Miracle-dz/UniFAR-FTUs huggingface dataset:
Span Morphing Using the Compliant Spar

This paper develops and models the Compliant Spar concept that allows the wing span to be varied to provide roll control and enhance the operational performance for a medium altitude long endurance (MALE) UAV. The wing semi-span is split into morphing partitions and the concept maybe incorporated in each partition; however only the tip partition is considered here. The Compliant Spar is made of compliant joints arrange in series to allow the partition to be flexible under axial (spanwise) loads but at the same time stiff enough to resist bending loads. Each compliant joint consists of two concentric overlapping AL 2024-T3 tubes joined together using elastomeric material. Under axial (spanwise) loading, the elastomeric material deforms in shear allowing the overlapping distance between the tubes to vary and hence the length (in the spanwise direction) of the joint/spar to vary. High fidelity modelling of the concept is performed. Then, structural optimisation studies are performed to minimise the axial stiffness and the structural mass of the concept for various design constraints. The flexible skin and actuation system to be used are also addressed.
POSTER : Packet Conductance for Statistical Intrusion Detection in Anonymous Networks

Intrusion detection systems have been widely studied for various types of networks and applications. In applications where end-hosts of a network seek anonymity, the communication between such hosts behind secured gateways are supported by a heterogeneous, mixed wired/wireless network core. The secured gateway fully encrypts and anonymizes all data packets sent through the network core. While attacks on data itself is possible via crypt-analysis, real-time intrusion detection by studying packet header information is not possible as these packets are fully encrypted. In this paper we propose a distributed intrusion detection system for such anonymized networks. Our system monitors the behavior of the network bandwidth consumption in real-time and uses a statistical inference system to classify anomalies in bandwidth consumption of the network.
K-Adaptability in Stochastic Programming

We address combinatorial optimization problems with uncertain objective functions, given by discrete probability distributions. Within this setting, we investigate the so-called K-adaptability approach: the aim is to calculate a set of k feasible solutions such that the objective value of the best of these solutions, calculated in each scenario independently, is optimal in expectation. We show that this problem is NP-hard even if the underlying certain problem is trivial, and present further complexity results concerning approximability and fixed-parameter tractability with respect to k. Moreover, we present exact solution methods as well as a heuristic for this problem and compare them in an extensive experimental evaluation, where the underlying problem is the Unconstrained Binary Optimization Problem, the Shortest Path Problem or the Spanning Tree Problem. It turns out that the performance and the ranking of these approaches strongly depends on the parameter k and on the number of scenarios.
Teaching a new method of partial fraction decomposition to senior secondary students: Results and analysis from a pilot study

In this paper, we introduce a new approach to compute the partial fraction decompositions of rational functions and describe the results of its trials at three secondary schools in Hong Kong. The data were collected via quizzes, questionnaire and interviews. In general, according to the responses from the teachers and students concerned, this new approach has potential to be introduced at the senior secondary level, as an alternative to the method of undetermined coefficients described in common secondary mathematics textbooks. Some remarks on the related pedagogical issues are included.
A High Performance Computing Study of a Scalable FISST-Based Approach to Multi-Target, Multi-Sensor Tracking

Finite Set Statistics (FISST) is a rigorous Bayesian multi-hypothesis management tool for the joint detection, classification and tracking of multi-sensor, multi-object systems. Implicit within the approach are solutions to the data association and target label-tracking problems. The full FISST filtering equations, however, are intractable. While FISST-based methods such as the PHD and CPHD filters are tractable, they require heavy moment approximations to the full FISST equations that result in a significant loss of information contained in the collected data. In this paper, we review Smart Sampling Markov Chain Monte Carlo (SSMCMC) that enables FISST to be tractable while avoiding moment approximations. We study the effect of tuning key SSMCMC parameters on tracking quality and computation time. The study is performed on a representative space object catalog with varying numbers of RSOs. The solution is implemented in the Scala computing language at the Maui High Performance Computing Center (MHPCC) facility.
Effect of Forage Feeding on Goat Meat Production: Carcass Characteristics and Composition of Creole Kids Reared Either at Pasture or Indoors in the Humid Tropics

