Miracle-dz/UniFAR-FTUs huggingface dataset:
Biological and Magnetic Contrast Evaluation of Shape-Selective Mn–Fe Nanowires

One-dimensional composite Mn-Fe oxide nanostructures of different sizes (nanoneedles, nanorods, and nanowires) were prepared by a linker-induced organization of manganese-doped iron oxide nanoparticles. The nanostructures were obtained by the treatment of MnFe2O4 nanoparticles in the presence of cystamine. The average lengths of nanoneedle, nanorod, and nanowire are approximately 400, 800, and 1000 nm, respectively. High-resolution transmission electron microscopy (HR-TEM), energy-dispersive X-ray (EDX) spectroscopy, and inductively coupled plasma-optical emission spectroscopy (ICP-OES) were employed to characterize the morphologies and the elemental contents of the nanostructures. As an example of their potential applications, these nanostructures were explored as the cell-labeling agents for magnetic resonance imaging (MRI). The magnetic contrast properties of the nanostructures were characterized by a 1.5 T (Tesla) whole body MR system. 10 mug/mL of the nanostructures caused substantial negative contrast. After in vitro incubation, the nanostructures could be effectively incorporated into the cells of a monocyte/macrophage cell line (RAW264.7). These cellspsila viability and proliferation potential were not affected when the labeling concentration was less than 50 mug/mL.
Social Media Discourse in Disaster Situations: A Study of the Deadly July 21, 2012 Beijing Rainstorm

The July 21, 2012 Beijing rainstorm was a devastating catastrophe that caused 79 deaths, raising a great deal of attention all over the world. This research aims to explore emotions, attitudes, and views of citizens during the period surrounding this major rainstorm from a social media perspective. The results show that first the rainstorm-related posts of micro-bloggers in Beijing outnumbered that in Hebei, which also outnumbered the other provinces of China. Second, within the most reposted Weibo posts, a strong proportion is storm related and private bloggers are found to be more influential than government agencies. Third, the four district groups in the Beijing metropolitan region expressed a high-low-high sentiment before, during and after the rainstorm, respectively, and a quite uneven trend was observed across the different groups. Topics extracted through LDA modeling also exhibited a striking space-time pattern.
Parallel Combinatorial Optimization Heuristics with GPUs

Local search metaheuristics can be used for solving hard optimization problems in science, engineering, economics and technology. By using Local search metaheuristics, we could obtain satisfactory resolution (approximate optimum) in a reasonable time. However, it is still very CPU time-consuming when solving large problem instances. As graphic process units (GPUs) have been evolved to support general purpose computing, they are taken as a major accelerator in scientific and industrial computing. In this paper, we present an optimized parallel iterated local search hill climbing algorithm efficiently accelerated on GPUs and test the algorithm with a typical case study of the Graph bisection in computational science.
A Hybrid Approach for Speaker Tracking Based on TDOA and Data-Driven Models

The problem of speaker tracking in noisy and reverberant enclosures is addressed in this paper. We present a hybrid algorithm, combining traditional tracking schemes with a new learning-based approach. A state-space representation, consisting of a propagation and observation models, is learned from signals measured by several distributed microphone pairs. The proposed representation is based on two data modalities corresponding to high-dimensional acoustic features representing the full reverberant acoustic channels as well as low-dimensional time difference of arrival (TDOA) estimates. The state-space representation is accompanied by a statistical model based on a Gaussian process used to relate the variations of the acoustic channels to the physical variations of the associated source positions, thereby forming a data-driven propagation model for the source movement. In the observation model, the source positions are nonlinearly mapped to the associated TDOA readings. The obtained propagation and observation models establish the basis for employing an extended Kalman filter. The simulation results demonstrate the robustness of the proposed method in noisy and reverberant conditions.
Stereo vision based negative obstacle detection

Autonomous robots also known as Unmanned Ground Vehicles (UGV) is of broad and current interest, in both research and industry. Many studies have been done in stereo vision based UGV vision in positive obstacle detection and avoidance, but negative obstacle detection and avoidance remains less explored. In this paper, we propose a new method to detect negative obstacles incorporating the knowledge gained from stereo vision and detecting saliency on the ground plane. Experimental results show that our method works well on detecting negative obstacles.
A compact CPW-fed dual-band open-ended ZOR antenna based on CRLH TL for wireless applications

