Miracle-dz/UniFAR-FTUs huggingface dataset:
Modific ation of Gold Nanoparticles for SERS Application in Emulsion and Lipid Systems

iv ACKNOWLEDGMENTS I would like to thank my advisor Dr. Lili He for her full support and encouragement throughout my research. The experience I gained and opportunities I had in this lab greatly exceeded my expectations, for which I am grateful. I would also like to thank Dr. Decker and Dr. Zhang for being members on my committee and for their input on my thesis. I would finally like to thank Dr. McClements for his input and assistance throughout my project. Aside from the professors, I would like to acknowledge and thank my lab mates, for their help and support over the years as well as lab members from throughout the department where their expertise was greatly appreciated. Gold nanoparticles produced using the Turkevich method were able to have their hydrophobicity modified using octanethiol in a novel method for SERS application. Both amphiliphic GNPs and hydrophobic GNPs were produced and differentiated by Raman signals. The amphiliphic GNPs were able to enhance the SERS signals of the protein emulsifier in the emulsion in situ and the hydrophobic GNPs were able to enhance the SERS signals from canola oil. Further purification of the hydrophobic GNPs proved to have higher enhancement and sensitivity, but still poor consistency which is typical of SERS. Monitoring lipid oxidation using Raman and SERS using alternative approaches was the primary objective of the thesis. The purified GNPs were capable of enhancing the canola oil over two weeks, but the poor consistency led to no major trends. Using normal Raman, a triphenylphosphine oxidation reaction was capable of producing a peroxide value correlation in a very simple and rapid manor. Using a gold nanoparticle modified stainless steel wire, the headspace volatiles from canola oil oxidation were able to be enhanced, but with poor consistency. Silver dendrites were used to enhance the canola oil signal but with poor resolution. A combination of silver dendrites and GNPs were used to slightly improve enhancement, but not as strong as GNPs alone.
Study of 802.11 Network Performance and Wireless Multicasting

IEEE 802.11 proposed a number of standards and each one had a specific goal. They can be configured in infrastructure mode or ad hoc mode. In the study of infrastructure mode, the focus was on 802.11b/g co-existence along with other single mode operations to analyze the throughput and traffic drop through simulation. In the study of ad hoc mode, the focus was to analyze the behavior of ah hoc networks with demand driven ad hoc routing protocols like – Ad-Hoc On-demand Distance Vector Routing, Dynamic Source Routing and Temporally-Ordered Routing Algorithm in terms of throughput. Finally an investigation is done to study wireless multicasting and its behavior.
Stability in Supply Chain Networks

This paper presents a theory of matching in vertical networks, generalizing the theory of matching in two-sided markets introduced by Gale and Shapley. Under natural restrictions, stable networks are guaranteed to exist. The set of stable networks is a lattice, with side-optimal stable networks at the extremes. Several other key results on two-sided matching also extend naturally to the more general setting.
Evaluation of a Diet Quality Index Based on the Probability of Adequate Nutrient Intake (PANDiet) Using National French and US Dietary Surveys

Background Existing diet quality indices often show theoretical and methodological limitations, especially with regard to validation. Objective To develop a diet quality index based on the probability of adequate nutrient intake (PANDiet) and evaluate its validity using data from French and US populations. Material and Methods The PANDiet is composed of adequacy probabilities for 24 nutrients grouped into two sub-scores. The relationship between the PANDiet score and energy intake were investigated. We evaluated the construct validity of the index by comparing scores for population sub-groups with ‘a priori’ differences in diet quality, according to smoking status, energy density, food intakes, plasma folate and carotenoid concentrations. French and US implementations of the PANDiet were developed and evaluated using national nutritional recommendations and dietary surveys. Results The PANDiet was not correlated with energy for the French implementation (r = −0.02, P>0.05) and correlated at a low level for the US implementation (r = −0.11, P<0.0001). In both implementations, a higher PANDiet score (i.e. a better diet quality) was associated with not smoking, having a lower-energy-dense diet, consuming higher amounts of fruits, vegetables, fish, milk and other dairy products and lower amounts of cheese, pizza, eggs, meat and processed meat, and having higher plasma folate and carotenoid concentrations after controlling for appropriate factors (all P<0.05, carotenoid data for US not available). Conclusions The PANDiet provides a single score that measures the adequacy of nutrient intake and reflects diet quality. This index is adaptable for use in different countries and relevant at the individual and population levels.
Umeclidinium in patients with COPD: a randomised, placebo-controlled study

