Effect of deep cryogenic treatment on the properties of 80CrMo12 5 tool steel

The effect of deep cryogenic treatment on the mechanical properties of 80CrMo12 5 tool steel was investigated. Moreover, the effects of stabilization (holding at room temperature for some periods before deep cryogenic treatment) and tempering before deep cryogenic treatment were studied. The results show that deep cryogenic treatment can eliminate the retained austenite, making a better carbide distribution and a higher carbide amount. As a result, a remarkable improvement in wear resistance of cryogenically treated specimens is observed. Moreover, the ultimate tensile strength increases, and the toughness of the sample decreases. It is also found that both stabilization and tempering before deep cryogenic treatment decrease the wear resistance, hardness, and carbides homogeneity compared to the deep cryogenically treated samples. It is concluded that deep cryogenic treatment should be performed without any delay on samples after quenching to reach the highest wear resistance and hardness.     

Cupric ion release and cytotoxicity for Yuangong Cu-IUDs and the release behavior of indomethacin for medicated 220 Cu-IUD

These years Yuangong copper-bearing intrauterine devices （Cu-IUDs） have been used because of less side effects in use. The corrosion of copper is essential to the success of contraception, and the release behavior of indomethacin from medicated Cu-IUD is related to its therapeutic effect. In this study, analytical methods were established to investigate the release behavior of cupric ion of three kinds of Yuangong Cu-IUDs and indomethacin of medicated Yuangong 220 Cu-IUD. Cu-IUDs were incubated in simulated uterine solution （SUS）. The concentrations of cupric ion and indomethacin were analyzed by flame atomic absorption spectrometer （FAAS） for 60 days and UV/vis -3310 spectrophotometer for 60 days, respectively. The morphology of copper after corrosion was characterized by SEM. In addition, we detected cytotoxicity by MTT of L929 mouse fibroblasts cells caused by extracts of the three Yuangong Cu-IUDs. The release behavior of cupric ion for three kinds of Yuangong Cu-IUDs was biphasic, which consisted of the initial burst release and then slow and constant release. In vitro release experiment confirmed a biphasic release of indomethacin from Yuangong 220. The copper wire of Yuangong Cu-IUDs showed uneven corrosion. The RGR value of Yuangong 365 Cu-IUD was smaller than that of medicated Yuangong 220 Cu-IUD and RGR value of medicated Yuangong 220 Cu-IUD was smaller than that of Yuangong 300 Cu-IUD. The cupric ion release and indomethacin release showed biphasic. Indomethacin increased the cupric ion release rate and might diminish the adverse effects caused by burst release of cupric ion. The toxicity grade of these three Yuangong Cu-IUDs was 4. We should canvass the adverse events of Cu-lUDs based on practical experiments, and try our best to reduce the toxicity of Cu-lUDs.     

A forensic method for efficient file extraction in HDFS based on three-level mapping

The large scale and distribution of cloud computing storage have become the major challenges in cloud forensics for file extraction. Current disk forensic methods do not adapt to cloud computing well and the forensic research on distributed file system is inadequate. To address the forensic problems, this paper uses the Hadoop distributed file system (HDFS) as a case study and proposes a forensic method for efficient file extraction based on three-level (3L) mapping. First, HDFS is analyzed from overall architecture to local file system. Second, the 3L mapping of an HDFS file from HDFS namespace to data blocks on local file system is established and a recovery method for deleted files based on 3L mapping is presented. Third, a multi-node Hadoop framework via Xen virtualization platform is set up to test the performance of the method. The results indicate that the proposed method could succeed in efficient location of large files stored across data nodes, make selective image of disk data and get high recovery rate of deleted files.     

