Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Analysis of a Three-phase Induction Motor under Non-sinusoidal and Unbalanced Conditions

-As a result of the increasing use of electronic devices and other non-linear loads, the wave-forms of the electricity supply voltage are being distorted and inequalities are appearing between the phases. This deterioration is associated with problems of electromagnetic incompatibility and reductions in the efficiency of loads such as motors. This paper investigates the additional losses caused to induction motors when connected to supply systems which suffer from these distortions or inequalities. A specific formula is presented for the power factor associated with motors supplied by non-sinusoidal or unbalanced voltages; this has the characteristic of reflecting both the machine’s efficiency and that of the supply voltage.
Relationship-Based Care and Behaviours of Residents in Long-Term Care Facilities

Introduction. In long-term care (LTC), person-centred approaches are encouraged. One such approach, relationship-based care (RBC), aims among other things to reduce residents' agitated behaviours. RBC has been used in numerous Quebec LTC facilities over the past decade but it has never been studied. Objective. Explore correlations between use of RBC by trained caregivers and the frequency of agitated and positive behaviours of residents with cognitive impairments. Methods. Two independent raters observed fourteen caregiver/resident dyads in two LTC facilities during assistance with hygiene and dressing. Checklists were used to quantify caregivers' RBC use and residents' agitated and positive behaviours. Results. Scores for RBC use were high, suggesting good application of the approach by caregivers. Correlation analyses showed that offering residents realistic choices and talking to them during care were associated with both positive and agitated behaviours (P from 0.03 to 0.003). However, many other components of RBC were not associated with residents' behaviours during care. Conclusions. There were only a few quantitative links between the RBC checklist items and the frequency of agitated or positive behaviours. Other studies with a more rigorous research design are needed to better understand the impact of relationship-based care on residents' behaviours.
SliceSort: efficient sorting of hierarchical data

Sorting is a fundamental operation in data processing. While the problem of sorting flat data records has been extensively studied, there is very little work on sorting hierarchical data such as XML documents. Existing hierarchy-aware sorting approaches for hierarchical data are based on creating sorted subtrees as initial sorted runs and merging sorted subtrees to create the sorted output using either explicit pointers or absolute node key comparisons for merging subtrees. In this paper, we propose SliceSort, a novel, level-wise sorting technique for hierarchical data that avoids the drawbacks of subtree-based sorting techniques. Our experimental performance evaluation shows that SliceSort outperforms the state-of-art approach, HErMeS, by up to a factor of 27%.
Protracted abstinence from distinct drugs of abuse shows regulation of a common gene network

Addiction is a chronic brain disorder. Prolonged abstinence from drugs of abuse involves dysphoria, high stress responsiveness and craving. The neurobiology of drug abstinence, however, is poorly understood. We previously identified a unique set of hundred mu‐opioid receptor‐dependent genes in the extended amygdala, a key site for hedonic and stress processing in the brain. Here we examined these candidate genes either immediately after chronic morphine, nicotine, Δ9‐tetrahydrocannabinol or alcohol, or following 4 weeks of abstinence. Regulation patterns strongly differed among chronic groups. In contrast, gene regulations strikingly converged in the abstinent groups and revealed unforeseen common adaptations within a novel huntingtin‐centered molecular network previously unreported in addiction research. This study demonstrates that, regardless the drug, a specific set of transcriptional regulations develops in the abstinent brain, which possibly contributes to the negative affect characterizing protracted abstinence. This transcriptional signature may represent a hallmark of drug abstinence and a unitary adaptive molecular mechanism in substance abuse disorders.
A Machine-Oriented Logic Based on the Resolution Principle

:tb. ~tract. Theorem-proving on the computer, using procedures based on the fund~mental theorem of Herbrand concerning the first-order predicate etdeulus, is examined with ~ view towards improving the efticieney and widening the range of practical applicability of these procedures. A elose analysis of the process of substitution (of terms for variables), and the process of t ruth-funct ional analysis of the results of such substitutions, reveals that both processes can be combined into a single new process (called resolution), i terating which is vastty more ef[ieient than the older cyclic procedures consisting of substitution stages alternating with truth-functional analysis stages. The theory of the resolution process is presented in the form of a system of first<~rder logic with . just one inference principle (the resolution principle). The completeness of the system is proved; the simplest proof-procedure based oil the system is then the direct implementation of the proof of completeness. Howew~r, this procedure is quite inefficient, ~nd the paper concludes with a discussion of several principles (called search principles) which are applicable to the design of efficient proof-procedures employing resolution as the basle logical process.
Unimodal size scaling of phytoplankton growth and the size dependence of nutrient uptake and use.

