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To clarify the effects of ferrite morphologies and contents on high-cycle fatigue property of pearlite–ferrite dual-phase (DP) steel used for fabrication of commercial vehicle crankshafts, two types of DP steels with different ferrite grain sizes (S10: 13.1 μm and S30: 21.4 μm) and ferrite contents (S10: ~ 9.5 vol.% and S30: ~ 30.4 vol.%) were prepared. Stress amplitude–logarithm of number of high cycles to failure fatigue of the two DP steels was evaluated. Experimental results showed a fatigue strength of 510 and 400 MPa for S10 and S30 steels, respectively, at 107 cycles. Fatigue cracks in S10 steel extended preferentially along the grain boundary, but it was easy for crack propagation to extend within a pearlite colony to form a zigzag crack morphology. Crack roughness was enhanced and high stress was introduced to the crack surface due to this kind of crack propagation behavior, which has positive effects on slowing down crack propagation. However, the crack propagation in S30 steel mainly occurred inside the soft equiaxed coarse ferrite grain. Analysis revealed that little stress was introduced to the crack surface. These results show that it is possible to improve high cycle fatigue strength of pearlite–ferrite DP steel by appropriately manipulating the volume fraction and microstructure morphology of ferrite phase.

Application of an electronic packaging material is always limited by its established coefficient of thermal expansivity (CTE) and poor plastic working capacity. Here, we explored that a ductile high-entropy alloy (HEA) could achieve adjustable CTE values via decomposing at 500 °C. The carbon-doped equiatomic FeCoCrNiMn HEAs (0.8, 1.0, and 1.3 at% C) decomposed into B2, L10, M23C6 and σ phases during annealing. The adjustable CTE of the carbon-doped HEAs can be ascribed to the relatively low CTE values of the formed B2, L10, M23C6, and σ phases. The CTE value of a single face-centered cubic (FCC)-structured FeCoCrNiMn-1.3 at% C HEA is 16.7 × 10–6 °C−1, yet it can be continuously adjusted to 11.3 × 10–6 °C−1 when the FCC matrix is gradually decomposed into the B2, L10, M23C6, and σ phases. Furthermore, the decomposition rate and fractions of the B2, L10, M23C6, and σ phases can be controlled via changing carbon concentration and rolling reduction. More importantly, the CTE range of the 1.3C HEAs meets the requirements of electronic packaging. This work provides a way to design HEAs with desirable CTE for electronic industry via annealing at intermediate temperature.

The evolution of ferrite grain and cementite lamella during cold rolling in a granular carbide–pearlite steel has been investigated. Particular attention has been given to a quantitative characterization of changes in the ferrite grains. Electron back-scattered diffraction and transmission electron microscopy observations show that the ultrafine ferrite (~ 388 nm) can be produced through low equivalent strain cold rolling without severe plastic deformation (SPD) and annealing. The average grain size of ferrite depends on the volume fraction, shape and distribution of granular carbides as well as interlamellar spacing of pearlite. A general explanation of granular carbides-assisted grain refinement is that the embedded carbides between natural barrier will significantly facilitate dislocation nucleation during cold rolling. Dislocation reaction occurs more drastically and quickly near these granular carbides. Such reactions promote the formation of high-angle grain boundaries. The formation of ultrafine ferrite grains and subgrains in steel after cold rolling to e = 1.4 strain makes the strength and ductility increased simultaneously compared with e = 0.6 cold-rolled steel. The results suggest a new material design strategy to obtain ultrafine-grained structure via the granular carbides assistance.

