Miracle-dz/UniFAR-FTUs huggingface dataset:
Applying Machine Learning Techniques to Short Term Load Forecasting

This thesis reports on the application of two machine learning techniques on the case of 24-ahead short term load forecasting (STLF). The methods used are Random Forests and Echo State Networks. Hierarchical linear models are used as baseline comparison. Four different cases of STLF will be combined in this research: Total power consumption of an area, power demand on the power supplier, power supply to the power network, and solar power generation (SPG). These variables are useful things to know in power supply planning by power suppliers and short term peak detection for network operators. To know these variables beforehand means to be able to economically and securely operate the power grid and power supply. Therefore constant research is being done to improve forecasting techniques. More recently it has become important to incorporate the supply by users into the forecasting system as more and more households install solar panels. A dataset was used from a neighbourhood in The Netherlands where most households are outfitted with solar panels and all households have smart meters. A large part of the project consisted of cleaning the data. Predictors were chosen from the dataset using domain knowledge and partly by Fourier analysis. Some measurements of weather data were added to the dataset using an interpolation between two stations of the KNMI. Four datasets were created; one for each case. These were split up for training, validation, and testing purposes. Random Forests and Echo State Networks use a number of hyper-parameters as initiation or training settings. These parameters were optimized on the training and validation sets using particle swarm optimization (PSO). The resulting optimal settings were used to train new models and test performance on the test sets. Comparison was done by testing the differences in RMSE with Welch’s t-test. The results are interesting. It was found that the linear model is quite a good performer in most cases, but is sometimes outperformed by the Random Forest. Solar power generation has appeared to be the hardest to predict and even the linear model is not performing well in this case. The Echo State Network seems to be unsuitable for this kind of forecasting in all cases.
Percutaneous Muscle Biopsies: Review of 900 Consecutive Cases at London Health Sciences Centre

Objective: In the present study we review our experience with 900 consecutive percutaneous muscle biopsies over the period 1993 to 2007. We examined the advantages and limitations of the procedure, biopsy site preferences, diagnostic range, frequency of diagnoses and quality of histopathology. Demographics, referral patterns and patients' perceptions of the procedure were also assessed. Methods: Cases were identified through the London Health Sciences Centre Department of Pathology database. Standard biopsy procedures were followed using a manual trocar style instrument. With a neuropathology technologist in attendance at all biopsies, biopsies were oriented in the fresh state and snap frozen. Results: Most referrals for muscle biopsy were from neuromuscular neurologists. The procedure was found to be efficient, well-tolerated and produced high quality specimens in all diagnostic categories. No major complications occurred. Failure to obtain an adequate tissue sample, although uncommon (<2%), was usually due to marked obesity, edema or muscle wasting. Bleeding at the site was rarely problematic and no wound infections were reported. Conclusions: Needle muscle biopsies represent an efficient alternative to open biopsies when peripheral nerve sampling is not required and when large tissue samples are not needed for extensive biochemical analyses.
Neuroplasticity Promoted by Task Complexity

One condition that appears to have a strong impact on the neuroplastic changes presumed to subserve skillful motor performance is the complexity of tasks during motor training. This paper reviews some of the morphological, physiological, and behavioral effects associated with training on difficult tasks involving in-depth information processing, compared to simpler tasks, requiring lesser processing.
Classification Methods : Systematic Literature Review of Primary Studies

Text classification or categorization (TC) is the process of assigning a text document to one or more predefined classes based on their content. TC falls at the crossroad of Machine learning (ML) and information retrieval (IR). There have been tremendous interest in this research area due to the large amount of textual data posted and widely shared on the World Wide Web [1]. TC has been used in different applications including topic identifications, spam filtering and sentiment analysis. While some data can be described as being static such as PDF files, HTML pages could be updated more frequently, while Tweets are retrievable in real-time. In general, Text classifiers can be categorized into two models: Generative and Discriminative. For instance Naïve Bayes (NB) is an example of a generative model that will first try to estimate parameters from p(x | y) and p(y) from the training data, then calculates p(y | x) by using Bayes theorem. Where p(x | y) stands for a conditional probability of x given y is true. It is called “generative” since we can generate new samples by sampling from the learned joint distribution p(x, y). In contrast, a discriminative model estimates parameters of p(x | y) directly from the training data without assuming anything about the input distribution p(x), such models include Support Vector Machines (SVM), Neural Networks and Decision Trees [2, 3]. SVM is considered a non-probabilistic binary linear classifier, it can be used for both classification or regression. For a given set of training samples, the SVM model is representation of these samples as mapped points in space, isolated by a gap to distinguish the different categories. Likewise, Decision Trees can be used as a predictive model. Their structure include leaves to represent classes (target values) and branches to represent conjunctions of features. However, in complex classification tasks, trees could fail to generalize from the training data (overfitting) or correctly illustrate a concept.
Comparison of different methods for detecting glaucomatous visual field progression.

