Miracle-dz/UniFAR-FTUs huggingface dataset:
Study on Characteristics of Interface Between Multiple Laminar Streams and Application for Secondary Etching Inside Microchannels

Typical microfluidc devices basically operate at low Reynolds numbers. The flowage in microchannels is entirely laminar. This letter quantifies both experimentally and numerically the diffusion across the interface between two aqueous solutions in pressure-driven laminar flow in microchannels. The relationship between the flow parameters at the inlets and the interface's location and diffusion extent is verified at high Peclet number (about 800~8000). The experimental results accord with the CFD results very well. A quantitative conclusion of the interface location and the diffusion extent between two streams near the walls, such as described in this letter, are required for the further development of the technology of microfabrication inside microchannels using fluid flow. The features of the micro structures, which are patterned with this fabrication method, are determined by interface location and the diffusion across the interface in a great degree. In the last part of this letter we give some the experiment results about the relationship of the flow parameters and the topography of the secondary patterned micro structures.
Improved Preamble-Aided Timing Estimation with pulse shaping filter for OFDM Systems

Orthogonal frequency division multiplexing (OFDM) is a multicarrier modulation technique in which the available spectrum band is divided into number of carriers and all of them are orthogonally modulated. It faces three main challenges i) high PAPR and ii) symbol timing offset iii) carrier frequency offset. In this paper we present a novel timing offset estimation method with pulse shaping filter for orthogonal frequency division multiplexing systems to avoid the ambiguity which occurs in Schmidt’s, Park and Adegbenga timing offset estimation method. The performance of the proposed scheme is presented in terms of mean-square error (MSE) and auto-correlation and cross-correlation metric for no channel distortion. Here we can see, the simulation result has smaller MSE than the other estimators. In this paper, furthermore ambiguity of unwanted peak of noise has been reduced.
Did the Molecules of Adaptive Immunity Evolve from the Innate Immune System?1

Abstract The antigen receptors on cells of innate immune systems recognize broadly expressed markers on non-host cells while the receptors on lymphocytes of the adaptive immune system display a higher level of specificity. Adaptive immunity, with its exquisite specificity and immunological memory, has only been found in the jawed vertebrates, which also display innate immunity. Jawless fishes and invertebrates only have innate immunity. In the adaptive immune response, T and B-lymphocytes detect foreign agents or antigens using T cell receptors (TCR) or immunoglobulins (Ig), respectively. While Ig can bind free intact antigens, TCR only binds processed antigenic fragments that are presented on molecules encoded in the major histocompatibility complex (MHC). MHC molecules display variation through allelic polymorphism. A diverse repertoire of Ig and TCR molecules is generated by gene rearrangement and junctional diversity, processes carried out by the recombinase activating gene (RAG) products and terminal deoxynucleotidyl transferase (TdT). Thus, the molecules that define adaptive immunity are TCR, Ig, MHC molecules, RAG products and TdT. No direct predecessors of these molecules have been found in the jawless fishes or invertebrates. In contrast, the complement cascade can be activated by either adaptive or innate immune systems and contains examples of molecules that gradually evolved from non-immune functions to being part of the innate and then adaptive immune system. In this paper we examine the molecules of the adaptive immune system and speculate on the existence of direct predecessors that were part of innate immunity.
The Distributive Implications of Patents on Indivisible Goods

Patents raise the price and reduce consumption of the patented good, but the resulting deadweight loss is thought to be worth bearing when patent protection is required as an incentive to invention. The newly-invented good generates a residual surplus, making people better off than they would be if the good had not been invented. This well-known argument is usually framed in a context where people are identical, everybody's demand curve for the newly-invented good is the same and everybody shares to some extent in the residual surplus. However, when the newly-invented good is indivisible - like a heart transplant or the treatment of AIDS, where, in effect, a person consumes either one full unit of the good or none - the effect of a patent is to concentrate the entire benefit of the patented good upon the rich, leaving the poor no better off than if the good had not been invented.
Assessing democratic legitimacy from a deliberative perspective: An analytical framework for evaluating the EU’s second pillar decision-making system

