Miracle-dz/UniFAR-FTUs huggingface dataset:
Demonstration of Hopf-link semimetal bands with superconducting circuits

Hopf-link semimetals exhibit exotic gapless band structures with fascinating topological properties, which have never been observed in nature. Here we demonstrate nodal lines with topological form of Hopf-link chains in artificial semimetal-bands. Driving superconducting quantum circuits with elaborately designed microwave fields, we mapped the momentum space of a lattice to a parameter space of the Hamiltonian for a Hopf-link semimetal. By measuring the energy spectrum, we directly imaged nodal lines in cubic lattices. By tuning the driving fields, we adjusted various parameters of Hamiltonian. Important topological features, such as link-unlink topological transitions and the robustness of the Hopf-link chain structure were investigated. Moreover, we extracted the linking number by detecting the Berry phase associated with different loops encircling nodal lines. This topological invariant clearly reveals the nontrivial topology of the Hopf-link semimetal. Our results provide knowledge for developing new materials and quantum devices.
A regression approach for estimating procurement cost

Cost growth in Department of Defense (DoD) weapon systems continues to be a scrutinized area of concern. One way to minimize unexpected cost growth is to derive better and more realistic cost estimates. In this vein, cost estimators have many analytical tools to ply. Previous research has demonstrated the use of a two-step logistic and multiple regression methodology to aid in this endeavor. We investigate and expand this methodology to cost growth in procurement dollar accounts for the Engineering and Manufacturing Development phase of DoD acquisition. We develop and present two salient statistical models for cost estimators to at least consider if not use in mitigating cost growth for existing and future government acquisition programs.
An extension of the identification algorithm for feedback active control of incomplete measured systems

In a previous paper [1], the identification algorithm is presented for feedback active controlled systems. However, this method can only be applied to complete measured systems. The aim of this paper is to present a combination of the identification algorithm and the modal superposition method to control the incomplete measured systems. The system response is expanded by modal eigenfunction technique. The external excitation acting on some first modes is identified with a time delay and with a small error depending on the sensor locations. Then the control forces will be generated to balance the identified excitations. A numerical simulation is applied to an eight story building subjected to base acceleration.
High Energy Positrons and Gamma Radiation from Decaying Constituents of a two-component Dark Atom Model

We study a two component dark matter candidate inspired by the Minimal Walking Technicolor model. Dark matter consists of a dominant SIMP-like dark atom component made of bound states between primordial helium nuclei and a doubly charged technilepton, and a small WIMP-like component made of another dark atom bound state between a doubly charged technibaryon and a technilepton. This scenario is consistent with direct search experimental ndings because the dominant SIMP component interacts too strongly to reach the depths of current detectors with sucient energy to recoil and the WIMP-like component is too small to cause signicant amount of events. In this context a metastable technibaryon that decays to e + e + , + + and + + can in principle explain the observed positron excess by AMS-02 and PAMELA, while being consistent with the photon
QATAR UNIVERSITY COLLEGE OF ENGINEERING BUILDING A TEST COLLECTION FOR SIGNIFICANT-EVENT DETECTION IN ARABIC TWEETS BY HIND ALI AL-MEREKHI

With the increasing popularity of microblogging services like Twitter, researchers discovered a rich medium for tackling real-life problems like event detection. However, event detection in Twitter is often obstructed by the lack of public evaluation mechanisms such as test collections (set of tweets, labels, and queries to measure the effectiveness of an information retrieval system). The problem is more evident when non-English languages, e.g., Arabic, are concerned. With the recent surge of significant events in the Arab world, news agencies and decision makers rely on Twitters microblogging service to obtain recent information on events. In this thesis, we address the problem of building a test collection of Arabic tweets (named EveTAR) for the task of event detection. To build EveTAR, we first adopted an adequate definition of an event, which is a significant occurrence that takes place at a certain time. An occurrence is significant if there are news articles about it. We collected Arabic tweets using Twitter’s streaming API. Then, we identified a set of events from the Arabic data collection using Wikipedias current events portal. Corresponding tweets were extracted by querying the Arabic data collection with a set of manually-constructed queries. To obtain relevance judgments for those tweets, we leveraged CrowdFlower’s crowdsourcing platform. Over a period of 4 weeks, we crawled over 590M tweets, from which we identified 66 events that cover 8 different categories and gathered more than 134k relevance judgments. Each event contains an average of 779 relevant tweets. Over all events, we got an average Kappa of 0.6, which is a substantially acceptable value. EveTAR was used to evaluate three state-of-the-art event detection algorithms. The best performing algorithms achieved 0.60 in F1 measure and 0.80 in both precision and recall. We plan to make our test collection available for research, including events description, manually-crafted queries to extract potentially-relevant tweets, and all judgments per tweet. EveTAR is the first Arabic test collection built from scratch for the task of event detection. Additionally, we show in our experiments that it supports other tasks like ad-hoc search.
Transferring Value Functions via Variational Methods

