Miracle-dz/UniFAR-FTUs huggingface dataset:
Cross-terms allocation of line losses using game theory

The power loss on a branch are the function of power flows which are caused by various participants present in the network and it varies nearly quadratic with respect to these power flows. There are several methods present in literature to unbundle or allocate the branch power losses based on different assumptions. In this paper, three game theoretic models are presented and compared, namely, Nash equilibrium, Shapley value and nucleolus, so as to allocate the cross-terms in branch power flow to each participant of the network. Additionally, these game theoretic models are compared with some of the classical methods present in the literature. Further, it is observed that all these game theoretic models give the same solution for the allocation of the branch losses. It satisfies all the axioms of fairness and provide unbiased solution for loss allocation. The results are discussed and illustrated using an example.
To Prescribe or Not to Prescribe? Consumer Access to Life-Enhancing Products

Abstract With rapid biotechnological advances in specialty drugs and direct-to-consumer advertising, consumers are under tremendous pressure to look, perform, feel, and live better. This is often accomplished through the use of life-enhancing products, sometimes referred to as performance-enhancing products, which can be accessed only through a gatekeeper, such as a physician. Integrating consumer and medical research, this article investigates how physicians make trade-offs between objective medical and nonmedical factors to determine consumers’ access to life-enhancing products by examining US pediatric endocrinologists’ prescription decisions for growth hormone (GH) for healthy but short children. The results of a conjoint study indicate that consumer medical criteria have less impact on a physician’s decision to prescribe GH if the consumer requests a prescription or the physician believes in the intangible product benefits, and more impact when the product is more expensive. A physician’s length of experience increases the impact of consumer medical criteria and decreases the influence of a consumer’s preference for a prescription on the decision to prescribe. Overall, this research shows that not all consumers have equal access to life-enhancing products; their access depends on a complex combination of medical and nonmedical factors related to the consumer, product, and the physician.
Analysis for Soware Engineering : How Far Can We Go ?

Sentiment analysis has been applied to various software engineering (SE) tasks, such as evaluating app reviews or analyzing developers’ emotions in commit messages. Studies indicate that sentiment analysis tools provide unreliable results when used out-of-the-box, since they are not designed to process SE datasets. The silver bullet for a successful application of sentiment analysis tools to SE datasets might be their customization to the speci c usage context. We describe our experience in building a software library recommender exploiting developers’ opinions mined from Stack Over ow. To reach our goal, we retrained—on a set of 40k manually labeled sentences/words extracted from Stack Over ow—a state-of-the-art sentiment analysis tool exploiting deep learning. Despite such an e ortand time-consuming training process, the results were negative. We changed our focus and performed a thorough investigation of the accuracy of commonly used tools to identify the sentiment of SE related texts. Meanwhile, we also studied the impact of di erent datasets on tool performance. Our results should warn the research community about the strong limitations of current sentiment analysis tools.
An Interference Contribution Rate Based Small Cells On/Off Switching Algorithm for 5G Dense Heterogeneous Networks

Dense heterogeneous network serves as one of the most promising technologies for the upcoming 5G network. However, with a larger number of densely deployed small cells, the problem of co-channel interference and power consumption of 5G small cell base stations (also known as S-gNBs) becomes more severe. In order to improve the power efficiency and alleviate the co-channel interference, S-gNBs in the network need to be dynamically switched on or off via appropriate criteria or strategies. This paper proposes an interference contribution rate (ICR) based small cell on/off switching algorithm. With the incorporation of network adjacency matrix (NAM), the proposed algorithm circumvents the complicated computation of the ICRs of the S-gNBs in the network and hence yields less computational complexity. In addition, the proposed algorithm identifies the S-gNBs that need to be switched on or off based on the NAM and the serving signal strength measurements of user equipment’s and therefore involves less signaling information in the procedure of switching on/off decision. Simulation results verify that the proposed algorithm effectively diminishes the co-channel interference among the small cells, ameliorates the network power efficiency and total data rate, and meanwhile maintains a lower traffic loss in the network.
Fast Ellipse Detection via Gradient Information for Robotic Manipulation of Cylindrical Objects

Robotic manipulation of objects requires a fast recognition from image stream. For many cylindrical object (e.g., cans, cups, pipes, bottles, etc.) this is possible through detection of ellipse depicting the circular top of the cylinder. Growing industrial and warehouse applications of robots drive the demand for fast and reliable detection of ellipses, while state-of-the-art methods are lacking in either speed or accuracy strength. We present a novel algorithm to perform fast and robust ellipse detection. First, the method utilizes the information of edge curvature to split curves into arcs. Next, the arc convexity–concavity is used to classify arcs into different quadrants of ellipses. Then, based on multiple geometric constraints the arcs can be grouped at low computational cost. Our method is compared with six state-of-the-art methods using three public image datasets. The comparison results show that the proposed algorithm outperforms other methods with high detection accuracy and fast detection speed. Lastly, the algorithm is applied to identifying cylindrical objects in real-time for arranging and tracking purposes.
Find Who to Look at: Turning From Action to Saliency

