Miracle-dz/UniFAR-FTUs huggingface dataset:
Construction of simulation model of a flying robot with variable attack angle mechanism

This paper discusses a simulation model of flying robots (developed in our previous paper) with a variable attack angle mechanism. The simulation model constructed in this paper are divided into three parts. The first part is a so- called attack angle model to compute the change of angle of attack. The second part is a so-called lift force model to compute lift force based on the computational result from the first part. In the second part, we compensate lift and drag coefficients with aero dynamical (experimental) data in previous studies on pitching (motion) wing since the variable attack angle mechanism realizes a pitching motion. The third part is a so- called power model to compute the electrical power of motors. The utility of the simulation model is verified by comparing simulation results with experimental results.
Self‐supervised learning to visually detect terrain surfaces for autonomous robots operating in forested terrain

Autonomous robotic navigation in forested environments is difficult because of the highly variable appearance and geometric properties of the terrain. In most navigation systems, researchers assume a priori knowledge of the terrain appearance properties, geometric properties, or both. In forest environments, vegetation such as trees, shrubs, and bushes has appearance and geometric properties that vary with change of seasons, vegetation age, and vegetation species. In addition, in forested environments the terrain surface is often rough, sloped, and/or covered with a surface layer of grass, vegetation, or snow. The complexity of the forest environment presents difficult challenges for autonomous navigation systems. In this paper, a self‐supervised sensing approach is introduced that attempts to robustly identify a drivable terrain surface for robots operating in forested terrain. The sensing system employs both LIDAR and vision sensor data. There are three main stages in the system: feature learning, feature training, and terrain prediction. In the feature learning stage, 3D range points from LIDAR are analyzed to obtain an estimate of the ground surface location. In the feature training stage, the ground surface estimate is used to train a visual classifier to discriminate between ground and nonground regions of the image. In the prediction stage, the ground surface location can be estimated at high frequency solely from vision sensor data. © 2012 Wiley Periodicals, Inc.
Laying out circuits on asynchronous cellular arrays: a step towards feasible nanocomputers?

Opinions differ widely as to the type of architecture most suitable for achieving the tremendous performance gains expected with computers built by nanotechnology. In this context little research effort has gone into asynchronous cellular arrays, an architecture that is promising for nanocomputers due to (1) its regular structure of locally interconnected cells, and (2) its asynchronous mode of timing. The first facilitates bottom-up manufacturing techniques like directed self-assembly. The second allows the cells’ operations to be timed randomly and independently of each other, mitigating the problems accompanying a central clock, like high power consumption and heat dissipation. The advantages of asynchronous timing notwithstanding, it makes computation less straightforward. Attempts to compute on asynchronous cellular arrays have therefore focused on simulating synchronous operation on them, at the price of more complex cells. Here we advance a more effective approach based on the configuration on an asynchronous cellular array of delay-insensitive circuits, a type of asynchronous circuit that is robust to arbitrary delays in signals. Our results may be a step towards future nanocomputers with a huge number of autonomously operating cells organized in homogeneous arrays that can be programmed by configuring them as delay-insensitive circuits.
Not all gamblers are created equal: gambling preferences depend on individual personality traits

Previous studies have presented evidence revealing that the dispositional personalities of consumers, or the traits associated with their gambling behaviors and risk preferences, differ across various gambling types. Those studies leave open the question of whether and how personality differences affect domain-specific gambling preferences. Using latent class analysis (LCA), we explored the latent classes of risk preference for different gambling types and the effects of personality traits on these classes. A total of 732 Macau residents completed a questionnaire assessing 13 gambling types and personality traits. Preferences for gambling shown by the participants varied between three latent classes of games: chance gambling, entertainment gambling, and technical gambling. Moreover, not all personality traits consistently predicted preferences for these gambling classes. For instance, aggression-hostility positively predicted a general preference for both chance gambling and technical gambling, but impulsive sensation-seeking positively predicted only a preference for chance gambling. However, neuroticism-anxiety traits showed no such predictive effect for any of the three gambling classes. This study suggests that personality patterns emerge for overall and specific gambling types and underscores the value of LCA to help resolve the longstanding debate over whether gambling is domain specific.
Cross-Talk between cAMP and MAPK Pathways in HSD11B2 Induction by hCG in Placental Trophoblasts

