Miracle-dz/UniFAR-FTUs huggingface dataset:
Design , fabrication and characterization of drug delivery systems based on lab-ona-chip technology

Lab-on-a-chip technology is an emerging field evolving from the recent advances of microand nanotechnologies. The technology allows the integration of various components into a single microdevice. Microfluidics, the science and engineering of fluid flow in microscale, is the enabling underlying concept for lab-on-a-chip technology. The present paper reviews the design, fabrication and characterization of drug delivery systems based on this amazing technology. The systems are categorized and discussed according to the scales at which the drug is administered. Starting with the fundamentals on scaling laws of mass transfer and basic fabrication techniques, the paper reviews and discusses drug delivery devices for cellular, tissue and organism levels. At the cellular level, a concentration gradient generator integrated with a cell culture platform is the main drug delivery scheme of interest. At the tissue level, the synthesis of smart particles as drug carriers using lab-on-a-chip technology is the main focus of recent developments. At the organism level, microneedles and implantable devices with fluid-handling components are the main drug delivery systems. For drug delivery to a small organism that can fit into a microchip, devices similar to those of cellular level can be used.
Confinement transition in a Kitaev-like honeycomb model with bond anisotropy

The honeycomb $K-\Gamma$ model is known to have both deconfined spin liquid and confined phases. We study here a confinement transition between the Kitaev spin liquid and a dimerized phase in the limit of strong bond anisotropy. By partially projecting out Majorana states we are able to map the model onto a model of weakly coupled Ising chains in a transverse field. Within this mapping the ordered Ising phase corresponds to the condensation of $Z_2$ fluxes, or confinement. Our results may improve our understanding of the extensively studied spin liquid candidate material $\alpha$-RuCl$_3$, where $K-\Gamma$ interactions are dominant.
Psychometric Evaluation Of The Turkish Version Of The Defensive Pessimism Scale

Problem Statement: Much theoretical interest and attention has been given to the cognitive strategies that individuals use in achievement related domains. Defensive-pessimism is a functional strategy in a demanding academic setting for those who easily become anxious. However, this is a new concept for Turkish researchers and there is currently no instrument for defensive pessimism. Therefore, an instrument is needed to measure defensive pessimism. Purpose of Study: The purpose of this study was to provide construct validation and normative data for the Turkish adaptation of DefensivePessimism Scale (DPS) in a sample of Turkish university students. In the educational settings, many teachers are not aware of defensive pessimism as a cognitive strategy because it is not included in their education. The use of this scale may have many implications in the educational setting. Method: First, translation and back translation of the questionnaire were carried out. Thereafter, LISREL 8.30 was used to perform a confirmatory factor analysis. Confirmatory factor analysis was used as this is a theorydriven method for assessing the fit of a-priori-specified model. SPSS 17.0 was also used for descriptive statistics. Findings and Results: Confirmatory factor analysis was conducted for both single and two-factor models of the Turkish version of the DPS. For the single model, after a slight modification, the following indices were obtained: χ² (51) = 121.04, p <. 01; χ²/df = 2.37; GFI =. 93; AGFI =. 89; RMSEA =. 07; and CFI =. 95. For the two factor model, χ² (51) = 138.39, p <. 01; χ²/df = 2.71; GFI =. 91; AGFI =. 87; RMSEA =. 08; and CFI =. 94 were found. The internal consistency of the DPS was assessed by Cronbach’s alpha, which was.81 for the total; . 78 for the negative expectation; and . 64 for reflectivity. McDonald’s Omega was also computed for the single
QMiner : Data Analytics Platform for Processing Streams of Structured and Unstructured Data

QMiner is an open source analytics platform for performing large scale data analysis written in C++ and exposed via a Javascript API. The paper presents five main design elements which focus on focus on storage, online and real-time processing as well as fast prototyping and give QMiner unique advantages as a data analytics platform for processing streams of structured and unstructured data. These design elements are incorporated in a five layer architecture represented by 1) storage and indexing, 2) stream aggregate, 3) feature extractor, 4) linear algebra and 5) analytics layers. The functionality of the platform is demonstrated by three representative usage examples containing code fragments: text classification, time series prediction and community detection in graphs.
Analysis on Aircraft Sortie Generation Rate Based on Multi-class Closed Queueing Network

Abstract — In order to research the sortie generation rate (SGR) of carrier-based aircrafts, an analytical method based on closed queueing network was put forward. A multi-class, multi-server non-preemptive (or Head of Line, HOL) closed queueing network model was analyzed. As the exact mathematical solution is not available, an approximate method based on reduced work-load assumption and mean value analysis (MVA) iteration was used to gain the performance of the network. As a result, this approximation method can provide the marginal distribution of customers at each service facility of the queueing network. Numerical results that show that the accuracy of the method is good was given. Keywords-SGR; closed queueing network; HOL; MVA I. INTRODUCTION The sortie generation rate (SGR) of carrier-borne crafts is the key component of aircraft carrier’s ability to project power against any enemy.In order to gain insight into SGR, queueing theory based on Fork-join closed queueing network have been used
Teaching and Learning Health Justice: Best Practices and Recommendations for Innovation.