Phytoplankton size structure is key for the ecology and biogeochemistry of pelagic ecosystems, but the relationship between cell size and maximum growth rate (μ(max) ) is not yet well understood. We used cultures of 22 species of marine phytoplankton from five phyla, ranging from 0.1 to 10(6) μm(3) in cell volume (V(cell) ), to determine experimentally the size dependence of growth, metabolic rate, elemental stoichiometry and nutrient uptake. We show that both μ(max) and carbon-specific photosynthesis peak at intermediate cell sizes. Maximum nitrogen uptake rate (V(maxN) ) scales isometrically with V(cell) , whereas nitrogen minimum quota scales as V(cell) (0.84) . Large cells thus possess high ability to take up nitrogen, relative to their requirements, and large storage capacity, but their growth is limited by the conversion of nutrients into biomass. Small species show similar volume-specific V(maxN) compared to their larger counterparts, but have higher nitrogen requirements. We suggest that the unimodal size scaling of phytoplankton growth arises from taxon-independent, size-related constraints in nutrient uptake, requirement and assimilation.
Energy Conservation in IEEE 802. 15. 4 Compliant Wireless Sensor Network using LT codes

In this paper, we investigate the energy efficiency of a IEEE 802. 15. 4 based RF transceiver using Direct Sequence Spread SpectrumOffset Quadrature Phase Shift Keying (DSSSOQPSK) modulation under 2.4 GHz frequency band in a WSN. Multi path propagation in a wireless environment affects the reliability of the network and therefore the sensor nodes wastes the battery power to ensure successful delivery of data by adopting Automatic Repeat reQuest (ARQ) schemes. Error control coding is one of the most suitable solutions to this reliability issue. In this paper, the use of one such Forward Error Correction (FEC) codes such as Luby Transform (LT) codes to reduce energy expenditure by the network nodes is analyzed under Rayleigh fading channel environment. The performance metrics considered for our study are Bit Error Rate (BER) and Energy spent per bit. Simulation results show that LT codes show improved performance under severe fading channel conditions, i. e. , for low SNR values (-5 to 2 dB) and ARQ scheme provides better performance under high SNR values (above 2 dB).
Image Quality Assessment Using Rough Fuzzy Integrals

In this paper, a generalized image quality metric based on rough fuzzy integrals is presented. The proposed assessment is to evaluate the image quality by categorizing the pixels of the image into the edge region, texture region and flat region considering the different effects of each region on the image quality. The rough fuzzy integrals are employed into the block of pixels to assess effectively and efficiently the image quality. The experimental results illustrate that the proposed quality metric has an effective evaluation on the image quality.
Color LinesView:AnApproach toVisualization of Families of Function Graphs

Data sets often include information that can be represented as a mapping that describes how a dependent variable depends on an independent variable. Such a mapping, usually represented as a function graph, can be parameterized to provide a family of function graphs. The challenge is how to efficiently aggregate individual function graph views to represent the whole family and allow visual analysis and search for patterns. We propose a novel view, called the color lines view, which provides a two dimensional, rectangular view where each line represents a single function graph. The points on the line correspond to values of the independent variable. The point colors represent the value of the dependent variable. The lines, placed next to each other in parallel, show a family of function graphs. The color lines view offers sorting and brushing features which support visual analysis procedures that are difficult to perform with previously existing views.
Beyond Weber's Law: A Second Look at Ranking Visualizations of Correlation

Models of human perception - including perceptual “laws” - can be valuable tools for deriving visualization design recommendations. However, it is important to assess the explanatory power of such models when using them to inform design. We present a secondary analysis of data previously used to rank the effectiveness of bivariate visualizations for assessing correlation (measured with Pearson's r) according to the well-known Weber-Fechner Law. Beginning with the model of Harrison et al. [1], we present a sequence of refinements including incorporation of individual differences, log transformation, censored regression, and adoption of Bayesian statistics. Our model incorporates all observations dropped from the original analysis, including data near ceilings caused by the data collection process and entire visualizations dropped due to large numbers of observations worse than chance. This model deviates from Weber's Law, but provides improved predictive accuracy and generalization. Using Bayesian credibility intervals, we derive a partial ranking that groups visualizations with similar performance, and we give precise estimates of the difference in performance between these groups. We find that compared to other visualizations, scatterplots are unique in combining low variance between individuals and high precision on both positively- and negatively correlated data. We conclude with a discussion of the value of data sharing and replication, and share implications for modeling similar experimental data.
A privacy framework for the Internet of Things

Privacy is a concept that is intertwined with data security, but its scope is significantly broader. Often considerations of privacy in the context of connected devices are limited to a consideration of the security of the data stored in those devices. In this article a broader scope is introduced and defined in terms of different classes of privacy and the implications for IoT devices and systems is discussed. Finally some ideas for an improved privacy framework for IoT are outlined and explained in the context of the literature and current state-of-art.
Terfenol-D elastomagnetic properties under varied operating conditions using hysteresis loop analysis