The intercritical annealing treatment at 650 and 700 °C results in two ultrafine-grained (UFG) dual-phase ferrite–austenite steels. The two steels exhibit different and special discontinuous yielding and pronounced Luders-like strain phenomena with large yielding strain which are related to their retained γ-austenite (RA) volume fractions and RA stabilities. The steel annealed at 650 °C shows an absent or very small strain hardening, while the steel annealed at 700 °C shows an obvious strain hardening upward curvature with increasing strain. The results show that before and during straining, the steel annealed at 650 °C exhibits a mixture of equiaxed and elongated UFG α-ferrite and austenite phases; however, the steel annealed at 700 °C exhibits only elongated UFG α and γ phases. It was found that most of the γ-austenite to α′-martensite transformation occurred at the initial deformation stage and very small or almost no transformation occurred afterward. This demonstrates that the strain-induced martensite (SIM) transformation (γ–α′) or transformation-induced plasticity (TRIP) effect dominates only at the initial deformation stage. RA remained stable, and no TRIP effect was observed at the final deformation stage. The load–unload–reload test was performed to evaluate the back stress (σb) hardening effect. It is believed that the pronounced strain hardening behavior at the later deformation stage is mainly associated with σb enhancement induced by the strain partitioning between the soft and hard phases due to SIM transformation during tensile deformation.

A dual phase (martensite–austenite) low carbon nickel-manganese transformation-induced plasticity (TRIP) steel was fabricated by heavily warm rolling (HWR), and the effect of annealing on the phase fraction, mechanical properties and tensile deformation behavior of the heavily warm rolled (HWRed) steel was investigated. The results showed that the reverse transformation of γ-austenite from α′-martensite occurs and that the γ-austenite volume fraction (VA) decreases from 91% to 55% as the annealing temperature increases from 400 °C to 800 °C, respectively. The HWRed steel annealed at 400 °C exhibits a high strength-high ductility combination with yield strength of 706 MPa, ultimate tensile strength (UTS) of 1573 MPa, total elongation (TEL) of 21.6%, and the product of the strength and elongation (PSE: UTS×TEL) is 34 GPa%. These excellent mechanical properties are principally attributed to the formation of a large volume fraction of austenite (γ) by the reverse transformation and subsequent TRIP effect during tensile deformation. It was found that the HWRed and annealed steels exhibit a special tensile behavior with a large yielding strain followed by pronounced strain hardening. The tensile curve can be readily divided into three obviously different stages. The strain-induced martensite (SIM) transformation (γ -α′) occurs in the early yielding deformation stage and in the intermediate rapidly hardening deformation stage, indicating that the TRIP effect dominates the process of these two stages. However, the retained γ-austenite remains very stable, and no TRIP effect is observed in the final hardening deformation stage. The load-unload reload (LUR) test was performed to evaluate the back stress (σb) hardening effect during tensile testing. It is believed that the pronounced strain hardening behavior after yielding is mainly associated with the σb enhancement induced by the strain partitioning between the soft retained γ-austenite and the hard α′-martensite due to the SIM transformation during tensile deformation.

A dual-phase (12 vol.% delta-ferrite + 78 vol.% austenite) high manganese twinning-induced plasticity (TWIP) steel was produced by hot rolled and annealing treatment. In comparison with the fully a...

An ultrafine-structured FeCoCrNiMn high-entropy alloy (HEA) was produced by severe cold rolling (SCR) with a 95 pct reduction, and the effect of annealing temperature over a wide range from 573 K to 1373 K (300 °C to 1100 °C) on the microstructure and mechanical properties of this SCRed HEA was systematically investigated. The results show that the microstructure of the single-phase FCC HEA after SCR is greatly refined and mainly comprises ultrafine crystalline, high-density dislocation cell and networks, which substantially improve the hardness and strength. The SCRed HEA starts to recrystallize at an annealing temperature of 773 K (500 °C) and is fully recrystallized at 973 K (700 °C) after 1 hours. The σ phases with a tetragonal structure form upon annealing at 773 K (500 °C), leading to a further enhancement in hardness and strength for the annealed HEA compared to the SCRed HEA. The average grain size remains below 500 nm when the SCRed HEA is annealed at 923 K (650 °C) for 1 hours, indicating that the ultrafine-structured HEA after SCR and subsequent annealing treatment possesses excellent microstructural thermal stability. The SCRed HEAs annealed at 923 K and 973 K (650 °C and 700 °C) exhibit excellent high strength–ductility combinations due to the formation of the fully recrystallized ultrafine-grained microstructure. The strain-rate sensitivity parameter (m value) with different grain sizes, from ultrafine to coarse, was evaluated by strain-rate jump tests. The m value of the HEA increases monotonically with increasing grain size and annealing temperature. Moreover, high-temperature tensile tests demonstrated that the annealed HEAs exhibit superplastic deformation behavior at 973 K and 1073 K (700 °C and 800 °C), and the formation of a Cr-rich phase is found during high-temperature deformation. It is believed that the plastic deformation facilitates the phase decomposition of fine-grained HEAs and grain boundary sliding is the key mechanism of high-temperature deformation.

