Miracle-dz/UniFAR-FTUs huggingface dataset:
Solar thermophotovoltaic efficiency potentials : surpassing photovoltaic device efficiencies

Solar energy has gained more attention in recent years due to increased concerns about the continued use of fossil fuels. Solar energy is a form of renewable energy, and solar energy technology does not release greenhouse gases responsible for climate change. While photovoltaic (PV) cells, which convert sunlight into electrical energy, are becoming more widely used, they are limited in their ability to convert sunlight into electricity. One of the limitations of PV energy generation is caused by the fact that only a limited portion of the energy spectrum of sunlight contributes to electricity generation. Solar thermophotovoltaics (TPV) aim to improve the efficiency with which sunlight can be converted to electrical energy by converting solar energy to thermal energy first before generating electrical energy with a PV cell. Instead of direct illumination by sunlight, the sunlight is absorbed by an intermediate material and then reemitted as a means of energy spectrum control, which in theory allows for more photons to generate useful electrical energy. The efficiency of solar TPV systems have been modeled. These models demonstrate that solar TPV devices have a higher potential efficiency than PV device counterparts. Yet, solar TPV devices are not yet suitable for any sustainable use, and there are many engineering challenges that need to be overcome in order to cross over from theory into practical use.
Investigation of a Novel 24-Slot/14-Pole Six-Phase Fault-Tolerant Modular Permanent-Magnet In-Wheel Motor for Electric Vehicles

In this paper, a six-phase fault-tolerant modular permanent magnet synchronous machine (PMSM) with a novel 24-slot/14-pole combination is proposed as a high-performance actuator for wheel-driving electric vehicle (EV) applications. Feasible slot/pole combinations of the fractional-slot concentrated winding six-phase PMSM are elicited and analyzed for scheme selection. The novel 24-slot/14-pole combination is derived from the analysis and suppression of the magnetomotive force (MMF) harmonics. By making use of alternate-teeth-wound concentrated winding configuration, two adjacent coils per phase and unequal teeth widths, the phase windings of the proposed machine is magnetically, thermally isolated, which offers potentials of modular design and fault tolerant capability. Taking advantage of the leakage component of winding inductance, 1.0 per unit short-circuit current is achieved endowing the machine with short-circuit proof capability. Optimal design of essential parameters aiming at low eddy current losses, high winding factor and short-circuit-proof ability are presented to pave the way for a high-quality system implementation.
Detering Dishonest Behaviour in Private Information Retrieval

It is useful to support Byzantine robustness in a Private Information Retrieval (PIR) protocol so that any database operators that are being used cannot maliciously aﬀect the records that a client is looking for. We analyze the problem of Byzantine servers in Goldberg’s IT-PIR protocol and relate the problem to the well known stag hunt game. To make this game more applicable, we expand it to include more than two agents, we deﬁne a multi-round game and incorporate the idea of punishing dishonest behaviour. Using the relationship between this problem and the that of collusion between PIR servers, we design a mechansim to deter servers from Byzantine behaviour.
Combining information from multi-stream features using deep neural network in speech recognition

The subject of the paper is the integration of multi-stream features in the framework of hybrid artificial neural network (ANN) - hidden Markov model (HMM). We investigate the use of log filter bank and MFCC features in multi-stream combination for phoneme recognition. An intermediate integration method is proposed to fuse the information from different sets of features. By exploiting deep learning algorithm to train the deep neural network (DNN), we explore different stream combination methods. Results of recognition experiments using DNN-HMM system on the TIMIT speech data show that the proposed approach is not only superior to the single best stream, which is relative 6.1% phone error rate (PER) reduction, but outperforms the other fusion strategies as well.
Teaching Robots How to Become Autonomous

In this paper we present a scheme to train a robot by a human teacher in a series of training phases from supervised training via correcting advises, reinforcement based training and internal critic to completely autonomous, unsupervised training. The controller of the robot consists of a network of artificial immune systems based RLAs that build up the hierarchical systemic architecture. In this paper we present new promising results from simulations and from a real robot system that has been trained with the proposed scheme.
A comparative study of vector fitting and orthonormal vector fitting techniques for EMC applications

Broadband rational approximations of multi- port systems are of great importance for accurate transient analysis of electrical systems. To this aim, fitting techniques have been found extremely useful in providing rational models for sampled data in the frequency domain. This paper presents a comparative study of two fitting methods, namely the standard Vector fitting (VF) and the orthonor- mal vector fitting (OVF) techniques. The latter is found to be better conditioned, reduces the numerical sensitivity to the choice of starting poles and limits the the number of iterations and, thus, the global cpu-time to obtain the rational approximation.
On Convergence of Discriminative Training Algorithm for Speaker Recognition

