Development of optical anisotropy in vitrains during carbonization

The purpose of this work was to examine the possible significance in the formation of metallurgical coke of the anisotropic spherical mesophase exemplified by that found during the carbonization of pitch-like materials, and to ascertain if the various types of optical anisotropy found in coke could form a basis for the characterization of cokes produced from different coals. Vitrains from a wide range of coals were carbonized at temperatures from 370 to 1000 °C and the types and amounts of optical anisotropy in the resulting semi-cokes and cokes were determined from microscopic examination, the anisotropic components being classified according to grain size of the granular mosaics and appearance. The anisotropy developed directly from the isotropic phase, appearing initially as a fine-grained mosaic. With increasing carbonization temperature, this fine-grained mosaic was transformed into progressively coarser-grained anisotropy, the extent of this transformation depending on the rank of the vitrain. It is therefore concluded that the formation, growth and coalescence of anisotropic spherical bodies, such as occurs during the carbonization of pitch, is not a necessary precursor of the mosaic anisotropy in coke. The type and amount of anisotropy developed provide a quantitative means of characterising different cokes.     

Entropy based spatial domain image watermarking and its performance analysis

Digital image watermarking technique based on LSB Substitution and Hill Cipher is presented and examined in this paper. For better imperceptibility watermark is inserted in the spatial domain. Further the watermark is implanted in the Cover Image block having the highest entropy value. To improve the security of the watermark hill cipher encryption is used. Both subjective and objective image quality assessment technique has been used to evaluate the imperceptibility of the proposed scheme.Further, the perceptual perfection of the watermarked pictures accomplished in the proposed framework has been contrasted and some state-of-art watermarking strategies. Test results demonstrates that the displayed method is robust against different image processing attacks like Salt and Peppers, Gaussian filter attack, Median filter attacks, etc.

     

Location of steel reinforcement in concrete using ground penetrating radar and neural networks

Ground-penetrating radar is becoming increasingly popular for use as a non-destructive assessment method for investigating reinforced concrete structures. The amount of data collected however can be very large and take a significant level of subjective experience to interpret. This study focuses upon the use of a neural network approach to automate and facilitate the post-processing of ground penetrating radar results. The radar data is reduced to a simplified data set by using an edge detection routine. Signal reflections from reinforcing bars displaying a hyperbolic image format are detected using a multi-layer perceptron (MLP) network with a single hidden layer containing 8 nodes to recognise a simplified hyperbolic shape. Training and testing of the network was carried out making use of an emulsion analogue tank, simulating the properties of concrete, and using real concrete specimens. The results showed that the use of a MLP neural network approach could be quite effective in automating the identification and location of embedded steel reinforcing bars from a radar investigation. Accurate estimation of depth, or cover, requires a reliable knowledge of the dielectric properties of the concrete, and recent work using a specially-developed wideband horn antenna for direct determination of in situ properties is also outlined.     

Evolution of the cranial computed tomography scan in child abuse

Computed tomography (CT) scans obtained at the time of clinical presentation have occasionally been reported to be normal in children with history and findings of significant abusive head injury. We have retrospectively observed abnormalities in "normal" scans of some similar children. We have also seen abnormalities develop on serial scanning. To determine how frequently these situations occur, we reviewed charts of 34 children with a final diagnosis of child abuse who also had cranial CT scans performed. Their CT scans were retrospectively reviewed by a pediatric radiologist. Eleven (11/34) CT scans had initially been interpreted as normal. Four (4/11) of these had been reinterpreted during the hospitalization as abnormal, affecting medical (1) and legal (3) outcome. Repeat scanning in three of the remaining seven resulted in surgical drainage of a subdural effusion (1) and affected legal outcome (2). Four of the seven initial scans felt normal throughout the hospitalizations were judged abnormal on retrospective review. This evaluation was confirmed in the two rescanned. Initial CT interpretation most often failed to appreciate changes in parenchymal density and small amounts of falcine or cortical subdural blood. Subsequent scans also showed evolving effusions and infarcts. Changes were noted in 1 1/2 to 5 days. The CT scan frequently shows subtle changes in the immediate posttrauma period. If the child does not recover promptly, subsequent scans frequently result in significant changes in clinical and legal management.     