Efficacy and safety of umeclidinium administered in a dry power inhaler were evaluated in moderate-to-very-severe chronic obstructive pulmonary disease patients. This was a randomised, placebo-controlled study assessing once-daily umeclidinium 62.5 and 125 &mgr;g over 12 weeks. The primary end-point was change from baseline in trough forced expiratory volume in 1 s (FEV1) on day 85. Secondary end-points were 0–6-h weighted mean and serial forced expiratory volume in 1 s. Other end-points were transitional dyspnoea index, health outcomes (St George’s Respiratory Questionnaire), pharmacokinetics and safety. 246 patients were enrolled; 168 completed the study. On day 85, umeclidinium 62.5 and 125 &mgr;g significantly improved least squares mean change from baseline in trough FEV1 (127 and 152 mL, respectively; p<0.001) compared with placebo. On day 84, umeclidinium 62.5 and 125 &mgr;g significantly improved least squares mean change from baseline in 0–6-h weighted mean (166 and 191 mL, respectively; p<0.001) and serial FEV1 at each time point (p≤0.003). Significant improvement in least squares mean transitional dyspnoea index focal score (1.0 and 1.3 units, respectively; p≤0.05) and change from baseline St George’s Respiratory Questionnaire total score (-7.9 and -10.87 units, respectively; p<0.001) were noted compared with placebo at week 12. The incidence of adverse events was low and similar across treatments. Umeclidinium 62.5 and 125 &mgr;g significantly improved lung function, dyspnoea and health status compared with placebo, and were well tolerated in chronic obstructive pulmonary disease patients over 12 weeks. Umeclidinium significantly improves lung function, dyspnoea and health status and is well tolerated http://ow.ly/qf7T9
Qualitative and quantitative aspects of cooperation mechanisms for monitoring in service-oriented architectures

Cooperation mechanisms for agents monitoring service-based workflows are a means to address the increasing complexity of modern enterprise architectures. These mechanisms are inspired by existing biological mechanisms and extend an existing decentralized monitoring architecture in order to handle deviations from Service Level Agreements autonomously. As the core cooperation mechanisms have been subject of our previous work and have been described in detail earlier, we present now an evaluation of our concepts regarding both effectiveness and efficiency, based on an implemented prototype.
XOS-SSH: A Lightweight User-Centric Tool to Support Remote Execution in Virtual Organizations

Large-scale virtual organizations (VOs) often comprise resource providers from different administrative domains, each probably with a specific security model. Grids try to solve this problem by providing a new security infrastructure featured with single-sign on (SSO). However, the usability of Grids is often impaired by the complexity of configuring and maintaining the new security infrastructure as well as adapting to new interfaces of security enabled services. The co-existing of different Grid platforms and SSO solutions among resource providers makes this situation even worse. In this paper, we present XOS-SSH, a lightweight user-centric tool to support remote execution of jobs among heterogeneous nodes of VOs. XOS-SSH is a modified version of the widely used OpenSSH tool based on several OS-level VO support mechanisms developed in XtreemOS project [23]. XOS-SSH adopts a pluggable framework that is capable of supporting different authentication schemes and making them transparent to shell users. The performance evaluation of XOS-SSH around NAS Parallel Benchmarks (NPB) shows that our current implementation incurs trivial overhead comparing to the unmodified one.
Matrix models for beta ensembles

This paper constructs tridiagonal random matrix models for general (β>0) β-Hermite (Gaussian) and β-Laguerre (Wishart) ensembles. These generalize the well-known Gaussian and Wishart models for β=1,2,4. Furthermore, in the cases of the β-Laguerre ensembles, we eliminate the exponent quantization present in the previously known models. We further discuss applications for the new matrix models, and present some open problems.
A Linguistic Approach to Games with Communication