PURPOSE To compare the performance characteristics of seven methods for analyzing glaucomatous visual field progression, using a combination of real patient data and computer simulation techniques. METHODS The initial and final visual field results, separated by 7 years and measured with the full-threshold 30-2 program of the Humphrey Field Analyzer (Carl Zeiss Meditec, Dublin, CA) of 76 patients with open-angle glaucoma were used. A computer simulation program generated 14 interim semiannual visual fields under conditions of high, moderate, and no variability. Progression was analyzed using the methods of the Advanced Glaucoma Intervention Study (AGIS), the Collaborative Initial Glaucoma Treatment Study (CIGTS), three criteria based on the Glaucoma Change Probability (GCP) analysis, and two criteria based on point-wise linear regression analysis (PLRA). Specificities were calculated by using the same visual field of each patient as both the initial and final field (no progression) under conditions of moderate and high variability. RESULTS Under the no-variability condition, progression rates were 18% for the AGIS, 36% for CIGTS, 47% to 62% for the three GCP methods, and 72% and 84% for the two PLRA methods. Progression rates increased with greater variability with the three GCP methods and decreased with all other methods. The time to detect confirmed progression was longest for the PLRA methods and shortest for the CIGTS and GCP methods. Under the moderate-variability condition, all methods yielded high specificity. The AGIS, CIGTS, and one of the GCP and PLRA methods were relatively resistant to high variability and maintained high specificities. CONCLUSIONS The AGIS and CIGTS methods had high specificity, but classified fewer cases of progression than the other methods. The GCP methods determined progression earliest; however, they were generally not as specific. Methods based on PLRA were specific but times to confirmed progression were the longest.
A Novel Boresight and Conical Pattern Reconfigurable Antenna With the Diversity of 360° Polarization Scanning

A novel design of boresight and conical pattern reconfigurable antenna with the capability of 360° polarization scanning is presented in this paper. At boresight mode, the antenna produces a continuously reconfigurable polarization over 360° in the azimuth plane, which provides adaptive-polarization reception between a transmitter and a receiver. The antenna is analyzed in terms of the electric field with an explanation of the generation of TM11 and TM02 resonant modes, which provides an innovative method of designing pattern reconfigurable antennas for the WLAN applications. The proposed method is validated by a good agreement between simulation and experimental results for an antenna designed to provide boresight and conical pattern switching at 2.4 GHz. The gain of 6.05 dBi and efficiency of 86.7% are measured in the boresight mode, corresponding to 4.39 dBi and 82.4% at conical mode.
Nest, Eggs, and Nesting Behavior of the Gray Trembler (Cinclocerthia gutturalis) on St. Lucia, West Indies

Abstract Few descriptions exist of the nesting behavior of the Gray Trembler (Cinclocerthia gutturalis), and the only nest description of this species seems incongruent with what is known about nesting behavior of other species of Mimidae. We report the first definitively described nest of the Gray Trembler in St. Lucia, West Indies in June–July 2007. We observed construction of, incubation at, and feeding of nestlings in an open-cup nest, similar in architecture to nests of other mimids, contradicting previous reports that Gray Trembler nests are domed, constructed of dried grass, and on top of palm (Cocos nucifera) trees.
Immunoimmobilization of Living Salmonella for Fundamental Studies and Biosensor Applications