Deep learning (DL) frameworks have been extensively designed, implemented, and used in software projects across many domains. However, due to the lack of knowledge or information, time pressure, complex context, etc., various uncertainties emerge during the development, leading to assumptions made in DL frameworks. Though not all the assumptions are negative to the frameworks, being unaware of certain assumptions can result in critical problems (e.g., system vulnerability and failures, inconsistencies, and increased cost). As the first step of addressing the critical problems, there is a need to explore and understand the assumptions made in DL frameworks. To this end, we conducted an exploratory study to understand self-claimed assumptions (SCAs) about their distribution, classification, and impacts using code comments from nine popular DL framework projects on GitHub. The results are that: (1) 3,084 SCAs are scattered across 1,775 files in the nine DL frameworks, ranging from 1,460 (TensorFlow) to 8 (Keras) SCAs. (2) There are four types of validity of SCAs: Valid SCA, Invalid SCA, Conditional SCA, and Unknown SCA, and four types of SCAs based on their content: Configuration and Context SCA, Design SCA, Tensor and Variable SCA, and Miscellaneous SCA. (3) Both valid and invalid SCAs may have an impact within a specific scope (e.g., in a function) on the DL frameworks. Certain technical debt is induced when making SCAs. There are source code written and decisions made based on SCAs. This is the first study on investigating SCAs in DL frameworks, which helps researchers and practitioners to get a comprehensive understanding on the assumptions made. We also provide the first dataset of SCAs for further research and practice in this area.

Decisions are an important type of artifacts in software development and maintenance, while decisions are not well-documented in projects due to limited human resources and budget. To this end, many studies focus on using automatic approaches to identify decisions from textual artifacts, e.g., mailing lists, issue tracking systems. In this paper, we present a replication study of our previous work (EASE2020), which conducted experiments to automatically identify decisions from the Hibernate developer mailing list. In addition, we utilized different datasets in the experiment with the aim of exploring the impact of the proprieties of dataset (i.e., the quality of positive samples, different negative samples in the dataset, and the size of the dataset) on classification results of decisions. The results show that (1) improving the quality of positive samples in the dataset can decently improve the classification results; (2) different negative samples in the dataset have an impact on the classification results; and (3) before the dataset size reaches 1200, increasing the size will improve the classification results.

This is the replication package for the paper: "Will Data Influence the Experiment Results?: A Replication Study of Automatic Identification of Decisions".It contains the source code and dataset of our experiment for thereplicationbyotherresearchers. In the meanwhile, we provide brief description of the files in the replicationpackage below. 1. main_code folder automatic_approach.pycontains the main source code of the automatic approach for identifying decisions in our experiment, which is conducted on MacOsand Python 3.7.9.Note that you mayget slightlydifferent experimentresults when conducting the experimentson different environment configurations. requirement.txt records all the installation packages and their version numbers needed for the current program to run.Youcan use "pip install -r requirement.txt" to rebuild the project and install all dependencies. Note that you mayget slightly different experimentresults when using different packages or versions. 2. dataset folder EASE2020 - 650 decisions.xlsxcontains 650decision sentencesfrom our previous work (EASE2020) EASE2020 - 650 non decisions.xlsxcontains 650 non-decision sentencesfrom our previous work (EASE2020) Our 844 relabeled decisions.xlsx contains 844 relabeled decisions in this work. Our 750 assumptions.xlsx contains 750 assumptions from our previous work (APSEC2019) 3. RQ1 folder experiment_RQ1.py contains the main source code of the experiment for answering RQ1, which is conducted on the same environment configuration as theautomatic_approach.py. 4. RQ2folder experiment_RQ2.py contains the main source code of the experiment for answering RQ2, which is conducted on the same environment configuration as theautomatic_approach.py. 5. RQ3folder experiment_RQ3.py contains the main source code of the experiment for answering RQ3, which is conducted on the same environment configuration as theautomatic_approach.py.

