenarios, since the limitation of testing resource and the impact on developers cannot be ignored in these scenarios.

Defect localization aims to locate buggy program elements (e.g., buggy files, methods or lines of code) based on defect symptoms, e.g., bug reports or program spectrum. However, when we receive the defect symptoms, the defect has been exposed and negative impacts have been introduced. Thus, one challenging task is: whether we can locate buggy program prior to appearance of the defect symptom at an early time (e.g., when buggy program elements are being checked-in). We refer to this type of defect localization as “Just-In-Time (JIT) Defect localization”. Although many prior studies have proposed various JIT defect identification methods to identify whether a new change is buggy, these prior methods do not locate the suspicious positions. Thus, JIT defect localization is the next step of JIT defect identification (i.e., after a buggy change is identified, suspicious source code lines are located). To address this problem, we propose a two-phase framework, i.e., JIT defect identification and JIT defect localization. Given a new change, JIT defect identification will identify it as buggy change or clean change first. If a new change is identified as buggy, JIT defect localization will rank the source code lines introduced by the new change according to their suspiciousness scores. The source code lines ranked at the top of the list are estimated as the defect location. For JIT defect identification phase, we use 14 change-level features to build a classifier by following existing approach. For JIT defect localization phase, we propose a JIT defect localization approach that leverages software naturalness with the N-gram model. To evaluate the proposed framework, we conduct an empirical study on 14 open source projects with a total of 177,250 changes. The results show that software naturalness is effective for our JIT defect localization. Our model achieves a reasonable performance, and outperforms the two baselines (i.e., random guess and a static bug finder (i.e., PMD)) by a substantial margin in terms of four ranking measures.

INTRODUCTION Resting heart rate (RHR) predicts future risk for cardiovascular disease (CVD). However, longitudinal studies investigating the relationship of RHR with cognitive decline are scarce. METHODS This population-based cohort study included 2147 participants (age≥60) in SNAC-K who were free of dementia and regularly followed from 2001-2004 to 2013-2016. RHR was assessed with electrocardiogram. Dementia was diagnosed following the Diagnostic and Statistical Manual of Mental Disorders 4th Revision criteria. Global cognitive function was assessed using Mini-Mental State Examination (MMSE). Data were analyzed using Cox and linear mixed-effects models. RESULTS RHR≥80 (vs. 60-69) bpm was associated with a multi-adjusted hazard ratio of 1.55 (95% confidence interval 1.06-2.27) for dementia. The association remained significant after excluding participants with prevalent and incident CVDs. Similarly, RHR≥80 bpm was associated with a multi-adjusted β-coefficient of -0.13 (-0.21 to -0.04) for MMSE score. DISCUSSION Higher RHR is associated with increased risk for dementia and faster cognitive decline independent of CVDs in a general population of elderly people.

Infection is the leading cause of admission and mortality in neonatal intensive care units. Immature immune function and antibiotic resistance make the treatment more difficult. However, there is no effective prevention for it. Recently, more and more researches are focusing on stem cell therapy, especially mesenchymal stem cells (MSCs); their potential paracrine effect confer MSCs with a major advantage to treat the immune and inflammatory disorders associated with neonatal infection. In this review, we summarize the basal properties and preclinical evidence of MSCs and explore the potential mechanisms of paracrine factors of MSCs for neonatal infection.

Smart contract, a term which was originally coined to refer to the automation of legal contracts in general, has recently seen much interest due to the advent of blockchain technology. Recently, the term is popularly used to refer to low-level code scripts running on a blockchain platform. Our study focuses exclusively on this subset of smart contracts. Such smart contracts have increasingly been gaining ground, finding numerous important applications (e.g., crowdfunding) in the real world. Despite the increasing popularity, smart contract development still remains somewhat a mystery to many developers largely due to its special design and applications. Are there any differences between smart contract development and traditional software development? What kind of challenges are faced by developers during smart contract development? Questions like these are important but have not been explored by researchers yet. In this paper, we performed an exploratory study to understand the current state and potential challenges developers are facing in developing smart contracts on blockchains, with a focus on Ethereum (the most popular public blockchain platform for smart contracts). Toward this end, we conducted this study in two phases. In the first phase, we conducted semi-structured interviews with 20 developers from GitHub and industry professionals who are working on smart contracts. In the second phase, we performed a survey on 232 practitioners to validate the findings from the interviews. Our interview and survey results revealed several major challenges developers are facing during smart contract development: (1) there is no effective way to guarantee the security of smart contract code; (2) existing tools for development are still very basic; (3) the programming languages and the virtual machines still have a number of limitations; (4) performance problems are hard to handle under resource constrained running environment; and (5) online resources (including advanced/updated documents and community support) are still limited. Our study suggests several directions that researchers and practitioners can work on to help improve developers’ experience on developing high-quality smart contracts.

Just-in-Time (JIT) defect prediction—a technique which aims to predict bugs at change level—has been paid more attention. JIT defect prediction leverages the SZZ approach to identify bug-introducing changes. Recently, researchers found that the performance of SZZ (including its variants) is impacted by a large amount of noise. SZZ may considerably mislabel changes that are used to train a JIT defect prediction model, and thus impact the prediction accuracy. In this paper, we investigate the impact of the mislabeled changes by different SZZ variants on the performance and interpretation of JIT defect prediction models. We analyze four SZZ variants (i.e., B-SZZ, AG-SZZ, MA-SZZ, and RA-SZZ) that are proposed by prior studies. We build the prediction models using the labeled data by these four SZZ variants. Among the four SZZ variants, RA-SZZ is least likely to generate mislabeled changes, and we construct the testing set by using RA-SZZ. All of the four prediction models are then evaluated on the same testing set. We choose the prediction model built on the labeled data by RA-SZZ as the baseline model, and we compare the performance and metric importance of the models trained using the labeled data by the other three SZZ variants with the baseline model. Through a large-scale empirical study on a total of 126,526 changes from ten Apache open source projects, we find that in terms of various performance measures (AUC, F1-score, G-mean and Recall@20%), the mislabeled changes by B-SZZ and MA-SZZ are not likely to cause a considerable performance reduction, while the mislabeled changes by AG-SZZ cause a statistically significant performance reduction with an average difference of 1–5 percent. When considering developers’ inspection effort (measured by LOC) in practice, the changes mislabeled B-SZZ and AG-SZZ lead to 9–10 and 1–15 percent more wasted inspection effort, respectively. And the mislabeled changes by B-SZZ lead to significantly more wasted effort. The mislabeled changes by MA-SZZ do not cause considerably more wasted effort. We also find that the top-most important metric for identifying bug-introducing changes (i.e., number of files modified in a change) is robust to the mislabeling noise generated by SZZ. But the second- and third-most important metrics are more likely to be impacted by the mislabeling noise, unless random forest is used as the underlying classifier.