We consider the problem of transferring value functions in reinforcement learning. We propose an approach that uses the given source tasks to learn a prior distribution over optimal value functions and provide an efficient variational approximation of the corresponding posterior in a new target task. We show our approach to be general, in the sense that it can be combined with complex parametric function approximators and distribution models, while providing two practical algorithms based on Gaussians and Gaussian mixtures. We theoretically analyze them by deriving a finite-sample analysis and provide a comprehensive empirical evaluation in four different domains.
Relationship between religious orientation and moral sensitivity in the decision making process among nurses

Decision making is an important part of nurses’ responsibilities in providing clinical interventions for patients. Religion is among the moral factors that affect the performance and clinical decision making of nurses. The present study aimed to investigate the relationship between religious orientation and moral sensitivity in the decision making process among nurses. This study was a cross-sectional, analytical research that was carried out on 170 nurses working in Gonabad hospitals. Subjects were selected by census, and data were collected using the Allport Religious Orientation Scale and the Moral Sensitivity Questionnaire. Data analysis was performed using SPSS version 16. In the present study, internal religious orientation mean was 36.39 ± 4.65, external religious orientation mean was 33.77 ± 6.89 and moral sensitivity mean was 159.21 ± 14.1, and therefore internal religious orientation was higher among the nursing staff. Moreover, Pearson's correlation test showed that there was a significant, positive relationship between internal religious orientation and moral sensitivity (P = 0.01, r = 0.17). Nevertheless, no significant relationship was found between external religious orientation and moral sensitivity (P = 0.86, r = 0.01). Based on the findings of this study, it seems that authorities need to develop and implement strategies to educate nurses on morality and spirituality, since members of this profession play an important part in the health and well-being of the community.
Influence of Span Ratio on Modals and Mode Direction Factors of Curved Box-girder Bridges

The vibration mode of curved box-girder bridge was decomposed into 4 mode direction factors by energy ratios. 4 twospan curved box-girder bridge models were built taking span ratio as parameter. The modal characteristics and the mode direction factors were analyzed along with varying span ratio. It is advised that the stiffness increases more slowly than mass for curved box-girder bridge, the vertical bending mode must be taken account, the torsion mode factor decreases with the decreasing of span ratio, the summit of longitudinal movement direction mode factor appears on higher modal orders, and high order modals must be considered when designing. Keywords-span ratio; curved box-girder bridges; modal; mode direction factor
Primary and successive events in the Madden–Julian Oscillation

Conventional analyses of the MJO tend to produce a repeating cycle, such that any particular feature cannot be unambiguously attributed to the current or previous event. We take advantage of the sporadic nature of the MJO and classify each observed Madden–Julian (MJ) event as either primary, with no immediately preceding MJ event, or successive, which does immediately follow a preceding event. 40% of MJ events are primary events. Precursor features of the primary events can be unambiguously attributed to that event. A suppressed convective anomaly grows and decays in situ over the Indian Ocean, prior to the start of most primary MJ events. An associated mid‐tropospheric temperature anomaly destabilises the atmosphere, leading to the generation of the active MJ event. Hence, primary MJ events appear to be thermodynamically triggered by a previous dry period, although stochastic forcing may also be important. Other theories predict that boundary‐layer convergence, humidity, propagation of dynamical structures around the Equator, sea surface temperatures, and lateral forcing by extratropical transients may all be important in triggering an event. Although precursor signals from these mechanisms are diagnosed from reanalysis and satellite observational data in the successive MJ events, they are all absent in the primary MJ events. Hence, it appears that these apparent precursor signals are part of the MJO once it is established, but do not play a role in the spontaneous generation of the MJO. The most frequent starting location of the primary events is the Indian Ocean, but over half of them start elsewhere, from the maritime continent to the western Pacific. Copyright © 2008 Royal Meteorological Society
Real-Time Visualization System for Deep-Sea Surveying