Stakeholders make various types of decisions with respect to requirements, design, management, and so on during the software development life cycle. Nevertheless, these decisions are typically not well documented and classified due to limited human resources, time, and budget. To this end, automatic approaches provide a promising way. In this paper, we aimed at automatically classifying decisions into five types to help stakeholders better document and understand decisions. First, we collected a dataset from the Hibernate developer mailing list. We then experimented and evaluated 270 configurations regarding feature selection, feature extraction techniques, and machine learning classifiers to seek the best configuration for classifying decisions. Especially, we applied an ensemble learning method and constructed ensemble classifiers to compare the performance between ensemble classifiers and base classifiers. Our experiment results show that (1) feature selection can decently improve the classification results; (2) ensemble classifiers can outperform base classifiers provided that ensemble classifiers are well constructed; (3) BoW + 50% features selected by feature selection with an ensemble classifier that combines Na\"ive Bayes (NB), Logistic Regression (LR), and Support Vector Machine (SVM) achieves the best classification result (with a weighted precision of 0.750, a weighted recall of 0.739, and a weighted F1-score of 0.727) among all the configurations. Our work can benefit various types of stakeholders in software development through providing an automatic approach for effectively classifying decisions into specific types that are relevant to their interests., Comment: The 25th International Conference on Evaluation and Assessment in Software Engineering (EASE)

BACKGROUND Drug use disorder has high potential for relapse and imposes an enormous burden on public health in China. Since the promulgation of the Anti-drug law in 2008, community-based rehabilitation has become the primary approach to treat drug addiction. However, multiple problems occurred in the implementation process, leading to a low detoxification rate in the community. Mobile health (mHealth) serves as a promising tool to improve the effectiveness and efficiency of community-based rehabilitation. Community-based addiction rehabilitation electronic system (CAREs) is an interactive system for drug users and their assigned social workers. OBJECTIVE The study aimed to examine the feasibility and preliminary efficacy of CAREs in community-based rehabilitation from the perspective of drug users and social workers in Shanghai, China. METHODS In this pilot randomized controlled trial, 40 participants were recruited from the community in Shanghai from January to May 2019. Participants randomized to the intervention group (n=20) received CAREs + community-based rehabilitation, while participants in the control group (n=20) received community-based rehabilitation only for 6 months. CAREs provided education, assessment, and SOS (support) functions for drug users. The assigned social workers provided service and monitored drug use behavior as usual except that the social workers in the intervention group could access the webpage end to obtain drug users’ information and fit their routine workflow into CAREs. The primary outcome was the feasibility of CAREs, reflected in the overall proportion and frequency of CAREs features used in both app and webpage end. The secondary outcomes were the effectiveness of CAREs, including the percentage of drug-positive samples, longest period of abstinence, contact times with social workers, and the change of Addiction Severity Index (ASI) from baseline to the 6-month follow-up. RESULTS The number of participants logged in to the app ranged from 7 to 20 per week, and CAREs had relatively high levels of continued patient use. Drug users preferred assessment and education features in the app end while their social workers showed high levels of use in urine results record and viewing assessment results on the webpage end. After the 6-month intervention, 3.3% (17/520) of samples in the intervention group and 7.5% (39/520) in the control group were drug-positive (F=4.358, P=.04). No significant differences were noted between the control and intervention groups in terms of longest duration of abstinence, number of contact times and ASI composite scores. CONCLUSIONS The study preliminarily demonstrated that with relatively good feasibility and acceptability, CAREs may improve the effectiveness and efficiency of the community-based rehabilitation, which provided instruction for further improvement of the system. CLINICALTRIAL ClinicalTrials.gov NCT03451344; https://clinicaltrials.gov/ct2/show/NCT03451344 INTERNATIONAL REGISTERED REPORT RR2-10.3389/fpsyt.2018.00556

