Miracle-dz/UniFAR-FTUs huggingface dataset:
Physical Activity and Public Health 1-2006 Using Focus Groups in the Consumer Research Phase of a Social Marketing Program to Promote Moderate-Intensity Physical Activity and Walking Trail Use in Sumter County , South Carolina

Introduction The use of social marketing approaches in public health practice is increasing. Using marketing concepts such as the “four Ps” (product, price, place, and promotion), social marketing borrows from the principles of commercial marketing but promotes beneficial health behaviors. Consumer research is used to segment the population and develop a strategy based on those marketing concepts. In a community-based participatory research study, 17 focus groups were used in consumer research to develop a social marketing program to promote walking and other moderate-intensity physical activities. Methods Two phases of focus groups were conducted. Phase 1 groups, which included both men and women, were asked to respond to questions that would guide the development of a social marketing program based on social marketing concepts. Phase 1 also determined the intervention’s target audience, which was irregularly active women aged 35 to 54. Phase 2 groups, composed of members of the target audience, were asked to further define the product and discuss specific promotion strategies. Results Phase 1 participants determined that the program product, or target behavior, should be walking. In addition, they identified price, place, and promotion strategies. Phase 2 participants determined that moderate-intensity physical activity is best promoted using the term exercise and offered suggestions for marketing walking, or exercise, to the target audience. Conclusion There have been few published studies of social marketing campaigns to promote physical activity. In this study, focus groups were key to understanding the target audience in a way that would not have been accomplished with quantitative data alone. The group discussions generated important insights into values and motivations that affect consumers’ decisions to adopt a product or behavior. The focus group results guided the development of a social marketing program to promote physical activity in the target audience in Sumter County, South Carolina. The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, the Centers for Disease Control and Prevention, or the authors’ affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above. www.cdc. gov/pcd/issues/2006/jan/05_0093. htm • Centers for Disease Control and Prevention 1 Ericka L. Burroughs, MA, MPH, Lara E. Peck, MPH, CHES, Patricia A. Sharpe, PhD, MPH, Michelle L. Granner, PhD, Carol A. Bryant, PhD, Regina Fields, MS, CHES
A Method of Spatial Mapping and Reclassification for High-Spatial-Resolution Remote Sensing Image Classification

This paper presents a new classification method for high-spatial-resolution remote sensing images based on a strategic mechanism of spatial mapping and reclassification. The proposed method includes four steps. First, the multispectral image is classified by a traditional pixel-based classification method (support vector machine). Second, the panchromatic image is subdivided by watershed segmentation. Third, the pixel-based multispectral image classification result is mapped to the panchromatic segmentation result based on a spatial mapping mechanism and the area dominant principle. During the mapping process, an area proportion threshold is set, and the regional property is defined as unclassified if the maximum area proportion does not surpass the threshold. Finally, unclassified regions are reclassified based on spectral information using the minimum distance to mean algorithm. Experimental results show that the classification method for high-spatial-resolution remote sensing images based on the spatial mapping mechanism and reclassification strategy can make use of both panchromatic and multispectral information, integrate the pixel- and object-based classification methods, and improve classification accuracy.
Confidence intervals and p‐values in clinical decision making

Clinical trials are usually performed on a sample of people drawn from the population of interest. The results of a trial are, therefore, estimates of what might happen if the treatment were to be given to the entire population of interest. Confidence intervals (CIs) provide a range of plausible values for a population parameter and give an idea about how precise the measured treatment effect is. CIs may also provide some useful information on the clinical importance of results and, like p‐values, may also be used to assess ‘statistical significance’. Although other CIs can be calculated, the 95% CI is usually reported in the medical literature. In the long run, the 95% CI of an estimate is the range within which we are 95% certain that the true population parameter will lie. Despite the usefulness of the CI approach, hypothesis testing and the generation of p‐values are common in the medical literature. The p‐value is often used to express the probability that the observed differences between study groups are due to chance. p‐values provide no information on the clinical importance of results.
Classifying Temporal Relations with Rich Linguistic Knowledge

We examine the task of temporal relation classification. Unlike existing approaches to this task, we (1) classify an event-event or eventtime pair as one of the 14 temporal relations defined in the TimeBank corpus, rather than as one of the six relations collapsed from the original 14; (2) employ sophisticated linguistic knowledge derived from a variety of semantic and discourse relations, rather than focusing on morpho-syntactic knowledge; and (3) leverage a novel combination of rule-based and learning-based approaches, rather than relying solely on one or the other. Experiments with the TimeBank corpus demonstrate that our knowledge-rich, hybrid approach yields a 15‐16% relative reduction in error over a state-of-the-art learning-based baseline system.
LMS-Verify: abstraction without regret for verified systems programming

