Influence of a cyclic butylene terephthalate oligomer on the processability and thermoelectric properties of polycarbonate/MWCNT nanocomposites

The thermoelectric properties of melt-processed nanocomposites consisting of a polycarbonate (PC) thermoplastic matrix filled with commercially available carboxyl (–COOH) functionalized multi-walled carbon nanotubes (MWCNTs) were evaluated. MWCNTs carrying carboxylic acid moieties (MWCNT-COOH) were used due the p-doping that the carboxyl groups facilitate, via electron withdrawing from the electron-rich π-conjugated system. Preliminary thermogravimetric analysis (TGA) of MWCNT-COOH revealed that the melt-mixing was limited at low temperatures due to thermal decomposition of the MWCNT functional groups. Therefore, PC was mixed with 2.5 wt% MWCNT-COOH (PC/MWCNT-COOH) at 240 °C and 270 °C. In order to reduce the polymer melt viscosity, a cyclic butylene terephthalate (CBT) oligomer was utilized as an additive, improving additionally the electrical conductivity of the nanocomposites. The melt rheological characterization of neat PC and PC/CBT blends demonstrated a significant decrease of the complex viscosity by the addition of CBT (10 wt%). Optical and transmission electron microscopy (OM, TEM) depicted an improved MWCNT dispersion in the PC/CBT polymer blend. The electrical conductivity was remarkably higher for the PC/MWCNT-COOH/CBT composites compared to the PC/MWCNT-COOH ones. Namely, the PC/MWCNT-COOH/CBT processed at 270 °C exhibited the best values with electrical conductivity; σ = 0.05 S/m, Seebeck coefficient; S = 13.55 μV/K, power factor; PF = 7.60 × 10⁻⁶μW/m K⁻², and thermoelectric figure of merit; ZT = 7.94 × 10⁻⁹. The PC/MWCNT-COOH/CBT nanocomposites could be ideal candidates for large-scale thermal energy harvesting, even though the presently obtained ZT values are still too low for commercial applications.     

Detecting invasive species with a bio-inspired semi-supervised neurocomputing approach: the case of Lagocephalus sceleratus

The need to protect the environment and biodiversity and to safeguard public health require the development of timely and reliable methods for the identification of particularly dangerous invasive species, before they become regulators of ecosystems. These species appear to be morphologically similar, despite their strong biological differences, something that complicates their identification process. Additionally, the localization of the broader space of dispersion and the development of invasive species are considered to be of critical importance in the effort to take proper management measures. The aim of this research is to create an advanced computational intelligence system for the automatic recognition, of invasive or another unknown species. The identification is performed based on the analysis of environmental DNA by employing machine learning methods. More specifically, this research effort proposes a hybrid bio-inspired computational intelligence detection approach. It employs extreme learning machines combined with an evolving Izhikevich spiking neuron model for the automated identification of the invasive fish species “Lagocephalus sceleratus” extremely dangerous for human health.

     

Global supply chain planning for pharmaceuticals

The shortening of patent life periods, generic competition and public health policies, among other factors, have changed the operating context of the pharmaceutical industry. In this work we address a dynamic allocation/planning problem that optimises the global supply chain planning of a pharmaceutical company, from production stages at primary and secondary sites to product distribution to markets. The model explores different production and distribution costs and tax rates at different locations in order to maximise the company's net profit value (NPV).Large instances of the model are not solvable in realistic time scales, so two decomposition algorithms were developed. In the first method, the supply chain is decomposed into independent primary and secondary subproblems, and each of them is optimised separately. The second algorithm is a temporal decomposition, where the main problem is separated into several independent subproblems, one per each time period. These algorithms enable the solution of large instances of the problem in reasonable time with good quality results.     

Supply chain optimisation for the process industries: Advances and opportunities

Supply chain management and optimisation is a critical aspect of modern enterprises and a flourishing research area. This paper presents a critical review of methodologies for enhancing the decision-making for process industry supply chains towards the development of optimal infrastructures (assets and network) and planning. The presence of uncertainty within supply chains is discussed as an important issue for efficient capacity utilisation and robust infrastructure decisions. The incorporation of business/financial and sustainability aspects is also considered and future challenges are identified.     

