国有企业管理创新

在市场经济条件下，国有企业要生存和发展，就必须重视和加强管理创新。文中就企业创新管理中的理念、决策、战略、组织结构、人本管理这5个方面的管理创新等问题进行了阐述，对企业创新管理具有一定的指导意义。     

Small-angle X-ray scattering studies on melting and recrystallization behaviors of poly(oxyethylene) crystallites in poly(d,l-lactide)/poly(oxyethylene) blends

Direct determination of the discrete distribution for crystalline lamellar thickness has been performed for poly(d,l-lactic acid)/poly(oxyethylene) (PDLLA/PEG) blends by conducting small-angle X-ray scattering (SAXS) measurements using synchrotron radiation. The PDLLA used was an random (racemic) copolymer of bio-based poly(l-lactic acid) (PLLA) and poly(d-lactic acid) (PDLA) with the lactide monomer ratio of l:d = 50:50. It is known that PLA is miscible with PEG in the amorphous state. In the current paper, we report comprehensive results on structural analyses of PDLLA/PEG blends in the course of heating and cooling process using SAXS to elucidate the change in the thickness distribution of the lamellae. As a consequence, it was found that the distribution of the lamellar thickness moves toward the larger value (in other words, lamellar thickening) as temperature approaches the melting point. Typically, the thickness distribution was dispersed in the range of 10–20 nm at room temperature and it changed toward 40 nm in the vicinity of the melting temperature. To the best of our knowledge, this is the first report of direct determination of the discrete distribution for the crystalline lamellar thickness and their in-situ changes in the course of the lamellar thickening process. As a result, the lamellar thickening was found to occur at much lower temperature for the blend samples with 10% and 20% of PDLLA contents as compared to the PEG 100% sample. This phenomenon can be ascribed to the melting point depression owing to the miscibility between PEG and PDLLA. Thereby, thinner lamellae were melted and thicker ones appeared at much lower temperature for the blends than for the PEG 100% sample. As for the average repeating distance (long period) of the lamellar stacks, an abrupt increase similar to the critical divergence was observed (from 25 nm to 50 nm) in the heating process. Not only for the melting behavior but also in the course of recrystallization, change in the lamellar-thickness distribution was uncovered, which shows strong hysteresis depending on what temperature the sample was cooled down from.     

Chromium(III) compounds as catalysts for Hydrocarbon Oxidation and hydroperoxide decomposition

Chromium stearate and chromium acetylacetonate are very active catalysts both for the oxidation of hydrocarbons by molecular oxygen and for the decomposition of organic hydroperoxides. During these reactions they also catalyze the oxidation of secondary alcohols to the corresponding ketones by organic hydroperoxides. From organic hydroperoxides and chromium(III)compounds chromium (VI) compounds are formed which are probably the effective agents oxidizing secondary alcohols to ketones.     

Fabrication and Thermal Dissipation Properties of Carbon Nanofibers Derived from Electrospun Poly(Amic Acid) Carboxylate Salt Nanofibers

Polyimides (PIs) possess excellent mechanical properties, thermal stability, and chemical resistance and can be converted to carbon materials by thermal carbonization. The preparation of carbon nanomaterials by carbonizing PI‐based nanomaterials, however, has been less studied. In this work, the fabrication of PI nanofibers is investigated using electrospinning and their transformation to carbon nanofibers. Poly(amic acid) carboxylate salts (PAASs) solutions are first electrospun to form PAAS nanofibers. After the imidization and carbonization processes, PI and carbon nanofibers can then be obtained, respectively. The Raman spectra reveal that the carbon nanofibers are partially graphitized by the carbonization process. The diameters of the PI nanofibers are observed to be smaller than those of the PAAS nanofibers because of the formation of the more densely packed structures after the imidization processes; the diameters of the carbon nanofibers remain similar to those of the PI nanofibers after the carbonization process. The thermal dissipation behaviors of the PI and carbon nanofibers are also examined. The infrared images indicate that the transfer rates of thermal energy for the carbon nanofibers are higher than those for the PI nanofibers, due to the better thermal conductivity of carbon caused by the covalent sp² bonding between carbon atoms.     

Phase Stability and Physical Properties of Cubic and Tetragonal ZrO2 in the System ZrO2–Y2O3–Ta2O5

The cubic ( c -ZrO₂) and tetragonal zirconia ( t -ZrO₂) phase stability regions in the system ZrO₂–Y₂O₃–Ta₂O₅ were delineated. The c -ZrO₂ solid solutions are formed with the fluorite structure. The t -ZrO₂ solid solutions having a c/a axial ratio (tetragonality) smaller than 1.0203 display high fracture toughness (5 to 14 MPa · m^1/2), and their instability/transformability to monoclinic zirconia ( m -ZrO₂) increases with increasing tetragonality. On the other hand, the t -ZrO₂ solid solutions stabilized at room temperature with tetragonality greater than 1.0203 have low toughness values (2 to 5 MPa · m^1/2), and their transformability is not related to the tetragonality.     