The past decade has witnessed the use of high-level features in saliency prediction for both videos and images. Unfortunately, the existing saliency prediction methods only handle high-level static features, such as face. In fact, high-level dynamic features (also called actions), such as speaking or head turning, are also extremely attractive to visual attention in videos. Thus, in this paper, we propose a data-driven method for learning to predict the saliency of multiple-face videos, by leveraging both static and dynamic features at high-level. Specifically, we introduce an eye-tracking database, collecting the fixations of 39 subjects viewing 65 multiple-face videos. Through analysis on our database, we find a set of high-level features that cause a face to receive extensive visual attention. These high-level features include the static features of face size, center-bias and head pose, as well as the dynamic features of speaking and head turning. Then, we present the techniques for extracting these high-level features. Afterwards, a novel model, namely multiple hidden Markov model (M-HMM), is developed in our method to enable the transition of saliency among faces. In our M-HMM, the saliency transition takes into account both the state of saliency at previous frames and the observed high-level features at the current frame. The experimental results show that the proposed method is superior to other state-of-the-art methods in predicting visual attention on multiple-face videos. Finally, we shed light on a promising implementation of our saliency prediction method in locating the region-of-interest, for video conference compression with high efficiency video coding.
Chitin Increases Mycobacterium ulcerans Growth in Acidic Environments

Species with a chitinous exoskeleton are overrepresented among the aquatic organisms carrying Mycobacterium ulcerans (MU) in nature and laboratory experiments have demonstrated the enhancing effects of chitin on the growth of MU. Field surveys identified pH as one of the key parameters delineating the distribution of MU in tropical regions. The present study investigated the relationship between chitin and pH in MU growth. By focusing on pH variations in the field, our results revealed that chitin enhanced MU growth in acidic environments. The present study provides new information on the ecological conditions favoring the development of this mycobacterium in nature.
Integrated railway timetable scheduling optimization model and rescheduling recovery optimization model: A systematic literature review

Research study in the railway optimization can be divided into strategic, tactical and operational area. In this paper, we will focus on discussing railway scheduling optimization and rescheduling recovery technique which (tactical and operational area) because these topics are usually complex and in the real case train is often delay from a predetermined schedule. We use Systematic Literate Review (SLR) to reviewing selected journals and articles which related with our research focus. The output of this Systematic Literature Review is possibility opportunity study in the railway scheduling and rescheduling recovery technique optimization. We also proposed an enhanced no-wait blocking parallel-machine job-shop-scheduling model optimization on railway scheduling and rescheduling recovery technique for our next research. The proposed model will consider new variable about rail line balancing using drum buffer rope, one of theory of constrained, and integrated with number of passenger car composition number and track backup rescheduling in partial double track rail line.
PHENOTYPE-DEPENDENT AND INDEPENDENT INERTIAL FOCUSING

The ability to precisely position flowing particles in microchannels would aid development of clinically useful cell identification and purification technologies. Here, we investigated the effect of fluid inertia on lateral focusing positions of flowing deformable cells over a wide range of flow rates. It was found that, at lower flow rates, cells can be differentially positioned in the lateral direction based on deformability variations, while, at higher flow rates fluid inertia dominates and all cell types, regardless of their mechanical properties, were more uniformly positioned. The presented study provides improved design criteria for high-throughput cell manipulation system development.
Dominance and Epistasis Interactions Revealed as Important Variants for Leaf Traits of Maize NAM Population

Leaf orientation traits of maize (Zea mays) are complex traits controlling by multiple loci with additive, dominance, epistasis, and environmental interaction effects. In this study, an attempt was made for identifying the causal loci, and estimating the additive, non-additive, environmental specific genetic effects underpinning leaf traits (leaf length, leaf width, and upper leaf angle) of maize NAM population. Leaf traits were analyzed by using full genetic model and additive model of multiple loci. Analysis with full genetic model identified 38∼47 highly significant loci (-log10PEW > 5), while estimated total heritability were 64.32∼79.06% with large contributions due to dominance and dominance related epistasis effects (16.00∼56.91%). Analysis with additive model obtained smaller total heritability (hT2 ≙ 18.68∼29.56%) and detected fewer loci (30∼36) as compared to the full genetic model. There were 12 pleiotropic loci identified for the three leaf traits: eight loci for leaf length and leaf width, and four loci for leaf length and leaf angle. Optimal genotype combinations of superior line (SL) and superior hybrid (SH) were predicted for each of the traits under four different environments based on estimated genotypic effects to facilitate maker-assisted selection for the leaf traits.
A Virtual Pilot-Assisted Channel Estimation Algorithm for MIMO-SCFDE Systems Over Fast Time-Varying Multipath Channels

