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OSS projects are being developed by globally distributed contributors, who often collaborate through the pull-based model today. While this model lowers the barrier to entry for OSS developers by synthesizing, automating and optimizing the contribution process, coordination among an increasing number of contributors remains as a challenge due to the asynchronous and self-organized nature of distributed development. In particular, duplicate contributions, where multiple different contributors unintentionally submit duplicate pull requests to achieve the same goal, are an elusive problem that may waste effort in automated testing, code review and software maintenance. While the issue of duplicate pull requests has been highlighted, to what extent duplicate pull requests affect the development in OSS communities has not been well investigated. In this paper, we conduct a mixed-approach study to bridge this gap. Based on a comprehensive dataset constructed from 26 popular GitHub projects, we obtain the following findings: (a) Duplicate pull requests result in redundant human and computing resources, exerting a significant impact on the contribution and evaluation process. (b) Contributors' inappropriate working patterns and the drawbacks of their collaborating environment might result in duplicate pull requests. (c) Compared to non-duplicate pull requests, duplicate pull requests have significantly different features, e.g., being submitted by inexperienced contributors, being fixing bugs, touching cold files, and solving tracked issues. (d) Integrators choosing between duplicate pull requests prefer to accept those with early submission time, accurate and high-quality implementation, broad coverage, test code, high maturity, deep discussion, and active response. Finally, actionable suggestions and implications are proposed for OSS practitioners.

Stochastic resonance is the use of nonlinear systems to synchronize an original signal with noise. This method is commonly used to extract useful signals by reducing noise and has been widely used for mechanical weak fault diagnosis. This paper analyzes the characteristics of a periodic non-sinusoidal potential function, considers the shape of the model, and introduces a time-delay. The steady-state probability density function, effective potential function, and signal-to-noise ratio are then analyzed. As a result, a signal detection method for periodic non-sinusoidal time-delay stochastic resonance (PNTSR) is proposed. Experimental and engineering data are used to explain the PNTSR through the simulation. It is found that the PNTSR method has better fault detection results when compared to the classic bi-stable stochastic resonance method.

In this study, D201 resin-hydrated iron oxide (HFO) nanocomposites were prepared for the first time for Cr(VI) removal in a high-gravity rotating packed bed (RPB), and then D201-HFO nanocomposites were characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), Fourier transform infrared (FTIR) spectroscopy and X-ray diffraction (XRD). The results show that HFO nanoparticles embedded in D201 resin exhibit an average particle size of 10.34 nm, a narrow particle size distribution and excellent dispersibility. The use of 0.05 g of D201-HFO nanocomposite prepared in the RPB (D201-HFO-R) leads to efficient removal of Cr(VI) from 10 mg/L to below 50 ppb, and it has a higher reaction rate constant (k = 0.959 h−1) than that prepared in a stirred tank reactor (STR). D201-HFO-R nanocomposites also exhibit higher competitive adsorption of Cr(VI) against CO32–, NO3– and SO42−. Notably, it still shows good removal efficiency for Cr(VI) after five adsorption-regeneration cycles. The removal mechanism of Cr(VI) by D201-HFO-R mainly involves complexation and electrostatic attraction. This study provides a simple and facile method to prepare D201-HFO nanocomposites in the RPB for wastewater treatment.

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 .

Communication and coordination between open source software (OSS) developers who do not work physically in the same location have always been the challenging issues. The pull-based development model, as the state-of-the-art collaborative development mechanism, provides high openness and transparency to improve the visibility of contributors’ work. However, duplicate contributions may still be submitted by more than one contributor to solve the same problem due to the parallel and uncoordinated nature of this model. If not detected in time, duplicate pull-requests can cause contributors and reviewers to waste time and energy on redundant work. In this paper, we propose an approach combining textual and change similarities to automatically detect duplicate contributions in the pull-based model at submission time. For a new-arriving contribution, we first compute textual similarity and change similarity between it and other existing contributions. And then our method returns a list of candidate duplicate contributions that are most similar to the new contribution in terms of the combined textual and change similarity. The evaluation shows that 83.4% of the duplicates can be found in average when we use the combined textual and change similarity compared with 54.8% using only textual similarity and 78.2% using only change similarity.

