Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatio-temporal fMRI analysis using Markov random fields

Functional magnetic resonance images (fMRI's) provide high-resolution datasets which allow researchers to obtain accurate delineation and sensitive detection of activation areas involved in cognitive processes. To preserve the resolution of this noninvasive technique, refined methods are required in the analysis of the data. In this paper, the authors first discuss the widely used methods based on a statistical parameter map (SPM) analysis exposing the different shortcomings of this approach when considering high-resolution data. First, the often used Gaussian filtering results in a blurring effect and in delocalization of the activated area. Secondly, the SPM approach only considers false alarms due to noise but not rejections of activated voxels. The authors propose to embed the fMRI analysis problem into a Bayesian framework consisting of two steps: (i) data restoration and (ii) data analysis. They, therefore, propose two Markov random fields (MRF's) to solve these two problems. Results on three protocols (visual, motor and word recognition) are shown for two SPM approaches and compared with the proposed MRF-approach.
Artificial Neural Network-based electricity price forecasting for smart grid deployment

A deregulated electricity market is one of the keystones of up-and-coming smart grid deployments. In such a market, forecasting electricity prices is essential to helping stakeholders with the decision making process. Electricity price forecasting is an inherently difficult problem due to its special characteristics of dynamicity and nonstationarity. In our research, we use an Artificial Neural Network (ANN) model on carefully crafted input features for forecasting hourly electricity prices for the next 24 hours. The input features are selected from a pool of features derived from information such as past electricity price data, weather data, and calendar data. A wrapper method for feature selection is used in which the ANN model is continuously trained and updated in order to select the best feature set. The performance of the proposed method is evaluated and compared with the published results of the state-of-the-art Pattern Sequence-based Forecasting (PSF) method on the same data sets and our method is observed to provide superior results.
Volume Rendering and Clipping for Air Pollution Visualization

In this paper, we present a novel and practical approach of volume rendering and clipping method to visualize large-scale air pollution data. A new viewpoint-based volume clipping method is used while rendering, which is fast and more interactive than previous methods, and 3D scene walkthrough is implemented in our system. We achieve expected experimental results and our research could benefit environment monitoring and many other applications associated with pollution visualization.
Exploring leadership styles from a mediator's perspective: A phenomenological study examining four specific organizational leadership styles and their relationship to four mediation styles as summarized by Leonard L. Riskin

If a successful organization is one where internal strife is minimized, then it stands to reason that a successful leader is an individual who knows how to handle conflict. Handling conflict, though, should not be approached intuitively, as the field of mediation has developed numerous techniques to understand and address conflict. To date, though, these techniques have not been categorized by leadership styles. The purpose of this study was to develop such a framework to understand how conflict resolution relates to the disciplines of mediation, organizational behavior, and organizational leadership. To further this endeavor, four themes were developed. These themes drew upon a quadrant categorization methodology proposed by Leonard L. Riskin, which was then compared to four leadership styles: situational leadership, transformational leadership, leader-member exchange theory, and servant leadership. A phenomenological methodology was used and eight leaders were interviewed to understand how they approach conflict within their respective organizations. Utilizing a story provided by each leader about a conflict each encountered on a frequent basis, the researcher asked a series of randomized questions based on the themes mentioned. By categorizing each leader’s response, the study found that a relationship worthy of further research existed between the position of the individual within the organization and how societal norms dictated the methodology the leader used to resolve his/her stated conflict. This relationship supported the use of specific tools developed in mediation that the leader and the organization could embrace to transcend conflict, allowing the organization and hence the leader to be more efficient.
Appearance and Persistence of Antibodies Against Different Virus Components After Regular Measles Infections

Different measles virus-specific antibody activities in acute, early (11 to 40 days after rash) and late (4 to 20 years postinfection) convalescent sera and gamma globulin were determined. Early immunoglobulin G antibodies gave a poor neutralization, which was increased 10- to 60-fold by addition of anti-gamma globulin. There was a high degree of correlation between titers of hemolysis-inhibiting (HLI) and hemagglutinating-inhibiting (HI) antibodies. However, in one out of fifteen late convalescent sera an HLI antibody titer of 640 in the presence of titer of only 20 in HI tests with Tween 80—either-treated antigen was found. Similar findings were made with sera from two patients with multiple sclerosis included in a parallel study. A somewhat higher titer of HI antibodies was demonstrable in these three sera when untreated material was used as antigen. These findings are interpreted in the following way. Antibodies against the hemagglutinin can block not only virus-specific agglutination but also lysis of red cells. In contrast, antibodies against the hemolysin, besides blocking the biological activity of this component, carry only a slight HI activity. This HI activity can be detected only by use of antigen preparations containing hemagglutinin-associated hemolysin. Complement-fixation (CF) and immunodiffusion tests (the latter were carried out with antigen preparations treated with 0.25% sodium dodecyl sulfate) demonstrated that, in almost all cases, antibodies against nucleocapsid structures dominated quantitatively among antibodies appearing in connection with and persisting after regular measles infections. Generally, only low titers of antibodies reacting with purified small particle hemagglutinin (HA; 10 to 14S) or additional structural or nonstructural components were identified in CF and immunodiffusion tests.
Structure Tensor Series-Based Large Scale Near-Duplicate Video Retrieval

