Miracle-dz/UniFAR-FTUs huggingface dataset:
Electrochemical Characterisation and Application of Multi Microelectrode Array Devices to Biological Electrochemistry

A new design for microelectrode array (MEA) devices fabricated by semiconductor-processing techniques is presented. The microelectrode surfaces consist of gold and are surrounded by an insulating silicon nitride layer. Each chip of these so-called Multi MEAs contains regular arrays with circular-shaped electrodes of eight different sizes: 1, 3, 5, 10, 50, 100, 500 and 1000μm. The Multi MEAs were electrochemically characterised by use of ferrocenecarboxylic acid. Well-defined cyclic voltammograms of the two small redox proteins, horse heart cytochrome c and amicyanin from Thiobacillus versutus, were obtained at variously surface-modified Multi MEAs. Furthermore, a very simple method to manufacture Multi MEAs with carbon surfaces is introduced.
Optimal filtering for polynomial system states with polynomial multiplicative noise

In this paper, the optimal filtering problem for polynomial system states with polynomial multiplicative noise over linear observations is treated proceeding from the general expression for the stochastic Ito differential of the optimal estimate and the error variance. As a result, the Ito differentials for the optimal estimate and error variance corresponding to the stated filtering problem are first derived. The procedure for obtaining a closed system of the filtering equations for any polynomial state with polynomial multiplicative noise over linear observations is then established, which yields the explicit closed form of the filtering equations in the particular cases of of a linear state equation with linear multiplicative noise and a bilinear state equation with bilinear multiplicative noise. In the example, performance of the designed optimal filter is verified for a quadratic state with a quadratic multiplicative noise over linear observations against the optimal filter for a quadratic state with a state-independent noise and a conventional extended Kalman-Bucy filter
The Culture Wars: How American and Japanese Businesses have Outperformed Europe's and why the Future will be Different

Foreword by Karl Otto Pohl. Acknowledgements. Introduction: Towards the Twenty--First Century. United by Difference: The Formation of a European Identity. The Business of America is Business: A Consideration of How America Gained its Business Hegemony. Miracle Workers: Development of the Japanese and German Economies. Knowing the Field: The Asian Runners and Riders. Towards a Definition of a European Company. Confronting Culture. The Cultural Iceberg: A Guide to Cultural Difference both Above and Below the Surface. European Culture: Asset or Liability? Europe: The Practical Realities. Conclusion. Bibliography. Index.
A novel probabilistic fuzzy set for uncertainties-based integration inference

The probabilistic fuzzy set and the related probabilistic fuzzy logic system is designed for handling the uncertainties with both stochastic and fuzzy features. In this paper, based on the review of probabilistic fuzzy logic system, a novel probabilistic fuzzy set is proposed. It considers random variation from center in the triangular membership function. The work presented will improve the potential application of probabilistic fuzzy sets.
A unified framework for tree search decoding: rediscovering sequential decoding

We consider receiver design for coded transmission over linear Gaussian channels. We restrict ourselves to the class of lattice codes and formulate the joint detection and decoding problem as a closest lattice point search (CLPS). Here, a tree search framework for solving the CLPS is adopted. In our framework, the CLPS algorithm decomposes into preprocessing and tree search stages. The role of the preprocessing stage is to expose the tree structure in a form matched to the search stage. Here, it is argued that the minimum mean square error decision feedback (MMSE-DFE) frontend is instrumental for solving the joint detection and decoding problem in a single search stage. It is further shown that MMSE-DFE filtering allows for using lattice reduction methods to reduce complexity, at the expense of a marginal performance loss, and solving under-determined linear systems. For the search stage, we present a generic method, based on the branch and bound (BB) algorithm, and show that it encompasses all existing sphere decoders as special cases. The proposed generic algorithm further allows for an interesting classification of tree search decoders, sheds more light on the structural properties of all known sphere decoders, and inspires the design of more efficient decoders. In particular, an efficient decoding algorithm that resembles the well known Fano sequential decoder is identified. The excellent performance-complexity tradeoff achieved by the proposed MMSE-Fano decoder is established via simulation results and analytical arguments in several MIMO and ISI scenarios.
Prosthetic Arteriovenous Grafts for Hemodialysis