Currently, there is no technique available to probe an individual bacterium in its physiological environment for a prolonged period of time to determine its response to environmental stimuli or to conduct measurements on it. Controlling and manipulating individual bacteria will facilitate fundamental studies of such bacterial characteristics as morphology, adhesion, biomineralization and mechanical properties under physiological conditions. Access to specific individual cells will open new research frontiers in areas such as differentiation among the individual offspring of a predetermined bacterium (Arnoldi et al. 1998; Chao et al. 2011; Gao et al. 2011). In many applications, it is necessary to immobilize bacteria on flat substrates or particles. For example, there have been many reports on using living bacteria as sensors for environmental monitoring because of their low cost, fast growth, easy genetic modification and handling, and sensitivity to a wide variety of environmental stimuli (Kuang et al. 2004; Mbeunkui et al. 2002; Premkumar et al. 2002). It is often necessary to immobilize living bacteria on a designated area of a detecting surface to build a practical sensor. Reliable, controllable and efficient immobilization of bacteria is crucial for the success of pathogen detection. Typically, a captured bacterium triggers an event that converts the capturing process into a signal which is detectable by optical, electrochemical, mechanical or other means (O'Kennedy et al. 2009). The technique developed by our group appears to be very promising for these applications. Before we describe our methodology here, we will give a brief review of the previous approaches and methods for the immobilization of bacteria on material surfaces.
Interactive augmented reality art book to promote Malaysia traditional game

Malaysia is a multicultural country and is known for its tourism industry. The uniqueness of Malaysian traditional games is one of the cultural aspects to promote tourism industry in Malaysia. This paper presents a mobile application that combines the use of printed media and marker-based augmented reality (AR) technology as an effective and engaging platform in promoting Malaysian traditional games. The mobile AR application developed in this study provides interactive features that allow users to interact and manipulate the 3D objects including videos using five assigned virtual buttons. User acceptance test was carried out with 15 users including foreigners to verify the effectiveness of the mobile AR application.
Performance Improvement of Traffic Classification Based on Application Traffic Locality

Application-level traffic classification is an essential requirement for stable network operation and resource management. The payload signature-based classifier is considered a reliable method for Internet traffic classification. However, with this system, processing speeds are slower when high volumes of traffic are being classified in high-speed networks in real time. In this paper, we propose a method for server IP-port pair cachebased traffic classification, with the aim of increasing the processing speed and completeness of payload signature-based traffic classification. This approach takes application traffic locality into consideration. Moreover, we propose a cache data management method that has the purpose of minimizing the utilization of cache memory and processing speed and maximizing level of completeness. When our proposed method was applied to a campus network, we observe 10 times improvement in processing speed and 10% increasing in completeness against the payload signature-based classifier without a server IP-Port pair cache.
SEASONAL CHANGES IN &OV0312;O2max AMONG DIVISION 1A COLLEGIATE WOMEN SOCCER PLAYERS

Aerobic capacity and body composition were measured at 3 time points over a 1-year period in 26 Division 1A women soccer players from Texas A&M University, in order to determine whether there were seasonal changes in these parameters. Subjects were tested in December, immediately following a 4-month competitive season; in April, following 15 weeks of strength and conditioning; and immediately prior to the start of the regular season in August, following a 12-week summer strength and conditioning program. A periodized strength and conditioning program design was incorporated in order to optimize anaerobic and oxidative capacity immediately prior to the regular competitive season. Significant differences in &OV0312;O2max were measured between August (49.24 ± 4.38 ml·kg−1·min−1) and December (44.87 ± 4.61 ml·kg−1·min−1). No significant changes in aerobic capacity were found between April (47.43 ± 4.01 ml·kg−1·min−1) and August (49.64 ± 5.25 ml·kg−1·min−1). Significant increases in body fat were measured between August (15.71 ± 2.92%) and December (18.78 ± 2.79%), before and after the competitive season, respectively. No significant changes in body fat were found between April (16.24 ± 2.95%) and August (15.71 ± 2.92%). The results of this study suggest that decreases in muscle mass over the course of a regular competitive season contribute to decreases in aerobic capacity in collegiate women soccer players. Although it is unknown whether this decrease in muscle mass is the result of inadequate training or a normal adaptation to the physiological demands imposed by soccer, the results of the current study suggest that resistance training volume should be maintained during the competitive season, in order to maintain preseason levels of muscle mass.
The influence of speech coding algorithms on automatic speech recognition

Examines the influence of different coders in the range from 64 kbit/sec to 4.8 kbit/sec on both a speaker independent isolated word recognizer and a speaker verification system. Applying systems trained with 64 kbit/sec to e.g. the 4.8 kbit/sec data increases the error rate of the word recognizer by a factor of three. For rates below 13 kbit/sec the speaker verification is more affected than the word recognition. The performance improves significantly if word models are provided for the individual coding conditions. Therefore, the authors use a Gaussian classifier for estimation of the coding condition of a test utterance. The combination of this classifier and coder specific word models yields a high overall recognition performance. <<ETX>>
Study of neuronal networks development from in-vitro recordings: A Granger causality based approach