In this paper, we present a discriminative training algorithm for GMM based speaker verification, and develop a convergence proof for the same. During training of the models, instead of performing MAP adaptation of the UBM, the model parameters of the GMM are estimated such that target scores are maximized while impostor scores are minimized. The focus of the algorithm is to estimate more accurately, the parameters of the elements of the mixture that are unique to a speaker. The algorithm uses an Expectation-Maximisation-like framework for estimation of parameters. It is shown that the algorithm converges with appropriate choice of a regularization parameter ($\alpha$). $\alpha$ balances target and impostor data during each iteration of the algorithm. Fast convergence can be obtained by modifying the regularization parameter after each iteration. It is shown that incrementing $\alpha$ is a sufficient condition for convergence. Further, an impostor data selection technique for fast implementation is addressed. The performance of the system is evaluated on NIST 2003 benchmark dataset. A relative improvement 9.5% is obtained using Mel Frequency Slope as feature.
An adaptive belief representation for target tracking using disparate sensors in Wireless Sensor Networks

Sensor diversity has been shown to increase the accuracy and robustness of Wireless Sensor Network (WSN) target tracking. However, difficulties can arise due to disparity between sensor types. This paper seeks to address specifically those sensor measurements which require additional information from another source before they can be used to create a location estimate; such as a microphone mote, which requires knowledge of the target's acoustic power before it can estimate a distance. A novel representation, called the Adaptive GRiD (Grid Representation of belief Distribution), is presented for such sensor measurements which facilitates fusion with other measurement types, overcoming disparity. This is accomplished using a state space which can expand to track extra parameters of the target apart from location, and subsequently contract when those parameters are no longer necessary. In this way the tracker can adapt to a variety of different sensor types whose measurements are mathematically related to target properties besides location. The proposed representation is evaluated for its effectiveness and suitability and shows promising results.
7 Monetary Policy and the Open Economy

No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher, except for reading and browsing via the World Wide Web. Users are not permitted to mount this file on any network servers. Follow links for Class Use and other Permissions. For more information send email to:
Adjustable Bounded Rectifiers: Towards Deep Binary Representations

Binary representation is desirable for its memory efficiency, computation speed and robustness. In this paper, we propose adjustable bounded rectifiers to learn binary representations for deep neural networks. While hard constraining representations across layers to be binary makes training unreasonably difficult, we softly encourage activations to diverge from real values to binary by approximating step functions. Our final representation is completely binary. We test our approach on MNIST, CIFAR10, and ILSVRC2012 dataset, and systematically study the training dynamics of the binarization process. Our approach can binarize the last layer representation without loss of performance and binarize all the layers with reasonably small degradations. The memory space that it saves may allow more sophisticated models to be deployed, thus compensating the loss. To the best of our knowledge, this is the first work to report results on current deep network architectures using complete binary middle representations. Given the learned representations, we find that the firing or inhibition of a binary neuron is usually associated with a meaningful interpretation across different classes. This suggests that the semantic structure of a neural network may be manifested through a guided binarization process.
Understanding the psychology of positive psychotic symptoms : an epidemiological and momentary assessment approach

Thewissen, V. , Bentall, R. , Lecomte, T. , van Os, J., & Myin-Germeys, I. (2007). Shortterm instability of self-esteem across the paranoia continuum. Schizophrenia Bulletin, 33(2), 224. Thewissen, V. , Bentall, R. , à Campo, J., van Lierop, T. , van Os, J., & Myin-Germeys, I. (2007). Psychologische processen bij paranoia: Een Experience Sampling onderzoek. Tijdschrift voor Psychiatrie, 49(suppl. 1), S65-S66.
Multi-stage Order Acceptance Model and the Heuristic Algorithm Based on Simulated Annealing

This paper studies the order acceptance problem with tardiness penalties faced by firm who has a pool of potential orders waiting to be accepted and processed through a single production line with multiple processing stages. We present an order acceptance decision model, named the multistage order acceptance model, to handle this problem, which is an extension of the Slotnick-Morton version of single-stage order acceptance model, aiming at maximizing the total profit of these potential orders to be processed through a multi-stage production line. Because of the complexity of the problem that integrates the operations of order acceptance and multi-stage job scheduling, we propose a heuristic algorithm, named Simulated Annealing Based on Partial Optimization (SABPO) algorithm, to solve the model with feasible solutions and acceptable computational time. Empirical experiments on synthetic datasets are carried out to examine the proposed algorithm and also to compare the performances between multi-stage model and single-stage model. The comparisons show that the multi-stage model can always suggest a better decision on order acceptance problem; some potential orders rejected by single-stage model are in fact profitable and will be accepted by multi-stage model.
Quantum Random State Generation with Predefined Entanglement Constraint

