Miracle-dz/UniFAR-FTUs huggingface dataset:
Awareness and Usage of E-Learning Materials among Students of National Open University of Nigeria (NOUN).

The study focused on awareness and usage of e-learning materials among students in the distance education programme of the National Open University of Nigeria (NOUN). The study is a descriptive survey which was guided by two research questions. The population for the study comprised of the 1, 512 year three students out of which 400 were sampled and used for the study. The instrument for data collection was a 30-item self-constructed checklist titled, “Distance Education Students’ Awareness and Usage of E-Learning Materials” (DESAUELM). It was validated by experts and the reliability co-efficient stood at 0.86. The data collected were analysed using frequencies and percentages. The findings showed that, the students are aware of majority of the e-learning materials. Also, the result on usage indicated that the students do not use majority of the e-learning materials. There was general low usage of the e-learning materials as revealed by the findings.
Effort and Comparison Income: Experimental and Survey Evidence

The authors test the hypothesis that individual effort on the job depends both on one's own income and on the individual's position in the relevant income distribution. Combining experimental evidence from a gift-exchange game with multi-country ISSP survey data, they analyze the extent to which relative income affects an individual's effort, finding that an individual's rank in the income distribution more strongly determines effort than does others' average income, which suggests that comparisons are more ordinal than cardinal. Their experiment also reveals that comparisons over time affect effort: individuals who received higher income offers or enjoyed higher income rank in the past exerted lower levels of effort for a given current income and rank.
Bayesian lasso regression

The lasso estimate for linear regression corresponds to a posterior mode when independent, double-exponential prior distributions are placed on the regression coefficients. This paper introduces new aspects of the broader Bayesian treatment of lasso regression. A direct characterization of the regression coefficients' posterior distribution is provided, and computation and inference under this characterization is shown to be straightforward. Emphasis is placed on point estimation using the posterior mean, which facilitates prediction of future observations via the posterior predictive distribution. It is shown that the standard lasso prediction method does not necessarily agree with model-based, Bayesian predictions. A new Gibbs sampler for Bayesian lasso regression is introduced. Copyright 2009, Oxford University Press.
Orthonormal Vector Fitting: A Robust Macromodeling Tool for Rational Approximation of Frequency Domain Responses

Vector Fitting is widely accepted as a robust macromodeling tool for approximating frequency domain responses of complex physical structures. In this paper, the Orthonormal Vector Fitting technique is presented, which uses orthonormal rational functions to improve the numerical stability of the method. This reduces the numerical sensitivity of the system equations to the choice of starting poles significantly and limits the overall macromodeling time
Clinical Significance of a Large Difference (≥ 2 points) between Biopsy and Post-prostatectomy Pathological Gleason Scores in Patients with Prostate Cancer

We investigated the clinical significance of large difference (≥ 2 points) between biopsy-derived (bGS) and post-prostatectomy Gleason scores (pGS). At 14 medical centers in Korea, 1,582 men who underwent radical prostatectomy for prostate cancer were included. According to the difference between bGS and pGS, the patients were divided into three groups: A (decreased in pGS ≥ 2, n = 30), B (changed in pGS ≤ 1, n = 1,361; control group), and C (increased in pGS ≥ 2, n = 55). We evaluated various clinicopathological factors of prostate cancer and hazards for biochemical failure. Group A showed significantly higher mean maximal percentage of cancer in the positive cores (max%) and pathological T stage than control. In group C, the number of biopsy core was significantly smaller, however, tumor volume and max% were significantly higher and more positive biopsy cores were presented than control. Worse pathological stage and more margin-positive were observed in group A and C than in control. Hazard ratio for biochemical failure was also higher in group A and C (P = 0.001). However, the groups were not independent factors in multivariate analysis. In conclusion, large difference between bGS and pGS shows poor prognosis even in the decreased group. However it is not an independent prognostic factor for biochemical failure.
Smart home system based on comparative analysis among AODV and DSDV protocols in MANET

The wireless sensor networks have accomplished advancement in various applications such as military, smart homes, patient monitoring, and intelligent building. In this paper, the smart home proposed system has been presented basis on two types of sensor. As well as the evaluation comparison among Destination Sequenced Distance Vector with Ad hoc On Demand Distance Vector has discussed to determine the optimal routing protocols. Motion sensor used for detecting any movement in this system while the MQ-7 is really suitable for detecting various gases. Three different factors namely pause interval time, node numbers and speed of nodes is estimated on the Network Simulator (ns-2) based on Linux operating system. Additionally, The performance metrics average end to end time delay, data packet delivery ratio and routing load which is analyzed. Our simulation outcome show that DSDV protocol has better evaluation compared with AODV protocol in lower delay interval and routing normalized load.
Title : Constructing module maps for integrated analysis of heterogeneous biological networks Running title : From heterogeneous networks to module maps