Stack Overflow is the most popular programming question and answer community that continuously receives a large number of questions every day. To ensure the quality of questions, the community grants privileges for the moderators and a group of experienced users to review the quality of questions and close the low-quality ones (e.g. duplicate or irrelevant questions). The review process is a typical crowdsourcing job that relies on users’ volunteer participation, and the current practices of closing questions in Stack Overflow face two aspects of challenges: (1) an obvious increase in both the absolute number and the percentage of “closed” questions; (2) a considerable decrease in participation willingness of experienced users to close questions. In order to solve the problem, we present a novel model of user willingness for reviewing and voting questions by incorporating four types of user activity history, including questions, answers, comments and votes of closing questions. Then we propose an automatic recommendation method based on the model to assign experienced users proper questions, to utilize the forces of them to close questions. The evaluation shows that the successful recommendation probability in the top 5, top 10, top 20, top 30, top 40, top 50 users are 48.23%, 58.93%, 68.83%, 74.27%, 78.13% and 81%, respectively.

To extract weak faults under strong noise, a method for feature extraction of weak faults with time-delayed feedback mixed potential stochastic resonance (TFMSR) is proposed. This method not only overcomes the saturation characteristics of classical bistable stochastic resonance (CBSR), but also verifies a new potential function model. Based on this model, considering the short memory characteristics of the CBSR method, a method is proposed that can add historical information to the negative feedback process of the stochastic resonance (SR). Through the combination of the above two methods, the weak fault extraction under strong background noise is realized. The article analyzes the effects of the delay term, feedback term, and system parameter on the effect of SR and uses the ant colony algorithm (ACA) to optimize the above parameters. Finally, through simulated and engineering experimental results, it is proved that the proposed method has more advantages than the CBSR method in weak fault feature extraction.

Oily wastewater caused by the increment of industrial wastewater discharge and frequent oil spills endangers organisms and the environment seriously. Membrane separation technology is an efficient way to remove oil from various oily wastewaters, however the lack of highly permeable and anti-fouling membranes has hampered its application worldwide. Here, we conceive a facile in situ hybridization hydrophilization and asymmetry engineering strategy to manufacture superhydrophilic hierarchical hydrolyzed polyacrylonitrile (PAN) nanofiber membranes for ultrafast separation of oil-in-water emulsion through the electrospinning technique. The hierarchical structures of nanofibers ensure the separation efficiency and reduce the mass transfer resistance of the membranes through tailoring the nanofiber structure. Interestingly, the multi-hydrophilic crosslinked network co-functionalized by several hydrophilic active oligomers significantly enhances the hydrophilicity and finely tailors the pore structure of the hydrolyzed PAN nanofiber membranes further. The well-designed nanofiber membranes exhibit n-octane-in-H2O emulsion permeances as high as 22 206 L m−2 h−1 bar−1 and toluene-in-H2O emulsion permeances as high as 29 840 L m−2 h−1 bar−1 with separation efficiency above 99.2%, outperforming most of the state-of-the-art membranes. More importantly, the nanofiber membranes exhibit excellent anti-fouling performance with flux recovery rate above 98% and irreversible fouling rate about 2%, showing strong promise in removing oil from the oil-in-water emulsion.

Lightweight concrete (LWC) has been developed and used in both structural and nonstructural applications. With an increasing demand for sustainable construction materials, geopolymer as an ...

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.

To develop high energy Li-O2 batteries (LOBs), it is important to optimize the air cathode structure. Therefore, MFe2O4@carbon cloth (M = Co, Fe) has been prepared by electrodeposition to be an effective cathode for LOBs. This cathode can effectively avoid the disturbance of a polymer binder during the discharge-charge process. Electrocatalytic and electrochemical measurements show that MFe2O4@carbon cloth can enhance the ORR/OER kinetics. In particular, CoFe2O4@carbon cloth exhibits a good initial discharge specific capacity (7259 mA h/g at 170 mA/g) and long cycle life (over 100 cycles at the upper limit specific capacity of 500 mA h/g at 170 mA/g). The favorable electrocatalytic properties of CoFe2O4@CC are ascribed to the presence of Co2+.