Commit messages recorded in version control systems contain valuable information for software development, maintenance, and comprehension. Unfortunately, developers often commit code with empty or poor quality commit messages. To address this issue, several studies have proposed approaches to generate commit messages from commit diffs . Recent studies make use of neural machine translation algorithms to try and translate git diffs into commit messages and have achieved some promising results. However, these learning-based methods tend to generate high-frequency words but ignore low-frequency ones. In addition, they suffer from exposure bias issues, which leads to a gap between training phase and testing phase. In this article, we propose CoRec to address the above two limitations. Specifically, we first train a context-aware encoder-decoder model that randomly selects the previous output of the decoder or the embedding vector of a ground truth word as context to make the model gradually aware of previous alignment choices. Given a diff for testing, the trained model is reused to retrieve the most similar diff from the training set. Finally, we use the retrieval diff to guide the probability distribution for the final generated vocabulary. Our method combines the advantages of both information retrieval and neural machine translation. We evaluate CoRec on a dataset from Liu et al. and a large-scale dataset crawled from 10K popular Java repositories in Github. Our experimental results show that CoRec significantly outperforms the state-of-the-art method NNGen by 19% on average in terms of BLEU.

A software system’s design determines many of its properties, such as maintainability and performance. An understanding of design is needed to maintain system properties as changes to the system occur. Unfortunately, many systems do not have up-to-date design documentation and approaches that have been developed to recover design often focus on how a system works by extracting structural and behaviour information rather than information about the desired design properties, such as robustness or performance. In this paper, we explore whether it is possible to automatically locate where design is discussed in on-line developer discussions. We investigate and introduce a classifier that can locate paragraphs in pull request discussions that pertain to design with an average AUC score of 0.87. We show that this classifier, when applied to projects on which it was not trained, agrees with the identification of design points by humans with an average AUC score of 0.79. We describe how this classifier could be used as the basis of tools to improve such tasks as reviewing code and implementing new features.

Feng-Juan Hu,1,2 Hu Liu,3 Xiao-Lei Liu,1,2 Shu-Li Jia,1,2 Li-Sha Hou,2 Xin Xia,2 Bi-Rong Dong1,2 1Center of Gerontology and Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan, People’s Republic of China; 2National Clinical Research Center of Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan, People’s Republic of China; 3Department of Liver Surgery, Liver Transplantation Division, Laboratory of Liver Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan, People’s Republic of ChinaCorrespondence: Bi-Rong DongCenter of Gerontology and Geriatrics, West China Hospital, Sichuan University, GuoXueXiang 37, Chengdu, Sichuan, 610041, People’s Republic of ChinaTel +86-28-85421550Fax +86-28-85421550Email birongdong123@outlook.comPurpose: Mid-upper arm circumference (MUAC) is a simple, noninvasive anthropometric indicator. This study evaluated the applicability of MUAC as an alternative screening instrument to appendicular skeletal muscle mass index (ASMI) for detecting sarcopenia, and determined the optimal MUAC cutoff values.Patients and Methods: A total of 4509 subjects ≥ 50 years of age from the West China Health and Aging Trend study were included in the present study. ASM was measured by bioelectrical impedance analysis. MUAC, calf circumference (CC), and grip strength were evaluated and the Short Physical Performance Battery and 3-m timed up-and-go test were administered. Low muscle mass was diagnosed based on Asian Working Group for Sarcopenia 2019 (AWGS2019) and updated European Working Group on Sarcopenia in Older People 2 (EWGSOP2) criteria.Results: ASMI was positively correlated with MUAC in both men (r=0.726, P< 0.001) and women (r=0.698, P< 0.001). The area under the receiver operating characteristic curve (AUC) for MUAC as an indicator of low muscle mass in men and women was 0.86 (95% confidence interval [CI]: 0.85– 0.88) and 0.85 (95% CI: 0.84– 0.86), respectively, according to AWGS2019 criteria; and 0.86 (95% CI: 0.85– 0.88) and 0.86 (95% CI: 0.85– 0.88), respectively, according to EWGSOP2 criteria. Optimal MUAC cutoff values for predicting low muscle mass were ≤ 28.6 cm for men and ≤ 27.5 cm for women. There was no significant difference between the AUCs of MUAC and CC in men according to the 2 reference standards (P=0.809), whereas the AUC of CC was superior to that of MUAC in women according to AWGS2019 (P< 0.001) and EWGSOP2 (P=0.008) criteria.Conclusion: MUAC is strongly correlated with ASMI among community-dwelling middle-aged and older adults in China. MUAC can be used as a simple screening instrument to ASMI for diagnosing sarcopenia, especially in men.Keywords: anthropometry, low muscle mass, diagnosis, older adults

This paper develops a dynamic incomplete-markets model to analyse the impact of equity-for-guarantee swaps (EGSs), a financial innovation in China, on the interaction between an entrepreneur's initial financing and future dynamic investment policies. We compare the main implications generated by EGSs and traditional fee-for-guarantee swaps (FGSs). We demonstrate a twofold effect of EGSs on the firm: EGSs increase the firm's borrowing ability ex ante and thus allow the entrepreneur to enjoy a greater reduction in exposure to idiosyncratic business risk and undertake larger initial projects. However, EGSs aggravate the ex post underinvestment problem. Moreover, we argue that this additional underinvestment is more severe when entrepreneurs have higher risk aversion, the firm's capital is less liquid, the long- and short-term shocks to the firm's cash flow are larger, the corporate tax rate is higher, and the government provides higher subsidies to guarantors. Thus, our model yields a novel practical implication whereby EGSs' positive and negative real effects should be fully considered when decisions are made regarding their use.

Elevated blood pressure (BP) is a growing burden worldwide, leading to over 10 million deaths each year. May Measurement Month (MMM) is a global initiative of the International Society of Hypertension (ISH) aimed at raising awareness of high BP and acting as a temporary solution to the lack of screening programmes worldwide. As part of MMM, screening in South Africa in 2017 and 2018 revealed that approximately one-third of adults had hypertension, that only half of hypertensives were receiving anti-hypertensive therapy and that only a third of those with hypertension had controlled BP. These data highlight the need for continued screening and awareness campaigns. An opportunistic cross-sectional survey of volunteers aged ≥18 years was carried out in May 2019. Blood pressure measurements, the definition of hypertension and statistical analyses followed the MMM protocol. The sites screened were general populations, pharmacies, and university campuses in preference to hospitals and clinics, aiming to raise awareness, and allow access to screening to those less likely to be aware of their BP. Of the 4727 individuals (age 40.9 ± 18.1 years) screened, 31.9% had hypertension. Of those with hypertension, only 42.5% were aware and 36.1% were receiving treatment for hypertension. A large proportion (48.5%) of individuals receiving antihypertensive medication had uncontrolled BP. In conclusion, the high proportions of individuals unaware of their hypertension and with uncontrolled BP highlight the need for hypertension awareness campaigns and more rigorous management of hypertension.

In this study, we investigated whether nutrition status mediates the relationship between cognitive decline and sarcopenia. Sarcopenia was assessed in 4023 community-dwelling older adults from West China using the AWGS 2014 diagnostic criteria. Cognitive function and nutrition status were assessed using the 10-item Short Portable Mental Status Questionnaire (SPMSQ) and Mini Nutrition Assessment-Short Form (MNA-SF) scale, respectively. Mediation model regression analysis demonstrated that nutrition status was negatively associated with sarcopenia (β = -0.521; 95% CI: -0.583 to -0.459). The indirect effects of cognitive decline on sarcopenia were significant after adjusting for age, sex, and ethnicity (β = 0.015; 95% CI: 0.012 to 0.017), but the direct effects of cognitive decline on sarcopenia were not statistically significant after adding nutrition status as a parameter in the mediation model analysis (β = -0.001; 95% CI: -0.008 to 0.005). Structural equation model (SEM) framework pathway analysis confirmed the association between nutrition status, cognitive decline, and sarcopenia. These findings demonstrate that the negative effects of cognitive decline on sarcopenia were mediated by nutrition status. We therefore postulate that maintaining a good nutrition status delays the negative effects of cognitive decline on sarcopenia in older adults.

