Predicting Merger Outcomes: The Accuracy of Stock Market Event Studies, Market Structure Characteristics, and Agency Decisions

Merger analysis is an exercise in prediction. This paper analyzes the accuracy of two leading methods of predicting merger outcomes—stock market event studies and an approach using market structure criteria—as well as the accuracy of antitrust agencies’ decisions about challenging mergers. The basis for these evaluations is a database of price effects of 41 mergers compiled from published retrospective studies of mergers. This paper finds that event studies systematically underpredict the incidence of anticompetitive outcomes, while market structure criteria overpredict competitive problems. Agency decisions correct much of that overprediction, however, which suggests that structural criteria may serve as an appropriate first screen

Do Chinese Farmers Misuse Pesticide Intentionally or Not?

Nonstandard pesticide-application behavior leads to excessive pesticide residue and even affects the quality and safety of agricultural products and agricultural sustainability. Based on 968 valid samples randomly selected in Jiangsu Province of China, it focuses on the impact of incident shock and yield fluctuation avoidance on the pesticide-application behavior of farmers. Then, it investigated the impact of intentional factors, such as insufficient cognition and lack of knowledge, on their improper pesticide-application behavior. This study shows that, besides the pursuit of improper income, inadequate awareness and preventive actions to avoid operational risks are also important factors in farmers’ nonstandard pesticide application. In addition, the study also shows that farmers who understand the responsibility unit of agricultural product quality and safety supervision are more inclined to choose standardized application of pesticides. The higher the education level of farmers, the higher the probability of standardized application of pesticides. Therefore, farmers’ nonstandard pesticide-application behavior is largely due to the farmers’ insufficient awareness of the harm of pesticide residues or the lack of trust in the efficacy of pesticides. Moreover, the study also shows that adverse selection phenomenon exists in pesticide-application training

Synthesis of Hexagonal Prism-like ZnO Nanorods and Their Structure and Optical Properties

Hexagonal prism-like Al-doped ZnO nanorods were prepared by a coprecipitation method followed by annealing. The effects of Al doping and annealing atmosphere on microstructure and optical properties were studied. X-ray diffraction (XRD) results show that the Al-doped nanorods have a pure hexagonal wurtzite structure and good crystallinity. Field emission scanning electron microscope (FESEM) and transmission electron microscope (TEM) experiments confirm the rod morphology, single crystal and a-axis preference growth of Al-doped ZnO. Energy Dispersive X-Ray Spectroscopy (EDX), Raman and UV-VIS-NIR spectra analyses indicate the successful substitutional doping of Al in the ZnO lattice. Diffuse reflectance at near-ultraviolet and visible wavelengths analyses indicate the suppression of the optical absorption for Al-doped ZnO compared to pristine ZnO, which may be related to the resistance to color center formation. The optimal optical properties Al-doped ZnO nanorods (high near-infrared absorption and no formation of the yellow coloration) are reached by combining H-2 atmosphere annealing with Al doping

Microwave Synthesis of AlFeCuCrNi /TiB

The Al-Fe-Cu-Cr-Ni-Ti-B system was microwaved to generate high entropy alloy matrix composites reinforced by TiB2 particles. The micro structure and reaction process of the composites were observed and investigated by modern analysis methods, including X-ray diffraction (XRD), scanning electron microscopy (SEM), X-ray energy dispersive spectroscopy (EDS) and differential scanning calorimeter (DSC) analysis. The results show that AlFeCuCrNi /TiB2 composites can be prepared by microwave heating method. The matrix structure was FCC, and the reinforcement TiB2 showed regular geometric morphology in the matrix and evenly distributed in the matrix when the volume fraction of the reinforcement is 10%. When the volume fraction of the reinforcement increased to 15%, TiB2 partially aggregates in the matrix, and the system activation energy was 195.69 kJ/mol

Titania Embedded with Nanostructured Sodium Titanate: Reduced Thermal Conductivity for Thermoelectric Application

Titania embedded with layer-cracking nanostructures (sodium titanate) was synthesized by a hydrothermal method and a subsequent sintering process. The structure and morphology were determined by x-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and N-2 adsorption-desorption experiments. In thermoelectric investigations, this nanocomposite has reduced thermal conductivity, where the minimum reaches about 2.4 W/m K at 700A degrees C. This value is relatively low among the transition-metal oxides. Strong boundary scattering at the interfaces of the layered nanostructures and point defect scattering resulting from volatilization of Na+ ions seem to be main reasons for the suppression of phonon heat transfer. On the other hand, the power factor shows no apparent deterioration. Our results suggest that introduction of proper layer-cracking nanostructures into thermoelectric hosts might be effective to enhance their performance