Forage diets provide good quality carcasses in sheep but very little is known in tropical goats. An experiment was designed with Creole male goats using grass-based systems to assess carcass yield, scores, cuts and composition. After weaning (84 d, 9.2 kg LW) two modes of forage feeding were compared with two replicates of each. Feeding groups were: PF for animals reared at pasture (n = 62) and IF when reared indoors (n = 60). Given that forage finishing will result in low ADG it appeared necessary to study different fattening lengths. The kids were equally divided into 4 groups: group A (n = 32), 4mo after weaning; group B (n = 32), 4mo after A; group C (n = 30), 3mo after B and group D (n = 28), 2mo after C. The animals grazed (in two sub-flocks) on irrigated tropical pastures managed in a rotational system (28 d of re-growth) at a mean stocking rate of 1,200 kg/ha/yr LW. The IF groups were reared in collective pens on a slatted floor (2 replicates of 7 or 8 kids each). They were fed the same stand of tropical grass (25% DM, 12% CP) as that of pasture that was cut daily and provided ad libitum. The ADG (-10%), the weights of omental fat (-60%) and fat in shoulder (-18%), the ultimate pH of carcass (-12%), the meat colour score (-24%), the "a" parameter accounting for redness (12%) and the DM and lipid contents (-4%) were significantly lower (p<0.05) in PF than in IF, while the liver was heavier (+23%, p<0.05). Feeding conditions seemed to be similar, thus, differences could be related to gastrointestinal parasitism in the PF system and hypotheses are discussed. Increasing the fattening duration, resulted in significant difference (p<0.01) in many traits: the weights at slaughter and of carcass increased by 40% and 60% from groups A to D and consequently the weights of body compartments and carcass cuts (1.5 to 2.0 fold more). When the results were presented as percentage of empty body weight and carcass weight, these preliminary results (carcass weight 9kg and yield 53%, muscle proportion 70%) and qualitative parameters (low fat score 2/5, fat proportion 5%), seem to be a good incentive for the sector to develop a niche market to meet consumer lean meat expectations. The indoors system could be implemented where there was low availability of grazing areas or problems of dog attacks.
Balancing of U-type assembly systems using simulated annealing

The paper presents a new simulated annealing (SA)-based algorithm for the assembly line-balancing problem with a U-type configuration. The proposed algorithm employs an intelligent mechanism to search a large solution space. U-type assembly systems are becoming increasingly popular in today's modern production environments since they are more general than the traditional assembly systems. In these systems, tasks are to be allocated into stations by moving forward and backward through the precedence diagram in contrast to a typical forward move in the traditional assembly systems. The performance of the algorithm is measured by solving a large number of benchmark problems available in the literature. The results of the computational experiments indicate that the proposed SA-based algorithm performs quite effectively. It also yields the optimal solution for most problem instances. Future research directions and a comprehensive bibliography are also provided here.
On Utilization and Stockpiling of Prescription Drugs When Co-Payments Increase: Heterogeneity Across Types of Drugs

This paper investigates prescription drug utilization changes following an exogenous shift in consumer co-payment caused by a reform in the Danish subsidy scheme for the general public. Two different types of medication are considered – insulin for treatment of the chronic condition diabetes and penicillin for treatment of non-chronic conditions. Using purchasing records for a 20% random sample of the Danish population, I show that increasing co-payments lower the utilization of both drugs. I demonstrate that individuals treated with drugs for chronic conditions react to the policy change by stockpiling on their medications. This has implications for other papers in the literature that use variation in subsidy rates over time to estimate the price elasticity of demand. This is not the case for penicillin however, where price elasticities are estimated to be in the -. 18 – -. 35 range. Further, I find that the lower part of the income distribution is more price responsive.
Cosmological constraints from applying SHAM to rescaled cosmological simulations