Preparation of Al<Subscript>72</Subscript>Ni<Subscript>8</Subscript>Ti<Subscript>8</Subscript>Zr<Subscript>6</Subscript>Nb<Subscript>3</Subscript>Y<Subscript>3</Subscript> amorphous powders and bulk materials

Amorphous Al₇₂Ni₈Ti₈Zr₆Nb₃Y₃ powders were successfully fabricated by mechanical alloying. The microstructure, glass-forming ability, and crystallization behavior of amorphous Al₇₂Ni₈Ti₈Zr₆Nb₃Y₃ powders were investigated by X-ray diffraction (XRD), transmission electron microscopy (TEM), and differential scanning calorimetry (DSC). The isothermal crystallization kinetics was analyzed by the Johnson–Mehl–Avrami equation. In the results, the supercooled liquid region of the amorphous alloy is as high as 81 K, as determined by non-isothermal DSC curves. The activation energy for crystallization is as high as 312.6 kJ·mol?1 obtained by Kissinger and Ozawa analyses. The values of Avrami exponent (n) imply that the crystallization is dominated by interface-controlled three-dimensional growth in the early stage and the end stage and by diffusion-controlled two- or three-dimensional growth in the middle stage. In addition, the amorphous Al₇₂Ni₈Ti₈Zr₆Nb₃Y₃ powders were sintered under 2 GPa at temperatures of 673 K and 723 K. The results show that the Vickers hardness of the compacted powders is as high as Hv 1215.     

Microstructural characteristics and mechanical properties of bobbin-tool friction stir welded 2024-T3 aluminum alloy

Cold-rolled 2024-T3 sheet alloy was subjected to bobbin-tool friction stir welding (BTFSW). The microstructural characteristics and mechanical properties of the nugget zone in the as-welded state were investigated. The results show that the equiaxed grain size of BTFSW 2024-T3 alloy decreases from 7.6 to 2.8 μm as the welding speed is increased from 80 to 120 mm/min; in addition, fine grains are generated in the nugget zone and the size distribution is non-uniform. All Al₂CuMg (S') precipitates dissolve into the Al matrix, whereas Mn-rich phases confirmed as T phases (Al₂₀Cu₂Mn₃, Al₆Mn, or Al₃Mn) remain unchanged. The optimized parameters for BTFSW are verified as the rotation speed of 350 r/min and the travel speed of 100 mm/min. The variations in precipitation and dislocation play more important roles than grain size in the nugget zone with respect to influencing the mechanical properties during the BTFSW process. After the BTFSW process, the fracture mode of base material 2024-T3 alloy transforms from ductile rupture to ductile-brittle mixed fracture.     

Superior machinability of steel enhanced with BN and MnS particles

The strategy that replacing part of MnS with BN was proposed in order to decrease the sulfur content in sulfur based free-cutting steel. The effects of BN and MnS inclusions on the microstructure and machinability of the steel were systematically investigated. The results show that most of the BN and MnS inclusions exist individually in the steel and only a small amount of them are in a composite state forming either isolated particles or clusters of particles. In the case of multi-phased steel, the theoretical calculation predicts that the volume of large BN particles should be 0.7 times of the volume of large MnS particles. The machinability of this type of BN and MnS alloy steel over a wide range of cutting speeds ranging from a low speed appropriate for drilling to a high speed appropriate for turning is confirmed as being equal to or superior to that of an MnS reference steel, even though the sulfur content in the composite steel is only half that of the MnS steel. The aptitude for cutting effect of 240 ppm nitrogen and 115 ppm boron in the composite steel is demonstrated to be equivalent or even better than 1000 ppm sulfur in MnS free-cutting steel.     