In this paper a model is constructed for games with communication under incomplete information. The model, based on natural language processing techniques, is developed and tested on a case of Bridge bidding. The approach emphasizes the information processing nature of such games. A new methodology for performance evaluation of bidding programs is proposed, and – finally – some conclusions from implementation of such a program and some speculations are presented.
Automated segmentation and volumetric analysis of brain components on MR imaging

As the world population has been growing old, the incidence of several brain diseases, including dementia, has risen. As a consequence, a greater demand for automated segmentation methods and volumetric analysis of the brain structure and its components has increased. This work is based on the use of image processing algorithms and previous knowledge statistical models for automated segmentation over 386 magnetic resonance exams. We analyze brain components considering two different image subsets: non-demented and demented individuals. Our results demonstrate gray and white matter loss, and CSF enlargement with aging.
A review of independent component analysis (ICA) based on Kurtosis Contrast Function

Independent component analysis (ICA) is a computational mehtod to solve blind source separation (BSS) problem. Different kinds of classic measure can be used for the estimation of nonGaussian sources by ICA. In this paper we review independent componenet analysis (ICA) technique based on Kurtosis contrast function. We briefly present the common independent component analysis algorithms that use Kurtosis as a criterion for non-Gaussian. Basid on the literatures, we compare these algrithms in terms of performance and advantaves.
Inductive learning for the Semantic Web: What does it buy?

Nowadays, building ontologies is a time consuming task since they are mainly manually built. This makes hard the full realization of the Semantic Web view. In order to overcome this issue, machine learning techniques, and specifically inductive learning methods, could be fruitfully exploited for learning models from existing Web data. In this paper we survey methods for (semi-)automatically building and enriching ontologies from existing sources of information such as Linked Data, tagged data, social networks, ontologies. In this way, a large amount of ontologies could be quickly available and possibly only refined by the knowledge engineers. Furthermore, inductive incremental learning techniques could be adopted to perform reasoning at large scale, for which the deductive approach has showed its limitations. Indeed, incremental methods allow to learn models from samples of data and then to refine/enrich the model when new (samples of) data are available. If on one hand this means to abandon sound and complete reasoning procedures for the advantage of uncertain conclusions, on the other hand this could allow to reason on the entire Web. Besides, the adoption of inductive learning methods could make also possible to dial with the intrinsic uncertainty characterizing the Web, that, for its nature, could have incomplete and/or contradictory information.
Hierarchical routing for multi-layer ad-hoc wireless networks with UAVs

Routing scalability in multi-hop wireless networks faces many challenges. The spatial concurrency constraint on nearby nodes sharing the same channel is the fundamental limitation. A previous theoretical study shows that the throughput furnished to each user is rapidly reduced as network size is increased. In order to solve this problem, we extended the hierarchical state routing scheme to a hierarchical multilayer environment. With the hierarchical approach, many problems caused by "flat" multi-hopping disappear. In the real battlefield, a multi-level physical heterogeneous network with UAVs provides an ideal support for the multi-area theater with a large number of fighting units. Extended hierarchical state relating (EHSR) shows very promising results in this hierarchical infrastructure.
GAMIFICATION FEATURES OF STREAM-EDUCATION COMPONENTS WITH EDUCATION ROBOTICS