Stereo vision for robotic applications in the presence of non-ideal lighting conditions

Many robotic and machine-vision applications rely on the accurate results of stereo correspondence algorithms. However, difficult environmental conditions, such as differentiations in illumination depending on the viewpoint, heavily affect the stereo algorithms’ performance. This work proposes a new illumination-invariant dissimilarity measure in order to substitute the established intensity-based ones. The proposed measure can be adopted by almost any of the existing stereo algorithms, enhancing it with its robust features. The performance of the dissimilarity measure is validated through experimentation with a new adaptive support weight (ASW) stereo correspondence algorithm. Experimental results for a variety of lighting conditions are gathered and compared to those of intensity-based algorithms. The algorithm using the proposed dissimilarity measure outperforms all the other examined algorithms, exhibiting tolerance to illumination differentiations and robust behavior.     

Optimization methodology of dynamic data structures based on genetic algorithms for multimedia embedded systems

Modern multimedia application exhibit high resource utilization. In order to efficiently run this kind of applications in embedded systems, the dynamic memory subsystem needs to be optimized. A key role in this optimization is played by the dynamic data structures that reside in every real-life application. This paper presents a novel and automated way to optimize dynamic data structures. The search space is pruned using genetic algorithms that converge to the best multilayered data structure implementation for the targeted applications.     

A mixed integer optimisation model for data classification

In this work, a mixed integer linear programming (MILP) model is proposed for the multi-class data classification problem using a hyper-box representation. The latter representation is particularly suitable for capturing disjoint data regions. The objective function used is the minimisation of the total number of misclassified data samples. In order to improve the training and testing accuracy of our approach, an iterative solution procedure is developed to assign potential multiple boxes to each single class. Finally, the applicability of the proposed approach is demonstrated through a number of illustrative examples. According to the computational results obtained, the proposed optimisation-based approach is competitive in terms of prediction accuracy when compared with various standard classifiers.     

An Efficient Location Management Protocol for Wireless ATM Customer Premises Networks

In this paper we present a new location management protocol for wireless ATM networks, called LMCP (Location Management and Control Protocol). This protocol is based on the PNNI (Private Network-to-Network Interface) routing functionality to advertise the movement of mobile terminals within predefined areas. Moreover, LMCP uses specialized entities to store and retrieve the current location area of the mobile terminals. These entities are located in mobility enhanced switches that control the execution of mobility procedures (e.g., handovers).The main benefit from the application of LMCP is the establishment of connections that do not contain any misrouted segments. Furthermore, it requires minor modifications to the PNNI and enables its inter-working with other location management mechanisms. The protocol is compared with other similar mechanisms, and its efficiency is demonstrated by the results of an analytical model.     

Modelling electricity storage systems management under the influence of demand‐side management programmes

Electricity storage systems (ESS) for bulk energy storage are principally used for load levelling purposes or for relieving the intermittency of renewables. Another use is electricity arbitrage through the rule of ‘buy low, sell high’. This operation tracks the market‐clearing price (MCP) profiles and produces profit by exploiting the differences between peak and off‐peak prices. The profits made in this way depend on technology characteristics and the market competition level. We investigate the influence of demand‐side management (DSM) on ESS profitability when the only income is from provision of electricity arbitrage services, by optimizing the time allocation of the charge and discharge operations. Two scenarios of DSM in the market have been selected for two management periods (MP): 1 day and 3 days. The longer MP is examined in order to investigate the potential for higher economic value when energy transfer to the next day is permitted. The key finding is that a very small load shifting from peaks to off‐peaks, due to DSM, significantly affects the ESS profit. The significant profit losses the ESS showed are a result of the high capital costs and the small difference of the peak and off‐peak electricity prices in the Greek market. Therefore, under the assumptions we have made for this research, any attempt to use ESS in ‘buy low, sell high’ operation is not profitable. Copyright © 2008 John Wiley & Sons, Ltd.     

Multi-parameter performance analysis for decentralized cognitive radio networks

In this paper, we investigate the impact of primary user activity, secondary user activity, interface switching, channel fading and finite-length queuing on the performance of decentralized cognitive radio networks. The individual processes of these service-disruptive effects are modeled as Markov chains based on cross-layer information locally available at the network nodes. A queuing analysis is conducted and various performance measures are derived regarding the packet loss, throughput, spectral efficiency, and packet delay distribution. Numerical results demonstrate the impact of various system parameters on the system performance, providing insights for cross-layer design and autonomous decision making in decentralized cognitive radio networks.