Phase transition in the subducted oceanic lithosphere and generation of the subduction zone magma

Two metamorphic processes, i.e. subsolidus dehydration and partial melting occurring in MORB, metasediments and peridotite of subducted oceanic lithosphere are discussed on the basis of available experimental work and phase equilibrium modeling. Phase diagrams of hydrous MORB show that in most cold subduction P-T （pressure-temperature） regimes a large portion of water in the basic layer has released below the onset of blueschist facies （〈 20 km）, and at a depth （60--70 km） of transition from lawsonite blueschist to lawsonite eclogite facies through glaucophane dehydration; only a smaller portion of water will escape from the slab through dehydration of lawsonite and chloritoid in the depth range suitable for arc magma formation; and a very small portion of water stored in lawsonite and phengite will fade into the deeper mantle. The role of amphibole for arc magma formation is still arguable. In cold subduction P-Tregimes, the dehydration of chlorite and talc in AI-poor metasediments, and chloritoid and carpholite in AI-rich metapelites at a depth around 80--100 km will make some con- tributions to the formation of arc magma. Comparatively, dehydration of serpentine in hydrated peri- dotite occurs at depths of 120--180 km, playing an important role in the arc magmatism. Subduction of oceanic crust along warm P-T regimes will cross the solidi at a depth over 80 km, resulting in partial melting under fluid-saturated and fluid-absent conditions in the metasediments involving biotite and phengite, and in the basic rocks involving epidote and amphibole. The melt compositions of the basic crust are adakitic at pressures 〈 3.0 GPa, but become peraluminous granitic at higher pressures.     

Sentiment Analysis of Code-Mixed Bambara-French Social Media Text Using Deep Learning Techniques

The global growth of the Internet and the rapid expansion of social networks such as Facebook make multilingual sentiment analysis of social media content very necessary. This paper performs the first sentiment analysis on code-mixed Bambara-French Facebook comments. We develop four Long Short-term Memory (LSTM)-based models and two Convolutional Neural Network (CNN)-based models, and use these six models, Naïve Bayes, and Support Vector Machines (SVM) to conduct experiments on a constituted dataset. Social media text written in Bambara is scarce. To mitigate this weakness, this paper uses dictionaries of character and word indexes to produce character and word embedding in place of pre-trained word vectors. We investigate the effect of comment length on the models and perform a comparison among them. The best performing model is a one-layer CNN deep learning model with an accuracy of 83.23 %.     

Analysis of fragrance composition in three cultivars of <Emphasis Type="Italic">Osmanthus fragrans</Emphasis> Albus group flower by gas chromatography-mass spectrometry

With supercritical CO2 fluid extraction(SCFE), essential oil was extracted from three cultivars of Xianning osmanthus. The fresh osmanthus flower was processed with a petroleum ether digestion method to produce the extractum. The yields of essential oil and extractum were 0.19 % and 0.13 % (m/m) respectively. The essential oil and fragrance composition and content extracted were analyzed with gas chromatography-mass spectrometer (GC-MS). The result showed that essential oil contained 36.99%(area/total area) of ionone, ionol and 13.11% of linalool; ionone and ionol contained in extractum were as high as up to 33.33%, while linalool up to 21.92%. Whether essential oil or extractum contains only about 40% fat acid and other ester matters. None of environmental estrogen (phthalic ester) was found in fragrance ingredients. The result also showed that the quality of O. fragrans Albus group fragrance in Xianning is better than that produced in Hangzhou and Anhui districts.     

Application of artificial neural networks in global climate change and ecological research: An overview

Fields that employ artificial neural networks (ANNs) have developed and expanded continuously in recent years with the ongoing development of computer technology and artificial intelligence. ANN has been adopted widely and put into practice by researchers in light of increasing concerns over ecological issues such as global warming, frequent El Niño-Southern Oscillation (ENSO) events, and atmospheric circulation anomalies. Limitations exist and there is a potential risk for misuse in that ANN model parameters require typically higher overall sensitivity, and the chosen network structure is generally more dependent upon individual experience. ANNs, however, are relatively accurate when used for short-term predictions; despite global climate change research favoring the effects of interactions as the basis of study and the preference for long-term experimental research. ANNs remain a better choice than many traditional methods when dealing with nonlinear problems, and possesses great potential for the study of global climate change and ecological issues. ANNs can resolve problems that other methods cannot. This is especially true for situations in which measurements are difficult to conduct or when only incomplete data are available. It is anticipated that ANNs will be widely adopted and then further developed for global climate change and ecological research.