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In this work, novel DPS/MWCNT composites were synthesized through filling multi-walled carbon nanotubes (MWCNTs) with dialkyl pentasulfide (DPS) using a wet-chemical method. A nanofluid that use the DPS/MWCNT composites as additives was then developed for achieving a high-performance turning. The performance of the developed nanofluid including dispersion stability, heat transfer capacity, and wettability was evaluated using thermophysical analytical methods. The machining performance of the DPS/MWCNT composites nanofluid was systematically investigated in the turning of AISI 52100 alloy steel, using a commercial emulsion coolant as a benchmark. The lubrication mechanism of the nanofluid involved in the turning process was reveal with the aid of X-ray photoelectron spectroscopy (XPS). The results showed that DPS was successfully filled into MWCNTs with a filling rate of around 25.7%. The nanofluid had an excellent dispersion performance and produced respective 105.0% and 23.6% improvements in the heat transfer and wetting performance, in comparison to a base fluid. During turning, the composites suspended in the nanofluid acted as microbearings to reduce the friction of sliding, while the DPS filled in the MWCNTs was released under pressure to generate a complex lubricating film at the tool-chip interface, leading to a low resistance to cutting. This thus resulted in 15% and 25% reductions in the cutting force and temperature as well as 16% and 22% improvements in the surface roughness and tool life, in comparison to a conventional turning.

In recent years, with the development of software development, a large number of developers develop software by invoking API. With the increasing number of APIs, how to accurately recommend the APIs to developers has become a urgently necessary task. In this paper, we discover that there is a relationship between the user and the API, and use such relationships and collaborative learning techniques to finish APIs recommendation. We propose a holistic framework that contains three models. In the models, we design a joint matrix factorization technique and try to discover the preference among APIs invocation process. In natural language processing, word embedding is widely used. In our models, we use doc2vec to turn the representation of users and APIs into vector representation and calculate the similarity separately to generate the relationships. Besides the two modes in users side and APIs side, we also propose an ensemble model fully leveraging the preference mined from both users side and APIs side. We conduct the experiments on a real-world dataset and the experimental results show that our models perform better than all compared methods.

Radiographic evaluation of the wisdom teeth (third molar) formation is a widely used age assessment method for adolescents and young adults. This systematic review examines evidence on the agreement between Demirjian’s development stages of the third molar and chronological age. We searched four databases up until May 2016 for studies reporting Demirjian’s stages of third molar and confirmed chronological age of healthy individuals aged 10–25 years. Heterogeneity test of the included studies was performed. We included 21 studies from all continents except Australia, all published after 2005. The mean chronological age for Demirjian’s stages varied considerably between studies. The results from most studies were affected by age mimicry bias. Only a few of the studies based their results on an unbiased age structure, which we argue as important to provide an adequate description of the method’s ability to estimate age. Observed study variation in the timing of Demirjian’s development stages for third molars has often been interpreted as differences between populations and ethnicities. However, we consider age mimicry to be a dominant bias in these studies. Hence, the scientific evidence is insufficient to conclude whether such differences exist. • There is significant heterogeneity between studies evaluating age assessment by Demirjian’s third molar development. • Most of the studies were subject to the selection bias age mimicry which can be a source of heterogeneity. • Presence of age mimicry bias makes it impossible to compare and combine results. These biased studies should not be applied as reference studies for age assessment.

IntroductionWe evaluated an intervention designed to teach 10- to 12-year-old primary school children to assess claims about the effects of treatments (any action intended to maintain or improve health). We report outcomes measured 1 year after the intervention.MethodsIn this cluster-randomised trial, we included primary schools in the central region of Uganda that taught year 5 children (aged 10 to 12 years). We randomly allocated a representative sample of eligible schools to either an intervention or control group. Intervention schools received the Informed Health Choices primary school resources (textbooks, exercise books and a teachers’ guide). The primary outcomes, measured at the end of the school term and again after 1 year, were the mean score on a test with two multiple-choice questions for each of the 12 concepts and the proportion of children with passing scores.ResultsWe assessed 2960 schools for eligibility; 2029 were eligible, and a random sample of 170 were invited to recruitment meetings. After recruitment meetings, 120 eligible schools consented and were randomly assigned to either the intervention group (n = 60 schools; 76 teachers and 6383 children) or the control group (n = 60 schools; 67 teachers and 4430 children). After 1 year, the mean score in the multiple-choice test for the intervention schools was 68.7% compared with 53.0% for the control schools (adjusted mean difference 16.7%; 95% CI, 13.9 to 19.5;P ConclusionUse of the learning resources led to a large improvement in the ability of children to assess claims, which was sustained for at least 1 year.Trial registrationPan African Clinical Trial Registry (www.pactr.org), PACTR201606001679337. Registered on 13 June 2016.

Background A strategy for minimising the time and obstacles to accessing systematic reviews of health system evidence is to collect them in a freely available database and make them easy to find through a simple ‘Google-style’ search interface. PDQ-Evidence was developed in this way. The objective of this study was to compare PDQ-Evidence to six other databases, namely Cochrane Library, EVIPNet VHL, Google Scholar, Health Systems Evidence, PubMed and Trip. Methods We recruited healthcare policy-makers, managers and health researchers in low-, middle- and high-income countries. Participants selected one of six pre-determined questions. They searched for a systematic review that addressed the chosen question and one question of their own in PDQ-Evidence and in two of the other six databases which they would normally have searched. We randomly allocated participants to search PDQ-Evidence first or to search the two other databases first. The primary outcomes were whether a systematic review was found and the time taken to find it. Secondary outcomes were perceived ease of use and perceived time spent searching. We asked open-ended questions about PDQ-Evidence, including likes, dislikes, challenges and suggestions for improvements. Results A total of 89 people from 21 countries completed the study; 83 were included in the primary analyses and 6 were excluded because of data errors that could not be corrected. Most participants chose PubMed and Cochrane Library as the other two databases. Participants were more likely to find a systematic review using PDQ-Evidence than using Cochrane Library or PubMed for the pre-defined questions. For their own questions, this difference was not found. Overall, it took slightly less time to find a systematic review using PDQ-Evidence. Participants perceived that it took less time, and most participants perceived PDQ-Evidence to be slightly easier to use than the two other databases. However, there were conflicting views about the design of PDQ-Evidence. Conclusions PDQ-Evidence is at least as efficient as other databases for finding health system evidence. However, using PDQ-Evidence is not intuitive for some people. Trial registration The trial was prospectively registered in the ISRCTN registry 17 April 2015. Registration number: ISRCTN12742235 .

High quality and high efficiency are very important factors in the mold industry and must be considered in machining technology development. Corners are one of the typical features in a cavity mold that can cause low accuracy, tool wear, cutter defection, and vibration. Therefore, off-line feedrate optimization in a corner-milling process has been proposed as a means for avoiding these drawbacks. The corner-milling process could be divided into five stages according to the contact conditions between the cutter and workpiece. An improved chip thickness model based on the actual trajectory of the cutter was presented to predict the movement of the cutter along a curved path. Various feedrate optimizations were presented with varying control parameters. In this paper, three feedrate optimizations were implemented using three control parameters, relative volume, milling force, and cutter deflection, based on the differential element method (DEM). A corner-milling force experiment indicated that the simulation results using a modified milling force model agreed well with actual experimental milling results. Following this, a feedrate optimization with multiple constraints was built, according to a combination of three feedrate optimizations and then consider the acceleration and deceleration limitation. The simulation by using the method in this paper demonstrated that the machining time of rough milling, semi-finish milling, and finish milling could be reduced. In addition, it was found that the resulting milling force varies within a narrow range.