With the huge amount of video data and its exponential growth in recent years, many new challenges, like storage, search and navigation, have arisen. Among these challenges, near-duplicate video retrieval aims to find clips that are identical or nearly identical in content to a query clip. This has attracted much attention due to its wide applications including copyright detection, commercial monitoring and news video tracking. In this paper, we propose a practical solution based on 3-D structure tensor model for this problem. We first propose a novel video representation, adaptive structure video tensor series, together with a robust similarity measure, to improve the retrieval effectiveness. Then, we design a dimensionality reduction technique for tensor series to improve the search efficiency. Finally, we prove the effectiveness and efficiency of the proposed method by extensive experiments on hundreds of hours of real video data.
Multiple-Source Cooperation: From Code-Division Multiplexing to Variable-Rate Network Coding

Multiple-source cooperation techniques, including conventional code-division multiplexing (CDM) and classic network coding (CNC), are investigated. We adopt a soft sum - product decoding algorithm for the CNC technique to efficiently process a large number of information streams and propose a flexible variable-rate network coding (VNC) technique, which is capable of attaining a near-optimum performance. Quantitatively, the proposed VNC method is capable of operating within 1 dB from the outage capacity of the quasi-static Rayleigh fading channel. The iterative decoding convergence of the multiple-source computation methods is analyzed with the aid of extrinsic information transfer charts.
Tensor factorization for cross lingual entity co-reference resolution in the linked open data

The main objective of this research was to identify co-referent entities located in several linked open data (LOD) sources that are described in various natural languages. The problem is approached from two perspectives. First, we do a multi-scale analysis of the RDF graph to discover structural similarities of entities. This was implemented as a tensor decomposition of the RDF graph with each predicate corresponding to a horizontal slide of the tensor. Hereafter, we used the term "structural evidence" to refer to the result of this analysis. Second, for each entity, we associated textual data coming from the Web of documents. Thus, after some preprocessing (viz. removing empty words, applying a weighting scheme such as tf-idf, .), we represented each entity in a high dimensional space with each dimension corresponding to a term. Next, through a Singular Value Decomposition (SVD), we find a subspace such that the sum of squared distances from the original space to the sub space is minimized. This dimensionality reduction allows us to find language independent similarities between entities. Hereafter, we use the term "textual evidence" to refer to the result of this analysis. Since the similarity information coming from the structural and the textual evidence are complementary to each other, a global similarity score is computed by aggregating the two evidences. We adopt a linear opinion pool, an approach which is commonly used in belief aggregation as an aggregation mechanism. In the end, for any given entity, we obtained a global similarity vector. The higher component values of this vector correspond to potential co-referent entities. All algorithms are implemented in Python. According to the experiment result conducted on the French and English DBpedia, our approach can bring high results.
User Profiling - A Short Review

In Today’s technology driven world user profiles are the virtual representation of each user and they include a variety of user information such as personal, interest and preference data. These profiles are the outcome of the user profiling process and they are essential to service personalization. Different methods, techniques and algorithms have been proposed in the literature for the user profiling process. This paper aims to give an overview on the user profiling and its related concepts, and discuss the pros and cons of current methods for the future service personalization. Furthermore, it also give details about the simulations which have been carried out with well known classification and clustering algorithms with real world user profile dataset. This work is based on the doctoral thesis of the author.
Decision-Making and Problem Solving