A compact CPW-fed dual-band open-ended zeroth order resonance (ZOR) antenna with the partial backed ground plane is investigated. The designed antenna comprises of a patch on which interdigital capacitor imposed on a patch, a thin stripline, which is connected with slotted stub and at bottom a rectangular patch connected with partial ground plane via thin inductive strip. By using the rectangular patch on the ground, the proposed antenna is able to generate dual bands, which satisfy the required bandwidth for wireless communication systems. The interdigital capacitance and gap between patch I and patch II forms an equivalent series capacitor (CLeq) while combine effect of top and bottom strip line forms shunt inductance (LLeq) which verifies the composite right/left-handed transmission line based on metamaterial MTM). The shunt capacitance (CR) is obtained between partial backed ground plane and CPW feed, whereas the series inductance (LR) is realized by signal patch. Presented antenna follows open ended boundary condition, so ZOR frequency depends on shunt components. The proposed dual-band MTM antenna with rectangular stub exhibits a simulated input impedance bandwidth (S11 < −10 dB) of 10.08% (1.13–1.25 GHz) for the first band and 35.79% (3.44–4.94 GHz) for the second band, respectively. The electrical size of designing antenna is 0.1 λ0 × 0.16λ0 × 0. 0064λ0 where λ0 is free space wavelength indicates its compactness. It is observed that at ZOR frequency (1.2GHz) and other frequency (3.9GHz), the proposed antenna showing dipolar type radiation pattern in xz-plane whereas in yz-plane it shows omnidirectional radiation pattern. The difference between co- and cross-pol levels in xz-plane is −41.6dB at ZOR frequency. In addition, the dual-band MTM antenna offers good gain and radiation efficiency in the working band which is useful for modern wireless communications such as L/S and C band applications.
Emerging trends and new developments in monoclonal antibodies: A scientometric analysis (1980–2016)

ABSTRACT This article aims to explore the intellectual landscape of the study of monoclonal antibody (mAb), mainly to identify thematic trends, landmark articles and emerging trends involving mAb. This work is based on 4 sets of bibliographic records retrieved from the Web of Science. The final data set, consisting of 7,385 bibliographic records, was combined from the 4 individual data sets. This study explores the document co-citation clusters of 7,385 bibliographic records to identify the origin of mAb and the hot research specialty of this domain by applying CiteSpace software. We examined the mAb evolution from 4 perspectives: (1) Clusters of cited references regarding mAb; (2) Cited authors as contributors to mAb research; (3) Institutions participating in mAb research; and (4) Cited journals regarding mAb. The technical development, drug development and clinical applications of mAbs were analyzed. Through data analysis, we have identified the new directions for the exploration of mAbs, interactions between mAb technologies and diseases, and evolving global collaboration among institutions.
Efficient modular glass box software model checking

Glass box software model checking incorporates novel techniques to identify similarities in the state space of a model checker and safely prune large numbers of redundant states without explicitly checking them. It is significantly more efficient than other software model checking approaches for checking certain kinds of programs and program properties. This paper presents Pipal, a system for modular glass box software model checking. Extending glass box software model checking to perform modular checking is important to further improve its scalability. It is nontrivial because unlike traditional software model checkers such as Java PathFinder (JPF) and CMC, a glass box software model checker does not check every state separately---instead, it checks a large set of states together in each step. We present a solution and demonstrate Pipal's effectiveness on a variety of programs.
Is students' satisfaction in electrical engineering courses influenced by gender?

Electrical/Electronic Engineering courses are often regarded as male courses. In this paper it is presented a study conducted in two Portuguese and two Brazilian high education institutions (six courses) where the goal was to analyze if gender affects students' perceptions and satisfaction regarding Electrical/Electronic Engineering courses. The analysis was based on 654 questionnaires rating 44 items (among the six groups: Teacher Involvement Perception, Student Interest, Student-Teacher interaction, Course organization and functioning, Infrastructures, and Overall satisfaction). The investigation was performed by year, from the first to the third year (1st cycle) and considering the six programs. Based on students' perceptions, some items showed differences that were significant, namely the ones regarding how teachers and students interact and how teachers challenge students.
UAV image matching based on surf feature and harris corner algorithm

The Speed-up Robust Features (SURF) algorithm has a good scale invariance in the image matching process. Its speed is fast, but it is not stable enough in the feature point extraction. Harris algorithm is an efficient corner detection algorithm, but it cannot handle the issue of scale variance in the image. Therefore, this paper considers the combination of the Speedup Robust Features algorithm and Harris algorithm in the image matching process. First, we use the Harris algorithm to extract the corner points of the two images and obtain the feature point set. Then we use the SURF algorithm to extract the feature points of the two corner set and obtain the new point set. Finally, we use the random sample consensus method to remove the error points, achieve an exact match points set and match the two images. Experiments show that the combination of the two algorithms can improve the quality of Unmanned Aerial Vehicle image matching with high efficiency and strong robustness.
Predicition models for delayed graft function: external validation on The Dutch Prospective Renal Transplantation Registry