We place constraints on the matter density of the Universe and the amplitude of clustering using measurements of the galaxy two-point correlation function from the Sloan Digital Sky Survey (SDSS). We generate model predictions for different cosmologies by populating rescaled N-body simulations with galaxies using the subhalo abundance matching (SHAM) technique. We find ΩM = 0.29 ± 0.03 and σ8 = 0.86 ± 0.04 at 68 per cent confidence by fitting the observed two-point galaxy correlation function of galaxies brighter than Mr = -18 in a volume-limited sample of galaxies obtained by the SDSS. We discuss and quantify potential sources of systematic error and conclude that while there is scope for improving its robustness, the technique presented in this paper provides a powerful low-redshift constraint on the cosmological parameters that is complementary to other commonly used methods.
Credit Frictions and the Bank Lending Channel: Evidence from a Group of European Banks

﻿Monetary policy decisions are transmitted into the economy through many channels, one of which is the bank lending channel. It is based on the central bank’s actions that affect loan supply and real spending. This paper examines, spanning the period 1999-2010, whether for the case of European banks the operation of the bank lending channel can be modeled in a manner that better conforms to current institutional realities, such as credit frictions. The recent literature on monetary policy takes into account credit frictions and investigates monetary implications. We use interest rate spreads, that is, the difference between the interest rate available to savers and borrowers, as an indicator of the disruptions in the financial situation and incorporate them into the model for the estimation of the bank lending channel across eurozone countries. The results indicate that these credit frictions have an impact on the lending growth process.
Occurrence, distribution and control of papaya mealybug, Paracoccus marginatus (Hemiptera: Pseudococcidae), an invasive alien pest in Sri Lanka

A hitherto unrecorded species of mealybug was discovered in early 2008 in the western provincial districts Colombo and Gampaha in Sri Lanka, infesting a large number of plant species. Investigations were done to identify the pest and to study its host range, nature of damage and distribution, and to design and implement control measures. The pest was identified as papaya mealybug, Paracoccus marginatus Williams & Granara de Willink (Hemiptera: Pseudococcidae), an invasive alien species originating from Mexico and/or Central America. The mealybug found to heavily infest more than 40 plant species including papaw, the major host, and several horticultural and floricultural crops like Plumeria, manioc, bread fruit, Alstonia macrophylla and Jatropha spp. By 2009 the pest had spread to other parts of the country including the North Western, Sabaragamuwa, Southern, North Central, Central and Eastern provinces. As an immediate control measure, imidacloprid 200SL, thiamethoxam 25%WG and Mineral oil were recommended for the control of this pest until biological control agents could be introduced.
Research on foreign-language teaching and learning in the Netherlands (2002–2006)

This overview of applied linguistics research in the Netherlands between 2002 and 2006 is the fifth in a cyclical series of country-specific reviews of published research on foreign-language teaching and learning. About 75 papers have been selected from about twenty journals, conference proceedings, books and reports edited during the period 2002–2006 reporting on experimental or quasi-experimental research that has considerable value for those working in the field of language teaching. The review reveals the issues that currently concern applied linguists and second-language researchers in the Dutch language community: redefining multilingualism, trying to find ways to help minority children achieve at school, keeping a critical eye on current teaching practices, and finding specific ways to improve second- and foreign-language teaching.
Size segregation of intruders in perpetual granular avalanches

Granular flows such as landslides, debris flows and avalanches are systems of particles with a large range of particle sizes that typically segregate while flowing. The physical mechanisms responsible for this process, however, are still poorly understood, and there is no predictive framework for ascertaining the segregation behaviour of a given system of particles. Here, we provide experimental evidence of individual large intruder particles being attracted to a fixed point in a dry two-dimensional flow of particles of otherwise uniform size. A continuum theory is proposed which captures this effect using only a single fitting parameter that describes the rate of segregation, given knowledge of the bulk flow field. Predictions of the continuum theory are compared with the experimental findings, both for the typical location and velocity field of a range of intruder sizes. For large intruder particle sizes, the continuum model successfully predicts that a fixed point attractor will form, where intruders are drawn to a single location.
Visual Recurrence Analysis As An AlternativeFramework For Time Series Characterisation