Introduction Prosthetic arteriovenous (AV) grafts are indicated in patients with failed AV fistula (AVF), exhausted superficial veins or unsuitable vessels. Increasing the proportion of prevalent hemodialysis (HD) patients using autogenous AVF should reduce the need for AV grafts and associated morbidity. This paper reviews the current role of prosthetic AV grafts in vascular access for HD. Technical considerations Prior to the insertion of a prosthetic AV graft, a comprehensive review of previous access procedures and full physical examination in addition to vessel mapping is required. Anastomotic technique should take into account the flow diffuser concept, graft geometry and an anastomotic angle of 15° in order to reduce the incidence of intimal hyperplasia. Results Many authors report 1 and 2-yr cumulative graft patency rates of 59–90% and 50–82%, respectively. The major drawbacks with synthetic grafts include: thrombosis, a five-fold increase in infection risk and steal syndrome. The choice between surgical and percutaneous methods of dealing with blocked AV grafts remains controversial, though percutaneous techniques are assuming an increasingly important role. Percutaneous strategies are successful in declotting access in 67–95% of cases. Stenting of stenotic lesions following thrombectomy improves secondary patency rates. Strategies for dealing with AV graft infection include antibiotic prophylaxis, partial, subtotal or total graft excision and the use of biological prosthesis. Conclusions Though more prone to complications than autogenous AVFs, AV grafts offer a short maturation period and are more amenable to thrombectomy by radiological or surgical means. Complex AV grafts may be appropriate in patients with exhausted access sites.
Iterative reasoning and markets : three experiments

We present in this thesis three distinct experiments, studying issues in behavioural economics and financial market. In the first paper, we study the level-k reasoning model in an experimental extension of Arad and Rubinsten (2012) “11-20 Money Request Game”. In the second paper, we introduce an experimental design where traders can buy or sell the rights for performing complex decisional task. The design seeks to test the allocative efficiency of markets. In the third paper, we introduce an Arrow-Debreu market where traders have diverse and partial information about the true state of nature. The design seeks to test the Rational Expectations model’s prediction that all relevant information will be aggregated into market prices.
Biological Learning and Control: Shadmehr and Mussa-ivaldi 7 Learning Faster

The title of this chapter sounds a little like a product that you might find advertised on TV in the wee hours of the night. " We will not only help you learn (your favorite task here) faster, but if you order now, we will also send you this amazing new . " It sounds like the promises of a snake oil salesman. But as we will see here, if we view learning in biological systems as a Bayesian state estimation process, then the mathematical framework that we developed provides us with some useful ideas on how to encourage the process of learning. 7.1 Increased sensitivity to prediction errors One of the properties of the state estimation framework is that the sensitivity to error (the Kalman gain) depends on the relative size of the state uncertainty with respect to the measurement uncertainty. Said in another way, the sensitivity to error depends on the ratio of the confidence in the state that we are trying to estimate with respect to the confidence of our measurements. For example, suppose that we are attempting to estimate state x , which is governed by the following generative model:     (1) () () () () () 0, 0, n n n x x x n n n y y y x ax N y x N          (7.1) The sensitivity to prediction error, written as our Kalman gain () n k , will depend on the ratio of the prior state uncertainty   1 nn p  with respect to measurement uncertainty   1 nn y p    :     1 () 1 nn n nn y p k p      (7.2) When the learner observes a prediction error but is uncertain about the sensory measurement (noise y  is large), he will have a relatively small learning rate. When the learner observes the same prediction error but is uncertain about his own predictions (the state uncertainty p is large), he will have a relatively large learning rate. Therefore, the state estimation framework predicts that through simple techniques, you should be able to up-regulate or down-regulate rates of adaptation.
Improve worst-case robustness of multi-antenna communication systems

The full benefits of multi-antenna communication systems rely on exploiting the channel state information at the transmitter (CSIT), which is, however, often imperfect in practice. We consider worst-case robust precoder designs to combat the imperfection of CSIT. It is shown that the robust precoder can be efficiently achieved through convex optimization.
Power Aware Job Scheduling in Multi-Processor System with Service Level Agreements Constraints

Conventional hardware based per-component and system-wide power management methods can save more power consumptions if they are in assistance with software-level adaptation. Since the conventional coarse-grained methods are not adaptive to various fluctuating workload in real scenarios, the system performance can be deteriorated greatly if the objective is only to minimize the total power consumptions separately, despite of the violations of Service Level Agreements (SLAs). In this paper a fine-grained job-level power aware scheduling algorithm is proposed to minimize power consumption in multi-processor system with SLA constraints. Simulation results show that the proposed algorithm can save significant power consumptions while still providing SLAs guarantees and the performance degradation is acceptable. The results also show that fine-grained job-level power aware scheduling can achieve better power/performance balancing between multiple processors than coarse grained methods.
A Wordnet based information retrieval on the Semantic Web

Most of the existing textual information retrieval approaches depend on a lexical match between words in user's requests and words in target objects. Typically only objects that contain one or more common words with those in the user's query are returned as relevant. This lexical based retrieval model is far from ideal. In this research an approach to semantic based information retrieval of semantically annotated documents is presented. The approach operates based on: (i). natural language understanding, (ii). the Wordnet ontology, and (iii). the Semantic web standards. Not only the information is annotated and searched on a semantic basis, but also the retrieval process can be enhanced by the use of rich vocabulary knowledge in the ontology.
Comparing strength of locality of reference - popularity, majorization, and some folk theorems