We describe the development and implementation of an online graduate bioethics program that weaves a theme of health justice throughout the curriculum. Our account relies on a constructionist model of curriculum development and adult teaching and learning theory. Our curriculum draws upon core values of Jesuit higher education, including content with particular attention to justice for marginalized and vulnerable members of society and pedagogical strategies that cultivate students’ capacities for critical thinking and engagement with ethics and justice issues in the context of healthcare. We propose four major contributions from the health justice literature as key content areas for inclusion in bioethics programs interested in focusing on health justice. We identify gaps in the literature and suggest how they might be addressed. Finally, we give examples of content, pedagogy, and preliminary findings from specific courses in our program, all in hopes of stimulating more conversation about how students learn about health justice.
Inconsistent food safety pressures complicate environmental conservation for California produce growers

Controlling human pathogens on fresh vegetables, fruits and nuts is imperative for California growers. A range of rules and guidelines have been developed since 2006, when a widespread outbreak of E. coli O157:H7 was linked to bagged spinach grown in California. Growers face pressure from industry and government sources to adopt specific control measures on their farms, resulting in a complex, shifting set of demands, some of which conflict with environmental stewardship. We surveyed 588 California produce growers about on-farm practices related to food safety and conservation. Nearly all respondents considered both food safety and environmental protection to be important responsibilities for their farms. Responses indicate that clearing vegetation to create buffers around cropped fields, removing vegetation from ditches and ponds, and using poison bait and wildlife fences are commonly used practices intended to reduce wildlife movements onto farm fields. The survey also revealed that on-farm practices vary substantially even among farms with similar characteristics. This variability suggests inconsistencies in food safety requirements, auditors’ interpretations or growers’ perception of the demands of their buyers. Although site-specific considerations are important and practices should be tailored to local conditions, our findings suggest growers, natural resources and food safety would benefit from clearer, more consistent requirements.
Application of machine learning techniques at BESIII experiment

BESIII is a currently running tau-charm factory with the largest samples of on threshold charm meson pairs, directly produced charmonia and some other unique datasets at BEPCII collider. Machine learning techniques have been employed to improve the performance of BESIII software. The studies for reweighing MC, particle identification and cluster reconstruction for the CGEM (Cylindrical Gas Electron Multiplier) inner tracker are presented.
Beyond ‘communication and control’: towards ethically complete rationales for brain-computer interface research

Rapid advances in neural engineering have led to an increasing use of human subjects in brain-computer interface (BCI) research. Here we ask whether the rationales articulated for this research have kept pace with related ethical requirements. To answer this question, we examined the content of peer-reviewed BCI research publications of studies involving human subjects. We analysed the publications for the rationale expressed for the research against a backdrop of journal type and study design. We discuss the results in the context of strategies that neural engineering researchers can adopt to ensure that the ethical dimensions of human subject research are not lost amid the technological drive for results.
A self stabilizing robust region ® nder applied to color and optical ̄ ow pictures

This paper presents a new robust yet very simple algorithm to ®nd a homogeneous region in an image. The algorithm is based on iteratively growing and shrinking a region until a stable state of the algorithm is reached and good ®t is found. We have shown that the algorithm works very well in color and optical ̄ow pictures, and overcomes some of the drawbacks of classic seed growing algorithms. q 2001 Elsevier Science B.V. All rights reserved.
Ghost imaging at an XUV free-electron laser

Radiation damage is one of the most severe resolution limiting factors in x-ray imaging, especially relevant to biological samples. One way of circumventing this problem is to exploit correlation-based methods developed in quantum imaging. Among these, there is ghost imaging (GI) in which the image is formed by radiation that has never interacted with the sample. Here, we demonstrate GI at an XUV free-electron laser by utilizing correlation techniques. We discuss the experimental challenges, optimal setup, and crucial ingredients to maximize the achievable resolution.
Quantum discriminant analysis for dimensionality reduction and classification