In this article, I outline an analytical framework allowing for an assessment of the democratic legitimacy of the decision-making system of the EU’s second pillar with reference to five criteria. The criteria are developed on the basis of a discourse-theoretical reading of a deliberative perspective on democracy. Empirical indicators for each criterion are specified and discussed. With this analytical framework the institutional and procedural aspects of the second pillar decision-making system can be evaluated for its democratic qualities or lack thereof.
Real Time Traffic Monitoring With Bayesian Belief Networks

Modern traffic management systems are, we believe, best implemented as multi-agent systems. When multiple agents have to make decisions on shared knowledge, this knowledge incorporates the uncertainty of underlying information and sensor systems. One approach to deal with uncertainty is the use of probabilistic models called Belief Networks. However, calculating with these models is a NP-hard problem. In order to apply this technology we had to break down its complexity for our specific case. This paper discusses the design choices that we made to boost the performance of our Bayesian belief network and thereby enabling this technique for real-time traffic monitoring in multi-agent systems.
Histone modifications change with age, dietary restriction and rapamycin treatment in mouse brain

The risk of developing neurodegenerative disorders such as Alzheimer's disease (AD) increases dramatically with age. Understanding the underlying mechanisms of brain aging is crucial for developing preventative and/or therapeutic approaches for age-associated neurological diseases. Recently, it has been suggested that epigenetic factors, such as histone modifications, maybe be involved in brain aging and age-related neurodegenerations. In this study, we investigated 14 histone modifications in brains of a cohort of young (3 months), old (22 months), and old age-matched dietary restricted (DR) and rapamycin treated BALB/c mice. Results showed that 7 out of all measured histone markers were changed drastically with age. Intriguingly, histone methylations in brain tissues, including H3K27me3, H3R2me2, H3K79me3 and H4K20me2 tend to disappear with age but can be partially restored by both DR and rapamycin treatment. However, both DR and rapamycin treatment also have a significant impact on several other histone modifications such as H3K27ac, H4K16ac, H4R3me2, and H3K56ac, which do not change as animal ages. This study provides the first evidence that a broad spectrum of histone modifications may be involved in brain aging. Besides, this study suggests that both DR and rapamycin may slow aging process in mouse brain via these underlying epigenetic mechanisms.
Fingertip tracking and multi-point gesture recognition

This paper presents a method of multi-point gesture recognition based on machine vision, which can achieve similar features of multi-touch like iPhone with a camera. Firstly we present a CamShift based method to track fingertips, then we present a method of multi-point gesture recognition based on BP neural network. The method was realized and tested through DirectShow, and results show that the proposed methods are reliable and efficient for the tracking of fingertips and for the recognition of multi-point gestures.
International Firm Lobbying and ED 8 Operating Segments

Exposure Draft 8 (ED 8) Operating Segments was introduced to replace the revised IAS 14 Segment Reporting and to align segment reporting requirements with their United States counterparts in SFAS 131. ED 8 proposed material changes in the identification, measurement and disclosure of corporate segment information. In response to the ED, there were 182 comment letters from various respondents including firms, professional associations, regulatory authorities and accounting firms. This paper investigates the influence of firm characteristics such as size, performance, and number of segments on firms' lobbying position choices on ED 8. Results reveal that larger firms were more likely to lobby in favour of ED 8, and firms with two or fewer segments were more likely to lobby against ED 8. It also provides evidence that relatively profitable firms operating in an environment of low competition are less inclined to support ED 8.
CSD: cell-based service discovery in large-scale robot networks