Overexposure of the fetus to glucocorticoids in gestation is detrimental to fetal development. The passage of maternal glucocorticoids into the fetal circulation is governed by 11beta-Hydroxysteroid Dehydrogenase Type 2 (HSD11B2) in the placental syncytiotrophoblasts. Human chorionic gonadotropin (hCG) plays an important role in maintaining placental HSD11B2 expression via activation of the cAMP pathway. In this study, we investigated the relationship between the activation of the cAMP pathway by hCG and subsequent phosphorylation of extracellular signal-regulated kinase1/2 (ERK1/2) or p38 mitogen-activated protein kinase (MAPK) pathways in the regulation of placental HSD11B2 expression in human placental syncytiotrophoblasts. We found that treatment of the placental syncytiotrophoblasts with either hCG or dibutyl cAMP (dbcAMP) could promote the phosphorylation of p38 and ERK1/2. Inhibition of p38 MAPK with SB203580 not only reduced the basal HSD11B2 mRNA and protein levels but also attenuated HSD11B2 levels induced by either hCG or dbcAMP. By contrast, inhibition of ERK1/2 with PD98059 increased the basal mRNA and protein levels of HSD11B2 and had no effect on HSD11B2 mRNA and protein levels induced by either hCG or dbcAMP. These data suggest that p38 MAPK is involved in both basal and hCG/cAMP-induced expression of HSD11B2, and ERK1/2 may play a role opposite to p38 MAPK at least in the basal expression of HSD11B2 in human placental syncytiotrophoblasts and that there is complicated cross-talk between hCG/cAMP and MAPK cascades in the regulation of placental HSD11B2 expression.
Section Ii-exercise Physiology & Sports Medicine Can Height Categories Replace Weight Categories in Striking Martial Arts Competitions? a Pilot Study

In most combat sports and martial arts, athletes compete within weight categories. Disordered eating behaviors and intentional pre-competition rapid weight loss are commonly seen in this population, attributed to weight categorization. We examined if height categories can be used as an alternative to weight categories for competition, in order to protect the health of athletes. Height and weight of 169 child and adolescent competitive karate athletes were measured. Participants were divided into eleven hypothetical weight categories of 5 kg increments, and eleven hypothetical height categories of 5 cm increments. We calculated the coefficient of variation of height and weight by each division method. We also calculated how many participants fit into corresponding categories of both height and weight, and how many would shift a category if divided by height. There was a high correlation between height and weight (r = 0.91, p<0.001). The mean range of heights seen within current weight categories was reduced by 83% when participants were divided by height. When allocating athletes by height categories, 74% of athletes would shift up or down one weight category at most, compared with the current categorization method. We conclude that dividing young karate athletes by height categories significantly reduced the range of heights of competitors within the category. Such categorization would not cause athletes to compete against much heavier opponents in most cases. Using height categories as a means to reduce eating disorders in combat sports should be further examined.
QoS aware power allocation for combined guaranteed performance and best effort users in OFDMA systems

One of the key issues in design of an orthogonal frequency division multiple access (OFDMA) based system is subcarrier and power allocation. Assuming knowledge of the instantaneous channel gains for all users, we propose a resource allocation scheme for an OFDMA based system to simultaneously provide two services, guaranteed performance (GP) and best effort (BE), with different quality-of-service (QoS). Subcarrier and power allocation are carried out sequentially to reduce the complexity, and an optimal power allocation procedure is derived. Furthermore, we present a reduced complexity suboptimal power allocation algorithm.
A personalized recommendation system based on product attribute-specific weights and improved user behavior analysis

The amount of information on the Web is increasing exponentially due to the rapid development of information communication technology. In the field of e-business in particular, research into recommendation systems that analyze user preferences and suggest related products has been an issue. Analyzing the preferences of users is very important for making recommendations. However, if there is a lack of user information when a predictive method is being applied, no suggestion can be made. Therefore, this paper proposes a personalized recommendation system that utilizes the information derived from analyzing the data regarding user behaviors. The proposed system observes the various actions of a user on the Web in order to accurately understand his or her intentions. In addition, the most preferred qualities of various products is inferred based on the ID3 algorithm and the Naive Bayesian algorithm is applied to analyze user preferences by giving different weight to different product attributes. In this way, the proposed system can provide accurate recommendations even in situations where data regarding the behavior of a user is lacking. In order to evaluate the efficiency of the proposed system, the recommendation results and the number of available behavior data were compared, thus confirming its effectiveness.
Statistical characterization of filtered phase noise in optical receivers

This article presents an accurate numerical approach to statistically characterize filtered phase noise, usually encountered in the analysis of heterodyne optical fiber communication receivers. Filtered phase noise is generated by simulation. Generated samples are used to estimate the probability density functions and moment generating functions of phase noise random variables. The proposed approach is valid for large as well as small phase noise. Furthermore, it comes close to providing analytical expressions for the computed statistical measures.
Permanent Magnet Machine Design Practice and Optimization

The complexity of the permanent magnet (PM) machine structure makes the optimal design of the PM machine always a difficult task. The multiple objectives of an optimal design make most classic optimization algorithms inapplicable, due to the nonlinearity and some discontinous variables. In this paper, two interior permanent magnet (IPM) machine design practices with modular stator structure and conventional stator structure are discussed. The design process is directly coupled with finite-element analysis (FEA) with the machine design guidelines embedded in the optimization process. Multivariable optimization methods based on Monte Carlo and differential evolution algorithms are applied in the design phase and the results are compared in this paper
Featured Model-Based Mutation Analysis