The performance of demand-driven caching depends on the locality of reference exhibited by the stream of requests made to the cache. In particular, it is expected that the stronger the locality of reference, the smaller the miss rate of the cache. For the independent reference model, this amounts to a smaller miss rate when the popularity distribution of requested objects in the stream is more skewed. We formalize this "folk theorem" through the companion concepts of majorization and Schur-concavity. This folk theorem is established for caches operating under a random on-demand replacement algorithm (RORA). However, the result fails to hold in general under the (popular) LRU and CLIMB policies, but can he established when the input has a Zipf-like popularity pmf with large skewness parameter. In addition, we explore how the majorization of popularity distributions translates into comparisons of three well-known locality of reference metrics, namely the inter-reference time, the working set size and the stack distance.
2 AUSTRALIAN ENVIRONMENTAL MANAGEMENT ACCOUNTING : AN EXPLORATORY STUDY OF CURRENT CORPORATE PRACTICE AND STRATEGIC INTENT

This paper investigates the current state of corporate Australia’s environmental management accounting practices and environmental management accountants’ perceptions of how environmental management accounting information should be accounted for and reported in the annual report of an environmentally sensitive corporation. The results indicate that many Australian companies have not yet developed a holistic approach to environmental costing, and that environmental management accountants believe that environmentally induced costs and expenses should be reported as notes to financial statements, rather than in the profit and loss statement in the corporate annual report. However results are inconclusive as to whether certain environmentally induced expenditure should be capitalised and amortised separately in the balance sheet, while there is some support for environmentally induced end-of-pipe and integrated technologies being recognised separately as assets in the balance sheet.
Functionality in Neurd Nets *

We investigate the functional capabilities of sparse networks of computing elements in accumulating knowledge through successive learning experiences . As experiences, we consider various combinations of episodic and concept learning, in supervised or unsupervised mode, of conjunctions and of disjunctions. For these we exhibit algorithms for learning in well defined senses. Each concept or episode is expressible in terms of concepts or episodes already known, and is thus learned hierarchically, without disturbing previous knowledge. Minimal assumptions are made about the computing elements, which are assumed to be classical threshold elements with states. Also we adhere to severe resource constraints. Each new concept or episode requires storage linear in the relevant parameters, and the algorithms take very few steps. We hypothesise that in our context functionality is limited more by the communication bottlenecks in the networks than by the computing capabilities of the elements and hence that this approach may prove useful in understanding biological systems even in the absence of accurate neurophysiological models.
Material fatigue simulation using a periodic time-homogenisation method

This paper deals with the numerical simulation of combined cycle fatigue, which is characterised by two periodic loads, whose frequencies are very different one from the other. Rather than using classical fatigue life estimations, a time transient evolution model is solved using a periodic time-homogenisation method. This latter is based on the assumption that the time scales associated with the two periodic loads are decoupled. Different results on academic as well as industrial examples are presented. An extension of the proposed method up to three time scales is eventually proposed in order to speed up the numerical simulations.
Vesselness-guided variational segmentation of cellular networks from 3D micro-CT

Advances in imaging techniques lead to nondestructive 3D visualization of biological tissue at a sub-cellular scale. As a consequence, new demands emerge to segment complex structures. For instance, synchrotron radiation micro-CT, makes it possible to image the lacunar-canalicular porosity in bone tissue. This structure contains a dense network of slender channels interconnecting the cells. Their size (~300-600 nanometers in diameter) is at the limit of the acquisition system resolution (280 nm) making their detection difficult. In this work is proposed a variational region growing segmentation method adapted for cellular networks. To control the evolution of the segmentation through tubular structures a vesselness map is introduced in the expression of the functional to minimize. The method is tested on synthetic images and applied to experimental data.
Novel approach for Interference Management in cognitive radio

Cognitive radio based on a software-defined radio, is an emerging wireless communication system today. It is regarded as fifth generation (5G) mobile system. Cognitive radio research can be categorized in three parts, which are Spectrum Management, Intelligence Management and Interference Management. Spectrum management detects white space and manages different spectrum issues between primary and secondary users. Intelligence management on the other hand use different artificial intelligence techniques such as Neural Networks, Rule based system, Genetic Algorithm etc. in order to develop efficient cognitive engine. Finally Interference management focus on implementation issues of cognitive radio by dealing with channel awareness, link quality and resource allocation which mainly depend on the right choice of transmit power. This paper proposes an algorithm based on MAC scheduling techniques and uses two parallel processes to control transmit power. The first process go through different mathematical calculations of SINR, channel capacity etc of all links between different radio nodes within a network. The second process deals with environmental conditions with the help of Fuzzy logic. These both processes works together to adjust the value of transmit power of every node in order to mitigate interference and maintain QoS of the service.
Critical Path Routing (CPR) Protocol for Mobile Ad Hoc Networks