Because fault characteristics are often difficult to extract from a strong noise background, it is essential for mechanical fault diagnosis to extract a weak characteristic signal with a very small signal-to-noise ratio from a noisy interference. Therefore, this paper proposes a new method for diagnosing weak faults in asymmetric potential stochastic resonance. Compared with the existing methods, the asymmetric potential stochastic resonance method not only has characteristics common to the symmetric potential stochastic resonance, but can also change the inclination of the barrier and slope of the wall to obtain a better model structure. The proposed method solves the local adjustment problem of the existing method from the perspective of potential structure and optimizes the asymmetric system shape to better target frequency detection during much interference from noise. After simulation, a bearing failure test, and rolling mill gearbox bearing failure experiments, we concluded that the asymmetric potential stochastic resonance detection technology can effectively identify faults. Compared with the symmetric potential stochastic resonance method, the proposed method has better recognition.

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.

Since the weak fault characteristics of mechanical equipment are often difficult to extract in strong background noise, stochastic resonance (SR) is widely used to extract the weak fault characteristics, which is able to utilize the noise to amplify weak fault characteristics. Although classical bistable stochastic resonance (CBSR) can enhance the weak characteristics by adjusting the parameters of potential model, when potential barrier height is adjusted potential well width is also changed and vice versa. The simultaneous change of both potential well width and barrier height is difficult to obtain a suitable potential model for better weak fault characteristic extraction and further fault diagnosis of machinery. For this reason, the output signal-to-noise ratio (SNR) of CBSR is greatly reduced, and the corresponding enhancement ability of weak fault characteristics is limited. In order to avoid the shortcomings, a new SR method is proposed to extract weak fault characteristics and further diagnose the faults of rotating machinery, where the classical bistable potential is replaced with a bistable confining potential to get the optimal SR. The bistable confining potential model not only has the characteristics of the classical bistable potential model but also has the ability to adjust the potential width, barrier height, and wall steepness independently. Simulated data are used to demonstrate the proposed new SR method. The results indicate that the weak fault characteristics can be effectively extracted from simulated signals with heavy noise. Experiments on the bearings and planetary gearboxes demonstrate that the proposed SR method can correctly diagnose the faults of rotating machinery and moreover has higher spectrum peak and better recognition degree compared with the CBSR method.

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).

The damage evolution study on 3D woven carbon-fiber reinforced composites has always been a challenge to the research community. In this paper, a new in situ experimental method is proposed to realize the non-destructive observation of the failure process of 3D woven carbon-fiber reinforced composites. Synchrotron radiation computer tomography (SRCT), an in situ load frame and new-style specimens are integrated. With the method, the 3D crack initiation and propagation can be continuously recorded by the SRCT scanner. Through analyzing the experimental results, the out-plane failure mechanism under tension and shear loads is found. Particularly, it is shown that the interface property is the leading factor affecting out-plane strengths. Moreover, the effect of braiding angle on the out-plane strengths in different directions is discussed. According to the failure modes, it can be concluded that with the increasing of the braiding angle, the out-plane tension strength will increase but the out-plane shear strength will decrease. The proposed in situ experimental method and the above findings are significant to calibration of failure prediction model and the novel design of woven composites.

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.

A novel in situ experimental-numerical approach combining the meso-scale Digital Image Correlation (DIC) and a cohesive zone model (CZM) is proposed to assess the in situ interfacial strength of 3D woven composites. In order to obtain a specimen with interface damage as the dominant damage mode and simple internal structure, the specimen is particularly designed and cut into a dog-bone thin slice, which contains an in situ typical form of yarn/yarn or yarn/matrix interfacial structure. Benefiting from the simple internal structure of the specimen, the geometry of the numerical simulation model can be established quickly based on the micrographs of the specimen. A cohesive zone model is used to evaluate the debonding behavior of interface between yarn/yarn or yarn/matrix. With meso-scale DIC, tensile experiments are carried out on the particularly designed specimens. All specimens are broken at the yarn/yarn or yarn/matrix interface. Local strains obtained by meso-DIC show a consistent trend with that predicted by simulation, which proves the accuracy of the simulation model. The effects of interfacial parameters on the simulated strength are discussed. Through the combination of experiment and simulation results, the range of interfacial normal strength can be estimated.