Model-based mutation analysis is a powerful but expensive testing technique. We tackle its high computation cost by proposing an optimization technique that drastically speeds up the mutant execution process. Central to this approach is the Featured Mutant Model, a modelling framework for mutation analysis inspired by the software product line paradigm. It uses behavioural variability models, viz., Featured Transition Systems, which enable the optimized generation, configuration and execution of mutants. We provide results, based on models with thousands of transitions, suggesting that our technique is fast and scalable. We found that it outperforms previous approaches by several orders of magnitude and that it makes higher-order mutation practically applicable.
A Geometric Nearest Point Algorithm for the Efficient Solution of the SVM Classification Task

Geometric methods are very intuitive and provide a theoretically solid approach to many optimization problems. One such optimization task is the support vector machine (SVM) classification, which has been the focus of intense theoretical as well as application-oriented research in machine learning. In this letter, the incorporation of recent results in reduced convex hulls (RCHs) to a nearest point algorithm (NPA) leads to an elegant and efficient solution to the SVM classification task, with encouraging practical results to real-world classification problems, i.e., linear or nonlinear and separable or nonseparable.
2 Graphs as a Symmetric Positive Semidefinite Matrix

Finding a new mathematical representation for graphs, which allows direct comparison between different graph structures, is still an open-ended research direction. Having such a representation in a common, well-understood metric space is the first prerequisite for a variety of machine learning algorithms like classification, clustering, etc, over graph datasets. In this study, we propose a symmetric positive semidefinite matrix with the (i, j)-th entry equal to the covariance between normalized vectors Ae and Ae as a representation for graph with adjacency matrix A. We argue that the covariance between vectors of the form Ae and Ae, given some i and j, is an informative feature. We present theoretical results supporting this argument. Our representation, being a covariance matrix in a fixed dimensional metric space, can be directly compared across different graph structures. This naturally provides a measure of similarity on graph objects. In the task of social network classification, our proposal outperforms the state-of-the-art methodologies. In addition, the computation can be performed in operations linear in the number of edges, which makes the proposed approach faster and scalable.
Investment and Institutions

We study how financial systems and institutional environments affect investment efficiency using a sample of some 300,000 firm-years from 48 countries. Based on a canonical investment model, we identify two possible channels by which institutional environments may affect investment: firm-level financial frictions and the macro-level required rate of return. We find that a good institutional environment, in particular strong corporate governance, reduces financial frictions and lowers the required rate of return, thereby enhancing efficiency in capital allocation. This result is broadly consistent with previous literature, but the mechanism identified here is novel and more precise.
Cervical Cancer Screening Service Uptake and Associated Factors among Age Eligible Women in Mekelle Zone, Northern Ethiopia, 2015: A Community Based Study Using Health Belief Model

Introduction Cervical cancer is the third most common cancer among women worldwide, with about 500,000 new patients diagnosed and over 250,000 deaths every year. Cervical cancer screening offers protective benefits and is associated with a reduction in the incidence of invasive cervical cancer and cervical cancer mortality. But there is very low participation rate in screening for cervical cancer among low and middle-income countries. Objective This study aimed to determine cervical cancer screening service uptake and its associated factor among age eligible women in Mekelle zone, northern Ethiopia, 2015. Methods A community based cross-sectional study was conducted in Mekelle zone among age eligible women from February to June 2015. Systematic sampling technique was used to select 1286 women in to the study. A pre-tested structured questionnaire was used to collect relevant data. Data was entered and cleaned using EPINFO and analyzed using SPSS version 20 software package. Bivariate and Multivariate logistic regression was performed to assess association between dependent and independent variables with 95% CI and p-value less than 0.05 was set for association. Results The study revealed that among 1186 age eligible women, only 235(19.8%) have been screened for cervical cancer. Age (AOR = 1.799, 95%CI = 1.182–2.739), history of multiple sexual partners (AOR = 1.635, 95%CI = 1.094–2.443), history of sexually transmitted disease (AOR = 1.635,95%CI = 1.094–2.443), HIV sero status (AOR = 5.614, 95%CI = 2.595–12.144), perceived susceptibility to cervical cancer (AOR = 2.225, 95%CI = 1.308–3.783), perceived barriers to premalignant cervical lesions screening (AOR = 2.256, 95%CI = 1.447–3.517) and knowledge on cervical cancer and screening (AOR = 2.355, 95%CI = 1.155–4.802) were significant predictors of cervical cancer screening service uptake. Conclusion Magnitude of cervical cancer screening service uptake among age eligible women is still unacceptably low. Age of the women, history of multiple sexual partners and sexually transmitted disease, HIV sero-positivity, Knowledge, Perceived susceptibility and Perceived Barrier were important predictors of cervical cancer screening service uptake.
A bacterial quercetin oxidoreductase QuoA-mediated perturbation in the phenylpropanoid metabolic network increases lignification with a concomitant decrease in phenolamides in Arabidopsis