In this paper we present a hybrid adaptive ad hoc routing protocol, named Critical Path Routing (CPR) that strives to incorporate the merits of both reactive and proactive ad hoc routing algorithms. The genuine aspect of CPR is that it initially starts-off as a conventional reactive Dynamic Source Routing (DSR) protocol. The network traffic is monitored in attempt to gradually discover pairs of highly interactive nodes that engage in sending data to each other more often than other nodes in the network. CPR then constructs Critical Paths (CPs) between these pairs of nodes and proactively safe guards these CPs. The aim of our work is to achieve low latency between highly active pairs of nodes, thus increasing the overall performance of the network. Simulation results showed that CPR outclassed DSR with a decrease of 23.7% in end-to-end delay when nodes were in a high degree of mobility, but with a relatively high overhead cost. An enhancement is added to our CPR protocol to decrease the relatively high overhead by decreasing the engagement of intermediate nodes in the proactive monitoring of CPs. The total overhead decreased to only 14.74%.
The optimization of an export/import company by use of a MIMO/4G channel

The aim of this paper is to present a new view in the transportation problem for the Export/Import process using 4G technology. This paper offers a description of the commercials transactions using the Workflow model in order to quantify the gain of the implementation using MIMO channel. The results show a gain of at least a 14%, because of the reduction in the errors in the channel with a gain of 4.5 dB at the receiver side.
The Allocation of Resources Under Uncertainty

We study the effects of uncertainty on the allocation of resources in the standard, static, general equilibrium, two-sector, two-factor model. The elasticity of substitution in production vs that in consumption plays a key role in determining whether uncertainty attracts or repels resources. Risk aversion matters, but to a smaller extent, while factor endowments and factor intensities play a more limited role.
Node Density-Based Adaptive Spray and Focus Routing in Opportunistic Networks

Message delivery is one of the challenging issues in opportunistic networks because of the short-lived connectivity environment. To deal with this issue, replication-based routing protocols inject a specific number of multiple message copies into the network to achieve the tradeoff between the delivery probability and overhead. However, in most of the current copy-limited routing algorithms, message copies would be premature to be distributed. In the case of high node density, a large number of message copies are limited to the small local area, which causes long delay and low delivery ratio. To address this problem, a node Density-based Adaptive Spray and Focus (DASF) routing algorithm is proposed in this paper. By estimating the node density of the current location, DASF could control the total number of message copies for allocation. Then it decides the proportion of message copies between encountered nodes according to the level of node activity. Simulation results show that the proposed DASF routing algorithm can improve delivery ratio and reduce the average message transmission delay when node density increases.
Indexing Speculative Pressure on an Exchange Rate Regime: A Case Study of Macedonia

This paper modifies an ad hoc index originated by Eichengreen et al (1995,1996), which is often used to document financial crises in emerging markets. By assuming nonlinear dynamics in a system of financial data, we successfully develop an alternative approach that not only captures the essence of the conventional index but also offers an indicator that leads the crises in-sample. This is very important for policy markers of transitional economies like Macedonia, for which the historical macroeconomic data are often inadequate for existing "early warning sign" systems of potential crises.
ON SOLUBILITY ENHANCEMENT TECHNIQUES

Oral route is the most desirable and preferred method of administering therapeutic agents for their systemic effects, but poorly solubility of drug is major challenge for formulation scientist. About 40% of orally administered drugs suffer from formulation difficulties related to their water insolubility. Dissolution rate, absorption, distribution and excretion of a moiety depend upon its solubility characteristics. On the basis of solubility, drugs are classified into four classes of the BCS classification. Solubility challenges are faced in the Class II and Class IV of the BCS system. To improve solubility and bioavailability of poorly soluble drug we use various methods or techniques. The methods like solid dispersion, complexation, liquisolid, hydrotropy, sonocrystallization, self emulsifying method, are commonly referred for solubility enhancement. In this review we concentrated on improvement of the solubility of poorly water soluble drugs by applying various methods.
Robust Model Predictive Control for a Class of Nonlinear Systems with Uncertainty

The purpose of this paper is to design a robust nonlinear model predictive controller for a class of single input single output nonlinear systems with uncertainty. The controller structure is designed by using backstepping technique, and the controller parameters are tuned online by using model predictive control. The overall model predictive control scheme is proved to guarantee stability by using Lyapunov function. The simulation of a continuous fermenter process also shows that the controller is valid and can be applied to practical system