Predicting forest height using the GOST, Landsat 7 ETM+, and airborne LiDAR for sloping terrains in the Greater Khingan Mountains of China

Sloping terrain of forests is an overlooked factor in many models simulating the canopy bidirectional reflectance distribution function, which limits the estimation accuracy of forest vertical structure parameters (e.g., forest height). The primary objective of this study was to predict forest height on sloping terrain over large areas with the Geometric-Optical Model for Sloping Terrains (GOST) using airborne Light Detection and Ranging (LiDAR) data and Landsat 7 imagery in the western Greater Khingan Mountains of China. The Sequential Maximum Angle Convex Cone (SMACC) algorithm was used to generate image endmembers and corresponding abundances in Landsat imagery. Then, LiDAR-derived forest metrics, topographical factors and SMACC abundances were used to calibrate and validate the GOST, which aimed to accurately decompose the SMACC mixed forest pixels into sunlit crown, sunlit background and shade components. Finally, the forest height of the study area was retrieved based on a back-propagation neural network and a look-up table. Results showed good performance for coniferous forests on all slopes and at all aspects, with significant coefficients of determination above 0.70 and root mean square errors (RMSEs) between 0.50 m and 1.00 m based on ground observed validation data. Higher RMSEs were found in areas with forest heights below 5 m and above 17 m. For 90% of the forested area, the average RMSE was 3.58 m. Our study demonstrates the tremendous potential of the GOST for quantitative mapping of forest height on sloping terrains with multispectral and LiDAR inputs

Predicting forest height using the GOST, Landsat 7 ETM+, and airborne LiDAR for sloping terrains in the Greater Khingan Mountains of China

\u3cp\u3eSloping terrain of forests is an overlooked factor in many models simulating the canopy bidirectional reflectance distribution function, which limits the estimation accuracy of forest vertical structure parameters (e.g., forest height). The primary objective of this study was to predict forest height on sloping terrain over large areas with the Geometric-Optical Model for Sloping Terrains (GOST) using airborne Light Detection and Ranging (LiDAR) data and Landsat 7 imagery in the western Greater Khingan Mountains of China. The Sequential Maximum Angle Convex Cone (SMACC) algorithm was used to generate image endmembers and corresponding abundances in Landsat imagery. Then, LiDAR-derived forest metrics, topographical factors and SMACC abundances were used to calibrate and validate the GOST, which aimed to accurately decompose the SMACC mixed forest pixels into sunlit crown, sunlit background and shade components. Finally, the forest height of the study area was retrieved based on a back-propagation neural network and a look-up table. Results showed good performance for coniferous forests on all slopes and at all aspects, with significant coefficients of determination above 0.70 and root mean square errors (RMSEs) between 0.50 m and 1.00 m based on ground observed validation data. Higher RMSEs were found in areas with forest heights below 5 m and above 17 m. For 90% of the forested area, the average RMSE was 3.58 m. Our study demonstrates the tremendous potential of the GOST for quantitative mapping of forest height on sloping terrains with multispectral and LiDAR inputs.\u3c/p\u3

Gravitational Surface Vortex Formation and Suppression Control: A Review from Hydrodynamic Characteristics

The energy-conversion stability of hydropower is critical to satisfy the growing demand for electricity. In low-head hydropower plants, a gravitational surface vortex is easily generated, which causes irregular shock vibrations that damage turbine performance and input-flow stability. The gravitational surface vortex is a complex fluid dynamic problem with high nonlinear features. Here, we thoroughly investigate its essential hydrodynamic properties, such as Ekman layer transport, heat/mass transfer, pressure pulsation, and vortex-induced vibration, and we note some significant scientific issues as well as future research directions and opportunities. Our findings show that the turbulent Ekman layer analytical solution and vortex multi-scale modeling technology, the working condition of the vortex across the scale heat/mass transfer mechanism, the high-precision measurement technology for high-speed turbulent vortexes, and the gas–liquid–solid three-phase vortex dynamics model are the main research directions. The vortex-induced vibration transition mechanism of particle flow in complex restricted pipelines, as well as the improvement of signal processing algorithms and a better design of anti-spin/vortex elimination devices, continue to draw attention. The relevant result can offer a helpful reference for fluid-induced vibration detection and provide a technical solution for hydropower energy conversion