This paper considers a novel virtual pilot-assisted channel estimation algorithm for multiple input multiple output single carrier frequency domain equalization (MIMO-SCFDE) systems over fast time-varying multipath channels. Given that the fast time-varying channels can no longer support the assumption that the channel frequency-domain response remains invariant for the duration of one SC-FDE symbol, most of the channel estimation methods cannot be directly applied to MIMO-SCFDE systems. We put forward an iterative interfrequency interference cancellation method for the time-varying multipath channels over one SC-FDE symbol. The proposed method utilizes the virtual pilot-assisted channel estimation and partial minimum-mean-square-error equalization to improve the accuracy of the estimation. Simulation results validate the improved performance of the proposed method for MIMO-SCFDE systems over fast time-varying multipath channels.
An examination of a modified Yo-Yo test to measure intermittent running performance in rugby players

Abstract This study examined how starting each shuttle in the prone position altered the internal, external and perceptual responses to the Yo-Yo Intermittent Recovery Test Level 1 (Yo-Yo IR1). Using a randomized crossover design, 17 male rugby players completed the Yo-Yo IR1 and prone Yo-Yo IR1 on two separate occasions. External loads (via microtechnology), , heart rate (HR) and rating of perceived exertion (RPE) were measured at 160, 280 and 440 m (sub-maximal) and when the test was terminated (peak). The pre-to-post change in blood lactate concentration (Δ[La]b) was determined for both tests. All data were analysed using effect sizes (ES) and magnitude-based inferences. Between-trial differences (ES ± 90% CL) indicated that total distance was most likely lower (−1.87 ± 0.19), whereas other measures of peak external load were likely to very likely higher during the prone Yo-Yo IR1 (0.62–1.80). Sub-maximal RPE was likely to most likely higher (0.40–0.96) and peak RPE very likely higher (0.63 ± 0.41) in the prone Yo-Yo IR1. The change in [La]b was likely higher after the prone Yo-Yo IR1. Mean HR was possibly lower at 440 m (−0.25 ± 0.29) as was peak HR (−0.26 ± 0.25) in the prone Yo-Yo IR1. , and were likely to very likely higher at 280 and 440 m (ES = 0.36–1.22), while peak values were possibly to likely higher (ES = 0.23–0.37) in the prone Yo-Yo IR1. Adopting a prone position during the Yo-Yo IR1 increases the internal, external and perceptual responses, placing greater emphasis on metabolically demanding actions typical of rugby.
Recurrent Multiresolution Convolutional Networks for VHR Image Classification

Classification of very high-resolution (VHR) satellite images has three major challenges: 1) inherent low intraclass and high interclass spectral similarities; 2) mismatching resolution of available bands; and 3) the need to regularize noisy classification maps. Conventional methods have addressed these challenges by adopting separate stages of image fusion, feature extraction, and postclassification map regularization. These processing stages, however, are not jointly optimizing the classification task at hand. In this paper, we propose a single-stage framework embedding the processing stages in a recurrent multiresolution convolutional network trained in an end-to-end manner. The feedforward version of the network, called FuseNet, aims to match the resolution of the panchromatic and multispectral bands in a VHR image using convolutional layers with corresponding downsampling and upsampling operations. Contextual label information is incorporated into FuseNet by means of a recurrent version called ReuseNet. We compared FuseNet and ReuseNet against the use of separate processing steps for both image fusions, e.g., pansharpening and resampling through interpolation and map regularization such as conditional random fields. We carried out our experiments on a land-cover classification task using a Worldview-03 image of Quezon City, Philippines, and the International Society for Photogrammetry and Remote Sensing 2-D semantic labeling benchmark data set of Vaihingen, Germany. FuseNet and ReuseNet surpass the baseline approaches in both the quantitative and qualitative results.
Measuring executive function skills in young children in Kenya

ABSTRACT Interest in measuring executive function skills in young children in low- and middle-income country contexts has been stymied by the lack of assessments that are both easy to deploy and scalable. This study reports on an initial effort to develop a tablet-based battery of executive function tasks, which were designed and extensively studied in the United States, for use in Kenya. Participants were 193 children, aged 3–6 years old, who attended early childhood development and education centers. The rates of individual task completion were high (65–100%), and 85% of children completed three or more tasks. Assessors indicated that 90% of all task administrations were of acceptable quality. An executive function composite score was approximately normally distributed, despite higher-than-expected floor and ceiling effects on inhibitory control tasks. Children’s simple reaction time (β = –0.20, p = .004), attention-related behaviors during testing (β = 0.24, p = .0005), and age (β = –0.24, p = .0009) were all uniquely related to performance on the executive function composite. Results are discussed as they inform efforts to develop valid and reliable measures of executive function skills among young children in developing country contexts.
Relationship of eye and hand movements during perturbed reaching tasks