Healing Effect of Controlled Anti‐Electromigration on Conventional and High‐Tc Superconducting Nanowires

The electromigration process has the potential capability to move atoms one by one when properly controlled. It is therefore an appealing tool to tune the cross section of monoatomic compounds with ultimate resolution or, in the case of polyatomic compounds, to change the stoichiometry with the same atomic precision. As demonstrated here, a combination of electromigration and anti‐electromigration can be used to reversibly displace atoms with a high degree of control. This enables a fine adjustment of the superconducting properties of Al weak links, whereas in Nb the diffusion of atoms leads to a more irreversible process. In a superconductor with a complex unit cell (La_2?x Ce_x CuO₄), the electromigration process acts selectively on the oxygen atoms with no apparent modification of the structure. This allows to adjust the doping of this compound and switch from a superconducting to an insulating state in a nearly reversible fashion. In addition, the conditions needed to replace feedback controlled electromigration by a simpler technique of electropulsing are discussed. These findings have a direct practical application as a method to explore the dependence of the characteristic parameters on the exact oxygen content and pave the way for a reversible control of local properties of nanowires.     

Uncertain supply chain network design considering carbon footprint and social factors using two-stage approach

Sustainable development has become one of the leading global issues over the period of time. Currently, implementation of sustainability in supply chain has been continuously in center of attention due to introducing stringent legislations regarding environmental pollution by various governments and increasing stakeholders’ concerns toward social injustice. Unfortunately, literature is still scarce on studies considering all three dimensions (economical, environmental and social) of sustainability for the supply chain. An effective supply chain network design (SCND) is very important to implement sustainability in supply chain. This study proposes an uncertain SCND model that minimizes the total supply chain-oriented cost and determines the opening of plants, warehouses and flow of materials across the supply chain network by considering various carbon emissions and social factors. In this study, a new AHP and fuzzy TOPSIS-based methodology is proposed to transform qualitative social factors into quantitative social index, which is subsequently used in chance-constrained SCND model with an aim at reducing negative social impact. Further, the carbon emission of supply chain is estimated by considering a composite emission that consists of raw material, production, transportation and handling emissions. In the model, a carbon emission cap is imposed on total supply chain to reduce the carbon footprint of supply chain. To solve the proposed model, a code is developed in AMPL software using a nonlinear solver SNOPT. The applicability of the proposed model is illustrated with a numerical example. The sensitivity analysis examines the effects of reducing carbon footprint cap, negative social impacts and varying probability on the total cost of the supply chain. It is observed that a stricter carbon cap over supply chain network leads to opening of more plants across the supply chain. In addition, carbon footprint of supply chain is found to be decreased in certain extent with the reduction in negative social impacts from suppliers. The carbon footprint of the supply chain is found to be reduced with increasing certainty of material supply from the suppliers. The total supply chain cost is observed to be augmented with increasing probability.     

Strategy, networks and systems in the global translation services market

The globalisation of markets has led to an increased demand for language translation services that support and enable communication between economic partners. For example, technical documents, software systems, business documents and web sites all need to be translated into multiple languages for individual national markets, and the information that they contain changes periodically. This paper sets out a theoretical framework that describes the architectures of business processes within and between separate firms that are used to support the delivery and management of services. This is done by coordinating the fit between externally generated problem complexity, from customers, and the internally generated complexity of different potential network configuration solutions. The theoretical framework is an architecture of how complexity is generated and managed at the different structural levels and across the different processual stages of an industry. A case study of thebigword, a major international translation services company, illustrates how the framework is applied in practice. The case study analyses the implementation of an electronic market platform which enables the coordination of the different stakeholders involved in the translation services market. These stakeholders include translators, translation services companies and their clients in a global business network.     

Reliable representations for association rules

Association rule mining has contributed to many advances in the area of knowledge discovery. However, the quality of the discovered association rules is a big concern and has drawn more and more attention recently. One problem with the quality of the discovered association rules is the huge size of the extracted rule set. Often for a dataset, a huge number of rules can be extracted, but many of them can be redundant to other rules and thus useless in practice. Mining non-redundant rules is a promising approach to solve this problem. In this paper, we first propose a definition for redundancy, then propose a concise representation, called a Reliable basis, for representing non-redundant association rules. The Reliable basis contains a set of non-redundant rules which are derived using frequent closed itemsets and their generators instead of using frequent itemsets that are usually used by traditional association rule mining approaches. An important contribution of this paper is that we propose to use the certainty factor as the criterion to measure the strength of the discovered association rules. Using this criterion, we can ensure the elimination of as many redundant rules as possible without reducing the inference capacity of the remaining extracted non-redundant rules. We prove that the redundancy elimination, based on the proposed Reliable basis, does not reduce the strength of belief in the extracted rules. We also prove that all association rules, their supports and confidences, can be retrieved from the Reliable basis without accessing the dataset. Therefore the Reliable basis is a lossless representation of association rules. Experimental results show that the proposed Reliable basis can significantly reduce the number of extracted rules. We also conduct experiments on the application of association rules to the area of product recommendation. The experimental results show that the non-redundant association rules extracted using the proposed method retain the same inference capacity as the entire rule set. This result indicates that using non-redundant rules only is sufficient to solve real problems needless using the entire rule set.