Technical Efficiency in the Iron and Steel Industry: A Stochastic Frontier Approach

In this paper we examine the technical efficiency of firms in the iron and steel industry and try to identify the factors contributing to the industry's efficiency growth, using a time-varying stochastic frontier model. Based on our findings, which pertain to 52 iron and steel firms over the period of 1978-1997, POSCO and Nippon Steel were the most efficient firms, with their production, on average, exceeding 95 percent of their potential output. Our findings also shed light on possible sources of efficiency growth in the industry. If a firm is government-owned, its privatization is likely to improve its technical efficiency to a great extent. A firm's technical efficiency also tends to be positively related to its production level as measured by a share of the total world production of crude steel. Another important source of efficiency growth identified by our empirical findings is adoption of new technologies and equipment. Our findings clearly indicate that continued efforts to update technologies and equipment are critical in pursuit of efficiency in the iron and steel industry.

Roles of zinc and metallothionein-3 in oxidative stress-induced lysosomal dysfunction, cell death, and autophagy in neurons and astrocytes

Zinc dyshomeostasis has been recognized as an important mechanism for cell death in acute brain injury. An increase in the level of free or histochemically reactive zinc in astrocytes and neurons is considered one of the major causes of death of these cells in ischemia and trauma. Although zinc dyshomeostasis can lead to cell death via diverse routes, the major pathway appears to involve oxidative stress

Curvature-enhanced spin-orbit coupling in a carbon nanotube

Structure of the spin-orbit coupling varies from material to material and thus finding the correct spin-orbit coupling structure is an important step towards advanced spintronic applications. We show theoretically that the curvature in a carbon nanotube generates two types of the spin-orbit coupling, one of which was not recognized before. In addition to the topological phase-related contribution of the spin-orbit coupling, which appears in the off-diagonal part of the effective Dirac Hamiltonian of carbon nanotubes, there is another contribution that appears in the diagonal part. The existence of the diagonal term can modify spin-orbit coupling effects qualitatively, an example of which is the electron-hole asymmetric spin splitting observed recently, and generate four qualitatively different behavior of energy level dependence on parallel magnetic field. It is demonstrated that the diagonal term applies to a curved graphene as well. This result should be valuable for spintronic applications of graphitic materials.Comment: 6 pages, 4 figures, to be published on Physical Review

Application of Recent Developments in Deep Learning to ANN-based Automatic Berthing Systems

Previous studies on Artificial Neural Network (ANN)-based automatic berthing showed considerable increases in performance by training ANNs with a set of berthing datasets. However, the berthing performance deteriorated when an extrapolated initial position was given. To overcome the extrapolation problem and improve the training performance, recent developments in Deep Learning (DL) are adopted in this paper. Recent activation functions, weight initialization methods, input data-scaling methods, a higher number of hidden layers, and Batch Normalization (BN) are considered, and their effectiveness has been analyzed based on loss functions, berthing performance histories, and berthing trajectories. Finally, it is shown that the use of recent activation and weight initialization method results in faster training convergence and a higher number of hidden layers. This leads to a better berthing performance over the training dataset. It is found that application of the BN can overcome the extrapolated initial position problem