It has been suggested that to make a goal-directed reach to grasp movement, precise visual information of target and hand is important. In spite of various studies relating to visual feedback in reach to grasp, few researchers have investigated the parameters of this behavior in relation to saccadic eye movement. Specifically, research in this area lacks insight about the contribution of visual feedback before and after the occurrence of saccadic eye movement. The purpose of this study was to investigate the influence of eye movement on the characteristics of the hand movement during a reach to grasp task. In this experiment, 9 college-age individuals performed the task of reaching and grasping a vertical dowel in two conditions: (1) with eye movement and (2) without eye movement (by fixating eye on central dowel). Further, we compared the performance in three sub-conditions: (1) full vision, (2) vision block before eye movement, and (3) vision block after eye movement. We found that presence or absence of either eye movement or continuous vision of target and hand does not modify the accuracy of grasp. Reach duration was shorter with full vision and reach trajectory was shorter when eye movement was coordinated with hand movement. We concluded that continuous vision of the hand and target is not necessary for the online control of these complex movements, but vision is necessary for optimizing the speed of the
Attention Bias to Sad Faces and Images: Which Is Better for Predicting Depression?

This study aims to compare emotion face and emotion image dot-probe tasks in predicting depression. The study uses descriptive correlational methods. The subjects studied during the research included the people between the ages of 19 - 40 years, who visited a particular psychology clinic in Tehran, Iran from 2011 to 2012. The patients studied received a clinical diagnosis, based on an unstructured interview, as per the 4th Edition of Diagnostic and Statistical Manual of Mental Disorders (DSM-IV), and a screening test using the Beck Depression Inventory (BDI, cut off point 21 and higher). Then 82 individuals were selected, using the convenience sampling method. Two computerized dot-probe tasks (emotional faces and images) were administered to the participants. Pearson’s correlation coefficients and univariate regression analysis showed that, in both tasks, attention bias was significantly linked with depression and could predict it (P < 0.01). Fisher’s Z-test further showed that the emotion face dot-probe task could predict depression more precisely than the emotion image dot-probe task (P < 0.01).
The Physical Mechanisms Behind the Strain-Induced Electron Mobility Increase in InGaAs-On-InP MOSFETs

The electron mobility in strained ultra-thin InGaAs-on-InP MOSFETs is investigated combining band-structure and physics-based modeling including all relevant scattering mechanisms and effects. The most important effect is Fermi-level pinning which occurs due to high density of interface states at InGaAs/oxide interface. Different interface states densities are considered in order to investigate impact of interface states on electron mobility in biaxially strained ultra-thin InGaAs-on-InP structures. We report the tensile biaxial strain values that can alleviate the unfavorable impact of interface states charge on electron transport.
Online Socialization and Delinquency: Expanding the Study of Peer Influence in Criminology

Criminologists typically study peer influence within traditional contexts such as schools and neighborhoods. Spurred by the ubiquitous use of electronic devices, recent research finds that online peer delinquency can also have deleterious effects on adolescent behavior. This has important implications for the study of delinquency since youth today are digital natives, meaning their developmental years are spent in a world reliant on technology and Internetconnected devices. Through the use of computer-mediated communication (CMC), adolescents can forge friendships with those found within the cyber context, a virtual environment unrestricted by the confines of space and time. This context not only provides access to a distinct group of online peers, it connects youth with their face-to-face (FTF) friends, thereby enabling instantaneous contact with offline associations. Furthermore, extant research on CMC has revealed that individuals are disinhibited and disclose personal information when communicating online. The cyber context thus (1) extends the view of peer groups by providing access to online peers, (2) increases the frequency, duration, and intensity of contact with existing FTF peers, and (3) affects the content of communication which may alter what is perceived as peer deviance. This dissertation uses self-reported data from a multi-site study of 3,641 middle school students to explore how youth interact with their friends as well as the association between peer delinquency and personal delinquency. Results indicate that few respondents prefer communicating online, but many find it easier to make friends online, and most communicate online with their FTF friends. The cyber context generally does not enable exposure to new peer delinquency; rather, it enhances the peer delinquency of those who are already exposed offline. Moreover, online peer delinquency from those who are not regularly seen in person emerges as a robust, independent predictor of self-reported delinquency. These results are discussed in terms of their implications for how criminologists should proceed with incorporating the cyber context into the study of delinquent peer influence.