Basic recurrence plot analysis has been extensively used as a technique for characterising financial time series. In this paper we examine recurrence plots of two of the most fundamental processes, i.e. the white noise process and the Wiener process. These recurrence plots are also used to ‘catalogue’ typical behaviour for different time lags for these two processes. The graphs are then used as a template to compare the recurrence graphs of the returns on eight reallife data sets taken from the NYSE.
The world price of exchange risk in the Pacific Basin equity markets

This paper investigates whether the foreign exchange risk is priced in the Pacific Basin equity markets. The test was performed in the conditional version which allows the world prices of market risk and exchange risk to vary over time. Being parsimonious, a principal component analysis is taken on these Pacific Basin interest rates to extract the common exchange rate factors. The results show that the international asset pricing model with exchange risk premia is better than the international asset pricing model without exchange risk premia to describe the Pacific Basin stock returns. This implies the world prices of exchange risk are present in the Pacific Basin equity markets.
Learning Long-term Planning in Basketball Using Hierarchical Memory Networks

We study the problem of learning to plan spatiotemporal trajectories over long time horizons using expert demonstrations. For instance, in sports, agents often choose action sequences with long-term goals in mind, such as achieving a certain strategic position. Conventional policy learning approaches, such as those based on Markov decision processes, generally fail at learning cohesive long-term behavior in such high-dimensional state spaces, and are only e↵ective when myopic planning leads to the desired behavior. The key difficulty is that such approaches use “shallow” planners that only learn a single state-action policy. We instead propose to learn a hierarchical planner that reasons about both longterm and short-term goals, which we instantiate as a hierarchical deep memory network. We showcase our approach in a case study on learning to imitate demonstrated basketball trajectories, and show that it generates significantly more realistic trajectories compared to non-hierarchical baselines as judged by professional sports analysts.
Segmentation and Classification of Lung Nodule in Chest Radiograph Image

Image segmentation plays a vital step in medical image processing. Lung cancer is the largest cause of tumor deaths. Since the nodules are commonly attached to blood vessels, detection of lung nodules is the challenging task . By early detection the lung cancer can be completely recovered. Especially in the case of lung nodule detection Computer Aided Detection (CAD) is effective for the improvement of radiologists‟ diagnosis. In this paper an efficient lung nodule detection scheme is developed by performing nodule segmentation through Fuzzy C-Means (FCM) and Virtual Dual Energy (VDE). Here the input image is considered as an radiograph image, then the lung is segmented by using Multi segment Active Shape Model (MASM). Finally neural network classifies as a nodule or non-nodule candidates.
Adapting Probabilistic Flooding in Energy Harvesting Wireless Sensor Networks

Technological advantages in energy harvesting have been successfully applied in wireless sensor network environments, prolonging network’s lifetime, and, therefore, classical networking approaches like information dissemination need to be readdressed. More specifically, Probabilistic Flooding information dissemination is revisited in this work and it is observed that certain limitations arise due to the idiosyncrasies of nodes’ operation in energy harvesting network environments, resulting in reduced network coverage. In order to address this challenge, a modified version of Probabilistic Flooding is proposed, called Robust Probabilistic Flooding, which is capable of dealing with nodes of about to be exhausted batteries that resume their operation after ambient energy collection. In order to capture the behavior of the nodes’ operational states, a Markov chain model is also introduced and—based on certain observations and assumptions presented here—is subsequently simplified. Simulation results based on the proposed Markov chain model and a solar radiation dataset demonstrate the inefficiencies of Probabilistic Flooding and show that its enhanced version (i.e., Robust Probabilistic Flooding) is capable of fully covering the network on the expense of increased termination time in energy harvesting environments. Another advantage is that no extra overhead is introduced regarding the number of disseminated messages, thus not introducing any extra transmissions and therefore the consumed energy does not increase.
Fast Method of Principal Component Analysis Based on L1-Norm Maximization Algorithm