Spatial prediction of landslide susceptibility using data mining-based kernel logistic regression,naive Bayes and RBFNetwork models for the Long County area (China)

The main goal of this study is to assess and compare three advanced machine learning techniques, namely, kernel logistic regression (KLR), naïve Bayes (NB), and radial basis function network (RBFNetwork) models for landslide susceptibility modeling in Long County, China. First, a total of 171 landslide locations were identified within the study area using historical reports, aerial photographs, and extensive field surveys. All the landslides were randomly separated into two parts with a ratio of 70/30 for training and validation purposes. Second, 12 landslide conditioning factors were prepared for landslide susceptibility modeling, including slope aspect, slope angle, plan curvature, profile curvature, elevation, distance to faults, distance to rivers, distance to roads, lithology, NDVI (normalized difference vegetation index), land use, and rainfall. Third, the correlations between the conditioning factors and the occurrence of landslides were analyzed using normalized frequency ratios. A multicollinearity analysis of the landslide conditioning factors was carried out using tolerances and variance inflation factor (VIF) methods. Feature selection was performed using the chi-squared statistic with a 10-fold cross-validation technique to assess the predictive capabilities of the landslide conditioning factors. Then, the landslide conditioning factors with null predictive ability were excluded in order to optimize the landslide models. Finally, the trained KLR, NB, and RBFNetwork models were used to construct landslide susceptibility maps. The receiver operating characteristics (ROC) curve, the area under the curve (AUC), and several statistical measures, such as accuracy (ACC), F-measure, mean absolute error (MAE), and root mean squared error (RMSE), were used for the assessment, validation, and comparison of the resulting models in order to choose the best model in this study. The validation results show that all three models exhibit reasonably good performance, and the KLR model exhibits the most stable and best performance. The KLR model, which has a success rate of 0.847 and a prediction rate of 0.749, is a promising technique for landslide susceptibility mapping. Given the outcomes of the study, all three models could be used efficiently for landslide susceptibility analysis.

     

Invariant 2D object recognition using KRA and GRA

Computer vision has been extensively adopted in industry for the last two decades. It enhances productivity and quality management, and is flexibility, efficient, fast, inexpensive, reliable and robust. This study presents a new translation, rotation and scaling-free object recognition method for 2D objects. The proposed method comprises two parts: KRA feature extractor and GRA classifier. The KRA feature extractor employs K-curvature, re-sampling, and autocorrelation transformation to extract unique features of objects, and then gray relational analysis (GRA) classifies the extracted invariant features. The boundary of the digital object was first represented as the form of the K-curvature over a given region of support, and was then re-sampled and transformed with autocorrelation function. After that, the extracted features own the unique property that is invariant to translation, rotation and scaling. To verify and validate the proposed method, 50 synthetic and 50 real objects were digitized as standard patterns, and 10 extra images of each object (test images) which were taken at different positions, orientations and scales, were acquired and compared with the standard patterns. The experimental results reveal that the proposed method with either GRA or MD methods is effective and reliable for part recognition.     

ZnO spintronics and nanowire devices

ZnO is a very promising material for spintronics applications, with many groups reporting room-temperature ferromagnetism in films doped with transition metals during growth or by ion implantation. In films doped with Mn during pulsed laser deposition (PLD), we find an inverse correlation between magnetization and electron density as controlled by Sn-doping. The saturation magnetization and coercivity of the implanted single-phase films were both strong functions of the initial anneal temperature, suggesting that carrier concentration alone cannot account for the magnetic properties of ZnO:Mn and factors such as crystalline quality and residual defects play a role. Plausible mechanisms for ferromagnetism include the bound magnetic polaron model or exchange that is mediated by carriers in a spin-split impurity band derived from extended donor orbitals. The progress in ZnO nanowires is also reviewed. The large surface area of nanorods makes them attractive for gas and chemical sensing, and the ability to control their nucleation sites makes them candidates for microlasers or memory arrays. Single ZnO nanowire depletion-mode metal-oxide semiconductor field effect transistors exhibit good saturation behavior, threshold voltage of ∼−3 V, and a maximum transconductance of 0.3 mS/mm. Under ultraviolet (UV) illumination, the drain-source current increased by approximately a factor of 5 and the maximum transconductance was ∼5 mS/mm. The channel mobility is estimated to be ∼3 cm²/Vss, comparable to that for thin film ZnO enhancement mode metal-oxide semiconductor field effect transistors (MOSFETs), and the on/off ratio was ∼25 in the dark and ∼125 under UV illumination. The Pt Schottky diodes exhibit excellent ideality factors of 1.1 at 25°C, very low reverse currents, and a strong photoresponse, with only a minor component with long decay times thought to originate from surface states. In the temperature range from 25°C to 150°C, the resistivity of nanorods treated in H₂ at 400°C prior to measurement showed an activation energy of 0.089 eV and was insensitive to ambient used. By contrast, the conductivity of nanorods not treated in H₂ was sensitive to trace concentrations of gases in the measurement ambient even at room temperature, demonstrating their potential as gas sensors. Sensitive pH sensors using single ZnO nanowires have also been fabricated.