High Volume Fly Ash (HVFA) concrete enables the utilization of fly ash (FA) to diminish greenhouse gas emission by decreasing the demand of ordinary Portland cement. Structures made of HVFA concrete, such as roadside barriers, tunnel cushions and building walls could be subjected to impact and blast loads during service life. Thus, dynamic performance of HVFA concrete is worthy of investigating for better analysis and design of concrete structures. This paper presents the quasi-static and dynamic properties of HVFA concrete containing FA contents of 40 and 60% (by wt.) as partial replacement of cement. The effect of 2% (by wt.) nano silica (NS) on the quasi-static and dynamic properties of HVFA concrete is also studied. The dynamic compressive tests are carried out by using a split Hopkinson pressure bar (SHPB) with 100 mm diameter. The failure processes and patterns as well as stress–strain curves of plain and HVFA concretes under different strain rates are compared. The strain rate effects on the compressive strength, modulus of elasticity and energy absorption capacities are analysed. The experimental results show that quasi-static and dynamic performances of HVFA concrete are enhanced by the addition of NS. With the increase of FA content, the damage level becomes more severe, and modulus of elasticity and energy absorption capacities of HVFA concretes become lower at the similar strain rate. Dynamic increase factors (DIF) of compressive strength for HVFA concretes are quantified and compared with the empirical formulae recommended by Euro-International Committee for Concrete (CEB) for normal concrete. Adding NS leads to lower DIF for compressive strength of HVFA concrete. Empirical formulae for DIF of compressive strength, modulus of elasticity and energy absorption capacity of HVFA concretes with and without nano silica as a function of strain rate are proposed. It is worth noting that the NS modified HVFA C60F38N2 in this study has higher compressive strength, modulus of elasticity and energy absorption capacity than the plain concrete (PC), which shows the potential to replace the normal concrete as a sustainable construction material.

This paper comprehensively compares the progressive damage simulation of 3D woven composites between the voxel and conformal mesh models. A progressive damage model for capturing the damage mechanisms of 3D woven composites is proposed, in which the longitudinal fiber fracture, transverse inter-fiber crack and matrix failure are detected by the Puck criterion, 3D hashin criteria and modified von Mises criterion, respectively. Benefiting from the periodicity of the woven architecture, a mesoscopic representative volume cell with high fidelity is constructed based on the microscope observations. The voxel and conformal meshing techniques are employed to discretize the periodic RVC model, and a mesh convergence analysis is performed to estimate the influence of element size. The comparison of the two discretization models is conducted in terms of fiber volume fraction, macroscopic stress-strain relations, damage developments and local stress fields. The results indicate that the damage initiations and developments obtained from voxel models are highly influenced by the spurious stress concentrations and artificial connections induced by the steplike elements.

This paper proposes an innovative honeycomb-based algorithm to generate the random fiber distributions geometry of the unidirectional laminate with high fiber volume fraction, which can reach 75% or even higher. This algorithm overcomes the jamming limit appeared in traditional hard-core algorithms and lots of iterative computation required in most of the published algorithms. Then a progressive damage model is used to evaluate the prediction capacity of the fiber distribution model generated by the new algorithm. The present results are compared with the experimental and predicted ones from other different fiber distribution models. Moreover, load direction effect on the predicted results is discussed using the coefficient of variation . Finally, a fiber distribution is generated with the fiber volume fraction of 75% to validate the prediction capability of the honeycomb-based distribution model with high fiber volume fraction.

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.

Tetramethyl thiuram disulfide (thiram) is a dithiocarbamate, which is widely used on seeds and storing food grains. The incorporation of thiram into the food chain could be a risk for both human beings and animals. Thiram-contaminated feed has been considered a common cause of tibial dyschondrolplasia (TD) in many avian species. The molecular mechanism of action of thiram on TD involving microRNA (miRNA) is not fully understood. For this purpose, the morbidity and pathologic changes were evaluated to understand the TD, and high-throughput RNA sequencing (RNA-Seq) was performed to explore the differentially expressed miRNAs (DEGs). RT-qPCR was used to confirm the validity as compared with sequencing data. The results showed that the marked alterations in the growth plate of the TD chickens were noticeable, with shrinking cells and irregular chondrocyte columns as compared with control group. In this study, we identified total 375 (p 0.1), 340 (p 0.05) and 266 (p 0.01) significant DEGs between the TD and control groups. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of DEGs showed that the target miRNAs were significantly enriched in different treatment groups, such as apoptosis, mRNA surveillance pathway, mitophagy-animal, etc. This study provides theoretical basis for in-depth understanding the pathogenesis of thiram-induced TD and explore the new insights towards the proposed molecular mechanism of specific miRNA as biomarkers for effective gene diagnosis and treatment of TD in broilers.