Entanglement plays an important role in quantum communication, algorithms, and error correction. Schmidt coefficients are correlated to the eigenvalues of the reduced density matrix. These eigenvalues are used in von Neumann entropy to quantify the amount of the bipartite entanglement. In this paper, we map the Schmidt basis and the associated coefficients to quantum circuits to generate random quantum states. We also show that it is possible to adjust the entanglement between subsystems by changing the quantum gates corresponding to the Schmidt coefficients. In this manner, random quantum states with predefined bipartite entanglement amounts can be generated using random Schmidt basis. This provides a technique for generating equivalent quantum states for given weighted graph states, which are very useful in the study of entanglement, quantum computing, and quantum error correction.
State Formation, Nation-Building, and Mass Politics in Europe: The Theory of Stein Rokkan

STATE FORMATION AND NATION-BUILDING BASIC CONCEPTS, MODELS, MAPS DIFFERENTIATIONS AND BOUNDARY-BUILDING CENTRES AND PERIPHERIES THE BASIC MODEL A MODEL AND CONCEPTUAL MAP OF EUROPE THE TERRITORIAL STRUCTURING OF EUROPE CONDITIONS OF STATE FORMATION AND NATION BUILDING NATION-BUILDING AND LANGUAGE THE SURVIVAL OF PERIPHERAL IDENTITY FEDERAL VERSUS UNITARY STRUCTURE B. MASS POLITICS III. THE DEMOCRATISATION OF EUROPE 1. EXIT AND VOICE 2. THE FOUR THRESHOLDS OF DEMOCRATISATION 3. NUMERICAL DEMOCRACY AND CORPORATE PLURALISM IV CLEAVAGE STRUCTURES AND PARTY SYSTEMS CLEAVAGES AND THEIR POLITICAL TRANSLATIONS CRITICAL JUNCTURES, ALLIANCES, AND OPPOSITIONS 3. PARTY SYSTEMS AND THE MODEL OF EUROPE
A heuristic gene regulatory networks model for cardiac function and pathology

Genome-wide association studies (GWAS) and next-generation sequencing (NGS) has led to an increase in information about the human genome and cardiovascular disease. Understanding the role of genes in cardiac function and pathology requires modeling gene interactions and identification of regulatory genes as part of a gene regulatory network (GRN). Feature selection and data reduction not sufficient and require domain knowledge to deal with large data. We propose three novel innovations in constructing a GRN based on heuristics. A 2D Visualised Co-regulation function. Post-processing to identify gene-gene interactions. Finally a threshold algorithm is applied to identify the hub genes that provide the backbone of the GRN. The 2D Visualized Co-regulation function performed significantly better compared to the Pearson's correlation for measuring pairwise associations (t=3.46, df=5, p=0.018). The F-measure, improved from 0.11 to 0.12. The hub network provided a 60% improvement to that reported in the literature. The performance of the hub network was then also compared against ARACNe and performed significantly better (p=0.024). We conclude that a heuristics approach in developing GRNs has potential to improve our understanding of gene regulation and interaction in diverse biological function and disease.
Key Variables Affecting Decisions of Bus Replacement Age and Total Costs

Recent budget and fiscal constraints make optimal fleet management more imperative than ever for transit agencies. Fleet data have consistently shown that bus operations and maintenance (O&M) costs per mile increase as buses age. From a purely economic perspective, there is a cost trade-off between the lower O&M costs of newer fleets and their higher initial capital costs. This trade-off has a significant impact on the optimal timing of purchase and replacement decisions. Realistic cost data and an optimization modeling framework were used to analyze the impact of purchase timing decisions on fleet costs per mile. The results indicate that (a) increases in diesel prices do not affect total bus fleet costs as much as increases in maintenance costs, (b) increases in maintenance costs and utilization per year reduce the optimal replacement age, (c) increases in utilization and fuel economy have a similar impact in terms of total fleet costs, and (d) bus purchase-price changes have a significant impact on the optimal replacement age. Given uncertain and hard-to-forecast market variables, a thorough sensitivity analysis is presented to ascertain the key variables that affect bus transit replacement timing.
Spectral Observer with Reduced Information Demand

Spectral observer is appropriate for investigating the frequency domain properties of signals. Since the spectral analysis is a very common method, this observer can be used widely in many applications. The paper introduces a novel resonator based spectral observer and control algorithm that require reduced amount of data for the operation, so they are appropriate for the utilization in signal processing systems with limited resources (e.g. wireless sensor networks). The data reduction is achieved by the deployment of the sign error principle. For the improvement of the transient properties of the sign error structures a new method is proposed that tunes the speed of the adaptation according to the norm of the error. This algorithm makes possible to find a trade-off between the settling time and the amount of data required for the operation. The algorithm can be used for recursive Fourier analysis of signals and control of linear systems. As a practical application a wireless active noise control system is presented.
Optimal scheduling in the hybrid-cloud