Remote robotic exploration holds vast potential for gaining knowledge about extreme environments, which is difficult to be accessed by humans. In the last two decades, various underwater devices were developed for detecting the mines and mine-like objects in the deep-sea environment. However, there are some problems in recent equipment, like poor accuracy of mineral objects detection, without real-time processing, and low resolution of underwater video frames. Consequently, the underwater objects recognition is a difficult task, because the physical properties of the medium, the captured video frames, are distorted seriously. In this paper, we are considering use of the modern image processing methods to determine the mineral location and to recognize the mineral actually within a little computation complex. We firstly analyze the recent underwater imaging models and propose a novel underwater optical imaging model, which is much closer to the light propagation model in the underwater environment. In our imaging system, we remove the electrical noise by dual-tree complex wavelet transform. And then we solve the nonuniform illumination of artificial lights by fast guided trilateral bilateral filter and recover the image color through automatic color equalization. Finally, a shape-based mineral recognition algorithm is proposed for underwater objects detection. These methods are designed for real-time execution on limited-memory platforms. This pipeline is suitable for detecting underwater objects in practice by our experiences. The initial results are presented and experiments demonstrate the effectiveness of the proposed real-time visualization system.
Extraction and Mining of Video Feature in Sport Videos

On the basis of analyzing the characteristics of sports video, the parameters of the feature generation are adjusted. According to the sports video library, three features of SD-VLAD (Soft Distribution-Vectors of Locally Aggregated Descriptors), BOC (Bag of Color) and shot type were selected as the description information of the image; the appropriate parameters were selected through experiments; the best parameter configuration for soccer video library was given. In order to detect the influence of parameters in SD-VLAD and BOC descriptors on the recognition effect of descriptors, and select the appropriate parameters, the experiment was carried out in part of the library of search web, and the experimental results were analyzed.
CT- guided transthoracic lung biopsy: a comparison of a co-axial core biopsy with non co-axial fine needle aspiration biopsy and factors influencing complications

Poster: "ECR 2018 / C-2636 / CT- guided transthoracic lung biopsy: a comparison of a co-axial core biopsy with non co-axial fine needle aspiration biopsy and factors influencing complications" by: "A. PEKER1, A. Gulcu1, N. S. GEZER1, M. K. SIMSEK1, E. YILMAZ1, P. Balci2; 1Izmir/TR, 2Izmir, . /TR"
Integrating subsistence producers from developing and emerging countries with the world market: the roles of Reactive and Proactive market orientation

Integrating subsistence producers from developing and emerging countries with the world market is seen as a way out poverty. Market integration analyses have been predominately the domain of economists who generally indicate that only socioeconomic and institutional factors such as cost of doing business and trade liberalizations exert influence on subsistence producers integration with markets. The extant literature has however overlooked the role of market orientation (MO) as a main driver of market integration of subsistence producers. Based on three streams of literatures (market integration, MO and embeddedness literatures), this study develops a framework in which MO is on the one hand seen as an antecedent of market integration and on the other hand rooted in embeededness of transaction and social relationships. A distinction is made between reactive and proactive MO. The framework is tested on data obtained from a sample of 183 shrimp fishers from Benin using a structural equation modeling. The results show that not proactive but reactive MO is the main driver of export market integration. In turn export market leads to better livelihood performance of shrimp fishers. Results also show that both MO dimensions are embedded-dependent, with favor support to reactive MO.
Reconciling Conflicting Evidence on the Origins of Comparative Development: A Finite Mixture Model Approach

In this paper, I revisit the controversy over the fundamental sources of comparative development. In contrast to much of the previous literature, my focus is on the appropriate specification of the empirical strategy. Using a finite mixture model approach and Monte Carlo simulations, I demonstrate that the standard linear estimation strategy may be mis-specified and as a result is likely to obscure the true effects of the variables used to explain cross-country income differences. My findings could potentially reconcile apparently conflicting results from the existing literature on the role of geography and institutions in comparative development.
How Personality Drives Network Benefits: Need for Cognition, Social Networks, and Information Amount

Notions of information search and information amount are central to several streams of management literature, including those on sensemaking and social networks. However, researchers have claimed that little is known about how managers actually gather information, and what factors determine the effectiveness of their search. This article proposes and tests a model of structural, personality, and contextual factors that affect the amount of information that managers gather, and shows how personality drives the benefits that individuals receive from their networks. Using a longitudinal research design in which managers record details of their search process over the course of several months, it specifically measures at a micro-level the time managers spend searching for information, as well as the amount of relevant and diverse information they gather. Results show that managers' need for cognition interacts with their network size and average tie strength to determine the amount of information they gather .
Risk as Impediment to Privatization? The Role of Collective Fields in Extended Agricultural Households

