Miracle-dz/UniFAR-FTUs huggingface dataset:
Culturally competent diabetes self-management education for Mexican Americans: the Starr County border health initiative.

OBJECTIVE To determine the effects of a culturally competent diabetes self-management intervention in Mexican Americans with type 2 diabetes. RESEARCH DESIGN AND METHODS A prospective, randomized, repeated measures study was conducted on the Texas-Mexico border in Starr County. A total of 256 randomly selected individuals with type 2 diabetes between 35 and 70 years of age, diagnosed with type 2 diabetes after 35 years of age, and accompanied by a family member or friend were included. The intervention consisted of 52 contact hours over 12 months and was provided by bilingual Mexican American nurses, dietitians, and community workers. The intervention involved 3 months of weekly instructional sessions on nutrition, self-monitoring of blood glucose, exercise, and other self-care topics and 6 months of biweekly support group sessions to promote behavior changes. The approach was culturally competent in terms of language, diet, social emphasis, family participation, and incorporation of cultural health beliefs. Outcomes included indicators of metabolic control (HbA(1c) and fasting blood glucose), diabetes knowledge, and diabetes-related health beliefs. RESULTS Experimental groups showed significantly lower levels of HbA(1c) and fasting blood glucose at 6 and 12 months and higher diabetes knowledge scores. At 6 months, the mean HbA(1c) of the experimental subjects was 1.4% below the mean of the control group; however, the mean level of the experimental subjects was still high (>10%). CONCLUSIONS This study confirms the effectiveness of culturally competent diabetes self-management education on improving health outcomes of Mexican Americans, particularly for those individuals with HbA(1c) levels >10%.
A new samples selecting method based on K nearest neighbors

Short text classification uses a supervised learning process, and it needs a huge amount of labeled data for training. This process consumes a lot of human resources. In traditional supervised learning problems, active learning can reduce the amount of samples that need to be labeled manually. It achieves this goal by selecting the most representative samples to represent the whole training set. Uncertainty sampling is the most popular way in active learning, but it has poor performance when it is affected by outliers. In our paper, we propose a new sampling method for training sets containing short text, which is denoted as Top-K Representative (TKR). However, the optimization process of TKR is a N-P hard problem. To solve this problem, a new algorithm, based on the greedy algorithm, is proposed to obtain the approximating results. The experiments show that our proposed sampling method performs better than the state-of-the-art methods.
Predicting copper-, iron-, and zinc-binding proteins in pathogenic species of the Paracoccidioides genus

Approximately one-third of all proteins have been estimated to contain at least one metal cofactor, and these proteins are referred to as metalloproteins. These represent one of the most diverse classes of proteins, containing metal ions that bind to specific sites to perform catalytic, regulatory and structural functions. Bioinformatic tools have been developed to predict metalloproteins encoded by an organism based only on its genome sequence. Its function and the type of metal binder can also be predicted via a bioinformatics approach. Paracoccidioides complex includes termodimorphic pathogenic fungi that are found as saprobic mycelia in the environment and as yeast, the parasitic form, in host tissues. They are the etiologic agents of Paracoccidioidomycosis, a prevalent systemic mycosis in Latin America. Many metalloproteins are important for the virulence of several pathogenic microorganisms. Accordingly, the present work aimed to predict the copper, iron and zinc proteins encoded by the genomes of three phylogenetic species of Paracoccidioides (Pb01, Pb03, and Pb18). The metalloproteins were identified using bioinformatics approaches based on structure, annotation and domains. Cu-, Fe-, and Zn-binding proteins represent 7% of the total proteins encoded by Paracoccidioides spp. genomes. Zinc proteins were the most abundant metalloproteins, representing 5.7% of the fungus proteome, whereas copper and iron proteins represent 0.3 and 1.2%, respectively. Functional classification revealed that metalloproteins are related to many cellular processes. Furthermore, it was observed that many of these metalloproteins serve as virulence factors in the biology of the fungus. Thus, it is concluded that the Cu, Fe, and Zn metalloproteomes of the Paracoccidioides spp. are of the utmost importance for the biology and virulence of these particular human pathogens.
Inducing dynamically stable walking in an underactuated prototype planar biped