We present quantum algorithms to efficiently perform discriminant analysis for dimensionality reduction and classification over an exponentially large input data set. Compared with the best-known classical algorithms, the quantum algorithms show an exponential speedup in both the number of training vectors M and the feature space dimension N. We generalize the previous quantum algorithm for solving systems of linear equations (2009 Phys. Rev. Lett. 103 150502) to efficiently implement a Hermitian chain product of k trace-normalized N ×N Hermitian positive-semidefinite matrices with time complexity of O ( log ( N ) ) . Using this result, we perform linear as well as nonlinear Fisher discriminant analysis for dimensionality reduction over M vectors, each in an N-dimensional feature space, in time O ( p polylog ( MN ) / ϵ 3 ) , where ϵ denotes the tolerance error, and p is the number of principal projection directions desired. We also present a quantum discriminant analysis algorithm for data classification with time complexity O ( log ( MN ) / ϵ 3 ) .
Characterization of a thermodenuder-particle beam mass spectrometer system for the study of organic aerosol volatility and composition

Abstract. This paper describes the development and evaluation of a method for measuring the vapor pressure distribution and volatility-dependent mass spectrum of organic aerosol particles using a thermodenuder-particle beam mass spectrometer. The method is well suited for use with the widely used Aerodyne Aerosol Mass Spectrometer (AMS) and other quantitative aerosol mass spectrometers. The data that can be obtained are valuable for modeling organic gas-particle partitioning and for gaining improved composition information from aerosol mass spectra. The method is based on an empirically determined relationship between the thermodenuder temperature at which 50% of the organic aerosol mass evaporates (T50) and the organic component vapor pressure at 25°C (P25). This approach avoids the need for complex modeling of aerosol evaporation, which normally requires detailed information on aerosol composition and physical properties. T50 was measured for a variety of monodisperse, single-component organic aerosols with known P25 values and the results used to create a logP25 vs. T50 calibration curve. Experiments and simulations were used to estimate the uncertainties in P25 introduced by variations in particle size and mass concentration as well as mixing with other components. A vapor pressure distribution and volatility-dependent mass spectrum were then measured for laboratory-generated secondary organic aerosol particles. Vaporization profiles from this method can easily be converted to a volatility basis set representation, which shows the distribution of mass vs. saturation concentration and the gas-particle partitioning of aerosol material. The experiments and simulations indicate that this method can be used to estimate organic aerosol component vapor pressures to within approximately an order of magnitude and that useful mass-spectral separation based on volatility can be achieved.
How Do Users Revise Answers on Technical Q&A Websites? A Case Study on Stack Overflow

To ensure the quality of its shared knowledge, Stack Overflow encourages users to revise answers through a badge system, which is based on quantitative measures (e.g., a badge is awarded after revising more than 500 answers). Prior studies show that badges can positively steer the user behavior on Stack Overflow (e.g., increasing user participation). However, little is known whether revision-related badges have a negative impact on the quality of revisions since some studies show that certain users may game incentive systems to gain rewards. In this study, we analyze 3,871,966 revision records that are collected from 2,377,692 Stack Overflow answers. We find that: 1) Users performed a much larger than usual revisions on the badge-awarding days compared to normal days; 25% of the users did not make any more revisions once they received their first revision-related badge. 2) Performing more revisions than usual in a single day increased the likelihood of such revisions being rolled back (e.g., due to undesired or incorrect revisions). 3) Users were more likely to perform text and small revisions if they performed many revisions in a single day. Our findings are concurred by the Stack Overflow community, and they highlight the need for changes to the current badge system in order to provide a better balance between the quality and quantity of revisions.
Normalizing Scientometric Indicators of Individual Publications Using Local Cluster Detection Methods on Citation Networks

—One of the major shortcomings of widely used scientometric indicators is that different disciplines cannot be compared with each other. The issue of cross-disciplinary normalization has been long discussed, but even the classiﬁcation of publications into scientiﬁc domains poses problems. Structural properties of citation networks offer new possibilities, however, the large size and constant growth of these networks asks for precaution. Here we present a new tool that in order to perform cross-ﬁeld normalization of scientometric indicators of individual publications relays on the structural properties of citation networks. Due to the large size of the networks, a systematic procedure for identifying scientiﬁc domains based on a local community detection algorithm is proposed. The algorithm is tested with different benchmark and real-world networks. Then, by the use of this algorithm, the mechanism of the scientometric indicator normalization process is shown for a few indicators like the citation number, P-index and a local version of the PageRank indicator. The fat-tail trend of the article indicator distribution enables us to successfully perform the indicator normalization process.
Horizontal Mergers and Divestment Dynamics in a Sunset Industry

Industries with declining demand tend to be riddled with chronic excess capital due to the presence of a business-stealing effect and fixed costs. This article highlights the potential of mergers to internalize this business-stealing effect and thereby promote divestment. Using the case of mergers in the Japanese cement industry, it examines whether such merger-induced divestment improves total welfare based on a dynamic model of divestment. The findings suggest that merged firms indeed tended to reduce capital more actively and that, as a result of these mergers, total welfare improved despite a reduction in the consumer surplus.
Jednokanálový zdroj signálu Single Channel Signal Source Jednokanálový zdroj signálu Multichannel Signal Source WT CT ST AT STFT STVD FCM SCBSS FIR / IIR filtering Wiener filtering Fixed filtering LPF HPFCWT DWT