Overwhelming lung inflammation is a key feature of acute lung injury (ALI). MicroRNAs (miRNAs) have been implicated in the regulation diverse cellular processes including the inflammatory response. However, little is known about their functions and molecular involvement in regulating the inflammatory process in ALI. Herein, we established a lipopolysaccharide (LPS)-induced ALI mouse model and used miRNA microarray analysis to investigate and compare the miRNA expression profiles in mouse lung tissues. We found that miR-92a was markedly upregulated in the lung tissues of ALI mice compared with that in normal lung tissues. This upregulation of miR-92a in LPS-induced ALI mice was further confirmed in lung tissues, splenocytes and bronchoalveolar lavage fluid (BALF) by quantitative real-time PCR. Inhibition of miR-92a by injection with antagomir-92a markedly reduced LPS-induced pathological changes associated with lung inflammation, and reduces lung wet/dry ratio (W/D ratio), and Evans blue dye extravasation (an indicator of lung epithelial permeability). Moreover, inhibition of miR-92a ameliorated the inflammatory response by reducing the repression of proinflammatory cytokines including tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β) and IL-6 in lung tissues. In addition, we identified that miR-92a inhibited the phosphatase and tensin homolog on chromosome ten (PTEN) by binding to its 3'-UTR in RAW264.7 murine macrophage cells. Western blot analysis demonstrated that inhibition of miR-92a may ameliorate inflammatory response through blocking PTEN/AKT/NF-κB signaling pathway in ALI mice. Collectively, these results have revealed a significant role of miR-92a in the lung inflammatory response associated with ALI in mice, and suggest that miR-92a may have potential as a prognostic indicator and novel therapeutic target for the treatment of ALI in future.

An ultrafine-grained (UFG) low-carbon medium-manganese steel was fabricated by the heavily warm rolling (HWR) and subsequent quenching, and the effects of annealing temperatures on microstructure and mechanical properties of the UFG HWRed steel were investigated. The results show that the HWRed steel exhibits simultaneous improvements in strength, uniform elongation and work hardening, which is mainly attributed to the refinement of martensitic microstructures. The HWRed steels comprise only α-phase when annealing at lower temperatures below to 550 °C and at higher temperatures above to 700 °C. Whereas, UFG γ-austenite is formed by reverse transformation when the HWRed steel was annealed at intermediate temperatures from 550 to 700 °C and the volume fraction increases with increasing annealing temperatures, consequently resulting in a dramatic increase in ductility of the annealed HWRed steels. It was found that the transformed UFG austenite and ferrite remained ~ 500 nm and ~ 800 nm in size when the HWRed steel was annealed at 650 and 700 °C for 1 h, respectively, showing an excellent thermal stability. Moreover, the HWRed steel annealed at 650 °C exhibits high strength-ductility combinations with a yield strength of 906 MPa, ultimate tensile strength (UTS) of 1011 MPa, total elongation (TEL) of 51% and product of strength and elongation (PSE: UTS × TEL) of 52 GPa%. It is believed that these excellent comprehensive mechanical properties are closely associated with the UFG austenite formation by reverse transformation and principally attributed to the transformation-induced plasticity (TRIP) effect.