Code summarization aims at generating a code comment given a block of source code and it is normally performed by training machine learning algorithms on existing code block-comment pairs. Code comments in practice have different intentions. For example, some code comments might explain how the methods work, while others explain why some methods are written. Previous works have shown that a relationship exists between a code block and the category of a comment associated with it. In this article, we aim to investigate to which extent we can exploit this relationship to improve code summarization performance. We first classify comments into six intention categories and manually label 20,000 code-comment pairs. These categories include “what,” “why,” “how-to-use,” “how-it-is-done,” “property,” and “others.” Based on this dataset, we conduct an experiment to investigate the performance of different state-of-the-art code summarization approaches on the categories. We find that the performance of different code summarization approaches varies substantially across the categories. Moreover, the category for which a code summarization model performs the best is different for the different models. In particular, no models perform the best for “why” and “property” comments among the six categories. We design a composite approach to demonstrate that comment category prediction can boost code summarization to reach better results. The approach leverages classified code-category labeled data to train a classifier to infer categories. Then it selects the most suitable models for inferred categories and outputs the composite results. Our composite approach outperforms other approaches that do not consider comment categories and obtains a relative improvement of 8.57% and 16.34% in terms of ROUGE-L and BLEU-4 score, respectively.

Software developers have heavily used online question-and-answer platforms to seek help to solve their technical problems. However, a major problem with these technical Q8A sites is “answer hungriness,” i.e., a large number of questions remain unanswered or unresolved, and users have to wait for a long time or painstakingly go through the provided answers with various levels of quality. To alleviate this time-consuming problem, we propose a novel D EEP A NS neural network–based approach to identify the most relevant answer among a set of answer candidates. Our approach follows a three-stage process: question boosting, label establishment, and answer recommendation. Given a post, we first generate a clarifying question as a way of question boosting. We automatically establish the positive , neutral + , neutral - , and negative training samples via label establishment. When it comes to answer recommendation, we sort answer candidates by the matching scores calculated by our neural network–based model. To evaluate the performance of our proposed model, we conducted a large-scale evaluation on four datasets, collected from the real-world technical Q8A sites (i.e., Ask Ubuntu, Super User, Stack Overflow Python, and Stack Overflow Java). Our experimental results show that our approach significantly outperforms several state-of-the-art baselines in automatic evaluation. We also conducted a user study with 50 solved/unanswered/unresolved questions. The user-study results demonstrate that our approach is effective in solving the answer-hungry problem by recommending the most relevant answers from historical archives.

Background Depression and anxiety are two common mental disorders in older people. Studies have reported that depression is strongly associate with frailty, but few studies focus on anxiety disorder and comorbid two mental disorders. In this study, we aimed to identify associations between comorbid depressive and anxiety symptoms with frailty in older adults. Methods 4,103 community-dwellings adults aged 60 and older from the baseline of the West China Health and Aging Trend (WCHAT) study were included. Frailty was measured by the Fried frailty phenotype criteria. The 15-item Geriatric Depression Scale (GDS-15) and 7-item Generalized Anxiety Disorder Scale (GAD-7) assessed for depressive and anxiety symptoms with a cut-off value of 5. Multinomial logistic regression was used to explore the association between different depressive and anxiety status and frailty. Results 8.7% of old adults suffered comorbid depressive and anxiety symptoms in the present study. The prevalence of pre-frailty and frailty was 47.0% and 6.7%, respectively. After adjusting for covariates, individuals with comorbid depressive and anxiety symptoms had higher odds of being pre-frail (OR=1.86, 95% CI=1.41, 2.45) and frail (OR=7.03, 95% CI=4.48, 11.05) compared to those without depressive and anxiety symptoms. In the comorbidity group, those with severe comorbid symptoms also had higher prevalence of pre-frailty and frailty. Limitations Depressive and anxiety symptom assessments were based on screening tools. Conclusions This study suggested comorbid depressive and anxiety symptoms were associated with frailty among Chinese older adults. Further efforts to screen and target depression and anxiety comorbidity may be warranted.

The morphological features of fish, such as the body length, the body width, the caudal peduncle length, the caudal peduncle width, the pupil diameter, and the eye diameter are very important indicators in smart mariculture. Therefore, the accurate measurement of the morphological features is of great significance. However, the existing measurement methods mainly rely on manual measurement, which is operationally complex, low efficiency, and high subjectivity. To address these issues, this paper proposes a scheme for segmenting fish image and measuring fish morphological features indicators based on Mask R-CNN. Firstly, the fish body images are acquired by a home-made image acquisition device. Then, the fish images are preprocessed and labeled, and fed into the Mask R-CNN for training. Finally, the trained model is used to segment fish image, thus the morphological features indicators of the fish can be obtained. The experimental results demonstrate that the proposed scheme can segment the fish body in pure and complex backgrounds with remarkable performance. In pure background, the average relative errors (AREs) of all indicators measured all are less than 2.8%, and the AREs of body length and body width are less than 0.8%. In complex background, the AREs of all indicators are less than 3%, and the AREs of body length and body width is less than 1.8%.

To report the blood pressure (BP) data obtained in the May Measurement Month (MMM) 2019 in China. Study participants were recruited if ≥18 years of age and had ideally not had their BP measured for ≥1 year. BP was measured three times consecutively with a 1-min interval in the sitting position, using a validated electronic BP monitor. Trained volunteer investigators administered a questionnaire to collect information on lifestyle, medical history, and use of medications. The measurement was performed in 238 387 participants in 250 sites across 31 China provinces. The majority of screening took place in hospitals or clinics (78.7%), with 17.1% in outdoor public areas and 4.2% in other settings. The study participants included 127 853 women (53.6%) and had a mean (±SD) age of 48.9 ± 16.2 years. The mean (of readings two and three) systolic/diastolic BP was 121.8/73.8 mmHg. In all hypertensive patients (n = 66 181, 27.8%), the awareness, treatment, and control rates of hypertension were 51.5%, 48.4%, and 29.1%, respectively. Linear regression models showed differences in systolic and diastolic BP according to sex and age and several other major characteristics, such as previous stroke, myocardial infarction, and diabetes mellitus, antihypertensive medication use and known hypertension, previous hypertension in pregnancy and current pregnancy, alcohol intake and current smoking, and body mass index. The MMM 2019 campaign has been successful in measuring BP in a large member of participants in China.