Improved methods for integrated analysis of heterogeneous large-scale omic data are direly needed. Here we take a network-based approach to this challenge. Given two networks, representing different types of gene interactions, we construct a map of linked modules, where modules are genes strongly connected in the first network and links represent strong inter-module connections in the second. We develop novel algorithms that considerably outperform prior art on simulated and real data from three distinct domains. First, by analyzing protein-protein interactions and negative genetic interactions in yeast we discover epistatic relations among protein complexes. Second, we analyze protein-protein interactions and DNA damage-specific positive genetic interactions in yeast, and reveal functional rewiring among protein complexes, suggesting novel mechanisms of DNA damage response. Finally, using transcriptomes of non-small cell lung cancer patients, we analyze networks of global co-expression and disease-dependent differential co-expression, and identify a sharp drop in correlation between two modules of immune activation processes, with possible microRNA control. Our study demonstrates that module maps are a powerful tool for deeper analysis of heterogeneous high throughput omic data.
A Comparison of Simulation and Hardware-in-the- Loop Alternatives for Digital Control of Power Converters

Debugging digital controllers for power converters can be a problem because there are both digital and analog components. This paper focuses on debugging digital controllers to be implemented in Field Programmable Gate Arrays or Application Specific Integrated Circuits, which are designed in hardware description languages. Four methods are proposed and described. All of them allow simulation, and two methods also allow emulation-synthesizing the model of the converter to run the complete closed-loop system in actual hardware. The first method consists in using a mixed analog and digital simulator. This is the easiest alternative for the designer, but simulation time can be a problem, specially for long simulations like those necessary in power factor correction or when the controller is very complex, for example, with embedded processors. The alternative is to use pure digital models, generating a digital model of the power converter. Three methods are proposed: real type, float type and fixed point models (in the latter case including hand-coded and automatic-coded descriptions). Float and fixed point models are synthesizable, so emulation is possible, achieving speedups over 20 000. The results obtained with each method are presented, highlighting the advantages and disadvantages of each one. Apart from that, an analysis of the necessary resolution in the variables is presented, being the main conclusion that 32-bit floating point is not enough for medium and high switching frequencies.
Four Brown Dwarfs in the Taurus Star-forming Region

We have identified four brown dwarfs in the Taurus star-forming region. They were first selected from R and I CCD photometry of 2.29 deg2 obtained at the Canada-France-Hawaii Telescope. Subsequently, they were recovered in the Two Micron All Sky Survey second incremental data release point source catalog. Low-resolution optical spectra obtained at the William Herschel Telescope allow us to derive spectral types in the range M7-M9. One of the brown dwarfs has very strong Hα emission (EW = -340 Å). It also displays Brγ emission in an infrared spectrum obtained with the Infrared Camera and Spectrograph on the Subaru telescope, suggesting that it is accreting matter from a disk. The K I resonance doublet and the Na I subordinate doublet at 818.3 and 819.5 nm in these Taurus objects are weaker than in field dwarfs of similar spectral type, consistent with low surface gravities as expected for young brown dwarfs. Two of the objects are cooler and fainter than GG Tau Bb, the lowest mass known member of the Taurus association. We estimate masses of only 0.03 M☉ for them. The spatial distribution of brown dwarfs in Taurus hints at a possible anticorrelation between the density of stars and the density of brown dwarfs.
Distributed Temporal Multiple Description Coding for Robust Video Transmission

The problem of multimedia communications over best-effort networks is addressed here with multiple description coding (MDC) in a distributed framework. In this paper, we first compare four video MDC schemes based on different time splitting patterns and temporal two- or three-band motion-compensated temporal filtering (MCTF). Then, the latter schemes are extended with systematic lossy description coding where the original sequence is separated into two subsequences, one being coded as in the latter schemes, and the other being coded with a Wyner-Ziv (WZ) encoder. This amounts to having a systematic lossy Wyner-Ziv coding of every other frame of each description. This error control approach can be used as an alternative to automatic repeat request (ARQ) or forward error correction (FEC), that is, the additional bitstream can be systematically sent to the decoder or can be requested, as in ARQ. When used as an FEC mechanism, the amount of redundancy is mostly controlled by the quantization of the Wyner-Ziv data. In this context, this approach leads to satisfactory rate-distortion performance at the side decoders, however it suffers from high redundancy which penalizes the central description. To cope with this problem, the approach is then extended to the use of MCTF for the Wyner-Ziv frames, in which case only the low-frequency subbands are WZ-coded and sent in the descriptions.
Competition , Choice and Primary School Performance