Metabolic perturbations by a gain-of-function approach provide a means to alter steady states of metabolites and query network properties, while keeping enzyme complexes intact. A combination of genetic and targeted metabolomics approach was used to understand the network properties of phenylpropanoid secondary metabolism pathways. A novel quercetin oxidoreductase, QuoA, from Pseudomonas putida, which converts quercetin to naringenin, thus effectively reversing the biosynthesis of quercetin through a de novo pathway, was expressed in Arabidopsis thaliana. QuoA transgenic lines selected for low, medium, and high expression levels of QuoA RNA had corresponding levels of QuoA activity and hypocotyl coloration resulting from increased anthocyanin accumulation. Stems of all three QuoA lines had increased tensile strength resulting from increased lignification. Sixteen metabolic intermediates from anthocyanin, lignin, and shikimate pathways had increased accumulation, of which 11 paralleled QuoA expression levels in the transgenic lines. The concomitant upregulation of the above pathways was explained by a significant downregulation of the phenolamide pathway and its precursor, spermidine. In a tt6 mutant line, lignifications as well as levels of the lignin pathway metabolites were much lower than those of QuoA transgenic lines. Unlike QuoA lines, phenolamides and spermidine were not affected in the tt6 line. Taken together, these results suggest that phenolamide pathway plays a major role in directing metabolic intermediates into the lignin pathway. Metabolic perturbations were accompanied by downregulation of five genes associated with branch-point enzymes and upregulation of their corresponding products. These results suggest that gene–metabolite pairs are likely to be co-ordinately regulated at critical branch points. Thus, these perturbations by a gain-of-function approach have uncovered novel properties of the phenylpropanoid metabolic network.
FSR 584 – a new globular cluster in the Galaxy?

Aims. We investigate the nature of the recently catalogued star cluster candidate FSR 584, which is projected in the direction of the molecular cloud W 3 and may be the nearest globular cluster to the Sun. Methods. 2MASS colour-magnitude diagrams, the stellar radial density profile, and proper motions are employed to derive fundamental and structural parameters. Results. The colour-magnitude diagram morphology and the radial density profile show that FSR584 is an old star cluster. With proper motions, the properties of FSR 584 are consistent with a metal-poor ([Fe/H] ≈ -2) globular cluster with a well-defined turnoff and evidence of a blue horizontal-branch. FSR 584 might be a Palomar-like halo globular cluster that is moving towards the Galactic plane, currently at ≈20 pc above it. The distance from the Sun is d ⊙ ≈ 1.4 kpc, and it is located at ≈ 1 kpc outside the Solar circle. The radial density profile is characterized by a core radius of R core = 0.3 ± 0.1 pc. However, we cannot exclude the possibility of an old open cluster. Conclusions. Near-infrared photometry coupled to proper motions support the scenario where FSR 584 is a new globular cluster in the Galaxy. The absorption is A v = 9.2 ± 0.6, which makes it a limiting object in the optical and explains why it has so far been overlooked.
A High Dynamic Range CMOS Variable Gain Amplifier for Mobile DTV Tuner

A high dynamic range RF variable gain amplifier (RFVGA) suitable for mobile digital television (DTV) tuners is presented. Variable gain is achieved using a capacitive attenuator and current-steering transconductance (Gm) stages, which provide high linearity with relatively low power consumption. A novel broadband input impedance matching scheme based on resistive shunt-feedback is proposed. This scheme allows the RFVGA to achieve a low noise figure. A gain control technique suitable for CMOS current-steering variable gain amplifiers is described; it features 1 dB per step resolution, independent of process and temperature variations. The chip is fabricated in six-metal 0.18mum CMOS technology and consumes 12.2mA current from 1.8V supply. The RFVGA achieves 16dB maximum gain, 33dB gain control range, a 4.3dB noise figure, and an IIP3 higher than 25dBm
Different Adenosine A 2 A Receptor Expression in Peripheral Cells from Elderly Patients with Vascular Dementia and Alzheimer ’ s Disease

The line between vascular dementia (VaD) and Alzheimer’s disease (AD) is often blurred. In this study we investigated whether adenosine A2A receptor (A2AR) expression can be used to differentiate between VaD and AD. We evaluated the expression of this receptor in the peripheral blood mononuclear cells of patients with VaD, mild cognitive impairment, AD, and controls. We found statistically significant lower levels of A2AR mRNA in VaD compared to AD subjects. These data suggest that A2AR expression may help in the differential diagnosis between VaD and AD.