If robots are deployed in large numbers in our environment in future, collaboration between the presumably specialized robots will be essential for a successful operation. The robots will set up mobile ad-hoc networks for communication and efficient routing and discovery protocols for such robot networks will provide the basic layer for a successful collaboration of the robots. In this paper we present a service discovery protocol that allows robots to efficiently discover available services in the network. It is specifically designed for large-scale robot networks. It takes into account the high dynamics of robot networks and exploits the position data of the robots to increase scalability and efficiency. A cell-based grid with master nodes in each cell forms the basic structure. Through proactive intra-cell communication and reactive inter-cell communication, scalability is ensured and the effects of node movements on the overall network are minimized. We implemented our solution in an example scenario.
Distributed speech translation technologies for multiparty multilingual communication

Developing a multilingual speech translation system requires efforts in constructing automatic speech recognition (ASR), machine translation (MT), and text-to-speech synthesis (TTS) components for all possible source and target languages. If the numerous ASR, MT, and TTS systems for different language pairs developed independently in different parts of the world could be connected, multilingual speech translation systems for a multitude of language pairs could be achieved. Yet, there is currently no common, flexible framework that can provide an entire speech translation process by bringing together heterogeneous speech translation components. In this article we therefore propose a distributed architecture framework for multilingual speech translation in which all speech translation components are provided on distributed servers and cooperate over a network. This framework can facilitate the connection of different components and functions. To show the overall mechanism, we first present our state-of-the-art technologies for multilingual ASR, MT, and TTS components, and then describe how to combine those systems into the proposed network-based framework. The client applications are implemented on a handheld mobile terminal device, and all data exchanges among client users and spoken language technology servers are managed through a Web protocol. To support multiparty communication, an additional communication server is provided for simultaneously distributing the speech translation results from one user to multiple users. Field testing shows that the system is capable of realizing multiparty multilingual speech translation for real-time and location-independent communication.
Asking Retrospective Questions in Household Surveys: Evidence from Vietnam

Asking retrospective questions about consumption and income has become an important part of household surveys and research in developing countries. While recall errors in retrospective data may generate estimation biases, the nature and the magnitude of the errors are largely unknown, especially in the context of developing countries. To fill this gap in the existing studies, we collect unique household data from Vietnam, a resurvey of respondents of the Vietnam Health and Living Standard Survey (VHLSS) 2006. This combined data allows us to investigate a variety of errors associated with recall surveys and the size of consumption categories in questionnaires. Our empirical results suggest that asking for total expenditure, rather than categorical expenditure, will cause fewer recall errors in a retrospective survey. This is especially true in the case of purchased or bartered consumption expenditure. Our results also suggest that while recall errors in the categorical sum of expenditure may exhibit mean-reverting patterns, retrospective total expenditure data is less likely to involve problems of mean reverting measurement error.
Unsupervised Blood Vessel Segmentation of Fundus Images Based on Region Features and Hierarchical Growth Algorithm

Automated blood vessel segmentation plays an important role in the diagnosis and treatment of various cardiovascular and ophthalmologic diseases. In this paper, a novel unsupervised segmentation algorithm is proposed to extract blood vessels from fundus images. At first, an enhanced vessel image is generated by morphological reconstruction, and then divided into three parts: preliminary vessel regions, background regions and undetermined regions. Secondly, a list of region features of blood vessels are defined and used to remove the noise regions in preliminary vessel regions and extract the skeletons of blood vessels. An intermediate vessel image is obtained by combing the denoised preliminary vessel regions with vessel skeletons. After that, a novel hierarchical growth algorithm, namely HGA, is proposed to label each pixel in undetermined regions as vessel or non-vessel in an incremental way, which takes the advantage of color and spatial information from the intermediate vessel image and the background regions. Finally, postprocessing is performed to remove the non-vessel regions. The proposed algorithm has low computational complexity and outperforms many other state-ofart supervised and unsupervised methods in terms of accuracy. It achieves a vessel segmentation accuracy of 96.0%, 95.7% and 95.1% in an average time of 10.72s, 15.74s and 50.71s on images from three publicly available fundus image datasets DRIVE, STARE, and CHASE DB1, respectively.
A Bi-objective Constrained Optimization Methodology Using a Hybrid Multi-Objective and Penalty Function Approach