Treatment of the individual patient with chronic myelogenous leukemia. 387 Adenoviral mediated gene transfer can be accomplished in human myeloid cell lines and is inhibited by all-trans retinoic acid-induced differentiation. 392 Measurement of catecholamines in mouse bone marrow by means of HPLC with electrochemi-cal detection. 395 Human leukemia k562 cells: induction to erythroid differentiation by guanine, guanosine and guanine nucleotides. 402 Parvovirus B19 infections in patients with chronic anemia. 406 Serum erythropoietin in the diagnosis of polycythemia vera. A follow-up study. 411 Superoxide release by human polymorphonuclear leukocytes in the presence of deferoxamine. 415 Bacteremia in patients with hematological malignancies. Analysis of risk factors, etiological agents and prognostic indicators. 420 Natural and acquired inhibitors of hemostasis in selected symptomatic outpatients with venous thromboembolic disease. 423 The clinical course of deep-vein thrombosis. Prospective long-term follow-up of 528 symptomatic patients. 429 Antiplatelet agents in thrombotic thrombocytopenic purpura (TTP). Results of a randomized multicenter trial by the Italian Cooperative Group for TTP. 440 Molecular heterogeneity of glucose-6-phosphate dehydrogenase (G6PD) variants in Italy. 446 The utility and cost-effectiveness of D-dimer measurements in the diagnosis of deep vein thrombosis. SHORT REPORTS 452 The natural history of trilinear myelodysplastic syndrome and erythroleukemia. 455 Ultrasound exploration in the workup of unexplained fever in the immunocompromized host: preliminary observations. 458 Long-lasting effect of cyclosporin-a on anemia associated with idiopathic myelofibrosis.
Compiling for template-based run-time code generation

Cyclone is a type-safe programming language that provides explicit run-time code generation. The Cyclone compiler uses a template-based strategy for run-time code generation in which pre-compiled code fragments are stitched together at run time. This strategy keeps the cost of code generation low, but it requires that optimizations, such as register allocation and code motion, are applied to templates at compile time. This paper describes a principled approach to implementing such optimizations. In particular, we generalize standard flow-graph intermediate representations to support templates, define a mapping from (a subset of) Cyclone to this representation, and describe a dataflow-analysis framework that supports standard optimizations across template boundaries.
Self-Advocacy Training in Secondary Schools for Students with Learning Disabilities

Self-Advocacy Training in Secondary Schools for Students with Learning Disabilities Ashleigh Smith Department of Counseling Psychology and Special Education Master of Science To be successful in the general education students with disabilities may require accommodations. Students with disabilities need to develop self-advocating skills to be able to request the accommodations needed in the general education. This article describes a study in which six high school students with learning disabilities were explicitly taught to recognize when an accommodation was needed, select the appropriate accommodation, request the accommodation and then implement the accommodation in the general education classroom. The study indicated that students were able to advocate for themselves by requesting accommodations from their general education teacher. Based on data collected though this study, it is important for self-advocacy training to be implemented in the curriculum for all students with disabilities.
Psychosocial Factors in Alcohol Use-Related Problems of Working Youth

This study investigated the psychosocial correlates of alcohol use related problems in a sample of 581 working adolescents (N = 4405), recruited from five vocational schools in Ankara in June 2004 with the CAGE questionnaire, The Beck Depression Inventory, the Beck Hopelessness Scale, the Spielberger State Anxiety Scale, and the Coopersmith Self-Esteem Inventory. Using a multivariate analysis, the anxiety and hopelessness scores, and the length of stay in Ankara were found to be related to alcohol-use problems of the working youth. The study's limitations were noted and future research was suggested.
On the Modeling of Spar-Type Floating Offshore Wind Turbines

In this paper an overview about floating offshore wind turbines (FOWT) including operating conditions, property and applicability of the barge, tension-leg, and spar floating platforms is described. The spar-floating offshore wind turbines (S-FOWT) have advantages in deepwater and their preliminary design, numerical modeling tools and integrated modeling are reviewed. Important conclusions about the nacelle and blade motions, tower response, effects of wind and wave loads, overall vibration and power production of the S-FOWT are summarized. Computationally-simplified models with acceptable accuracy are necessary for feasibility and pre-engineering studies of the FOWT. The design needs modeling and analysis of aero-hydro-servo dynamic coupling of the entire FOWT. This paper also familiarizes authors with FOWT and its configurations and modeling approaches.
Funcionamiento y comunicación familiar y consumo de sustancias en la adolescencia: el rol mediador del apoyo social

Resumen En el presente estudio se analiza el apoyo social como un recurso protector para el ajuste de los adolescentes. Concretamente, se estudian tanto los efectos directos como los mediadores del apoyo social entre las características de funcionamiento y comunicación familiar y el consumo de sustancias de los adolescentes. Con este objetivo, 431 chicos y chicas de 15 a 17 años cumplimentaron una batería de instrumentos para la medida de las variables de funcionamiento y comunicación familiar, apoyo social percibido y la estimación de consumo de sustancias. Los resultados muestran que el apoyo procedente del padre predice negativamente el consumo mientras que el procedente de la pareja del adolescente lo hace de forma positiva. Sin embargo, el apoyo social no media la relación entre características familiares y consumo de sustancias. Finalmente, diferentes explicaciones e implicaciones teóricas y metodológicas de estos resultados son discutidas.
Earlier Metabolizable Energy Intake Level Influences Heat Production during a Following 3-Day Fast in Laying Hens