Cardiovascular (CV) diseases are burdened by high mortality and morbidity, being responsible for half of the deaths in Europe. Although hypertension is recognized as the most important CV risk factor, hypertension awareness, and blood pressure (BP) control are still unsatisfactory. In 2017 and 2018, respectively >10 000 and >5000 individuals took part in the May Measurement Month (MMM) campaign in Italy, of whom 30.6% and 26.3% were found to have high BP, respectively. To raise public awareness on the importance of hypertension and to collect BP data on a nation-wide scale in Italy. In the frame of the MMM campaign, an opportunistic cross-sectional survey of volunteers aged ≥18 years was carried out in May 2019. BP measurement, the definition of hypertension, and statistical analysis followed the standard MMM protocol. Screening was conducted in multiple sites by health personnel. Among the 10 182 people screened (females: 52.3%, mean age 58 ± 16years) mean BP was 127/78 mmHg, and 3171 (31.1%) participants had arterial hypertension, of whom 62.1% were aware of being hypertensive. Diabetes, body mass index >25 kg/m2 were associated with higher BP and previous myocardial infarction with lower BP. For the third consecutive year we collected a nation-wide snapshot of BP control in a large sample of individuals. The high participation, with some yearly fluctuations likely due to the limitations of the sampling technique, confirms the power of this kind of health campaign in reaching a significant number of people to raise awareness on health topics.

Elevated blood pressure (BP) is the leading cause of global mortality, but control rates remain poor because most patients, especially in Africa, are unaware. May Measurement Month (MMM) is an annual global BP screening campaign that was initiated by the International Society of Hypertension (ISH) in 2017 to raise awareness of raised BP. Following participation in 2017 and 2018, Kenya participated again in 2019 and the results are reported here. Screening was carried out in 30 sites by volunteers coordinated by the Kenya Cardiac Society. Participants had three BP readings by standard methods with the last two being averaged and recorded. Heart rate, weight, height, socio-demographic parameters, and co-morbidities were documented. Hypertension was defined as a systolic BP (SBP) ≥140 mmHg and/or a diastolic BP (DBP) ≥90 mmHg or being on treatment with at least one antihypertensive medication. A total of 33 992 participants were screened, mean age was 42.5 (SD 16.8) years and 58.7% of participants were female. Only 27.3% had their BPs checked within the preceding 12 months. After multiple imputation, 26.1% were hypertensive, of whom 34.5% were aware of their hypertension and 31.5% were on treatment. Of those on treatment, 59.7% were controlled translating to 18.8% of all hypertensives. Being on treatment for hypertension, overweight, obese or having had hypertension in previous pregnancy were associated with increased SBP and DBP, while diabetes was associated with raised SBP. Two-thirds of hypertensives were unaware. Only a third of those aware were on treatment, with about 60% of these controlled. Lack of awareness remains a significant barrier to BP control. Programmes to raise awareness such as MMM are significant in raising population awareness.

Hypertension is a growing burden worldwide. According to the World Health Organization, approximately 1.13 billion people worldwide suffer from hypertension. May Measurement Month (MMM) is a global initiative of the International Society of Hypertension aimed at raising awareness of high blood pressure (BP) and to act as a temporary solution to the lack of screening programmes worldwide. We provide the results of the 2019 MMM (MMM19) edition in Mauritius. This cross-sectional survey of participants aged ≥ 18 years was carried out in May 2019. Hypertension was defined as systolic BP of at least 140 mmHg or diastolic BP of at least 90 mmHg or in those on antihypertensive medication. Blood pressure measurement and statistical analysis followed the standard MMM protocol. Screening was conducted by the Non-Communicable Diseases and Health Promotion Unit, which is under the aegis of the Ministry of Health and Wellness, mainly in workplaces and community centres, in both rural and urban areas across Mauritius. Of the 8262 individuals screened, after multiple imputations, 950 (14%) had untreated hypertension. May Measurement Month 2019 was the largest BP screening campaign undertaken in Mauritius. These results suggest that MMM19 is useful in the identification of potential patients with raised BP.

Hypertension is a major preventable cause of death worldwide. May Measurement Month (MMM) is a global initiative aimed at raising awareness of high blood pressure (BP) and identifying individuals with increased BP. A cross-sectional survey of volunteers aged ≥18 years old was carried out in May 2019 in Chile. Participating sites were distributed across the country, most of them from the Public Health System outpatient clinics. In addition, clinical research sites, universities, and private clinics participated. Blood pressure measurement protocol, hypertension categories, and statistical analysis followed the MMM protocol. Hypertension was diagnosed as mean systolic BP ≥140 mmHg and/or diastolic BP ≥90 mmHg or receiving antihypertensive medication. Overall, 6876 individuals were screened. After multiple imputations, hypertension prevalence was 35.4%, of which 65.9% were aware of their condition. While 60.1% were on antihypertensive medication and 34.4% of the total number of hypertensives had their BP controlled. Of participants not receiving antihypertensive treatment, 17.9% were identified as hypertensive. The MMM 2019 survey demonstrated a high proportion of participants with hypertension, with only one-third of these individuals having controlled BP (systolic BP

The May Measurement Month (MMM) 2019 campaign aimed to raise awareness of the health issues surrounding raised blood pressure (BP) among the general public. It also sought to identify and facilitate reduction of BPs of participants who require intervention to lower their BP according to current country treatment guidelines. Participants aged ≥18 years were recruited on site through interactions with the study team, educational fliers, and as voluntary walk-ins in response to the media engagement prior to the campaign. Blood pressures were measured using validated upper-arm cuff electronic devices provided by Omron Healthcare in partnership with International Society of Hypertension. With the participant seated, their back supported and legs resting uncrossed on the ground, three BP and heart rate readings were taken and recorded, 1 min apart. Participants’ basic demographic data were also collected. Hypertension was defined as being on treatment for hypertension, or a systolic BP ≥140 mmHg and/or a diastolic BP ≥90 mmHg (based on the mean of the last two of three readings). Of the 5459 screened participants, 1750 (32.1%) had hypertension, of whom 784 (44.8%) were aware they were hypertensive and 726 (41.5%) were on antihypertensive medication. Among those on antihypertensive treatment, 47.0% had their BP controlled (

Hypertension, the foremost cause of global morbi-mortality, is linked with a high mortality from numerous cardiovascular endpoints. The May Measurement Month (MMM) campaign is an annual initiative of the International Society of Hypertension (ISH) to collect information on blood pressure (BP) and other risk factors for cardiovascular disease (CVD) in adults. MMM2019 in the Democratic Republic of the Congo (DRC) was an opportunistic cross-sectional survey of volunteers aged ≥18 years that took place in Kinshasa and Mbuji-Mayi after the training of observers to familiarize with the ISH ad hoc methods. We screened 29 857 individuals (mean age: 40 years; 40% female). Hypertension was present in 7624 (25.5%) individuals. Of them, 2520 (33.1%) were aware, 1768 (23.2%) on treatment with 910 (51.5%) controlled BP (systolic BP

According to the Non-communicable disease Risk Factors Survey of 2018, more than one-fifth (21.0%) of adults aged 25 years or older have hypertension and one-third of the adults did not have their blood pressure (BP) measured in their lifetime in Bangladesh. The National Heart Foundation of Bangladesh participated in May Measurement Month (MMM) 2017 and 2018 as well as this 2019 as a part of a global initiative aimed at raising awareness of high BP and to act as a temporary solution to the lack of screening programmes worldwide. This opportunistic screening of voluntary participants aged ≥18 years was carried out from May to July 2019. Data were collected from 100 screening sites in 16 districts in Bangladesh. BP measurement, the definition of hypertension, and statistical analysis followed the MMM protocol. Data on 24 941 individuals were analysed. Among the participants, 12 658 (50.8%) were female. After multiple imputation, 6990 (28.0%) had hypertension. Among the 6990 participants with hypertension, 5007 (71.6%) were on antihypertensive medication and 5331 (76.3%) were aware of having hypertension. Among 6990 participants with hypertension, 3217 (46.0%) had controlled BP (