Single objective evolutionary constrained optimization has been widely searched and researched by plethora of researchers in last two decades. On the other hand, multi-objective constraint handling using evolutionary algorithms has not been actively proposed. However, real-world multi-objective optimization problems consist of one or many non-linear and non-convex constraints. In the present work, we develop an evolutionary algorithm based constraint handling methodology, to deal with constraints in multi-objective optimization problems. The method is a combination of an evolutionary multi-objective optimization coupled with classical weighted sum approach based local search method and is an extended version of our previously developed constraint handling method for single objective optimization [4]. A constrained bi-objective problem is converted into a tri-objective problem where the additional objective is formed using summation of constrained violation. The proposed method is applied to four constrained multi-objective problem. The non-dominated solutions are compared with a standard evolutionary multiobjective optimization algorithm (NSGA-II) with respect to hypervolume and attainment surface. The simulation results illustrates the effectiveness of the proposed approach.
Genetic Relationships, Carbendazim Sensitivity and Mycotoxin Production of the Fusarium Graminearum Populations from Maize, Wheat and Rice in Eastern China

Members of the Fusarium graminearum species complex (FGSC) are important pathogens on wheat, maize, barley, and rice in China. Harvested grains are often contaminated by mycotoxins, such as the trichothecene nivalenol (NIV) and deoxynivalenol (DON) and the estrogenic mycotoxin zearalenone (ZEN), which is a big threat to humans and animals. In this study, 97 isolates were collected from maize, wheat, and rice in Jiangsu and Anhui provinces in 2013 and characterized by species- and chemotype-specific PCR. F. graminearum sensu stricto (s. str.) was predominant on maize, while most of the isolates collected from rice and wheat were identified as F. asiaticum. Fusarium isolates from three hosts varied in trichothecene chemotypes. The 3-acetyldeoxynivalenol (3ADON) chemotype predominated on wheat and rice population, while 15ADON was prevailing in the remaining isolates. Sequence analysis of the translation elongation factor 1α and trichodiene synthase indicated the accuracy of the above conclusion. Additionally, phylogenetic analysis suggested four groups with strong correlation with species, chemotype, and host. These isolates were also evaluated for their sensitivity to carbendazim and mycotoxins production. The maize population was less sensitive than the other two. The DON levels were similar in three populations, while those isolates on maize produced more ZEN. More DON was produced in carbendazim resistant strains than sensitive ones, but it seemed that carbendazim resistance had no effect on ZEN production in wheat culture.
Antibacterial Activity of Chosen Mangrove Plants Against Bacterial Specified Pathogens

Study of marine organisms for their bioactive potential, being an important part of marine ecosystem, has picked up the rhythm in recent years with the growing recognition of their importance in human life. The antibacterial activity of the mangrove leaves of Avicennia marina, Ceriops decandra and (non italic) Bruguiera cylindrica were tested against antibiotic resistant pathogens (ARB) viz. Staphylococcus aureus, Streptococcus pneumonia, Klebsiella pneumoniae , Pseudomonas aeruginosa and eye pathogens (EP) viz. E. coli, Proteus, Acinetobacter and Staphylococcus epidermidis. Most of the plant extracts showed promising antibacterial activity against both the bacterial groups. However, Maximum antibacterial activity was observed with the leaf extract of A. marina with the zone of inhibition 7.12±0.11mm against P. aeruginosa followed by B. cylindrica (6.8±0.84mm) and C. decandra (6.02±0.02). Moreover, the leaf extract of A. marina (11.21±0.74mm) showed highest zone of inhibition against eye pathogens of S. epidermidis followed by and against Acinetobacter 9.21±0.94 and E.coli 8.23±0.86 respectively. The A. marina has Minimmum Inhibitory Concentartion value of 50µg. ml as against P. aeruginosa , Acinetobacter sp and E. coli followed by MIC value of 100 µg ml against 1 1 Klebsiella pneumoniae and Staphylococcus epidermidis. The presence of active compounds such a s flavonoids, anthroquinone, phenolic group, alkaloids and triterpenoids might be the responsible factors for bioactivity against chosen bacterial pathogens. The results provided evidence that, the chosen plants might indeed be the potential sources for the biological activity of antibiotic resistant bacteria and eye pathogens.
Applying LSTM in GUIs to learn interactive behavior

