Experiences with large-scale operational trials of ALTO-enhanced P2P filesharing in an intra-ISP scenario

Application Layer Traffic Optimization (ALTO) has recently gained attention in the research and standardisation community as a way for a network operator to guide the peer selection process of distributed applications by providing network layer topology information. In particular P2P applications are expected to gain from ALTO, due to the many connections peers form among each other, often without taking network layer topology information into account. In this paper, we present results of an extensive intra-ISP trial with an ALTO-enhanced P2P filesharing software. In summary, our results show that—depending on the concrete setting and on the distribution of upload capacity in the network—ALTO enables an ISP to save operational costs significantly while not degrading application layer performance noticeably. In addition, based on our experience we are able to give advice to operators on how to save costs with ALTO while not sacrificing application layer performance at all.     

Small‐signal and noise modeling of class of HEMTs using knowledge‐based artificial neural networks

In this article, the neural network approach is exploited for development of bias‐dependent small‐signal and noise models of a class of microwave field effect transistor (FETs) made in the same technology but differing in the gate width. The prior knowledge neural approach is applied. Introducing gate width at the input of proposed neural networks, as well as the S/noise parameters of a device that belongs to the same class as the modeled device representing the prior knowledge, leads to very accurate scattering and noise parameters' modeling, as exemplified by modeling of class of pseudomorphic high electron mobility transistor (pHEMT) devices. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2013.     

Large, huge or gigantic? Identifying and encoding intensity relations among adjectives in WordNet

We propose a new semantic relation for gradable adjectives in WordNet, which enriches the present, vague, similar relation with information on the degree or intensity with which different adjectives express a shared attribute. Using lexical-semantic patterns, we mine the Web for evidence of the relative strength of adjectives like “large”, “huge” and “gigantic” with respect to their attribute (“size”). The pairwise orderings we derive allow us to construct scales on which the adjectives are located. To represent the intensity relation among gradable adjectives in WordNet, we combine ordered scales with the current WordNet dumbbells based on the relation between a pair of central adjectives and a group of undifferentiated semantically similar adjectives. A new intensity relation links the adjectives in the dumbbells and their concurrent representation on scales. Besides capturing the semantics of gradable adjectives in a way that is both intuitively clear as well as consistent with corpus data, the introduction of an intensity relation would potentially result in several specific benefits for NLP.     

A case study validation of a knowledge-based approach for the selection of requirements engineering techniques

Requirements engineering (RE) is a critical phase in the software engineering process and plays a vital role in ensuring the overall quality of a software product. Recent research has shown that industry increasingly recognizes the importance of good RE practices and the use of appropriate RE techniques. However, due to the large number of RE techniques, requirements engineers find it challenging to select suitable techniques for a particular project. Unfortunately, technique selection based on personal experience has limitations with regards to the scope, effectiveness and suitability of the RE techniques for the project at hand. In this paper, a Knowledge-based Approach for the Selection of Requirements Engineering Techniques (KASRET) is proposed that helps during RE techniques selection. This approach has three major features. First, a library of requirements techniques was developed which includes detailed knowledge about RE techniques. Second, KASRET integrates advantages of different knowledge representation schemata and reasoning mechanisms. Thus, KASRET provides mechanisms for the management of knowledge about requirements techniques and support for RE process development. Third, as a major decision support mechanism, an objective function evaluates the overall ability and cost of RE techniques, which is helpful for the selection of RE techniques. This paper makes not only a contribution to RE but also to research and application of knowledge management and decision support in process development. A case study using an industrial project shows the support of KASRET for RE techniques selection.

Superior Thermoelectric Performance of Textured Ca3Co4−xO9+δ Ceramic Nanoribbons

Calcium cobaltite Ca₃Co_4−xO_9+δ (CCO) is a promising p-type thermoelectric (TE) material for high-temperature applications in air. The grains of the material exhibit strong anisotropic properties, making texturing and nanostructuring mostly favored to improve thermoelectric performance. On the one hand multitude of interfaces are needed within the bulk material to create reflecting surfaces that can lower the thermal conductivity. On the other hand, low residual porosity is needed to improve the contact between grains and raise the electrical conductivity. In this study, CCO fibers with 100% flat cross sections in a stacked, compact form are electrospun. Then the grains within the nanoribbons in the plane of the fibers are grown. Finally, the nanoribbons are electrospun into a textured ceramic that features simultaneously a high electrical conductivity of 177 S cm⁻¹ and an immensely enhanced Seebeck coefficient of 200 µV K⁻¹ at 1073 K are assembled. The power factor of 4.68 µW cm⁻¹ K⁻² at 1073 K in air surpasses all previous CCO TE performances of nanofiber ceramics by a factor of two. Given the relatively high power factor combined with low thermal conductivity, a relatively large figure-of-merit of 0.3 at 873 K in the air for the textured nanoribbon ceramic is obtained.     

Mitigating Detrimental Effect of Self-Doping Near the Anode in Highly Efficient Organic Solar Cells

Poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) has been one of the most established hole transport layers (HTL) in organic solar cells (OSCs) for several decades. However, the presence of PSS⁻ ions is known to deteriorate device performance via a number of mechanisms including diffusion to the HTL-active layer interface and unwanted local chemical reactions. In this study, it is shown that PSS⁻ ions can also result in local p-doping in the high efficiency donor:non-fullerene acceptor blends – resulting in photocurrent loss. To address these issues, a facile and effective approach is reported to improve the OSC performance through a two-component hole transport layer (HTL) consisting of a self-assembled monolayer of 2PACz ([2-(9H-Carbazol-9-yl)ethyl]phosphonic acid) and PEDOT:PSS. The power conversion efficiency (PCE) of 17.1% using devices with PEDOT:PSS HTL improved to 17.7% when the PEDOT:PSS/2PACz two-component HTL is used. The improved performance is attributed to the overlaid 2PACz layer preventing the formation of an intermixed p-doped PSS⁻ ion rich region (≈5–10 nm) at the bulk heterojunction-HTL contact interface, resulting in decreased recombination losses and improved stability. Moreover, the 2PACz monolayer is also found to reduce electrical shunts that ultimately yield improved performance in large area devices with PCE enhanced from 12.3% to 13.3% in 1 cm² cells.