The nonlinear migration of C atom between nearest interstitial sites in fcc iron has been studied in the paper. It reveals an optimum migration pathway in the preferred crowdion direction in its infancy; the pathway is finally along the tetrahedral direction. This nonlinear tendency can be intensified by a repulsive force from another near neighbor C atom in the crowdion direction. We introduce an interaction model based on Coulomb’s force, which indicates that this nonlinear migration mechanism is a foregone conclusion with a certain atomic geometric configuration. However, for a multiple-C system, we find that there exists a typical preferred orientation, which is a continuously stable structure maintained by the C atoms migrating in the crowdion direction. The first neighbor migrated structure is the main formation as a result of this preferential orientation. Based on this, we derive a novel diffusion equation by introducing a proportionality coefficient $$ K_{\rho } $$ , which can be closely related to the C structures absorbed on the surface during carburization. The diffusion coefficient will increase dramatically with a small perturbation of $$ K_{\rho } $$ . Moreover, a spatially heterogeneous occupation is introduced on a much larger scale. The results not only show that it is a relatively stable state but that the C diffusion coefficient is significantly larger than that of the other disorder-free C states. Meanwhile, it provides a repulsive force for nonlinear migration in the micro-cell. As a result, a greater diffusion rate will be achieved from both spatial heterogeneity and the nonlinear migration of C atoms.

An ultrafine lamellar martensitic steel was fabricated by heavy warm rolling of undercooled austenite and subsequent quenching. The martensite transformed from warm rolled austenite was composed of ultrafine heterogeneous lamellar plates, which were further subdivided into laths and twins. The warm rolled steel after low temperature tempering exhibited excellent mechanical properties with a yield strength of 2343 MPa, ultimate tensile strength of 2586 MPa and total elongation of 9.4%. The combination of ultrahigh strength and high ductility was primarily attributed to the united effect of ultrafine lamellar plates, high density dislocations, twins and retained austenite. After high temperature tempering, the strength of warm rolled steel decreased dramatically, which was attributed to the severe recovery of martensite.

A bulk nanostructured twinning-induced plasticity (TWIP) steel with high ductility and high strength was fabricated by cryogenic asymmetry-rolling (cryo-ASR) and subsequent recovery treatment. It was found that the cryo-ASRed TWIP steels exhibit simultaneous improvements in the ductility, strength and work hardening. Typical microstructures of the cryo-ASR TWIP steel were characterized by shear bands and intensive mechanical nano-sized twins induced by cryogenic deformation. These mechanical nano-scale twins remain thermally stable during the subsequent recovery treatment. It is believed that the ductility enhancement and high work-hardening ability for the cryo-ASR TWIP steels should be mainly attributed to the high-density pre-existing nano-scale twins.

To take advantage of the high strength of high-carbon martensitic steel and simultaneously avoid insufficient plasticity, a novel continuous carbon-gradient sheet steel (CGSS) with a high-carbon surface and medium-carbon core was developed as a gradient material using surface carburizing and warm rolling. An exceptional strength–ductility combination was achieved. On the premise of carbides precipitation in high-carbon martensitic matrix, tempered CGSS at 200 °C exhibits excellent mechanical property with an ultimate tensile strength of 2440 MPa and a total elongation of 4.6 %. Enlarged-strain-component in the tensile direction is the primary cause for higher plasticity in the high-carbon surface. Inhomogeneous fracture mode indicates the typical restraint of crack propagation in the core, suggesting greater toughness for the tempered CGSS.

MgFe2O4 spinel with abundant oxygen vacancy was synthesized by a simple precipitation method, and tested in photocatalytic reduction of CO2 with water vapor as reductant. A series of characterization including XRD, XPS, EPR, PL spectrum, UV–vis DRS and TPD-CO2 were performed to investigate the influence of calcination temperature on morphology, optical and electronic properties of MgFe2O4 spinel. The results demonstrated that the oxygen vacancy concentration increases first and then decreases with the increase of calcination temperature. By introducing oxygen vacancies, the recombination of photogenerated electron-hole pairs was significantly suppressed, visible light absorption and chemisorption capacity of CO2 were dramatically boosted. Mg-Fe-750 with the richest oxygen vacancies exhibits the highest photocatalytic activity, for which the production rate of CO and H2 was 24.4 and 34.3 μmol/gcat/h, respectively.