Background Delayed graft function (DGF) is a common complication after kidney transplantation in the era of accepting an equal number of brain- and circulatory-death donor kidneys in the Netherlands. To identify those cases with an increased risk of developing DGF, various multivariable algorithms have been proposed. The objective was to validate the reproducibility of four predictive algorithms by Irish et al. (A risk prediction model for delayed graft function in the current era of deceased donor renal transplantation. Am J Transplant 2010;10:2279-2286) (USA), Jeldres et al. (Prediction of delayed graft function after renal transplantation. Can Urol Assoc J 2009;3:377-382) (Canada), Chapal et al. (A useful scoring system for the prediction and management of delayed graft function following kidney transplantation from cadaveric donors. Kidney Int 2014;86:1130-1139) (France) and Zaza et al. (Predictive model for delayed graft function based on easily available pre-renal transplant variables. Intern Emerg Med 2015;10:135-141) (Italy) according to a novel framework for external validation. Methods We conducted a prospective observational study with data from the Dutch Organ Transplantation Registry (NOTR). Renal transplant recipients from all eight Dutch academic medical centers between 2002 and 2012 who received a deceased allograft were included (N = 3333). The four prediction algorithms were reconstructed from donor, recipient and transplantation data. Their predictive value for DGF was validated by c-statistics, calibration statistics and net benefit analysis. Case-mix (un)relatedness was investigated with a membership model and mean and standard deviation of the linear predictor. Results The prevalence of DGF was 37%. Despite a significantly different case-mix, the US algorithm by Irish was best reproducible, with a c-index of 0.761 (range 0.756 - 0.762), and well-calibrated over the complete range of predicted probabilities of having DGF. The US model had a net benefit of 0.242 at a threshold probability of 0.25, compared with 0.089 net benefit for the same threshold in the original study, equivalent to correctly identifying DGF in 24 cases per 100 patients (true positive results) without an increase in the number of false-positive results. Conclusions The US model by Irish et al. was generalizable and best transportable to Dutch recipients with a deceased donor kidney. The algorithm detects an increased risk of DGF after allocation and enables us to improve individual patient management.
How cognitive biases works on word alignment

The gap between the daily reasoning and logical reasoning is called cognitive biases. We applied this cognitive biases to word alignment. At first, we expected that cognitive bias would improve the alignment error rate (AER). However, cognitive biases doesn't improve the performance compared to the standard method for word alignment. While applying cognitive bias to word alignment simply doesn't outperform the accuracy by existing basic method, they showed better performance than most fundamental methods which use co-occurrence measurement.
System for quantitative analysis of coronary calcification via electron-beam computed tomography

Electron beam computed tomography (EBCT) has provided a new tool for identification and possible quantification of coronary arterial plaque calcium. EBCT is the only imaging modality currently available which generates images of the spatial, temporal, and contrast resolution required for the identification of small foci of calcium and the potential for accurate quantification of calcium. Meanwhile, interest in quantification of coronary arterial calcium via EBCT and its correlation with severity of coronary atherosclerosis is increasing. Data remain inconclusive, but it appears that the reproducibility of quantitative grading of the extent of calcification by EBCT may be limited, in part, by the arbitrary nature of the scoring algorithm employed within the analysis tools currently provided by the EBCT manufacturer. It has not been possible to objectively determine optimum values for minimum plaque area and brightness threshold or to quantitatively determine whether single optimal values even exist. Also, although the current system tabulates the score, area, and mean attenuation for each plaque, the locations of the plaques are not reported.
Stance classification of multi-perspective consumer health information

While search engines are effective in answering direct factual questions such as, 'What are the symptoms of a disease X? ', they are not so effective in addressing complex consumer health queries, which do not have a single definitive answer, such as, 'Is treatment X effective for disease Y? '. Instead, the users are presented with a vast number of search results with often contradictory perspectives and no definitive answer. We denote such queries as Multi-Perspective Consumer Health Information (MPCHI) queries for which there is no single 'Yes or No' answer. While ascertaining the credibility of the claims requires domain expertise, an efficient categorization of the search results according to their stance (support or oppose) to the queries will help the searcher in decision making. Hence, this paper focuses on the problem of stance classification for MPCHI data at sentence level, presenting a new data set for MPCHI queries. Unlike typical debate or argumentative text, the linguistic characteristics of MPCHI is quite different, with extensive use of scientific formal language and absence of opinion bearing words. Hence, such inherently different characteristic of MPCHI text requires going beyond traditional Bag of Words (BoW) features for stance classification. Hence, we propose using a rich non-traditional set of features such as medical semantic relations, stance vectors, sentiment polarity, textual entailment, and study their impact on MPCHI stance classification using an SVM and a neural network classifier. We find that using novel non-traditional features improves MPCHI stance classification performance over traditional BoW model by 24% for the SVM classifier, and 44% for the neural network classifier respectively, for the best feature combination.
Alcohol use among recent immigrant Latino/a youth: acculturation, gender, and the Theory of Reasoned Action