This article discusses the results of the May Measurement Month (MMM) 2019 campaign, which contributed to a third round of MMM hypertension screening campaigns carried out in Albania, a transitional country in the Western Balkans. The hypertension screening campaign in Albania was carried out during the period 1–31 May 2019 in 30 sites in many districts of the country. Overall, 19 154 participants aged ≥18 years were included (approximately 68% of these were women), with an overall mean age of 47.0 ± 15.3 years. Blood pressure (BP) was measured with OMRON sphygmomanometers (Omron Healthcare, Kyoto, Japan). Hypertension was defined as systolic BP ≥140 mmHg, or diastolic BP ≥90 mmHg, or on treatment for hypertension. Self-reported data consisted of height and weight, pre-existing conditions, including smoking status and alcohol consumption. Overall, the proportion of participants with hypertension was 38.6%. Less than two-thirds (64.7%) of hypertensive individuals were aware of their condition. Also, less than half (48.3%) of participants on antihypertensive medication had controlled BP (

Stack Overflow has been heavily used by software developers as a popular way to seek programming-related information from peers via the internet. The Stack Overflow community recommends users to provide the related code snippet when they are creating a question to help others better understand it and offer their help. Previous studies have shown that a significant number of these questions are of low-quality and not attractive to other potential experts in Stack Overflow. These poorly asked questions are less likely to receive useful answers and hinder the overall knowledge generation and sharing process. Considering one of the reasons for introducing low-quality questions in SO is that many developers may not be able to clarify and summarize the key problems behind their presented code snippets due to their lack of knowledge and terminology related to the problem, and/or their poor writing skills, in this study we propose an approach to assist developers in writing high-quality questions by automatically generating question titles for a code snippet using a deep sequence-to-sequence learning approach. Our approach is fully data-driven and uses an attention mechanism to perform better content selection, a copy mechanism to handle the rare-words problem and a coverage mechanism to eliminate word repetition problem. We evaluate our approach on Stack Overflow datasets over a variety of programming languages (e.g., Python, Java, Javascript, C# and SQL) and our experimental results show that our approach significantly outperforms several state-of-the-art baselines in both automatic and human evaluation. We have released our code and datasets to facilitate other researchers to verify their ideas and inspire the follow up work.

Two new credit guarantee contracts, called option-for-guarantee swaps (OGSs) and equity-for-guarantee swaps (EGSs), have become the important financial innovations/instruments over the past decade in China’s credit guarantee market and provide small and medium-sized enterprises (SMEs) with stable finance. In this paper we introduce a competitive credit guarantee market along with these two new financial contracts in a classical contingent claims model to analyze SME financing and investment and the incentives that these instruments create for entrepreneurs over the business cycle. We compare the main effects produced by OGSs and EGSs. The model based on our calibration of parameters predicts that financing through an EGS entails a higher market leverage ratio and a higher production capacity and, thus, a higher ex ante firm value than an otherwise identical firm financed with an OGS. We also show that, overall, EGS financing is effective at reducing the asset substitution incentive, while OGSs reduce the debt overhang problem. These findings offer a possible explanation for why these two new credit guarantee contracts are adopted by entrepreneurs in China’s credit guarantee market, and the optimal choice between OGSs and EGSs depends on priorities (attenuate the asset substitution incentive, reduce the debt overhang problem, or enhance value).

Two-dimensional (2D) transition metal dichalcogenides with intrinsically passivated surfaces are promising candidates for ultrathin optoelectronic devices that their performance is strongly affected by the contact with the metallic electrodes. Herein, first-principle calculations are used to construct and investigate the electronic and interfacial properties of 2D MoTe2 in contact with a graphene electrode by taking full advantage of them. The obtained results reveal that the electronic properties of graphene and MoTe2 layers are well preserved in heterostructures due to the weak van der Waals interlayer interaction, and the Fermi level moves toward the conduction band minimum of MoTe2 layer thus forming an n type Schottky contact at the interface. More interestingly, the Schottky barrier height and contact types in the graphene-MoTe2 heterostructure can be effectively tuned by biaxial strain and external electric field, which can transform the heterostructure from an n type Schottky contact to a p type one or to Ohmic contact. This work provides a deeper insight look for tuning the contact types and effective strategies to design high performance MoTe2-based Schottky electronic nanodevices.

To estimate the prevalence of frailty and prefrailty among older adults with chronic pain and review the longitudinal association between frailty status and chronic pain. Frailty and prefrailty are common in persons with chronic pain. Chronic pain is a risk factor for developing frailty among older persons. Non-frail older persons with chronic pain are more likely to experience physical frailty after an average follow-up of 5.8 years. Frailty and chronic pain are prevalent among older adults. However, no study has systematically reviewed the association between frailty and chronic pain in older adults. Therefore, we aimed to estimate the prevalence of frailty and prefrailty among older adults with chronic pain and review the longitudinal association between frailty status and chronic pain. Embase, Medline, Pubmed, and Cochrane library were searched from inception to March 2020. The methodological quality of the studies was assessed using the Newcastle Ottawa Scale. Random effect models and Mantel–Haenszel weighting were adopted to synthesize the estimates. Among the initial 846 articles retrieved, 24 were included in the review (12 cross-sectional, and 12 longitudinal). The pooled prevalence in persons with chronic pain was 18% (95% CI 14–23%; I2 = 98.7%) for frailty and 43% (95% CI 36–51%; I2 = 98.2%) for prefrailty. The pooled prevalence of chronic pain was 50% (95% CI 45–55%; I2 = 88.3%) for individuals with frailty and 37% (95% CI 31–42%; I2 = 97.1%) for individuals with prefrailty. Persons with chronic pain were 1.85 (95% CI 1.49–2.28; I2 = 93.2%) times more likely to develop frailty after an average follow-up of 5.8 years compared to those without. Frailty and prefrailty are common in persons with chronic pain. Chronic pain among non-frail older persons significantly predicts the incidence of frailty after an average follow-up of 5.8 years. Future studies should explore the efficacy of different pain management strategies in reducing physical frailty and clarify the association of other types of frailty (cognitive, social and psychological) with chronic pain.

Hypertension remains the dominant cardiovascular risk factor worldwide. May Measurement Month (MMM) is an annual global programme of the International Society of Hypertension aimed at screening for undetected hypertension in the general population. We report the outcome of MMM 2018 in Nigeria. An opportunistic screening of adults aged at least 18 years was conducted in the six geopolitical zones of Nigeria in the month of May, 2018. Screening for hypertension was done by trained volunteers with the use of validated digital and mercury sphygmomanometers following the MMM protocol. Hypertension was defined as blood pressure (BP) ≥140/90 mmHg or the use of BP-lowering medication. There were 6398 participants (53.0% female) with a mean (SD) age of 41.7 (15.0) years. Hypertension was present in 36.4% of the participants with 51.1% of the hypertensives aware of their status, 41.8% on medication, of whom 43.1% were controlled. Overall, only 18.0% of all hypertensive participants had their BP under control. The proportion with hypertension is high, and awareness, treatment, and control rates are low. Concerted efforts are needed to improve awareness and treatment of hypertension in Nigeria in order to reduce the high rate of complications associated with uncontrolled BP.