Pulses caused by rotating mechanical faults are weak and often submerged in strong background noise, which can affect the accuracy of fault detection. To solve this problem, we study the stochastic resonance phenomenon of a tristable potential system based on strong noise background and also investigate the influence of time-delayed feedback on this stochastic resonance model. The effects of time-delayed feedback strength on potential energy, steady-state probability density function, and signal-to-noise ratio (SNR) are discussed. The results show that stochastic resonance can be enhanced or suppressed by adjusting the delay time and feedback strength. Combined with bearing fault diagnosis simulation research and experimental verification evaluation, the proposed time-delayed feedback tristable stochastic resonance fault diagnosis method is more effective than the classical stochastic resonance method.

This paper investigates the compressive damage mechanisms of 3D woven composites through a coupled numerical-experimental approach. A comprehensive progressive damage model, capable of characterizing the fiber kinking failure, transverse inter-fiber crack, matrix damage and interfacial debonding, is developed to capture the damage accumulations by incorporating a novel fracture angle-dependent stress reduction scheme, and the numerical implementation is accomplished accordingly. According to microscopic observations, a periodic representative volume cell with sufficient fidelity, including fiber yarns, matrix pockets and yarns/matrix interface, is extracted from the whole interlaced architecture. Moreover, the influence of inhomogeneous fiber initial misalignments on the compressive performances is parametrically investigated by adopting three stochastic distributions, which are implemented through a compiled user-defined subroutine. To validate the reliability of the proposed progressive damage model, some corresponding experiments of 3D woven composites are conducted. The numerically predicted strengths and damage development of constituents exhibit good consistency with the available experimental results.

This paper used a simulation-based method to predict the life cycle energy performance of residential buildings in different climate zones of China. After comparison, GISS-E2-R was selected as the general climate model (GCM) for generating future weather data, and 15 locations in different climate zones of China were involved. To predict the life cycle building performance, we created a Python procedure to generate future weather data files for every year from 2020 to 2099 for each location and performed simulations on a parametric simulation tool. According to the simulation results, heating energy is expected to decrease, and cooling energy is expected to increase in the future under the influence of climate change. The more climate change there is, the greater the heating energy and cooling energy of the building change. There are three trends in residential building energy demand. For the locations where heating energy dominates, there will be a significant decrease in total energy demand. For the locations in central China, the increase in cooling energy will offset part of the decrease in heating energy; therefore, there will be a relatively small drop in the total energy demand. For the locations where cooling energy dominates, the total energy demand is expected to rise. Finally, a simple sensitivity analysis was conducted to explore the impact of the passive design of the window-to-wall ratio (WWR), solar heat gain coefficient (SHGC), wall construction and roof construction on the life cycle energy demand under representative concentration pathway (RCP) 8.5 and 4.5.

Geopolymer as eco-friendly and alternative cementitious material has been intensively investigated. In a previous study, Expanded Polystyrene (EPS) of volume fractions of 10%, 20% and 30% was mixed into the ambient-cured plain geopolymer mortar (GM) to form lightweight geopolymer composite (LGC). The static mechanical properties were tested and reported. The developed LGC can be used in various applications such as road barriers, tunnel cushions and lightweight building products i.e. bricks and panels, which could be subjected to dynamic loads such as impact or blast. Therefore, its dynamic properties need to be investigated. In this study, dynamic compressive properties of LGC were investigated by using O100-mm split Hopkinson pressure bar (SHPB). The failure processes and the failure modes of plain GM and LGC specimens with various EPS contents under different strain rates, as well as the stress–strain curves and the energy absorption capacity were compared. The strain rate effect on the dynamic compressive strength, axial strain at peak stress and normalized energy absorption capacities were compared and analysed. The test results showed that dynamic compressive properties and energy absorption capacity of LGC with more EPS contents were more sensitive to the strain rate. Based on the testing results, the empirical formulae of dynamic increase factor of compressive strength and energy absorption versus strain rate were proposed.

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.