This paper presents an experimental study of the effects of varied magnetic bias, AC magnetic field amplitude and frequency on the characteristics of hysteresis loops produced in a magnetostrictive transducer. The study uses a magnetostrictive transducer designed at Iowa State University that utilizes an 11.5 cm (4.54 in) long by 1.27 cm (0.5 in) diameter cylindrical Terfenol-D rod. This transducer allows controlled variation of the following operating conditions: mechanical prestress, magnitude and frequency of AC magnetic field, and magnetic bias. By performing extensive experimental tests, material property trends can be developed for use in the optimization of transducer design parameters for different applications. For the results presented, the magnetic bias, the AC magnetic field amplitude, and the frequency of excitation were independently varied while temperature, mass load and prestress were kept constant. The minor hysteresis loops of the strain versus applied magnetic field, flux density versus applied magnetic field, and magnetization versus applied magnetic field are presented and compared. Material property trends identified from the minor loops are presented for the axial strain coefficient, permeability, susceptibility, and energy losses.
Combination of PSO Algorithm and Naive Bayesian Classification for Parkinson Disease Diagnosis

Parkinson is a neurological disease which quickly affects human’s motor organs. Early diagnosis of this disease is very important for its prevention. Using optimum training data and omitting noisy training data will increase the classification accuracy. In this paper, a new model based on the combination of PSO algorithm and Naive Bayesian Classification has been presented for diagnosing the Parkinson disease, in which optimum training data are selected by PSO algorithm and Naive Bayesian Classification. In this paper, according to the obtained results, Parkinson disease diagnosis accuracy has been 97.95% using the presented method, which is indicative of the superiority of this method to the previous models of Parkinson disease diagnosis.
A Secure Virtualized Logging Scheme for Digital Forensics in Comparison with Kernel Module Approach

Digital forensics encompasses the process of identifying the perpetrator and the criminal method by analyzing the logs generated in the computer. Log files record the activities of the computer and by reading them one can know what kind of event happened at a certain time. Therefore, secure logs with the integrity property are essential. In this paper, we discuss two approaches to achieve the integrity of logs- the kernel module and virtualization, and compare them. Although virtualization is more inefficient in performance than the kernel module, it provides more security properties for logs. Thus, we then focus on the virtualization approach with a detailed proposal, which describes ways to preserve logs without tampering and deletion.
Hidden Part Models for Human Action Recognition: Probabilistic versus Max Margin

We present a discriminative part-based approach for human action recognition from video sequences using motion features. Our model is based on the recently proposed hidden conditional random field (HCRF) for object recognition. Similarly to HCRF for object recognition, we model a human action by a flexible constellation of parts conditioned on image observations. Differently from object recognition, our model combines both large-scale global features and local patch features to distinguish various actions. Our experimental results show that our model is comparable to other state-of-the-art approaches in action recognition. In particular, our experimental results demonstrate that combining large-scale global features and local patch features performs significantly better than directly applying HCRF on local patches alone. We also propose an alternative for learning the parameters of an HCRF model in a max-margin framework. We call this method the max-margin hidden conditional random field (MMHCRF). We demonstrate that MMHCRF outperforms HCRF in human action recognition. In addition, MMHCRF can handle a much broader range of complex hidden structures arising in various problems in computer vision.
CONVERGENCE RESULTS FOR THE FLUX IDENTIFICATION IN A SCALAR CONSERVATION LAW

Here we study an inverse problem for a quasilinear hyperbolic equation. We start by proving the existence of solutions to the problem which is posed as the minimization of a suitable cost function. Then we use a Lagrangian formulation in order to formally compute the gradient of the cost function introducing an adjoint equation. Despite the fact that the Lagrangian formulation is formal and that the cost function is not necessarily differentiable, a viscous perturbation and a numerical approximation of the problem allow us to justify this computation. When the adjoint problem for the quasi-linear equation admits a smooth solution, then the perturbed adjoint states can be proved to converge to that very solution. The sequences of gradients for both perturbed problems are also proved to converge to the same element of the subdifferential of the cost function. We evidence these results for a large class of numerical schemes and particular cost functions which can be applied to the identification of isotherms for conservation laws modeling distillation or chromatography. They are illustrated by numerical examples.
Evaluation of RNA Markers for Early Detection of Cervical Neoplasia in Exfoliated Cervical Cells