—In data-analysis problems with a large number of dimension, principal component analysis based on L2-norm (L2-PCA) is one of the most popular methods, but L2-PCA is sensitive to outliers. Unlike L2-PCA, PCA-L1 is robust to outliers because it utilizes the L1-norm, which is less sensitive to outliers. Furthermore, the bases obtained by PCA-L1 is invariant to rotations. However, PCA-L1 needs long time to calculate bases, because PCA-L1 employs an iterative algorithm to obtain each basis, and requires to calculate an eigenvector of autocorrelation matrix as an initial vector. The autocorrelation matrix needs to be recalculated for each basis. In this paper, we propose a fast method to compute the autocorrelation matrices. In order to verify the proposed method, we apply L2-PCA, PCA-L1, and the proposed method to face recognition. Simulation results show that the proposed method provides same recognition performance as PCA-L1, and is superior to L2-PCA, while the execution time is less than PCA-L1.
Comparison of the RC Model and the WR Model Based on CHILDES

The acquisition of the verb past tense has often been used to help to figure out children’s real process of language acquisition. This paper aims to make a comparison between Charles Yang’s Rules and Competition Model (the RC model) and Steven Pinker’s Words and Rules Model (the WR model) based on real language acquisition data selected from CHILDES (Child Language Data Exchange System). Chomsky’s Universal grammar is the foundation of both models. The comparison has been done from three aspects: the role of input frequency, overregularization errors, and the origin of irregular past tense. The finding of the study indicates that the RC model can well explain the role of input frequency in verb classes and the similarity between verb and the past tense while the WR model’s explanation is vague in this point. Overregularization errors are more like an inevitable learning phenomenon that sheds light on phonological rules in the RC model instead of simple memory failures in the WR model. The WR model well explains the origin of irregular past tense while the RC model does not mention this point.
Seasonal variation in nutritional compositions of spider plant (Cleome gynandra L.) in south Côte d'Ivoire Accepted 28 October, 2014

1Laboratory of Nutrition and Food Security, University Nangui Abrogoua, 02 BP 801 Abidjan 02, Côte d’Ivoire. 2ICRAF (International Centre for Research in Agroforestry), 08 BP 2824 Abidjan 08, Côte d’Ivoire. 3Laboratory of Genetics, University Felix Houphouet Boigny, 22 BP 582 Abidjan 22, Côte d’Ivoire 4National Agronomic Research Center, 01 BP 1740 Abidjan 01 Côte d’Ivoire 5University Jean Lorougnon Guédé, BP 150 Daloa, Côte d’Ivoire. A survey on leafy vegetables consumption was carried out in the district of Abidjan. Samples of spider plant, were collected from the fields during the dry and the rainy seasons. Nutrient analysis of spider plant leaf were performed on dry matter, pH, proteins, sugars, fibers, vitamin C, ß-carotene, minerals and oxalic acid content. The survey revealed that spider plant was less known and consumed. Oxalic acid level is high in rainy season (10.17 mg/100g DW). Soluble fibers content is around 13.33 % in dry season and 16% in rainy season while, insoluble fibers rate is 25.1% in dry season and 23.07% in rainy season. The most β-carotene level was determined in rainy season (110.26 μg/100g FW). Iron content was about 23.93 mg/100g DW in dry season and 10.17 mg/100g DW in rainy season. Calcium content was high in rainy season (45.25 g/100g DW) whereas potassium content is more elevated in dry season (109.38 g/100g DW). Except vitamin C, the other nutrients differ statistically within the seasons. Spider plant could be harvested and consumed in the both seasons and there is a need to raise awareness on its nutritional benefit to promote its production and consumption.