The magnesium oxide (MgO) / ozone (O3) / hydrogen peroxide (H2O2) system was proposed in this study for oxidative degradation of formaldehyde in wastewater in a rotating packed bed (RPB), and the effects of initial pH, high gravity factor β, MgO dosage, O3 concentration and H2O2 concentration on the formaldehyde removal rate were investigated. The results showed that at an initial pH of 9, a high gravity factor of 40, a MgO powder dosage of 4 g/L, a H2O2 concentration of 5 g/L, a O3 dosage of 65 mg/L, a liquid flow of 90 L/h and a gas flow rate of 100 L/h, a 99.93 % and 93.98 % removal rate could be achieved for formaldehyde (2200 mg/L) and chemical oxygen demand (COD) after 60 min, respectively, and the final formaldehyde concentration was 1.45 mg/L, which met the Grade 2 standard set in the Chinese National Standards for Integrated Wastewater (GB 8978-1996). The degradation rates of formaldehyde and COD by RPB-MgO/O3/H2O2, bubbling reactor (BR)-MgO/O3/H2O2, RPB-O3/H2O2 and RPB-MgO/O3 were also compared in the study. The results showed that RPB-MgO/O3/H2O2 allowed for more efficient degradation of formaldehyde in wastewater. Possible degradation mechanisms of formaldehyde in wastewater were proposed based on ion chromatography.

Three-dimensionally mesoporous La1-xSrxFeO3 (x = 0, 0.4, 0.6) precursors have been synthesized through a facile solvothermal process. After high-temperature sintering, La1-xSrxFeO3 still exhibits uniform morphology and good dispersibility, which provides a porous structure and favorable surface area. Particularly, La0.4Sr0.6FeO3 shows the biggest surface area of 58 m2 g-1. Doping also induces the generation of oxygen vacancies and Fe4+, which is beneficial for the conductivity and catalytic activity of the materials. Complete with favorable structure and electrochemical activity, La0.4Sr0.6FeO3 exhibits bifunctional catalytic activity in alkaline solution. Applied as a cathode catalyst in Li-O2 batteries, it shows a larger discharge capacity of 23 905 mA h g-1 and a better cycling stability of 100 cycles.

In this manuscript, Fe(II) inhibition of anammox and its recovery were investigated, and the performance, kinetics and statistical features were comprehensively studied simultaneously. Anammox was suppressed and completely inhibited by the addition of 109.29 and 378.57 mg/L Fe(II), respectively, via uncompetitive inhibition. Nitrite inhibition of anammox was best fitted by the Edwards model and Aiba model. EDTA-2Na wash (0.5, 1.0, 1.5, and 2.0 mM) had a limited effect on anammox recovery, while the addition of 2.0 mM betaine accelerated anammox recovery. Prolonged betaine addition caused an unintended reduction of anammox activity, though it self-recovered after the withdrawal of betaine. The modified Boltzmann model most accurately simulated the processes of anammox recovery using the EDTA-2Na wash, betaine regulation and self-recovery, and the modified Stover-Kincannon model was able to assess the results of anammox recovery. The one-sample t-test was successfully applied to determine the effects of these three recovery strategies on inhibited anammox, which were short-term disinhibition or long-term recovery effects. The above-mentioned results demonstrate that an intermittent addition of betaine, which is a better alternative to frequently-used but poorly-degradable EDTA, may be a useful and environmentally friendly recovery strategy for Fe(II)-inhibited anammox reactor.

Rechargeable Li–O2 batteries (LOBs) have attracted great attentions for its high theoretical energy density, and were considered to be the next potential energy storage devices. However, the high overpotential and poor cycling performance hinder the further development of LOBs. In this study, a binder-free air electrode of MnCo2O4 nanosheets supporting on the carbon cloth, was directly prepared by electrodeposition and calcination. MnCo2O4@carbon cloth possesses a flaky-porous and interconnected network nanostructure. The MnCo2O4@carbon cloth electrode could operate more than 108 cycles at 340 mA/g with a limiting specific capacity of 500 mA h/g. And it could deliver a first discharge specific capacity of 7238 mA h/g at 200 mA/g with the charge/discharge overpotential of 1.46 V. And it could achieve 3000 mA h/g when the current density was increased up to 800 mA/g. Such good electrocatalytic activity towards oxygen reduction reaction should derive from the high valence state of Mn3+ and Mn4+, and the three-dimensional interconnected mesoporous structure which can benefit oxygen diffusion and electrolyte infiltrate to form triple-phase regions in favour of oxygen reduction.