The emerging hybrid cloud architecture allows organizations to optimize their computation needs and costs by maintaining their private computational infrastructure at high utilization and meeting peak requirements by offloading selected tasks to the public cloud. Consequently, there is a need to devise efficient systems equipped with online task cloudbursting algorithms that optimize the overall cost while maintaining adequate quality of service. Such algorithms must take into account the difference in communication and computational requirements associated with different tasks. For example, it is clear that when two tasks have the same local computational requirements, the one with the lower cloudbursting cost is a better candidate to be off-loaded and sent to the cloud. In this paper, we address the case in which arriving tasks have the same computational cost but different communication costs. We design scheduling system based on online decision algorithms driven by the user's local infrastructure constraints. We model the online scheduling problem as Markov Decision Process (MDP) problem and provide optimal policies for scheduling tasks either locally or remotely. We further explore the usage of MDP in different scenarios and prove the structural properties of the optimal policies in order to incorporate them into the decision engine. The design of the practical scheduling system is supported by the analytical results and numerical evaluations. We demonstrate the practical computational advantage of threshold type policies and provide an insight into their dependence on system parameters.
Patients’ Perspective of the Design of Provider-Patients Electronic Communication Services

Information Delivery is one the most important tasks in healthcare practice. This article discusses patient’s tasks and perspectives, which are then used to design a new Effective Electronic Methodology. The system design methods applicable to electronic communication in the healthcare sector are also described. The architecture and the methodology for the healthcare service portal are set out in the proposed system design.
Experiences of disengagement from mental health services: an interpretative study

Whilst there is evidence of a range of effective treatments available for people with severe mental health problems (SMHP), people frequently disengage from mental health services (MHS). This thesis investigates experiences of disengagement of people with SMHP and comprises two studies: 1) semi-structured interviews to elicit the experiences of people with SMHP and a history of disengagement from MHS; and 2) building on those findings, focus groups with staff from assertive outreach teams (specialising in providing care for people with SMHP and a history of disengagement). The participants were those perceived as the most disengaged from MHS yet they were willing to engage with the research. Interpretative phenomenological analysis was used to develop themes for individual participants and then across the participants. Disengagement from MHS was part of a wider experience of a limited connection to social structures, including an ambivalent and complex relationship with MHS. There was a sense of sadness in all aspects of the participants’ experience but they had developed strategies to reinforce personal resilience and to reassert personal identity. The findings of this thesis can be employed to better understand the context of disengagement from MHS and consequently better inform future engagement with this client group.
The welfare of zoo-housed chimpanzees (Pan troglodytes) with special attention to auditory enrichment

Modern zoos house millions of wild animals but also aim to conduct animal research, educate visitors about the natural world and support conservation programmes via funding and encouraging wildlife-friendly actions by the public. However, the benefit to conservation efforts and species living in the wild, through fundraising and increasing public awareness, may come at the cost of the animals living within zoos. In this thesis I focussed on the welfare of zoo-housed chimpanzees due to their endangered status and public popularity. I investigated potential social, dietary and visitor related triggers for anxiety-related behaviours (yawning and scratching) and regurgitation and reingestion (R/R). Despite interesting trends, no immediate triggers for these behaviours were found. However, long-term data showed the frequency with which individuals engage in R/R reduced over time when part of a complex group inhabiting an enclosure encouraging natural behaviours and social dynamics. I also examined if music, frequently broadcast to captive chimpanzees, provides any enriching effect on their welfare. Both observational and experimental research suggested that whilst music does not appear to have any obvious positive welfare effects it is equally not detrimental. The final study explored whether public education, one of assumed benefits associated with having animals in zoos, is achieved via ‘Keeper Talks’. They were effective at demonstrating to adult visitors that touchscreens are an effective form of enrichment for chimpanzees but the talks had no significant effect on their knowledge of other issues covered, or any area of knowledge in young people. Further investigation showed that zoo professionals have inaccurate estimates of visitor knowledge and that assessment methods do not always align with educational goals. Overall, the findings in this thesis highlight the importance of research within zoos, which can lead to a better understanding of barriers to good animal welfare, utilisation of only positive welfare interventions and maximisation of educational potential.
Magnesium Borohydride: From Hydrogen Storage to Magnesium Battery**

Beyond hydrogen storage: The first example of reversible magnesium deposition/stripping onto/from an inorganic salt was seen for a magnesium borohydride electrolyte. High coulombic efficiency of up to 94 % was achieved in dimethoxyethane solvent. This Mg(BH(4))(2) electrolyte was utilized in a rechargeable magnesium battery.