Apparition is a crowdsourcing [11] system for rapid pro-totyping interface design. It has a shared canvas through which designers interact with the paid workers. The can-vas provides a wizard-of-oz experience, where the designer seems to be working only with the software, while under the hood the canvas helps crowdsourced workers to co-ordinate tasks so that no two workers overlap over the same task and no tasks remain untouched. In this paper, we have applied long short-term memory (LSTM) recurrent neural network to learn interactive behavior in animation.
Phase cross-correlation of the wmap ilc map and foregrounds

We present a circular cross-correlation tests for the phases of the Internal Linear Combination Map (ILC) and WMAP’s foregrounds for all K–W frequency bands at the range of multipoles l ≤ 50. We have found significant deviations from the expected Poissonian statistics for the ILC and the foregrounds phases. Our analysis shows that the low multipole range of the ILC power spectrum contains some of the foregrounds residues. Subject headings: cosmology: cosmic microwave background — cosmology: observations — methods: data analysis
Novel region-based modeling for human detection within highly dynamic aquatic environment

One difficult challenge in autonomous video surveillance is on handling highly dynamic backgrounds. This difficulty is compounded if foreground objects of interest are partially hidden by specular reflections or glare. In this paper, we provide numerous insights into the technical difficulties faced in developing an automated video surveillance system within a hostile environment: an outdoor public swimming pool. For robust detection performance, we focused on two central aspects: (i) effective modeling of the dynamic outdoor aquatic background with rapid illumination changes, splashes and random spatial movements of background elements, owing to the movement of water ripples; and (ii) enhancing the visibility of swimmers that are partially hidden by specular reflections. Several innovations have been introduced from scratch in this paper. The first is the development of a scheme that models the background as regions of dynamic homogeneous processes. This model facilitates the implementation of an efficient spatial searching scheme for background subtraction that could exploit long-range spatial dependencies between pixels. The second is the implementation of a spatio-temporal filtering scheme that enhances the detection of swimmers that are partially hidden by specular reflections of artificial nighttime lighting, serving as a pre-processing module to foreground detection for nighttime operation. These various algorithms have been tightly integrated under a unified framework and demonstrated on a busy Olympic-sized outdoor public pool.
Bridging Social Capital Formation In A Faith-Based Organization

BRIDGING SOCIAL CAPITAL FORMATION IN A FAITH-BASED ORGANIZATION by ANGELA A. KAISER August 2010 Advisor: Dr. Anna Maria Santiago Major: Social Work Degree: Doctor of Philosophy Previous research on faith-based organizations suggest that these organizations are highly effective at forging relationships between individuals from different racial, ethnic, and religious backgrounds, known as bridging social capital. This study examines the process of bridging social capital formation within MOSES, a faith-based organization located in Detroit, Michigan. Through the use of in-depth interviews, participant observation, archival research, and a member survey, I documented the forms of social capital present within the organization. I also explored whether bridging relationships were forming within the organization and the strategies that facilitated their formation. Diversity within the organization, recognition of member differences, and barriers to social capital formation were also examined. Finally, I assessed mechanisms for unifying and mobilizing members within the organization. The study depicts how one faith-based organization has attempted to bridge the gaps between organization and community members from different