Government education policy in England, like in the US, has been increasingly geared towards increasing competitiveness between schools, at both primary and secondary levels. Theory suggests that in a world where parental preference over school quality has a key role to play in school admissions, competition for pupils between neighbouring schools could provide performance incentives, and parental choice could lead to allocative efficiency gains. Although the economic rationale for choice and competition is clear, most of the evidence that exists relates to the US and there is rarely an attempt to distinguish between the two concepts. We exploit a large and detailed pupil census, with information on pupil and school locations, to establish whether primary schools in England that are in a position of potential competition over pupil admissions perform better than those that are in a more geographically isolated, monopolistic situation. We also explore whether pupils who live in places where a wide range of choices is available achieve more than those whose choices seem more limited. In simple least squares regression models, we find a positive, but small association between pupil performance and competition indices; however, this could be related to endogenous school location or pupil sorting. Instrumental variables results based on admissions district boundaries do in fact suggest that there might very little gains to be had from improving pupil choice and school competition; indeed, it is only in faith schools that competition seems to be linked to performance, and even then only in terms of their competitive position in relation to other faith schools. JEL
Quark mass and condensate in HQCD

We extend the Sakai-Sugimoto holographic model of QCD (HQCD) by including the scalar bi-fundamental ``tachyon" field in the 8-brane-anti-8-brane probe theory. We show that this field is responsible both for the spontaneous breaking of the chiral symmetry, and for the generation of (current algebra) quark masses, from the point of view of the bulk theory. As a by-product we show how this leads to the Gell-Mann-Oakes-Renner relation for the pion mass.
Chemokine Decoy Receptor D6 Plays a Negative Role in Human Breast Cancer

Chemokine binding protein D6 is a promiscuous decoy receptor that can inhibit inflammation in vivo; however, the role it plays in cancer is not well known yet. In this study, we showed for the first time that human breast cancer differentially expressed D6 and the expression could be regulated by some cytokines. More importantly, overexpression of D6 in human breast cancer cells inhibits proliferation and invasion in vitro and tumorigenesis and lung metastasis in vivo. This inhibition is associated with decreased chemokines (e.g., CCL2 and CCL5), vessel density, and tumor-associated macrophage infiltration. Furthermore, D6 expression is inversely correlated to lymph node metastasis as well as clinical stages, but positively correlated to disease-free survival rate in cancer patients. Therefore, D6 plays a negative role in the growth and metastasis of breast cancer. (Mol Cancer Res 2008;6(8):1276–88)
Part-segment features for articulated pose estimation

We propose part-segment (PS) features for estimating an articulated pose in still images. The proposed PS feature evaluates the image likelihood of each body part (e.g. head, torso, and arms) robustly to background clutter and nuisance textures on the body and clothing. In contrast to similar segmentation features, part segmentation is improved by part-specific shape priors that are optimized by training images with fully-automatically obtained seeds. The extracted PS feature is fused complementarily with gradient features using discriminative training and adaptive weighting for robust and accurate evaluation of part similarity.
GRAPHS , CAUSALITY

Structural equation modeling (SEM) has dominated causal analysis in the social and behavioral sciences since the 1960s. Currently, many SEM practitioners are having diiculty articulating the causal content of SEM and are seeking foundational answers. Recent developments in the areas of graphical models and the logic of causality show potential for alleviating such diiculties and thus for revitalizing structural equations as the primary language of causal modeling. This paper summarizes several of these developments, including the prediction of vanishing partial correlations, model testing, model equivalence, parametric and nonparametric identiiability, control of confounding , and covariate selection. These developments clarify the causal and statistical components of structural equation models and the role of SEM in the empirical sciences .
The Emergence and Volatility of Homesickness in Exchange Students Abroad: A Smartphone-Based Longitudinal Study

Previous research on the determinants of homesickness has tended to produce inconsistent results and relied mostly on cross-sectional assessments. To capture the longitudinal perspective, we conducted a smartphone app-based study, monitoring the emergence and volatility of homesickness in international university exchange students (n = 148). Applying an experience sampling method (ESM), homesickness was measured every second day over a period of 3 months followed by a post hoc questionnaire to assess potential moderators. Multilevel modeling revealed that whereas age, Extraversion, Conscientiousness, Openness to Experience, voluntariness, previous stays abroad, support from host university, geographical distance, co- and host national identification, language proficiency, and pre-data collection duration of stay did not yield any effects, being male, scoring high on Neuroticism as well as Agreeableness, having difficulties in sociocultural adaptation, and being at the beginning of the stay (as opposed to later on) were related to higher levels of homesickness. Corroborating the latter finding, curve estimation regression analyses showed that homesickness normally peaks immediately after relocation and fades away afterward. Together with the low overall intensities of homesickness found in the present sample, the results suggest that homesickness is a common but mild adverse by-product of international student mobility.
Color texture classification by normalized color space representation