This paper presents the experimental implementation and validation of a framework for the systematic design, analysis, and performance enhancement of controllers that induce stable walking in N-link underactuated planar biped robots. Controllers designed via this framework act by enforcing virtual constraints - holonomic constraints imposed via feedback - on the robot's configuration. The stability properties of the resulting walking motions may be easily analyzed in terms of a two-dimensional sub-dynamic of the full walking model. The experimental validation is performed on RABBIT, a 5-link prototype constructed by the French project Commande de Robots a Pattes of the CNRS-GdR Automatique.
Effect of Management Control System (MCS), Activity Based Management (ABM), and Value Chain on Effectiveness, Efficiency, Economic Value Added (EVA) and Its Impact on Profitability in Construction Service Companies in East Java Province

purpose of the study was to analyze the effect of Management Control System (MCS), Activity Based Management (ABM), and Value Chain on Effectiveness, Efficiency, Economic Value Added (EVA) and Its Impact Against Profitability in Construction Service Companies in East Java Province. The type of research used in this study is quantitative research. Quantitative research is a method used to test certain theories by examining the relationship of how much influence between variables is the level of business competition and Islamic business ethics on the behavior of Muslim entrepreneurs with statistical analysis. This research is a survey research because it uses a data collection tool in the form of a questionnaire / questionnaire. The results of the questionnaire were then tested using validity test, reliability test. The population in this study were construction service companies incorporated in the Association of Indonesian National Construction Implementation Association (GAPENSI) East Java Province with K1 sub-qualifications of 978 companies, K2 as many as 1369 companies and K3 as many as 1194 companies with a total of 3541 construction service companies (contractors). The sample used in this study was 97 contractors. For this study data analysis used theapproach Partial Least Square (PLS). The results of this study indicate that; variable Management Control System (MCS) significantly affects the effectiveness variables, (2) variable Management Control System (MCS) significantly affects the variable efficiency, (3) the variable Management Control System (MCS) significantly affects the variable Economic Value Added (EVA),(4) Management Control System (MCS) variable has a significant effect on profitability variable (5) Activity Based Management (ABM) variable has a significant effect on effectiveness variable (6) Activity Based Management (ABM) variable influences efficiency variable (7)variable Activity BasedManagement (ABM) has a significant effect on the variables Economic Value Added (EVA), (8) Activity Based Management (ABM) variables have a significant effect on profitability variables, (9)variables Value Chain have no significant effect on effectiveness variables, (10)variables Value Chain significant effect on the efficiency variable (11) variable Value Chain experienc the effect is not significant on the variables Economic Value Added (EVA), (12)variables Value Chain have a significant effect on profitability variables, (13) effectiveness variables have a significant effect on profitability variables, (14) efficiency variables have a significant effect on profitability variables, (15) variables Economic Value Added (EVA) has a significant effect on profitability variables.
Sum Power Minimization Under Rate Constraints in Multiuser OFDM Systems

For a multi-cell system using OFDM, this paper considers the multiuser power control problem in a frequency-selective interference channel. In particular, the aim of this work is to develop an iterative algorithm for user power control and bit loading on OFDM subcarriers, which minimizes the sum power under a constraint on the user bit rates. The performance of the proposed algorithm is compared against two previous algorithms in terms of sum power, probability of convergence within a fixed number of iterations, and mean number of iterations required for convergence.
Flatten hierarchies for large-scale hierarchical text categorization

Hierarchies are very popular in organizing documents and web pages, hence automated hierarchical classification techniques are desired. However, the current dominant hierarchical approach of top-down method suffers accuracy decrease compared with flat classification approaches, because of error propagation and bottom nodes' data sparsity. In this paper we flatten hierarchies to relieve such accuracy decrease in top-down method, which aims to make hierarchies both effective enough to make large-scale classification tasks feasible, and simple enough to ensure high classification accuracy. We propose two flattening strategies based on these two causes of the accuracy decrease, and experimental results show that the flattening strategy designed for error propagation is more effective, which suggests that hierarchies with lots of branches at top layers can provide high classification accuracy. Besides, we analyze the computational complexity before and after flattening, which approximately agree with the experimental results.
Active Learning of constraints using incremental approach in semi-supervised clustering

Semi-supervised clustering aims to improve clustering performance by considering user-provided side information in the form of pairwise constraints. We study the active learning problem of selecting must-link and cannot-link pairwise constraints for semi-supervised clustering. We consider active learning in an iterative framework; each iteration queries are selected based on the current clustering outcome and constraints available. We use the neighborhood framework where the pairwise points having the must-link belong to the same neigborhood and cannot-link pairwise points belong to the different neighborhood. If two points belong to the same neighborhood then they belong to the same cluster and viceversa. We will use the Glass Identification Data Set from the UCI machine learning repositories and investigate the improvement in clustering time using the Incremental Clustering. Keywords— Active learning, Semi-supervised clustering, Incremental Approach, Pairwise constraints.
ST-14 Handling Missing Data with Multiple Imputation Using PROC MI in SAS

