Can Political Skill Enhance Business and IT Knowledge?

ABSTRACT

We examine whether Chief Information Officers’ (CIOs’) political skill enhances their IT and business knowledge as a means of influencing executive teams’ commitment to strategic and operational IT initiatives. We empirically examine these relationships using data collected from 139 CIOs. The results suggest CIOs’ business and IT knowledge is significantly related to influencing executive team commitment to strategic and operational IT initiatives, but political skill only enhances business knowledge for influencing executive team commitment to operational IT initiatives.     

Supply chain financing with advance selling under disruption

We study a financing problem in a supply chain (SC) consisting of one supplier and one buyer under supply disruption. The supplier could face a disruption at its end which could effectively reduce its yield in case of disruption, thereby resulting in supply yield uncertainty. The retailer can finance the supplier using advance selling that can help mitigate the impact of disruption. We model this problem as a Stackelberg game, where the supplier as the leader announces the wholesale price and the retailer responds by deciding its optimal order quantity given stochastic demand and an exogenous fixed retail price. The supplier then commences production and a disruption can happen with a known probability. We assume that under disruption the quantity delivered is a fraction of the initial quantity ordered by the retailer. The retailer loses any unmet demand. We analyze three different scenarios of the Stackelberg game, namely no advance selling with disruption, advance selling without disruption, and advance selling with disruption. Our results indicate that advance selling can be used to mitigate the impact of supply disruption and at the same time could lead to an increase in the overall SC profit.     

基于IEEE 802.15.4/ZigBee的定位监测系统

本文使用飞思卡尔的ZigBee技术实现了一个低成本、低功耗的定位监测系统,该系统适用于室内环境,而这正是其他定位系统所不能比拟的.本文可帮助系统设计者了解协作式定位的基本概念.     

Nanoparticle stability: Processing pathways for solvent removal

Processing of hydrophobic organic compounds into nanoparticle form by rapid precipitation requires the use of organic solvents in which the compounds are initially dissolved. Mixing with an antisolvent induces supersaturation, nucleation and particle growth. In most cases the nanoparticle is to be dispersed in an aqueous phase and so the antisolvent is water and the organic solvent is a water-miscible organic. Ultimately the organic solvent must be removed because its presence either has toxicological consequences or it reduces particle stability against Ostwald ripening. Dialysis is commonly used to remove organics; however, the process is slow and costly. We present an efficient and scalable process of solvent removal based on flash evaporation. The non-ideality of the water:tetrahydrofuran (THF) activity coefficient makes the process especially effective. The results are compared against phase equilibria calculations and two flash stages are shown to reduce THF concentrations by over 95%, and bring the residual solvent concentration within acceptable limits for drug applications.     

Efficient and Simple Method for Designing Chaotic S‐Boxes

A substitution box (S‐box) plays a central role in cryptographic algorithms. In this paper, an efficient method for designing S‐boxes based on chaotic maps is proposed. The proposed method is based on the mixing property of piecewise linear chaotic maps. The S‐box so constructed has very low differential and linear approximation probabilities. The proposed S‐box is more secure against differential and linear cryptanalysis compared to recently proposed chaotic S‐boxes.     

Life cycle assessment of solar PV based electricity generation systems: A review

Sustainable development requires methods and tools to measure and compare the environmental impacts of human activities for various products viz. goods, services, etc. This paper presents a review of life cycle assessment (LCA) of solar PV based electricity generation systems. Mass and energy flow over the complete production process starting from silica extraction to the final panel assembling has been considered. Life cycle assessment of amorphous, mono-crystalline, poly-crystalline and most advanced and consolidate technologies for the solar panel production has been studied.     

The role of carbon capture technologies in greenhouse gas emissions-reduction models: A parametric study for the U.S. power sector

This paper analyzes the potential contribution of carbon capture and storage (CCS) technologies to greenhouse gas emissions reductions in the U.S. electricity sector. Focusing on capture systems for coal-fired power plants until 2030, a sensitivity analysis of key CCS parameters is performed to gain insight into the role that CCS can play in future mitigation scenarios and to explore implications of large-scale CCS deployment. By integrating important parameters for CCS technologies into a carbon-abatement model similar to the EPRI Prism analysis (EPRI, 2007), this study concludes that the start time and rate of technology diffusion are important in determining emissions reductions and fuel consumption for CCS technologies. Comparisons with legislative emissions targets illustrate that CCS alone is very unlikely to meet reduction targets for the electric-power sector, even under aggressive deployment scenarios. A portfolio of supply and demand-side strategies is needed to reach emissions objectives, especially in the near term. Furthermore, model results show that the breakdown of capture technologies does not have a significant influence on potential emissions reductions. However, the level of CCS retrofits at existing plants and the eligibility of CCS for new subcritical plants have large effects on the extent of greenhouse gas emissions reductions.     

A dynamic threshold-based local mesh ternary pattern technique for biomedical image retrieval

Many content-based image retrieval techniques like local binary pattern (LBP), local ternary pattern (LTP), local mesh peak valley edge pattern (LMePVEP), local mesh ternary pattern (LMeTerP), etc. extract texture features of an image for retrieval purposes. These techniques use fixed threshold to encode the input image and selection of such threshold value is not guided, that is, a chosen threshold may not be optimal for all images in the database. Moreover the performance of these texture-based static threshold algorithms also decreases if the input images are noisy. In this paper, a dynamic threshold value-based local mesh ternary pattern method is proposed in which the threshold value is chosen from the neighborhood of a central pixel using median of all pixels. The proposed modification reduces the overall effect of noise component and thereby improves the average retrieval rate (ARR) and average retrieval precision (ARP) of the original technique. The proposed modified technique has been compared with five other image retrieval approaches to prove its worthiness - the original local mesh ternary pattern technique (LMeTerP), a local ternary pattern technique (LTP), a Best ensemble technique, a multi-label classification CNN model and a CNN-based model of the proposed approach using a VIA ELCAP lung database and an Emphysema database. An improvement of 3.92% in ARR and 2.53% in ARP is observed over the basic local mesh ternary pattern method. Further the proposed modification has been combined with CNN concept and its results have also been analyzed.