In this chapter, the photovoltaic properties occurring in single-wall carbon nanotubes (SWNTs)–donor conjugated polymer composites are reported. Photovoltaic devices based on the dispersed heterojunction concept, containing a blend of SWNTs and soluble poly(3-octylthiophene) (P3OT) were studied. The nanotubes not only act as electron acceptors, but also allow the electrons to transport efficiently along their length. The diodes (Al/polymer–nanotube com-posite/ITO) with low nanotube concentration (1%) show photovoltaic behavior, with an open circuit voltage of 0.75 V. The short circuit current is increased by two orders of magnitude compared with the pristine polymer diodes and the fill factor (ff) also increases from 0.3 to 0.4 for the nanotube–polymer cells. It is proposed that the photovoltaic response of these devices is based on the introduction of internal polymer–nanotube junctions within the polymer matrix, which due to a photoinduced electron transfer from the polymer to the nanotube contribute to enhanced charge separation and collection.

By incorporating a helium jet in the filtered arc ion deposition system previously used to produce hard diamond-like amorphous carbon film, carbon composite materials consisting of both amorphous carbon and nanoparticles can be deposited, some of which are both hard and elastic. These remarkable mechanical properties have been attributed to the possible sp3 linking of sp2 dominated fullerene-like nanotube. We investigate issues involved in the determination of atomic bonding character of carbon atoms in such structured materials, using electron energy loss spectroscopy.

In this paper some of the optimized wind turbine layouts in a windfarm, as presented by many authors, have been chosen as basis for further evaluation and study. The objective of most of earlier studies was to minimize cost per kW of power produced. This paper focuses from different perspective of optimization of turbine rotor diameters and hub heights to improve overall windfarm efficiency at a reduced cost. Differential Evolution (DE) algorithm is employed to optimize rotor diameters and hub heights of turbines in a windfarm.

Fundamentals of Information Systems contains articles from the 7th International Workshop on Foundations of Models and Languages for Data and Objects (FoMLaDO '98), which was held in Timmel, Germany. These articles capture various aspects of database and information systems theory:
identification as a primitive of database models
deontic action programs
marked nulls in queries
topological canonization in spatial databases
complexity of search queries
complexity of Web queries
attribute grammars for structured document queries
hybrid multi-level concurrency control
efficient navigation in persistent object stores
formal semantics of UML
reengineering of object bases and integrity dependence

Long-tailed learning aims to tackle the crucial challenge that head classes dominate the training procedure under severe class imbalance in real-world scenarios. However, little attention has been given to how to quantify the dominance severity of head classes in the representation space. Motivated by this, we generalize the cosine-based classifiers to a von Mises-Fisher (vMF) mixture model, denoted as vMF classifier, which enables to quantitatively measure representation quality upon the hyper-sphere space via calculating distribution overlap coefficient. To our knowledge, this is the first work to measure representation quality of classifiers and features from the perspective of distribution overlap coefficient. On top of it, we formulate the inter-class discrepancy and class-feature consistency loss terms to alleviate the interference among the classifier weights and align features with classifier weights. Furthermore, a novel post-training calibration algorithm is devised to zero-costly boost the performance via inter-class overlap coefficients. Our method outperforms previous work with a large margin and achieves state-of-the-art performance on long-tailed image classification, semantic segmentation, and instance segmentation tasks (e.g., we achieve 55.0% overall accuracy with ResNetXt-50 in ImageNet-LT). Our code is available at https://github.com/VipaiLab/vMF_OP.

Computer-aided systems are widely used in digital dentistry to help human experts for accurate and efficient diagnosis and treatment planning. In this paper, we study the problem of tooth defect segmentation in 3-Dimensional (3D) mesh scans, which is a prerequisite task in many dental applications. Existing models usually perform poorly in this task due to the highly imbalanced characteristic of tooth defects. To tackle this issue, we propose a novel triple-stream graph convolutional network named TripleNet to learn multi-scale geometric features from mesh scans for end-to-end tooth defect segmentation. With predefined geometrical features as inputs and a focal loss for training guidance, we achieve state-of-the-art performance on 3D tooth defect segmentation. Our work exhibits the great potential of artificial intelligence for future digital dentistry.

This book not only presents the state-of-the-art research on knowledge modelling, knowledge retrieval and knowledge reuse, but also elaborates the Collaborative Knowledge Management (CKM) paradigm and the architecture for the next generation of knowledge management systems. Although knowledge management has been extensively studied, particularly in the fields of business management and engineering design, there is a lack of systematic methodologies for addressing the integrated and collaborative dimension of knowledge management during the collaborative process of designing and developing complex systems, products, processes and services. The rapid development of information technologies, together with their applications in engineering and management, has laid the foundation for a Collaborative Knowledge Management (CKM) paradigm. The book specifically discusses this paradigm from a computational perspective.

Three-dimensional integrated circuits (3-D ICs) have attracted much interest in the past one decade because they can achieve technically breakthrough and significant enhancement in their electrical performance in comparison with that of conventional two-dimensional ICs. As the key technique for realization of ultra-high density integration and miniaturized packaging of 3-D ICs, through-silicon via (TSV) provides vertical electrical connection between different functional chips through multilayered silicon dies [1–3]. Moreover, it offers an opportunity for heterogeneously flexible integration, which is a powerful and effective solution called as “More-than-Moore” technology. Till now, there are numerous papers published on modeling, characterizing, and fabricating many different TSV structures for development of various 3-D ICs [4–13]. The most common TSV filling materials currently being used are copper (Cu), tungsten (W), and even doped poly-silicon. However, there are still some reliability and thermal management issues to be further studied in the realization of TSV-based 3-D ICs.

This chapter describes the efforts in modeling nanoscale transistors, with an emphasis on modeling quantum transport and multiphysics phenomena. It discusses a simulation approach based on the non-equilibrium Green’s function (NEGF) formalism and multiphysics approach. The chapter gives examples of applying the simulation capability to assess transistors based on 2D semiconductor materials near the scaling limits. It applies the multiphysics simulation capability to ultra-thin-body silicon on insulator metal oxide semiconductor field effect transistor. Short-channel effect limits further miniaturization of field effect transistors. Self-heating effects, which highlight the importance of modeling coupled with electron-thermal transport, can play an important role in nanoscale transistors. The NEGF formalism meets the needs of nanodevice modeling by providing an atomistic description, treatment of open boundary and non-equilibrium transport, and treatment of inelastic scattering. The thermal transport equation is solved self-consistently by coupling it to the NEGF formalism with phonon scattering.

Nanobacteria are very small mineral forming bacteria recently discovered in mammalian blood and tissues. They are the first mineral forming bacteria found in blood and the first heat and gamma irradiation resistant bacteria detected in man. They seem to have a genetic organization, which permits them to compensate for their small size and slow metabolism, and endure in their environmental niche. These properties together with their very slow growth rate meant that they were not detected until about 10 years ago. Their extraordinary nature also hindered attempts to publish this data. These autonomously replicating microorganisms were approaching the theoretical limit of self-replicating life with a size of only one hundredth of that of usual bacteria (Kajander, 1992; Åkerman et al, 1993, Kajander et al, 1994). Morphologically similar organisms had been discovered by Folk (1993) in sedimentary rocks and in hot-springs in travertine, and these he had given the name Nanobacteria. Such bacteria seem to contribute to the formation of many kinds of minerals. These bacteria may secrete molecules acting as nucleation centers for depositing biogenic minerals, e.g., apatite, around them in aquatic environments (Mojzsis et al, 1996). In a similar way, nanobacteria from blood can surround themselves with mineral deposits.