The multiple imputation was developed as a general method for inference with missing data. Instead replacing the missing observation with a single value, multiple imputation method replaces each missing value with multiple plausible values. PROC MI in SAS creates multiply imputed data sets for incomplete multivariate data. This study reviews multiple imputation as an analytic strategy for missing data and applies PROC MI to impute missing data in a Medical Expenditure Panel Survey.
A continuous-time multi-stage noise-shaping delta-sigma modulator with analog delay

A new continuous-time multi-stage noise-shaping delta-sigma modulator has been designed. This modulator provides high resolution and robust stability characteristics which are the primary advantages of the conventional multi-stage noise-shaping architecture. At the same time, previous critical challenges that degraded the overall performance of multi-stage noise-shaping delta-sigma modulators are eliminated through several unique techniques. Additionally, these techniques relax the requirements of each component of the proposed delta-sigma modulator. As a result, this new delta-sigma modulator architecture can provide several advantages that are not obtainable in other modulator architectures. Thesis Supervisor: Hae-Seung Lee Title: Professor
Invariant Moments based War Scene Classification using ANN and SVM: A Comparative Study

- In this paper we are trying to classify a war scene from the natural scene. For this purpose two set of image categories are taken viz., opencountry & war tank. By using Invariant Moments features are extracted from the images. The extracted features are trained and tested with (i) Artificial Neural Networks (ANN) using feed forward back propagation algorithm and (ii) Support Vector Machines (SVM) using radial basis kernel function with p=5. The comparative results are proving efficiency of Support Vector Machines towards war scene classification problems by using Invariant Moment feature extraction method. It can be concluded that the proposed work significantly and directly contributes to scene classification and its new applications. The complete work is experimented in Matlab 7.6. 0 using real world dataset.
Reading movies: an integrated DVD player for browsing movies and their scripts

We have built over the last few years an integrated browser and query interface for watching a movie synchronized with its script. The system is demonstrated with the movie 'The Wizard of Oz', which was fully synchronized with its script using automated tools described elsewhere. Searchable textual descriptions and synchronization metadata (hyperlinks) are generated dynamically on the server side. Video content navigation runs in a DVD player embedded in a web browser on the client side. Thus, the owner of the DVD can connect to our site and view, search and navigate the movie shot by shot.
Web Resource Cacheable Edge Device in Fog Computing

The bandwidth of the network is precious. As cloud computing provides powerful computation power and more and more devices are connected to the Internet there is a need to seek for methods to reduce the traffic. Fog computing was proposed to adding the computation and storage ability to the edge devices. The requests from the end device can be pre-processed or even responded at the edge device. In this work, the ability of Web Resource Caching is added to the edge device to serve as a caching proxy server. In order to obtain more caching storage, the end devices are also exploits to provide some caching space. As the whole caching storage is enlarged, large resource like media files are able to be cached near to the end devices once any of the end devices in the same network just recently requested it. The result shows that the proposed architecture has better downloading latency compares to the single caching proxy approach.
The Influential Motivations of Green IT Device Use and the Role of Reference Group Perspective

In this study we investigate the determinants of behavior intention to use green IT device for reducing electronic consumption by focusing on the end user aspects of a pro-environmental behavior. We tried to understand motivation theory in explaining the causal relationship between motivation aspects and perceived usefulness. By using a reference group theory, we emphasized on how the reference group moderates the motivations and perceived usefulness relationship. We used Partial Least Square (PLS) to analyze the data sample of 104 and found that intrinsic motivation (perceived enjoyment) is significantly related to the perceived usefulness as well as extrinsic motivation (saving money, legislative pressure) is strongly related to the perceived usefulness. In sum, the perceived usefulness has a strong impact on sustainable use of green IT device. Also, we found that a reference group moderates partially the independent variables and mediation variable.
Top-k high utility itemset mining based on utility-list structures

Top-k high utility itemset mining refers to the discovery of top-k patterns using a user-specified value k by considering the utility of items in a transactional database. Since existing top-k high utility itemset mining algorithms are based on the pattern-growth method, they search the patterns in two steps. Therefore, the generation of many candidates and additional database scan for calculating exact utilities are unavoidable. In this paper, we propose a new algorithm, TKUL-Miner, to mine top-k high utility itemsets efficiently. It utilizes a new utility-list structure which stores necessary information at each node on the search tree for mining the itemsets. The proposed algorithm has a strategy using search order for specific region to raise the border minimum utility threshold rapidly. Moreover, two additional strategies for calculating smaller overestimated utilities are suggested to prune unpromising itemsets effectively. Experimental results on various datasets showed that the TKUL-Miner outperforms other recent algorithms both in runtime and memory efficiency.
Attentional Modulation of Motion Integration of Individual Neurons in the Middle Temporal Visual Area