Performance critical software is almost always developed in C, as programmers do not trust high-level languages to deliver the same reliable performance. This is bad because low-level code in unsafe languages attracts security vulnerabilities and because development is far less productive, with PL advances mostly lost on programmers operating under tight performance constraints. High-level languages provide memory safety out of the box, but they are deemed too slow and unpredictable for serious system software. Recent years have seen a surge in staging and generative programming: the key idea is to use high-level languages and their abstraction power as glorified macro systems to compose code fragments in first-order, potentially domain-specific, intermediate languages, from which fast C can be emitted. But what about security? Since the end result is still C code, the safety guarantees of the high-level host language are lost. In this paper, we extend this generative approach to emit ACSL specifications along with C code. We demonstrate that staging achieves ``abstraction without regret'' for verification: we show how high-level programming models, in particular higher-order composable contracts from dynamic languages, can be used at generation time to compose and generate first-order specifications that can be statically checked by existing tools. We also show how type classes can automatically attach invariants to data types, reducing the need for repetitive manual annotations. We evaluate our system on several case studies that varyingly exercise verification of memory safety, overflow safety, and functional correctness. We feature an HTTP parser that is (1) fast (2) high-level: implemented using staged parser combinators (3) secure: with verified memory safety. This result is significant, as input parsing is a key attack vector, and vulnerabilities related to HTTP parsing have been documented in all widely-used web servers.
Optimization of Circular Ring Microstrip Antenna Using Genetic Algorithm

Circular ring microstrip antennas have several interesting properties that make it attractive in wireless applications. Although several analysis techniques such as cavity model, generalized transmission line model, Fourier-Hankel transform domain and the method of matched asymptotic expansion have been studied by researchers, there is no efficient design tool that has been incorporated with a suitable optimization algorithm. In this paper, the cavity model analysis along with the genetic optimization algorithm is presented for the design of circular ring microstrip antennas. The method studied here is based on the well-known cavity model and the optimization of the dimensions and feed point location of the circular ring antenna is performed via the genetic optimization algorithm, to achieve an acceptable antenna operation around a desired resonance frequency. The antennas designed by this efficient design procedure were realized experimentally, and the results are compared. In addition, these results are also compared to the results obtained by the commercial electromagnetic simulation tool, the FEM based software, HFSS by ANSOFT.
Stochastic Complementarity for Local Control of Discontinuous Dynamics

We present a method for smoothing discontinuous dynamics involving contact and friction, thereby facilitating the use of local optimization techniques for control. The method replaces the standard Linear Complementarity Problem with a Stochastic Linear Complementarity Problem. The resulting dynamics are continuously differentiable, and the resulting controllers are robust to disturbances. We demonstrate our method on a simulated 6-dimensional manipulation task, which involves a finger learning to spin an anchored object by repeated flicking.
Efficient Feature Selection and Classification of Protein Sequence Data in Bioinformatics

Bioinformatics has been an emerging area of research for the last three decades. The ultimate aims of bioinformatics were to store and manage the biological data, and develop and analyze computational tools to enhance their understanding. The size of data accumulated under various sequencing projects is increasing exponentially, which presents difficulties for the experimental methods. To reduce the gap between newly sequenced protein and proteins with known functions, many computational techniques involving classification and clustering algorithms were proposed in the past. The classification of protein sequences into existing superfamilies is helpful in predicting the structure and function of large amount of newly discovered proteins. The existing classification results are unsatisfactory due to a huge size of features obtained through various feature encoding methods. In this work, a statistical metric-based feature selection technique has been proposed in order to reduce the size of the extracted feature vector. The proposed method of protein classification shows significant improvement in terms of performance measure metrics: accuracy, sensitivity, specificity, recall, F-measure, and so forth.
Determination of serum carbohydrate-deficient transferrin in the diagnosis of alcoholic liver disease.

BACKGROUND Alcoholic liver disease (ALD) is a serious and potentially fetal consequence of alcohol use. The diagnosis of ALD is based on alcohol consumption, physical signs and symptoms, and laboratory tests. The aim of the present study was to assess the reliability of carbohydrate-deficient transferrin (CDT) in the diagnosis of ALD. METHODS According to the diagnostic criteria for ALD by the Chinese Medical Association in 1995, 76 patients with ALD, 55 patients with alcoholism, 32 patients with nonalcoholic liver disease (NALD), and 27 healthy subjects (controls) were studied. Serum CDT was assayed by isoelectric focusing immunofixation and Comassie blue staining. The levels of alamine aminotransferase (ALT), aspartate aminotransferase (AST), and gamma glutamyltransferase (GGT) were also examined. RESULTS The positive rate of CDT in the patients with ALD was 93.4%(71/76), which was higher than that in those with alcoholism (52.7%, 29/55, P<0.001), in those with NALD(9.4%, 3/32, P<0.001), and in healthy controls, respectively. The sensitivity and specificity of CDT for ALD was 93.4% and 71.9%, respectively. CONCLUSION CDT may help diagnose alcoholic liver disease.
Opioid Antagonist Naltrexone for the Treatment of Pathological Gambling in Parkinson Disease