Gumennykova Tamara, Doctor of pedagogical sciences, professor, professor of the Department of the social sciences, E-mail: gumennikova100@gmail.com , ORCID: 0000-0002-6223-7711 Blazhko Oleksandr, Candidate of Technical Sciences, Associate Professor, Associate Professor of the System Software Department, E-mail: blazhko@ieee.org , ORCID: 0000-0001-7413-275X, Odessa, Ukraine Luhova Tetiana, Candidate of Art Sciences, Associate Professor of the Information Activity and Media Communications Department, E-mail: lug2308@gmail.com, ORCID: 0000-0002-3573-9978 Troianovska Yuliia, Senior Lecturer of the Information System Department, E-mail: troyanovskaja@gmail.com, ORCID: 0000-0002-6716-9391 Melnyk Serhii, Candidate of Social Communication Sciences, Associate Professor, E-mail: miroschnick@ukr.net, ORCID: 0000-0003-3996-9970 Riashchenko Oksana, Teacher of Informatics, E-mail: oksanaivanovna561@gmail.com, ORCID: 0000-0002-9242-0752 Odessa National Polytechnic University, Odessa, Ukraine Prydunai Branch of Private joint-stock company “Higher educational institution “Interregional Academy of Personnel Management” Secondary school No. 10, Izmail, Ukraine
Approximation Algorithms for the Feedback Vertex Set Problem with Applications to Constraint Satisfaction and Bayesian Inference

A feedback vertex set of an undirected graph is a subset of vertices that intersects with the vertex set of each cycle in the graph. Given an undirected graph G with n vertices and weights on its vertices, polynomial-time algorithms are provided for approximating the problem of finding a feedback vertex set of G with smallest weight. When the weights of all vertices in G are equal, the performance ratio attained by these algorithms is 4-(2/n). This improves a previous algorithm which achieved an approximation factor of $O(\sqrt{\log n})$ for this case. For general vertex weights, the performance ratio becomes $\min\{2\Delta^2, 4 \log_2 n\}$ where $\Delta$ denotes the maximum degree in G. For the special case of planar graphs this ratio is reduced to 10. An interesting special case of weighted graphs where a performance ratio of 4-(2/n) is achieved is the one where a prescribed subset of the vertices, so-called blackout vertices, is not allowed to participate in any feedback vertex set. It is shown how these algorithms can improve the search performance for constraint satisfaction problems. An application in the area of Bayesian inference of graphs with blackout vertices is also presented.
Workload Characteristics of the DAS-2 Supercomputer

This paper presents a comprehensive characterization of the DAS-2 1 workloads using twelve-month scientiﬁc traces. Metrics that we characterize include system utilization, job arrival rate and interar-rival time, job size (degree of parallelism), job run time, memory usage, and job queue wait time. Differences with previous reported workloads are recognized and statistical distributions are ﬁtted for generating synthetic workloads with the same characteristics. This study provides a realistic basis for experiments in resource management and evaluations of different scheduling strategies in a multi-cluster environment.
mTOR kinase is needed for the development and stabilization of dendritic arbors in newly born olfactory bulb neurons

Neurogenesis is the process of neuron generation, which occurs not only during embryonic development but also in restricted niches postnatally. One such region is called the subventricular zone (SVZ), which gives rise to new neurons in the olfactory bulb (OB). Neurons that are born postnatally migrate through more complex territories and integrate into fully functional circuits. Therefore, differences in the differentiation of embryonic and postnatally born neurons may exist. Dendritogenesis is an important process for the proper formation of future neuronal circuits. Dendritogenesis in embryonic neurons cultured in vitro was shown to depend on the mammalian target of rapamycin (mTOR). Still unknown, however, is whether mTOR could regulate the dendritic arbor morphology of SVZ‐derived postnatal OB neurons under physiological conditions in vivo. The present study used in vitro cultured and differentiated SVZ‐derived neural progenitors and found that both mTOR complex 1 and mTOR complex 2 were required for the dendritogenesis of SVZ‐derived neurons. Furthermore, using a combination of in vivo electroporation of neural stem cells in the SVZ and genetic and pharmacological inhibition of mTOR, it was found that mTOR was crucial for the growth of basal and apical dendrites in postnatally born OB neurons under physiological conditions and contributed to the stabilization of their basal dendrites. © 2016 Wiley Periodicals, Inc. Develop Neurobiol 76: 1308–1327, 2016
Sequential unscented Kalman filter for radar target tracking with range rate measurements