We examined how spatially directed attention affected the integration of motion in neurons of the middle temporal (MT) area of visual cortex. We recorded from single MT neurons while monkeys performed a motion detection task under two attentional states. Using 0% coherent random dot motion, we estimated the optimal linear transfer function (or kernel) between the global motion and the neuronal response. This linear kernel filtered the random dot motion across direction, speed, and time. Slightly less than one-half of the neurons produced reasonably well defined kernels that also tended to account for both the directional selectivity and responses to coherent motion of different strengths. This subpopulation of cells had faster, more transient, and more robust responses to visual stimuli than neurons with kernels that did not contain well defined regions of integration. For those neurons that had large attentional modulation and produced well defined kernels, we found attention scaled the temporal profile of the transfer function with no appreciable shift in time or change in shape. Thus, for MT neurons described by a linear transfer function, attention produced a multiplicative scaling of the temporal integration window.
High Resolution Fluorescence Imaging of Cancers Using Lanthanide Ion-Doped Upconverting Nanocrystals

During the last decade inorganic luminescent nanoparticles that emit visible light under near infrared (NIR) excitation (in the biological window) have played a relevant role for high resolution imaging of cancer. Indeed, semiconductor quantum dots (QDs) and metal nanoparticles, mostly gold nanorods (GNRs), are already commercially available for this purpose. In this work we review the role which is being played by a relatively new class of nanoparticles, based on lanthanide ion doped nanocrystals, to target and image cancer cells using upconversion fluorescence microscopy. These nanoparticles are insulating nanocrystals that are usually doped with small percentages of two different rare earth (lanthanide) ions: The excited donor ions (usually Yb3+ ion) that absorb the NIR excitation and the acceptor ions (usually Er3+, Ho3+ or Tm3+), that are responsible for the emitted visible (or also near infrared) radiation. The higher conversion efficiency of these nanoparticles in respect to those based on QDs and GNRs, as well as the almost independent excitation/emission properties from the particle size, make them particularly promising for fluorescence imaging. The different approaches of these novel nanoparticles devoted to “in vitro” and “in vivo” cancer imaging, selective targeting and treatment are examined in this review.
The Point of View of Undergraduate Health Students on Interprofessional Collaboration: A Thematic Analysis

Interprofessional education (IPE) is essential to prepare future professionals for interprofessional collaboration (IPC). Learning together is essential for students because it is a way to understand the roles of other colleagues, improve their skills, knowledge, competencies, and attitudes to collaborate with the interprofessional teams. To explore how undergraduate students who attend IPE courses define IPC, a qualitative study using semistructured interviews followed by a thematic analysis was performed. Four main themes were identifed: IPC as a resource, requirements for IPC, emotions linked to IPC, and tutor's role to facilitate students' perception of IPC. Students considered IPE important to build IPC, where clinical placement tutors play a key role. The most important findings of the present study include the students' considerations about the importance of IPE when building their IPC definition and the key role played by the tutor during the placement in building IPC in clinical practice.
Chapter 1 Supervised Control of Complex Systems Satisfying a Bounded Amplitude Performance Criterion

Hybrid dynamical systems arise when discrete and continuous systems are interfaced together. A common example of such systems are found in complex computer-controlled systems arising in manufacturing and process control. There are two fundamental approaches to the design of such systems. The rst approach nds its roots in traditional control systems synthesis methods such as Lyapunov stability theory and modern robust control methods. The second approach originates in computer science and is primarily concerned with the ver-iication of programs through symbolic model checking algorithms. The primary objective of this talk is show how both of these perspectives can be integrated together to provide a systematic approach for the design of complex dynamical systems. In particular, this talk introduces a method for coordinating the behaviour of several non-linear model following systems. Coordination is attained by switching between controllers for the various subsystems so that a bounded amplitude performance criterion is met by the entire system. We use recent results in the synthesis and analysis of switched bounded amplitude linear parameter varying (LPV) control systems to estimate bounds on modal switching times and then show how these switching time estimates can be used to extract a timed automaton or Petri net representing the overall system's timed behaviour. The timed automaton represents a high-level symbolic representation of the system's behaviour which can be analyzed using recent results extending symbolic model checking methods to real-time systems.