As in the case of cooperatives, collective fields in extended agricultural households act as an insurance device but entail inefficiencies arising from the incentives to free ride on coworkers’ efforts. Privatization provides good incentives but decreases the level of risk sharing. The classical analysis of this trade-off rules out another major risk-sharing mechanism, namely income transfers. This paper is a first attempt to merge the two insurance mechanisms: collective production, which is plagued by free riding, and income transfers, which are hampered by limited commitment. Privatization of land is shown to interact with incentives to abide by the insurance agreement, so that the trade-off between risk sharing and production may or may not be maintained with income transfers. We show that an increase in the value of the household members’ exit option or a decrease in patience decreases the optimal rate of privatization, while larger households are more likely to privatize land.
Estimated annual economic loss from organ condemnation, decreased carcass weight and milk yield due to bovine hydatidosis (Echinococcus granulosus, Batsch, 1786) in Ethiopia.

Post-slaughter survey data collected over a period of 15 years (1985-1999) by the Faculty of Veterinary Medicine (FVM), Addis Ababa University (AAU) were used as a basis for the analysis of estimation of the economic significance of bovine hydatidosis in Ethiopia. The analysis output revealed a 35.15% mean annual prevalence of bovine hydatidosis. The total estimated economic loss from organ condemnation, carcass weight and milk yield decrease amounts to 1,691,266,200 ETB (101,203,734 USD). This is equivalent to a loss of 475.40 ETB (28.45 USD) per every infected slaughtered cattle and 249.00 ETB (14.90 USD) per every infected milking cow. The results of the present study showed that hydatidosis is a highly prevalent cattle disease in Ethiopia with considerable direct and indirect economic losses. The magnitude of loss per infected subject warrants the need to mount an integrated nation-wide hydatidosis control program involving public education, canine health care, proper handling and disposal of infected organs, stray dog population management and institution of strict standard operating procedures (SOPs) in slaughterhouses and public health legislative reinforcements. The authors recommend that hydatidosis control program should be designed and implemented in the context on ‘One Health’ initiative by integrating it with other zoonotic disease control program and in partnerships with animal and public health actors as well as other relevant stakeholders.
Estimating Dhaka Stock Market Volatility: A Comparison between Standard and Asymmetric GARCH Models

This paper compares and estimates standard and asymmetric GARCH models with daily returns data of the DSI index (All Share Price Index) of the Dhaka Stock Exchange from 28 March 2005 to 30 November 201 0. The maximum likelihood estimation (MLE) technique is used to estimate the parameters of the chosen models. Results indicate that GARCH has lower log-likelihood than the asymmetric GJR-GARCH model, which implies that GJRGARCH model is a better performing model to estimate and to forecast volatility. Results from other hypothesis tests indicate that volatility process has unit root i.e. volatility process is nonstationary, expected returns do not always depend on volatility, and the conditional variance (volatility) of future asset price is a symmetric function of changes in price at Dhaka stock market.
Demand evidence and think critically: Building research excellence in tomorrow’s scientists

Biomedical science students represent an essential resource for the future scientific workforce. As research scientists are more than their content specialities they must embody core values that govern the development and certification of knowledge. However, the ethos of science is often overlooked in University education in favour of increasing levels of content delivery. This results in graduates with rich content knowledge but limited ability in the generic skills and ethos of a research scientist. To address these concerns, authentic scientific research and communication experiences based on an apprenticeship model were developed to provide realistic insight into scientific ethos and to foster critical thinking, analytical and communication skills. By adapting transitional pedagogy commonly associated with First Year Experience and the Research Skills Development framework, our research-led teaching initiatives allow students who are considering a career in science true insight into what such a career entails. Students take on the role of ‘scientists in training’, stepping out of their academic comfort zone within a safe environment where ‘perfect’ data are no longer generated and adapting to setbacks forms part of the learning process. Success is reflected by positive feedback from students and peers, increased assessment performance, and graduates continuing in science careers.
Predicting the Tensile and Air Permeability Properties of Woven FabricsUsing Artificial Neural Network and Linear Regression Models

The objective of this paper is to investigate the predictability of some of woven fabric properties using artificial neural network (ANN) and regression models. For achieving this purpose, a neural network with three layers was adopted. The regression model was of type a multiple – linear regression one. The independent variables were weft yarn count, twist multiplier and weft density; and the dependent ones were tensile strength, breaking extension and air permeability of the woven fabrics. The ANN and regression models were assessed using the Root means square error (RMSE) and the coefficient of determination (R2-value). The findings of this study revealed that ANN is superior to regression model in predicting the woven fabric properties.