Numerous molecular biomarkers have been suggested for early detection of cervical cancer, but their usefulness in routinely collected exfoliated cells remains uncertain. We used quantitative reverse transcription-PCR to evaluate expression of 40 candidate genes as markers for high-grade cervical intraepithelial neoplasia (CIN) in exfoliated cervical cells collected at the time of colposcopy. Samples from the 93 women with CIN3 or cancer were compared with those from 186 women without disease matched (1:2) for age, race, and high-risk human papillomavirus status. Normalized threshold cycles (Ct) for each gene were analyzed by receiver operating characteristics to determine their diagnostic performance in a split sample validation approach. Six markers were confirmed by an area under the curve >0.6 in both sample sets: claudin 1 (0.75), minichromosome maintenance deficient 5 (0.71) and 7 (0.64), cell division cycle 6 homologue (0.71), antigen identified by monoclonal antibody Ki-67 (0.66), and SHC SH2-domain binding protein 1 (0.61). The sensitivity for individual markers was relatively low and a combination of five genes to a panel resulted in 60% sensitivity with 76% specificity, not positively increasing this performance. Although the results did not indicate superiority of RNA markers for cervical cancer screening, their performance in detecting disease in women referred for colposcopy suggests that the genes and pathways they highlight could be useful in alternative detection formats or in combination with other screening indicators. (Cancer Epidemiol Biomarkers Prev 2007;16(2):295–301)
Supporting Rapid Mobility via Locality in an Overlay Network

In this paper, we present Mobile Tapestry, an extension to the Tapestry overlay network protocol, that enables scalable, fault-tolerant, and timely delivery of network messages, including multimedia streams, to and from rapidly moving nodes. Mobile Tapestry efficiently supports individual mobile nodes and, by using an approach we call hierarchical mobility, it also supports large groups of mobile nodes simultaneously moving together. Mobile Tapestry leverages the Tapestry''s locality mechanisms to reduce the latency and bandwidth of mobility update traffic, while eliminating the routing inefficiencies and availability problems of IP-based mobility protocols, such as Mobile IP. Our simulation results show that Mobile Tapestry significantly outperforms Mobile IP by providing lower latency and higher fault tolerance for message delivery.
Manganese (Mn) Oxidation Increases Intracellular Mn in Pseudomonas putida GB-1

Bacterial manganese (Mn) oxidation plays an important role in the global biogeochemical cycling of Mn and other compounds, and the diversity and prevalence of Mn oxidizers have been well established. Despite many hypotheses of why these bacteria may oxidize Mn, the physiological reasons remain elusive. Intracellular Mn levels were determined for Pseudomonas putida GB-1 grown in the presence or absence of Mn by inductively coupled plasma mass spectrometry (ICP-MS). Mn oxidizing wild type P. putida GB-1 had higher intracellular Mn than non Mn oxidizing mutants grown under the same conditions. P. putida GB-1 had a 5 fold increase in intracellular Mn compared to the non Mn oxidizing mutant P. putida GB-1-007 and a 59 fold increase in intracellular Mn compared to P. putida GB-1 ∆2665 ∆2447. The intracellular Mn is primarily associated with the less than 3 kDa fraction, suggesting it is not bound to protein. Protein oxidation levels in Mn oxidizing and non oxidizing cultures were relatively similar, yet Mn oxidation did increase survival of P. putida GB-1 when oxidatively stressed. This study is the first to link Mn oxidation to Mn homeostasis and oxidative stress protection.
More efficient computation of the complex error function

Gautschi has developed an algorithm that calculates the value of the Faddeeva function w(z) for a given complex number z in the first quadrant, up to 10 significant digits. We show that by modifying the tuning of the algorithm and testing the relative rather than the absolute error we can improve the accuracy of this algorithm to 14 significant digits throughout almost the whole of the complex plane, as well as increase its speed significantly in most of the complex plane. The efficiency of the calculation is further enhanced by using a different approximation in the neighborhood of the origin, where the Gautschi algorithm becomes ineffective. Finally, we develop a criterion to test the reliability of the algorithm's results near the zeros of the function, which occur in the third and fourth quadrants.
Exploration of Data mining techniques in Fraud Detection : Credit Card

Data mining has been increasing as one of the chief key features of many security initiative s. Often, used as a means for detection of fraud, assessing risk a s well. Data mining involves the use of data analys is tools to discover unknown, valid patterns as well as relatio nships in large data sets. Decades have seen a mass ive growth in the use of credit cards as a transactional medium. Data mining become even more common in both the pri vate and public sectors. Data mining has been used widely in industries such as Banking, Insurance, Medicine an d Retailing to reduce costs, enhance Research and increase Sale s. Credit cards are much safer from theft than is c ash and also a promising area for buying and sales. Credit Cards a e growing as a popular medium of transaction. Ther efore, Fraud Detection involves monitoring the behavior of users /cu tomers in order to estimate, detect or avoid un esirable behavior in future. In this paper, we investigated he factors and various techniques involved in cred it card fraud detection during/after transaction as well. KEY TERMS: Data Mining, Credit Card. Fraud, Detection Tools