ABSTRACT Objective. Latino/a youth are at risk for alcohol use. This risk seems to rise with increasing US cultural orientation and decreasing Latino cultural orientation, especially among girls. To ascertain how acculturation may influence Latino/a youth alcohol use, we integrated an expanded multi-domain model of acculturation with the Theory of Reasoned Action. Design. Participants were 302 recent Latino/a immigrant youth (141 girls, 160 boys; 152 from Miami, 150 from Los Angeles) who completed surveys at 4 time points. Youth completed measures of acculturation, attitudes toward drinking, perceived subjective norms regarding alcohol use, intention to drink, and alcohol use. Results. Structural equation modeling indicated that collectivistic values predicted more perceived disapproval of drinking, which negatively predicted intention to drink. Intention to drink predicted elevated alcohol use. Conclusion. Although the association between collectivistic values and social disapproval of drinking was relatively small (β = .19, p < .05), findings suggest that collectivistic values may help protect Latino/a immigrant youth from alcohol use by influencing their perceived social disapproval of drinking, leading to lower intention to drink. Educational preventive interventions aimed at reducing or preventing alcohol use in recent Latino/a immigrant youth could promote collectivistic values and disseminate messages about the negative consequences of drinking.
A terrain-based hybrid approach towards DEM interpolation

Visualization of geospatial entities generally adopts digital elevation models (DEMs) that are interpolated to establish 3D co-ordinates for the entire terrain. The accuracy of the generated terrain models depends on the type of interpolation mechanism adopted. Generally available methods in this context use point data values and their distribution; however, terrain characteristics such as shape are not generally considered in the interpolation. We propose an adaptive interpolation strategy that considers the terrain features for improving the estimation accuracy. In this research, we have compared the proposed approach with different contemporary interpolation techniques, such as inverse distance weighting, kriging (KRG), nearest neighbour and spline methods. Comparative analysis of the proposed technique with contemporary approaches revealed that considerable success has been achieved with the procedure. Investigations have also been done to analyse the sensitivity of the approach to terrain smoothness.
A new iterative online dynamic identification method of robots from only force/torque data

This paper deals with a new iterative online dynamic identification method of robot. The robot is closed-loop controlled with a computed torque control (CTC) law that linearizes and decouples the non-linear and coupled dynamics of the robot. Usually, the parameters of the Inverse Dynamic Model (IDM), which calculates the CTC, are identified with an off-line identification method which uses the Inverse Dynamic Identification Model (IDIM) to calculate the joint force/torque as a linear relation in the dynamic parameters. This allows using linear least-squares techniques to estimate the parameters that minimize the 2-norm error between the actual joint force/torque and the calculated joint force/torque (IDIM-LS method). This method requires well tuned band-pass filtering of the joint position to estimate the joint velocity and acceleration and cannot take into account variations of the parameters (friction, payload) in CTC. The new method overcomes these 2 drawbacks. The IDIM-LS solution is periodically calculated over a moving time window to update the IDM of the CTC, and the IDIM is calculated with the noise-free data of the trajectory generator, which avoids using the noisy derivatives of the actual joint position. This method called IDIM-ILIC, works like an Iterative Learning Identification and Control procedure. An experimental setup on a prismatic joint validates the procedure with stationary parameters and with a variation of the payload.
Intersecting Social Inequalities and Body Mass Index Trajectories from Adolescence to Early Adulthood

This study combines multiple-hierarchy stratification and life course perspectives to address two research questions critical to understanding U.S. young adult health. First, to what extent are racial-ethnic inequalities in body mass index (BMI) gendered and/or classed? Second, do racial-ethnic, gender, and socioeconomic inequalities in BMI widen or persist between adolescence and early adulthood? Using data from the National Longitudinal Survey of Youth 1997 cohort and growth curve models, results suggest that among white, black, and Hispanic American men and women ages 13 to 31, racial-ethnic inequality in BMI is greatest among women. Black women experience the highest adolescent BMI and the greatest increases in BMI with age. Furthermore, socioeconomic resources are less protective against weight gain for blacks and Hispanics, with the nature of these relationships varying by gender. Findings present a more nuanced picture of health inequality that renders visible the disproportionate burden of poor health experienced by marginalized groups.