Hypertension is a global public health challenge, but awareness, treatment, and control rates remain low. May Measurement Month (MMM) creates a platform for awareness creation and opportunistic screening which can identify significant numbers with raised blood pressure (BP). We present results of the 2018 edition of MMM in Cameroon. A cross-sectional survey of self-selected volunteers aged ≥18 years was conducted in May 2018. The definition of hypertension and statistical analyses followed the standard protocol. Data were collected from 28 public (indoors and outdoors) and private locations which were assigned unique site identification numbers, over a 30-day period. Of the 8883 participants screened, 53.8% were male and the median age was 35 (interquartile range 21) years, and 30.0% reported not having a previous BP measurement. After imputation, 1867 (21.0%) were hypertensive, 645 (34.5%) were aware of their diagnosis, and 507 (27.2%) were on antihypertensive medication, among whom 242 (47.8%) had controlled BP. In linear regression models adjusting for age, sex, and the use of antihypertensive medication, systolic and diastolic BP were significantly higher in participants aware of their diagnosis, those on antihypertensive medication, and who reported alcohol intake of 1–3 times per month and, one or more times per week. Hypertension prevalence is the lowest, and awareness, treatments, and control rates the highest reported in epidemiological studies in Cameroon in the last two decades. Further surveys will be useful in evaluating the impact, if any, of the MMM campaign on the burden of hypertension in the Cameroon.

Elevated blood pressure (BP) is a growing burden worldwide, leading to over 10 million deaths each year. May Measurement Month (MMM) is a global initiative of the International Society of Hypertension (ISH) aimed at raising awareness of high BP and acting as a temporary solution to the lack of screening programmes worldwide. As part of MMM, screening in South Africa in 2017 revealed that 24.5% of adults (mean age = 31 years) have hypertension and only half of those with hypertension had controlled BP. These data highlight the need for continued screening and awareness campaigns. An opportunistic cross-sectional survey of volunteers aged ≥18 years was carried out in May 2018. Blood pressure measurements, the definition of hypertension and statistical analyses followed the MMM protocol. The sites screened were general populations and university campuses in preference to hospitals and clinics, aiming to raise awareness and allow access to screening to those less likely to be aware of their BP. In total, 2965 individuals (age 40.5 ± 18.2 years) were screened. After multiple imputation for missing BP readings, 34.6% had hypertension, only 56.7% of those with hypertension were aware, 21.2% of those not receiving treatment for hypertension were hypertensive, and a large proportion (42.5%) of individuals receiving antihypertensive medication had uncontrolled BP. These results suggest that opportunistic screening campaigns can identify significant numbers with undiagnosed and uncontrolled hypertension. The high proportions of individuals with undiagnosed and treated uncontrolled hypertension highlight the need for hypertension awareness campaigns and more rigorous management of hypertension.

Bug reports play an important role in the process of debugging and fixing bugs. To reduce the burden of bug report managers and facilitate the process of bug fixing, a great amount of software engineering research has been invested toward automated bug report management techniques. However, the verdict is still open whether such techniques are actually required and applicable outside the domain of theoretical research. To fill this gap, we conducted a survey among 327 practitioners to gain their insights into various categories of automated bug report management techniques. Specifically, we asked the respondents to rate the importance of such techniques and provide the rationale. To get deeper insights into practitioners’ perspective, we conducted follow-up interviews with 25 interviewees selected from the survey respondents. Through the survey and the interviews, we gained a better understanding of the perceived usefulness (or its lack) of different categories of automated bug report management techniques. Based on our findings, we summarized some potential research directions in developing techniques to help developers better manage bug reports.

Herein, we reported 3 cases of angiosarcoma (AS) of the head and neck in old patients, and the etiology, pathogenesis, histopathology, immunohistochemistry, diagnosis, differential diagnosis and prognosis were discussion. Case 1, a male patient aged 86 years old was admitted due to purplish erythema on the head and face and progressive purplish edema in the orbit of the eye for 3 years. He was misdiagnosed with connective tissue disease in another hospital, and a second biopsy and histopathological examination confirmed the AS of the head and neck. Case 2, a male patient aged 85 years were admitted due to erythemalike hyperplasia in the left anterior head with necrosis and scar formation at the center for 2 months. He was misdiagnosed with folliculitis in another hospital, and histopathological examination confirmed the AS of the head and neck. Case 3, a male patient aged 87 years were admitted due to large erythema in the right scalp with ulcer and scar formation for 1 month, and histopathological examination confirmed the AS of the head and neck. One of them was lost to follow, one died 5 months after confirmed diagnosis, and one achieved favorable outcome after radiotherapy and received further follow up. The early clinical manifestations of the three patients were different and lacked characteristics. The disease progressed rapidly and the prognosis was poor. At present, surgery combined with postoperative radiotherapy is the preferred treatment. Through this article, the following conclusions can be obtained: erythema in the head and face of the elderly, rapid progresses and painful, pathological examinations must be performed, and changes in blood vessels and vascular endothelial cells with or without crack formation and red blood cell extravasation should be carefully observed during pathological examinations in case of misdiagnosis. It is hoped that these three cases can serve as a reminder for clinicians, for reducing misdiagnosis and miss diagnosis, choosing appropriate treatment methods, and judging their prognosis. Therefore, early diagnosis and surgical treatment are extremely important to improve the prognosis of AS patients.

Primary mediastinal large B-cell lymphoma (PMBCL) is relatively infrequent and generally has a good prognosis with standard immunochemotherapy. However, treatment options are limited for patients with relapsed/refractory PMBCL who are ineligible for stem cell transplantation. In this report, we treated a refractory PMBCL patient, who did not respond to salvage chemotherapy, with combined nivolumab and radiotherapy. The patient achieved a complete remission with mild adverse reactions and has survived without relapse 2 years after treatment.

UI design is an integral part of software development. For many developers who do not have much UI design experience, exposing them to a large database of real-application UI designs can help them quickly build up a realistic understanding of the design space for a software feature and get design inspirations from existing applications. However, existing keyword-based, image-similarity-based, and component-matching-based methods cannot reliably find relevant high-fidelity UI designs in a large database alike to the UI wireframe that the developers sketch, in face of the great variations in UI designs. In this article, we propose a deep-learning-based UI design search engine to fill in the gap. The key innovation of our search engine is to train a wireframe image autoencoder using a large database of real-application UI designs, without the need for labeling relevant UI designs. We implement our approach for Android UI design search, and conduct extensive experiments with artificially created relevant UI designs and human evaluation of UI design search results. Our experiments confirm the superior performance of our search engine over existing image-similarity or component-matching-based methods and demonstrate the usefulness of our search engine in real-world UI design tasks., Accepted to TOSEM 2020