The present study was conducted to estimate energy requirements for maintenance in laying hens by using indirect calorimetry and energy balance. A total of 576 28-wk-old Nongda-3 laying hens with dwarf gene were randomly allocated into four ME intake levels (86.57, 124.45, 166.63 and 197.20 kcal/kg body weight (BW)0.75 per d) with four replicates each. After a 4 d adaptation period, 36 hens from one replicate were maintained in one of the two respiration chambers to measure the heat production (HP) for 3 d during the feeding period and subsequent 3 d fast. Metabolizable energy (ME) intake was partitioned between heat increment (HI), HP associated with activity, fasting HP (FHP) and retained energy (RE). The equilibrium FHP may provide an estimate of NE requirements for maintenance (NEm). Results showed that HP, HI and RE in the fed state increased with ME intake level (p<0.05). Based on the regression of HP on ME intake, the estimated ME requirements for maintenance (MEm) was 113.09 kcal/kg BW0.75 per d when ME intake equals HP. The FHP was decreased day by day with the lowest value on the third day of starvation. Except for lowest ME intake level, the FHP increased with ME intake level on the first day of starvation (p<0.05). The FHP at the two higher ME intake levels were greater than that at the two lower ME intake levels (p<0.05) but no difference was found between the two lower ME intake levels. Linear regression of HP from the fed state to zero ME intake yielded a value of 71.02 kcal/kg BW0·75 per d, which is higher than the extrapolated FHP at zero ME intake (60.78, 65.23 and 62.14 kcal/kg BW0.75 per d for the first, second and third day of fasting, respectively). Fasting time, lighting schedules, calculation methods and duration of adaptation of hens to changes in ME intake level should be properly established when using indirect calorimetry technique to estimate dietary NE content, MEm and NEm for laying hens.
Small field dose delivery evaluations using cone beam optical computed tomography-based polymer gel dosimetry

This paper explores the combination of cone beam optical computed tomography with an N-isopropylacrylamide (NIPAM)-based polymer gel dosimeter for three-dimensional dose imaging of small field deliveries. Initial investigations indicate that cone beam optical imaging of polymer gels is complicated by scattered stray light perturbation. This can lead to significant dosimetry failures in comparison to dose readout by magnetic resonance imaging (MRI). For example, only 60% of the voxels from an optical CT dose readout of a 1 l dosimeter passed a two-dimensional Low's gamma test (at a 3%, 3 mm criteria, relative to a treatment plan for a well-characterized pencil beam delivery). When the same dosimeter was probed by MRI, a 93% pass rate was observed. The optical dose measurement was improved after modifications to the dosimeter preparation, matching its performance with the imaging capabilities of the scanner. With the new dosimeter preparation, 99.7% of the optical CT voxels passed a Low's gamma test at the 3%, 3 mm criteria and 92.7% at a 2%, 2 mm criteria. The fitted interjar dose responses of a small sample set of modified dosimeters prepared (a) from the same gel batch and (b) from different gel batches prepared on the same day were found to be in agreement to within 3.6% and 3.8%, respectively, over the full dose range. Without drawing any statistical conclusions, this experiment gives a preliminary indication that intrabatch or interbatch NIPAM dosimeters prepared on the same day should be suitable for dose sensitivity calibration.
An Innovative Imputation and Classification Approach for Accurate Disease Prediction

Imputation of missing attribute values in medical datasets for extracting hidden knowledge from medical datasets is an interesting research topic of interest which is very challenging. One cannot eliminate missing values in medical records. The reason may be because some tests may not been conducted as they are cost effective, values missed when conducting clinical trials, values may not have been recorded to name some of the reasons. Data mining researchers have been proposing various approaches to find and impute missing values to increase classification accuracies so that disease may be predicted accurately. In this paper, we propose a novel imputation approach for imputation of missing values and performing classification after fixing missing values. The approach is based on clustering concept and aims at dimensionality reduction of the records. The case study discussed shows that missing values can be fixed and imputed efficiently by achieving dimensionality reduction. The importance of proposed approach for classification is visible in the case study which assigns single class label in contrary to multi-label assignment if dimensionality reduction is not performed.