Abdominal fetal ElectroCardioGrams (fECGs) carry a wealth of information about the fetus including fetal Heart Rate (fHR) and signal morphology during different stages of pregnancy. Here we report our results on the implementation and evaluation of two non-adaptive signal processing methods suitable for fECG signal extraction, namely: the Independent Component Analysis (ICA) and the Principal Component Analysis (PCA) Methods. We used the fetal heart rate extracted from fECG signals (in Beats Per Minute BPM) and Signal-to-Noise Ratio (SNR) as effective performance evaluation metrics for our applied methods. Our findings demonstrated that given adequate SNR, these methods produced excellent results in accurate determination of fHR. Furthermore, we found out that compared to the PCA Method, the ICA Method produces a lower variance in the detection of the fHR.
transcription factors High-resolution DNA-binding specificity analysis of yeast Material Supplemental

Open Access Open Access option. Genome Research Freely available online through the License Commons Creative . http://creativecommons.org/licenses/by-nc/3.0/ described at as a Creative Commons License (Attribution-NonCommercial 3.0 Unported License), ). After six months, it is available under http://genome.cshlp. org/site/misc/terms.xhtml for the first six months after the full-issue publication date (see This article is distributed exclusively by Cold Spring Harbor Laboratory Press
Sequence analysis lncRNAnet : Long Non-coding RNA Identification using Deep Learning

Motivation: Long non-coding RNAs (lncRNAs) are important regulatory elements in biological processes. LncRNAs share similar sequence characteristics with messenger RNAs (mRNAs), but they play completely different roles, thus providing novel insights for biological studies. The development of next-generation sequencing (NGS) has helped in the discovery of lncRNA transcripts. However, the experimental verification of numerous transcriptomes is time consuming and costly. To alleviate these issues, a computational approach is needed to distinguish lncRNAs from the transcriptomes. Results: We present a deep learning-based approach, lncRNAnet, to identify lncRNAs that incorporates recurrent neural networks (RNNs) for RNA sequence modeling and convolutional neural networks (CNNs) for detecting stop codons to obtain an open reading frame (ORF) indicator. LncRNAnet performed clearly better than the other tools for sequences of short lengths, on which most lncRNAs are distributed. In addition, lncRNAnet successfully learned features and showed 7.83%, 5.76%, 5.30%, and 3.78% improvements over the alternatives on a human test set (HT) in terms of specificity, accuracy, F1-score, and area under the curve (AUC), respectively. Availability: Data and codes are available in http://data.snu.ac.kr/pub/lncRNAnet. Contact: sryoon@snu.ac.kr
Assessing the adoption of Maximum Residue Level ( MRL ) : in the Tea Small Holding sector of Kandy district , Sri Lanka

The subject of food safety has at present caused a great concern, the international organizations and developed countries have imposed very stringent food safety laws and regulations. In line with the above safety regulations, recent past Japan and European Union (EU) have stipulated the Maximum Residue Level (MRL) for various pesticides which are used in tea fields. Conversely, it may not only include the Japan and EU but in future all other tea importing countries and globally also impose such stringent safety laws and regulations for our tea. Therefore adoption to MRL by tea small holders (TSH) is imperative to open more of the world markets for Sri Lankan tea and gives a much needed competitive edge. This study was aimed to assess the current situation of tea small holding sector with respect to MRL and to find out the determinants of MRL adoption by the tea small holders. A questionnaire based survey was carried out to collect the data from a random sample of 145 tea small holders in Kandy district. Both descriptive statistical methods and Multiple Linear Regression were used to analyze the data. According to the descriptive analysis, all the tea small holders of the sample had adopted at least three recommended practices on MRL. Nevertheless significant amount of respondents had not implemented some important recommendations. Result revealed that the adoption of recommended practices on MRL of tea small holders was significantly determined by the farming experience, education level of tea small holder, awareness on MRL and use of hired labor for tea farming.
Lower bounds for Lov´ asz-Schrijver systems and beyond, using multiparty communication complexity

We prove that an ω(log 3 n) lower bound for the three-party number-on-the-forehead (NOF) communication complexity of the set-disjointness function implies an n ω(1) size lower bound for tree-like Lov´ asz-Schrijver systems that refute unsatisfiable CNFs. More generally, we prove that an n Ω(1) lower bound for the (k + 1)-party NOF communication complexity of set-disjointness implies a 2 n Ω(1) size lower bound for all tree-like proof systems whose formulas are degree k polynomial inequalities.