To estimate the prevalence of sarcopenia in different ethnic groups and the association with cultural life styles in west China. A cross-sectional study. The communities in Yunnan, Guizhou, Sichuan, and Xinjiang provinces. 4500 participants aged 50 years or older in west China were enrolled in this study. Sarcopenia was defined according to the diagnostic algorithm of the Asia Working Group for Sarcopenia (AWGS). We measured gait speed, handgrip strength and muscle mass by using bioelectrical impedance analysis (BIA) for all eligible participants. Life-style information were collected by reviewers. Relationships between sarcopenia and ethnic groups were analyzed using univariate and multivariate analyses. We found 869 (19.31%) adults aged 50 years old or older were sarcopenia. The mean age is 62.4±8.3 years. The main ethnic groups enrolled in this study is Han, Tibetan, Qiang, Yi and Hui. The crude prevalence of sarcopenia is 22.3% in Han, 18.2% in Tibetan, 11.8% in Qiang, 34.7% in Yi and 26.7% in Hui. Compared to Han, after adjusting sex and age, Qiang has a lower prevalence of sarcopenia (odds ratio [OR]: 0.44, 95% CI 0.35–0.55), Yi has a higher prevalence of sarcopenia (OR: 1.78, 95% CI 1.29–2.43). While adding adjusting other potential cofounders, sarcopenia is still less prevalent in Qiang (OR: 0.44, 95% CI 0.34–0.57). The crude prevalence of sarcopenia is 22.3% in Han, 18.2% in Tibetan, 11.8% in Qiang, 34.7% in Yi and 26.7% in Hui. Sarcopenia was less prevalent in Qiang compared with Han. Further studies to determine related factors of sarcopenia among different ethnic groups are recommended.

The rapidly decreasing computation and memory cost has recently driven the success of many applications in the field of deep learning. Practical applications of deep learning in resource-limited hardware, such as embedded devices and smart phones, however, remain challenging. For binary convolutional networks, the reason lies in the degraded representation caused by binarizing full-precision filters. To address this problem, we propose new circulant filters (CiFs) and a circulant binary convolution (CBConv) to enhance the capacity of binarized convolutional features via our circulant back propagation (CBP). The CiFs can be easily incorporated into existing deep convolutional neural networks (DCNNs), which leads to new Circulant Binary Convolutional Networks (CBCNs). Extensive experiments confirm that the performance gap between the 1-bit and full-precision DCNNs is minimized by increasing the filter diversity, which further increases the representational ability in our networks. Our experiments on ImageNet show that CBCNs achieve 61.4% top-1 accuracy with ResNet18. Compared to the state-of-the-art such as XNOR, CBCNs can achieve up to 10% higher top-1 accuracy with more powerful representational ability. Also, CBCNs approximately achieve a storage reduction about 32 times. In particular, our method shows strong generalization on the object recognition task, i.e., face recognition, facial expression recognition and person re-identification.

In this work, fabrication and characterization of a nanostructured rare-earth-doped ZnMoO4 will be reported. Photoluminescence properties and quantum efficiency of the rare-earth-doped ZnMoO4 with different dopant concentrations have been investigated. These samples were synthesized by sol-gel method. Lattice structure of the fabricated samples was characterized by X-ray powder diffraction (XRD); absorption spectrum was performed on UV-2600 photo spectrometer; PL excitation and emission spectra were recorded by Fluorescence Spectrometers; quantum efficiency was measured by an integrating sphere photoluminescence (PL) system. The results showed that the optimized doping concentration of Eu3+ was around 10 mol% for the highest quantum efficiency at 616 nm emission peak and 465 nm excitation peak. The highest internal quantum efficiency was 91% at low power density excitation (around 50 μW/mm2). Introduction of Mn2+ to Eu3+-doped ZnMoO4 lead to reduced quantum efficiency and electronic lifetime, which can be attributed to defects inside the crystal lattice and energy transfer from Eu3+ to Mn2+ (more non-radiative transition occur).

Developers use bug reports to triage and fix bugs. When triaging a bug report, developers must decide whether the bug report is valid (i.e., a real bug). A large amount of bug reports are submitted every day, with many of them end up being invalid reports. Manually determining valid bug report is a difficult and tedious task. Thus, an approach that can automatically analyze the validity of a bug report and determine whether a report is valid can help developers prioritize their triaging tasks and avoid wasting time and effort on invalid bug reports. In this study, motivated by the above needs, we propose an approach which can determine whether a newly submitted bug report is valid . Our approach first extracts 33 features from bug reports. The extracted features are grouped along 5 dimensions, i.e., reporter experience, collaboration network, completeness, readability and text. Based on these features, we use a random forest classifier to identify valid bug reports. To evaluate the effectiveness of our approach, we experiment on large-scale datasets containing a total of 560,697 bug reports from five open source projects (i.e., Eclipse, Netbeans, Mozilla, Firefox and Thunderbird). On average, across the five datasets, our approach achieves an F1-score for valid bug reports and F1-score for invalid ones of 0.74 and 0.67, respectively. Moreover, our approach achieves an average AUC of 0.81. In terms of AUC and F1-scores for valid and invalid bug reports, our approach statistically significantly outperforms two baselines using features that are proposed by Zanetti et al. [104] . We also study the most important features that distinguish valid bug reports from invalid ones. We find that the textual features of a bug report and reporter's experience are the most important factors to distinguish valid bug reports from invalid ones.

Deep learning has gained tremendous traction from the developer and researcher communities. It plays an increasingly significant role in a number of application domains. Deep learning frameworks are proposed to help developers and researchers easily leverage deep learning technologies, and they attract a great number of discussions on popular platforms, i.e., Stack Overflow and GitHub. To understand and compare the insights from these two platforms, we mine the topics of interests from these two platforms. Specifically, we apply Latent Dirichlet Allocation (LDA) topic modeling techniques to derive the discussion topics related to three popular deep learning frameworks, namely, Tensorflow, PyTorch and Theano. Within each platform, we compare the topics across the three deep learning frameworks. Moreover, we make a comparison of topics between the two platforms. Our observations include 1) a wide range of topics that are discussed about the three deep learning frameworks on both platforms, and the most popular workflow stages are Model Training and Preliminary Preparation. 2) the topic distributions at the workflow level and topic category level on Tensorflow and PyTorch are always similar while the topic distribution pattern on Theano is quite different. In addition, the topic trends at the workflow level and topic category level of the three deep learning frameworks are quite different. 3) the topics at the workflow level show different trends across the two platforms. e.g., the trend of the Preliminary Preparation stage topic on Stack Overflow comes to be relatively stable after 2016, while the trend of it on GitHub shows a stronger upward trend after 2016. Besides, the Model Training stage topic still achieves the highest impact scores across two platforms. Based on the findings, we also discuss implications for practitioners and researchers.