This methodological work is aimed at providing a Granger causality based approach to the study of neuronal networks development in vitro. The analysis procedure makes use of tools derived from statistics and network theory for accessing network development of in-vitro neuronal cultures from their electrical activity, recorded through Multi Electrode Arrays (MEAs). The preliminary results that will be presented here show the potential of this approach for characterizing in a quantitative way the developmental stages of neuronal networks and provide some evidences which are consistent with direct in-vitro and in-vivo observations reported by other authors.
Dynamics and storage capacity of neural networks with small-world topology

We study the storage of phase-coded patterns as stable dynamical attractors in recurrent spin neural networks with small-world topology. The synaptic strength of existent connections is determined by a learning rule based on spike-time-dependent plasticity (STDP), with an asymmetric time window depending on the relative timing between pre-and post-synaptic activity. We store multiple patterns and study the network capacity in sparse networks with different topologies. We study networks where each neuron is connected only to a small number z « N of other neurons. Connections can be short range, between neighboring neurons placed on a regular lattice, or long range, between randomly chosen pairs of neurons. We find that a small fraction of long range connections is able to amplify the capacity of the network. This imply that a small-world-network topology maybe optimal, as a compromise between the cost of long range connections and the capacity increase.
Estimation of leakage power using low-power cut technology

Low-power design becomes a challenge for test. In this paper, we propose a novel low-power scan architecture-PowerCut to minimize power consumption during scan test, which is based on a scan chain modification technique. On one hand, a blocking logic is inserted into the scan chain to reduce the dynamic power and a controlling unit is also inserted to decrease the leakage power during the shift cycle. On the other hand, using genetic algorithm, the right values of control signals are found out to control the process. Experiments results indicate that this architecture can effectually reduce power during scan test, and leads to a small increase in area.
Penetration Enhancement Techniques

Penetration Enhancers have vast potential and if utilized thoroughly, can be beneficial route in the drug delivery. Penetration Enhancers facilitates the drug delivery in various ways. They are therefore also referred as sorption enhancers or accelerants. This review focusses on the various enhancement techniques which are widely used to increase the bioavailability of the drugs. This review explores the various penetration enhancers with their mechanism of action and their roles in enhancement techniques. One of the reasons why the Transdermal drug delivery has not been successful compared to other delivery system, is the impervious nature of the versatile skin. It has been reported that individual penetration enhancer are not able to bring that effect which is achieved by the combination of penetration enhancer or mixture of penetration enhancer. Different penetration enhancers act at different site of action and have different mechanism of action ranging from altering metabolic activity within the skin, or exerting an influence on the thermodynamic activity of the drug in its vehicle. The major limitation in transdermal drug delivery is the diffusion of drug molecules to cross, one of the most versatile barriers of skin. A number of skin penetrations enhancers have been investigated and explored for skin penetration enhancement. Further, developments and initiatives are required to evaluate the mechanism of enhancement techniques. A thorough understanding of this penetration enhancer has been discussed and future implications explored.
Un discours figuré chez Héliodore : « comment, en disant l’inverse de ce qu’on veut, on peut accomplir ce qu’on veut sans sembler dire l’inverse de ce qu’on veut »

Cette communication, qui traite du discours d’Hydaspe dans les Ethiopiques 10, 16, au cours duquel le roi proclame sa volonte de sacrifier sa fille, mais dans des termes qui incitent en fait son auditoire a empecher le sacrifice, montre que la presentation qu’en fait Heliodore manifeste une ambiguite tout a fait caracteristique: tout d’abord, le narrateur suggere qu’Hydaspe a veritablement l’intention d’accomplir le sacrifice, mais il laisse ensuite entendre qu’il a subverti son propre discours pour obtenir un effet inverse de celui qui est proclame. Cette contradiction est analysee au niveau du personnage, du public fictionnel et du lecteur, jusqu’a conclure que le lecteur reproduit dans son for interieur la reponse du peuple ethiopien au trope rhetorique du roi. Ce type de discours, qui signifie le contraire de ce qu’il dit, est qualifie de logos eschematismenos par les rhetoriciens de l’Antiquite; on en retrouve les caracteristiques dans le discours d’Hydaspe. Un exemple «canonique» de logos eschematismenos est le discours de Phenix dans le chant IX de l’Iliade. Or Heliodore cite un vers de la reponse d’Achille a Phenix, dans lequel il demasque le stratageme rhetorique. Ce faisant, le romancier demasque lui-meme, d’une facon metalitteraire et intertextuelle, la nature de son propre texte.