This paper proposes a novel approach to color texture characterization and classification. Rather than developing new textural features, we propose to derive a family of new, reduced dimensionality color spaces named P/sub 1/P/sub 2/, that allow a good classification performance by the use of classical energy-distribution features, defined in a scalar spectral domain. The dimensionality reduction approach can be traced back to color constancy normalization and the reduced ordering principle and exhibits a strong perceptual background. We develop an adaption procedure for the selection of the proper color space within the new P/sub 1/P/sub 2/ family. The overall classification performance is very promising and the proposed methodology surmounts the current color texture characterization by features extracted from the luminance spectrum only.
Present and future global distributions of the marine Cyanobacteria Prochlorococcus and Synechococcus

The Cyanobacteria Prochlorococcus and Synechococcus account for a substantial fraction of marine primary production. Here, we present quantitative niche models for these lineages that assess present and future global abundances and distributions. These niche models are the result of neural network, nonparametric, and parametric analyses, and they rely on >35,000 discrete observations from all major ocean regions. The models assess cell abundance based on temperature and photosynthetically active radiation, but the individual responses to these environmental variables differ for each lineage. The models estimate global biogeographic patterns and seasonal variability of cell abundance, with maxima in the warm oligotrophic gyres of the Indian and the western Pacific Oceans and minima at higher latitudes. The annual mean global abundances of Prochlorococcus and Synechococcus are 2.9 ± 0.1 × 1027 and 7.0 ± 0.3 × 1026 cells, respectively. Using projections of sea surface temperature as a result of increased concentration of greenhouse gases at the end of the 21st century, our niche models projected increases in cell numbers of 29% and 14% for Prochlorococcus and Synechococcus, respectively. The changes are geographically uneven but include an increase in area. Thus, our global niche models suggest that oceanic microbial communities will experience complex changes as a result of projected future climate conditions. Because of the high abundances and contributions to primary production of Prochlorococcus and Synechococcus, these changes may have large impacts on ocean ecosystems and biogeochemical cycles.
A Local Monte Carlo Tree Search Approach in Deterministic Planning

Much recent work in satisficing planning has aimed at striking a balance between coverage - solving as many problems as possible - and plan quality. Current planners achieve near perfect coverage on the latest IPC benchmarks. It is therefore natural to investigate their scaling behavior on more difficult instances. Among state of the art planners, LAMA (Richter, Helmert, and Westphal 2008) is able to generate high quality plans, but its coverage drops off rapidly with increasing prob- lem complexity. The Arvand planner (Nakhost and Müller 2009) scales to much harder instances but generates lower quality plans. This paper introduces a new algorithm, Monte Carlo Random Walk-based Local Tree Search (MRW-LTS), which uses random walks to selectively build local search trees. Experiments demonstrate that MRW-LTS combines a scaling behavior that is better than LAMA’s with a plan quality that is better than Arvand’s.
Identifying Optimal Lipid Raft Characteristics Required To Promote Nanoscale Protein-Protein Interactions on the Plasma Membrane

ABSTRACT The dynamic lateral segregation of signaling proteins into microdomains is proposed to facilitate signal transduction, but the constraints on microdomain size, mobility, and diffusion that might realize this function are undefined. Here we interrogate a stochastic spatial model of the plasma membrane to determine how microdomains affect protein dynamics. Taking lipid rafts as representative microdomains, we show that reduced protein mobility in rafts segregates dynamically partitioning proteins, but the equilibrium concentration is largely independent of raft size and mobility. Rafts weakly impede small-scale protein diffusion but more strongly impede long-range protein mobility. The long-range mobility of raft-partitioning and raft-excluded proteins, however, is reduced to a similar extent. Dynamic partitioning into rafts increases specific interprotein collision rates, but to maximize this critical, biologically relevant function, rafts must be small (diameter, 6 to 14 nm) and mobile. Intermolecular collisions can also be favored by the selective capture and exclusion of proteins by rafts, although this mechanism is generally less efficient than simple dynamic partitioning. Generalizing these results, we conclude that microdomains can readily operate as protein concentrators or isolators but there appear to be significant constraints on size and mobility if microdomains are also required to function as reaction chambers that facilitate nanoscale protein-protein interactions. These results may have significant implications for the many signaling cascades that are scaffolded or assembled in plasma membrane microdomains.