Previous literature has reported that physical frailty (PF) closely associated with cognitive impairment (CI). In this study, we aim to describe and evaluate the prevalence and associated factors with different patterns of PF and cognitive impairment. A cross-sectional study. Community-dwelling older adults aged ≥60 from the West China Health and Aging Trend (WCHAT) study were included in this study. Participants were assessed the PF and cognitive impairment. PF was assessed using the physical phenotype as defined by Fried’s criteria. Cognitive impairment was identified using the Short Portable Mental Status Questionnaire (SPMSQ). According different patterns of PF and cognitive impairment, participants were divided into 4 groups: not-PF and cognitive intact (“neither” group), not-PF and cognitive impairment (“CI only” group), PF and cognitive intact (“PF only” group), and PF and cognitive impairment (“both” group). Multinomial logistic regression was used to explore the association between medical conditions and different patterns of PF and cognitive impairment after adjusting the demographic characteristics. Among 4,103 participants (age 67.8 ± 5.9 years, female 58.3%), 78.8%, 3.9%, 14.5% and 2.9% were “neither”, “PF only”, “CI only”, and “both”, respectively. The prevalence of “PF only”, “CI only” and “both” were associated with age, lower education level and single status. In addition, there was substantial ethnicity heterogeneity in the prevalence of different patterns of PF and cognitive impairment. Comparing with “neither”, PF and/or cognitive impairment had higher association with depression, ADLs impairment, and malnutrition. Of note, obesity was only significantly associated with “PF only”, but not associated with “CI only” or “both”. We found the substantial demographic and medical conditions disparities in different patterns of PF and cognitive impairment. Further research should focus on the efficient and practical screen to predict the risk of “PF only”, “CI only” and “both”.

Frailty and poor oral health are both common age-related conditions. However, the association between oral health and frailty has not been explored thoroughly among older Chinese adults. We aimed to investigate the associations between number of teeth, denture use, and frailty among older adults in western China. Cross-sectional study. Community-based. Participants were 4037 community-dwelling individuals aged 60 and older from the West China Health and Aging Trend (WCHAT) study. Frailty was assessed using the physical frailty phenotype (PFP) and categorized as non-frail, prefrail and frail. In addition, number of teeth and denture use were examined by calibrated dentists. Multinomial logistic regression models were performed to examine the association between number of teeth, denture use, and frailty. The overall prevalence of frailty was 6.7% among 4037 participants aged 60 and older in western China. Participants with no more than 20 teeth were associated with higher odds of being prefrail or frail whether wearing dentures or not. Denture using could not help lower the odds of being prefrail or frail for older adults with fewer teeth. This study suggested that frailty was associated with having fewer teeth (≤20 teeth) among older Chinese adults. Future research involving targeted interventions addressing number of teeth may help provide information to establish effective strategies for frailty prevention in older adults.

Because of functionality evolution, or security and performance-related changes, some APIs eventually become unnecessary in a software system and thus need to be cleaned to ensure proper maintainability. Those APIs are typically marked first as deprecated APIs and, as recommended, follow through a deprecated-replace-remove cycle, giving an opportunity to client application developers to smoothly adapt their code in next updates. Such a mechanism is adopted in the Android framework development where thousands of reusable APIs are made available to Android app developers. In this work, we present a research-based prototype tool called CDA and apply it to different revisions (i.e., releases or tags) of the Android framework code for characterising deprecated APIs. Based on the data mined by CDA, we then perform an empirical study on API deprecation in the Android ecosystem and the associated challenges for maintaining quality apps. In particular, we investigate the prevalence of deprecated APIs, their annotations and documentation, their removal and consequences, their replacement messages, developer reactions to API deprecation, as well as the evolution of the usage of deprecated APIs. Experimental results reveal several findings that further provide promising insights related to deprecated Android APIs. Notably, by mining the source code of the Android framework base, we have identified three bugs related to deprecated APIs. These bugs have been quickly assigned and positively appreciated by the framework maintainers, who claim that these issues will be updated in future releases.

The rapid adoption of Smartphone devices has caused increasing security and privacy risks and breaches. Catching up with ever-evolving contemporary smartphone technology challenges leads older adults (aged 50+) to reduce or to abandon their use of mobile technology. To tackle this problem, we present AppMoD, a community-based approach that allows delegation of security and privacy decisions a trusted social connection, such as a family member or a close friend. The trusted social connection can assist in the appropriate decision or make it on behalf of the user. We implement the approach as an Android app and describe the results of three user studies (n=50 altogether), in which pairs of older adults and family members used the app in a controlled experiment. Using app anomalies as an ongoing case study, we show how delegation improves the accuracy of decisions made by older adults. Also, we show how combining decision-delegation with crowdsourcing can enhance the advice given and improve the decision-making process. Our results suggest that a community-based approach can improve the state of mobile security and privacy.

Technical debt (TD) is a metaphor to describe the situation where developers introduce suboptimal solutions during software development to achieve short-term goals that may affect the long-term software quality. Prior studies proposed different techniques to identify TD, such as identifying TD through code smells or by analyzing source code comments. Technical debt identified using comments is known as Self-Admitted Technical Debt (SATD) and refers to TD that is introduced intentionally. Compared with TD identified by code metrics or code smells, SATD is more reliable since it is admitted by developers using comments. Thus far, all of the state-of-the-art approaches identify SATD at the file-level. In essence, they identify whether a file has SATD or not. However, all of the SATD is introduced through software changes. Previous studies that identify SATD at the file-level in isolation cannot describe the TD context related to multiple files. Therefore, it is beneficial to identify the SATD once a change is being made. We refer to this type of TD identification as “Change-level SATD Determination”, which determines whether or not a change introduces SATD. Identifying SATD at the change-level can help to manage and control TD by understanding the TD context through tracing the introducing changes. To build a change-level SATD Determination model, we first identify TD from source code comments in source code files of all versions. Second, we label the changes that first introduce the SATD comments as TD-introducing changes. Third, we build the determination model by extracting 25 features from software changes that are divided into three dimensions, namely diffusion, history and message, respectively. To evaluate the effectiveness of our proposed model, we perform an empirical study on 7 open source projects containing a total of 100,011 software changes. The experimental results show that our model achieves a promising and better performance than four baselines in terms of AUC and cost-effectiveness (i.e., percentage of TD-introducing changes identified when inspecting 20 percent of changed LOC). On average across the 7 experimental projects, our model achieves AUC of 0.82, cost-effectiveness of 0.80, which is a significant improvement over the comparison baselines used. In addition, we found that “Diffusion” is the most discriminative dimension among the three dimensions of features for determining TD-introducing changes.

The incidence of acute myeloid leukemia (AML) increases with age. The overall prognosis remains poor for older patients. Studies on the efficacy of decitabine, an epigenetic agent, in older patients with AML have reported conflicting results.For this meta-analysis, we performed a literature search and collected 38 studies (including 3298 patients with AML) to evaluate the role of decitabine in elderly patients with AML. We used complete response (CR) or overall response (OR) rate as indicators of effectiveness.Patients treated with decitabine have a higher CR/OR rate than those treated with low-dose cytarabine (CR, 2.60; 95% confidence interval [CI], 1.64-4.14; OR, 4.88; 95% CI, 1.98-12.04) or CAG/HAG (low-dose epirubicin and cytarabine with granulocyte stimulating factor/low-dose homoharringtonine and cytarabine with granulocyte stimulating factor) regimens (CR, 2.53; 95% CI, 1.98-3.23; OR, 2.89; 95% CI, 2.24-3.73). However, patients treated with decitabine had a CR rate equivalent to those treated with intensive chemotherapy (CR, 0.58; 95% CI, 0.28-1.22; P = .15). Use of decitabine in combination with other regimens resulted in a higher CR/OR rate than did use of decitabine alone (P .001); there was no significant difference in infection rates and early death rates (P.05).The findings presented in this article show that decitabine is effective and safe for the treatment of older patients with AML.