To solve the radar target tracking problem with range rate measurements, in which the errors between range and range rate measurements are correlated, a sequential unscented Kalman filter (SUKF) is proposed in this paper. A pseudo measurement is constructed by block-partitioned Cholesky factorization first, this can keep the range, bearing and elevation (or two direction cosine) measurements unchanged, while the errors between the original range and range rate measurement are decorrelated; then based on the UKF, the bearing, elevation (or two direction cosine) and the pseudo measurement are sequentially processed to enhance the filtering precision and the computational efficiency simultaneously. Validity and consistency of the new proposed algorithm is verified by Monte-Carlo simulation.
Sensor-free corner shape detection by wireless networks

Due to the rapid growth of the smartphone applications and the fast development of the Wireless Local Area Networks (WLANs), numerous indoor location-based techniques have been proposed during the past several decades. Floorplan, which defines the structure and functionality of a specific indoor environment, becomes a hot topic nowadays. Conventional floorplan techniques leverage smartphone sensors combined with WiFi signals to construct the floorplan of a building. However, existing approaches with sensors cannot detect the shape of a corner, and the sensors cost huge amount of energy during the whole floorplan constructing process. In this paper, we propose a sensor-free approach to detect the shape of a certain corner leveraging WiFi signals without using sensors on smartphones. Instead of utilizing traditional wireless communication indicator Received Signal Strength (RSS), we leverage a finer-grained indicator Channel State Information (CSI) to detect the shape of a certain corner. The evaluation of our approach shows that CSI is more robust in sensor-free corner shape detection, and we have achieved over 85% detection accuracy in simulation and over 70% detection accuracy in real indoor experiments.
Vibrotactile jacket for perception enhancement

By nature, human beings perceive their environment mostly using sight and audition. Vibrotactile feedback has proven satisfying results in the domains of simple multimodal interaction, for immersion and navigation purposes. The scope of this research is to evaluate the additional value of tactile feedback on the user upper body limbs to pass 3D directional information. This paper presents the development of a vibrotactile jacket and its software interface. A validation concept test bench has been setup to measure the effect of our vibrotactile device onto the response time to localize a target in a virtual environment over visual and auditory cue. Early results are encouraging by showing clear benefits of our vibrotactile system while the complexity of the multimodal environment is increasing.
A language competition model for new minorities

This article presents a new model describing a language competition situation between a local majority language and a migrant minority language. Migrants enter the society, form families, and produce offspring. Adults raise their children in either one of the two languages or both. Children then attend school, learn additional languages as adults, and produce a new cohort with its own linguistic repertoire. Families and adults are utility maximizing actors, who take into account instrumental aspects of languages, such as their communicative range, as well as identity-related aspects. A general macro-level model describes how the linguistic composition of a population facing migration changes over time. Furthermore, a specific functional form of the general model is proposed and steady states are analyzed. Finally, for illustrative purposes, the model is applied to the case of Spanish and English in the United States.
A Rough Sets Partitioning Model for Mining Sequential Patterns with Time Constraint

Abstract — now a days, data mining and knowledge discovery methods are applied to a variety of enterprise and engineering disciplines to uncover interesting patterns from databases. The study of Sequential patterns is an important data mining problem due to its wide applications to real world time dependent databases. Sequential patterns are inter-event patterns ordered over a time-period associated with specific objects under study. Analysis and discovery of frequent sequential patterns over a predetermined time-period are interesting datamining results, and can aid in decision support in many enterprise applications. The problem of sequential pattern mining poses computational challenges as “a long frequent sequence” contains enormous number of frequent subsequences.Also useful results depend on the right choice of event window. In this paper, we have studied the problem of sequential pattern mining through two perspectives, one the computational aspect of the problem and the other is incorporation and adjustability of time constraint. We have used Indiscernibility relation from theory of rough sets to partition the search space of sequential patterns and have proposed a novel algorithm that allows previsualization of patterns and allows adjustment of time constraint prior to execution of mining task.The algorithm Rough Set Partitioning is atleast ten times faster than the naive time constraint based sequential pattern mining algorithm GSP. Besides this an additional knowledge of time interval of sequential patterns is also determined with the method. Keywords- Data mining, Sequential patterns, indiscernibility relation, partitioning etc.