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.

High-efficiency and low-cost electrocatalysts are generally believed to be the critical factor and have been highly researched to catalyze the oxygen reduction reaction (ORR) during the operation of Li-O2 battery (LOB). The catalysts with better ORR performance are essential for high-performance LOBs. Herein, a binder-free MnO x @carbon cloth cathode composed of Mn3O4 nanoparticles and Mn2O3 nanosheets were directly synthesized on the carbon cloth by electrodeposition and subsequently heat treatment at different temperature (from 200 °C to 400 °C). With the increase of temperature, the Mn3O4 nanospheres gradually transformed into Mn2O3 nanosheets. The MnO x obtained at 350 °C exhibited the best ORR performance. And MnO x -350 °C could operate more than 80 cycles at 340 mA g-1 with a limiting specific capacity of 1000 mAh g-1, and its first discharge specific capacity could nearly achieve 8000 mAh g-1 at 200 mA g-1.

Nitrate is a byproduct of the anaerobic ammonium oxidation (anammox) process and is related to its electron transfer. However, little is known about the influence of nitrate on the anammox process. In this work, the biphasic effect of exogenous nitrate on the anammox process was investigated in an upflow biofilter (UBF) reactor with ammonium as the sole electron donor. The responses of anammox to increased nitrate were analyzed by one-way ANOVA test and found to be significantly different under a constant and decreased nitrite condition (p 0.01). With a single increase in nitrate and constant ammonium and nitrite in the influent, the total nitrogen removal rate (TNRR) of anammox was uninhibited, but stoichiometry deviated and nitrate production always showed a linear decrease. In contrast, anammox exhibited a range of activity with constant ammonium and simultaneously increased nitrate and decreased nitrite in the influent, including a continuous reduction of TNRR, a nonpersistent ammonium overconsumption and a pronounced nonlinear response of nitrate production. Correlation analysis shows that the lack of ammonium overconsumption was accompanied by the disappearance of nitrate underproduction. Kinetic models of product formation were effectively used to explore the nitrate production behavior of anammox subjected to increased nitrate, and the metabolite of nitrate was divided into a growth negative coupling type and growth (partial) coupling type under a constant and decreased nitrite condition, respectively. These findings collectively suggest that nitrate has a biphasic effect on the anammox process and is correlated with the availability of nitrite.

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.

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.

The widespread use of pull-requests boosts the development and evolution for many open source software projects. However, due to the parallel and uncoordinated nature of development process in GitHub, duplicate pull-requests may be submitted by different contributors to solve the same problem. Duplicate pull-requests increase the maintenance cost of GitHub, result in the waste of time spent on the redundant effort of code review, and even frustrate developers' willing to offer continuous contribution. In this paper, we investigate using text information to automatically detect duplicate pull-requests in GitHub. For a new-arriving pull-request, we compare the textual similarity between it and other existing pull-requests, and then return a candidate list of the most similar ones. We evaluate our approach on three popular projects hosted in GitHub, namely Rails, Elasticsearch and Angular.JS. The evaluation shows that about 55.3% -- 71.0% of the duplicates can be found when we use the combination of title similarity and description similarity.

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.

Vibration information is widely used in the fault diagnosis of rotating machinery, but the weak components reflecting early faults are often difficult to extract because of strong background noise. Among the variety of weak information extraction methods, the stochastic resonance (SR) method has the unique advantage of transferring noise energy to weak signals and has great application prospect in weak signal extraction. The current underdamped SR method with two adjustment parameters cannot achieve an optimal potential model structure, so it is easy to fall prey to the local optimization problem without having a better feature extraction method. Therefore, in this paper, we present a multi-parameter constrained potential underdamped stochastic resonance method by investigating the potential model and signal-to-noise ratio of the system. Moreover, the weighted ant colony algorithm is introduced to optimize potential parameters and a damping factor is used to improve the ability of weak fault information extraction. Furthermore, several types of bearing failure experiments were implemented to verify the effectiveness of the proposed method. The results show that the proposed method has excellent weak signal extraction ability and is suitable for early fault diagnosis.

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.

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.