AbstractPathological gambling (PG) is a potential complication related to the treatment of Parkinson disease (PD) with dopamine agonists (DA). The cause of this disorder is unknown, but altered dopamine neurotransmission may be involved. ObjectiveWe evaluated the efficacy and tolerability of the opioid antagonist naltrexone in the treatment of PG in PD. MethodsOur cases included 3 patients with PD who developed PG after DA treatment. ResultsPathological gambling did not improve after reduction or discontinuation of DA. These patients responded poorly to serotonin reuptake inhibitors, whereas treatment with opioid antagonist naltrexone resulted in the remission of PG. Naltrexone treatment was well tolerated. In one patient, higher dose of naltrexone resulted in hepatic abnormalities, which resolved after dosage reduction. ConclusionsThe opioid antagonist naltrexone could be an effective option for the treatment of PG in PD.
Computing Pattern in Pervasive Healthcare

The Pervasive or Ubiquitous computing is the embedded technologies which involve computing and communication capability, which seamlessly integrated with the end user. The concept of pervasive computing composed of Artificial Intelligence, Human Computing Interface and Context-aware Computing which makes computing to appear everywhere and anywhere. Many research is going in Pervasive computing in that healthcare is one of the field which is fast growing. The pervasive computing and healthcare is combined and known as Pervasive Healthcare. It address those technologies and concept that integrate healthcare more seamlessly to our everyday life. In this project, a framework using the architectural pattern such as pipe and filter pattern and model view controller pattern is proposed. The framework will provide intricate monitoring, and probabilistic and filter algorithm to take the decision accurately.
A Survey of Interdependent Information Security Games

Risks faced by information system operators and users are not only determined by their own security posture, but are also heavily affected by the security-related decisions of others. This interdependence between information system operators and users is a fundamental property that shapes the efficiency of security defense solutions. Game theory is the most appropriate method to model the strategic interactions between these participants. In this survey, we summarize game-theoretic interdependence models, characterize the emerging security inefficiencies, and present mechanisms to improve the security decisions of the participants. We focus our attention on games with interdependent defenders and do not discuss two-player attacker-defender games. Our goal is to distill the main insights from the state of the art and to identify the areas that need more attention from the research community.
Does influenza vaccination prevent asthma exacerbations in children?

OBJECTIVE Influenza can exacerbate asthma, particularly in children. The effectiveness of influenza vaccine in preventing influenza-related asthma exacerbations, however, is not known. We evaluated influenza vaccine effectiveness in protecting children against influenza-related asthma exacerbations. STUDY DESIGN We conducted a population-based retrospective cohort study with medical and vaccination records in 4 large health maintenance organizations in the United States during the 1993-1994, 1994-1995, and 1995-1996 influenza seasons. We studied children with asthma who were 1 through 6 years of age and who were identified by search of computerized databases of medical encounters and pharmacy dispensings. Main outcome measures were exacerbations of asthma evaluated in the emergency department or hospital. RESULTS Unadjusted rates of asthma exacerbations were higher after influenza vaccination than before vaccination. After adjustment was done for asthma severity by means of a self-control method, however, the incidence rate ratios of asthma exacerbations after vaccination were 0.78 (95% CI: 0.55 to 1.10), 0.59 (0.43 to 0.81), and 0.65 (0.52 to 0.80) compared with the period before vaccination during the 3 influenza seasons. CONCLUSIONS After controlling for asthma severity, we found that influenza vaccination protects against acute asthma exacerbations in children.
Topic-Centered Aggregation of Presentations for Learning Object Repurposing

Large collections of learning content, developed using applications such as Microsoft PowerPoint can be found in numerous institutions and organizations. The reusability of such custom-made learning content are however not supported in traditional applications. There is a need to annotate the available learning objects with suitable meta-data at various level of granularity. This paper investigates the provision of a tool that allows users to compare and efficiently repurpose presentations collected in some institution’s content repository. Our preliminary experiments have shown that it is indeed possible to extend the reusability of learning objects beyond its originally intended usage. By adopting a layered approach of similarity checking, topic-centered content aggregation and repurposing has been achieved. The prototype system has also shed insights on the much-needed support mechanisms for the on-the-job, incremental elicitation of metadata.
Research on weapon system portfolio selection based on combat network modeling

Joint operations and combat system-of-systems encounter have become major developing trends of modern warfare. Weapon system portfolio selection attracts much attention, because it is closely related to the production, deployment and operation of weapons, which is a crucial factor determining the outcome of the war. This paper presents a portfolio selection model of the weapon systems based on the combat network. Firstly, we describe the portfolio selection problem of weapon systems based on the combat network, introduce the concept of operation loop, and analyze difficulties and strategies of the optimization problem. Then on this basis, the combat network of the weapon equipment system is modeled, and the operational capability evaluation index of weapon system-of-systems based on the operational loop is constructed. Portfolio selection model based on combat network is established to maximize the combined combat capability of the weapon as an optimization target, and the capacity demand and the expense restriction as constraints. Finally, we take the missile defense system as a example to demonstrate the whole calculation process and results of the weapon system portfolio selection based on the combat network model. It shows that our proposed method can achieve a very good performance to solve weapon system portfolio selection problems.
Predicting health inspection results from online restaurant reviews

Informatics around public health are increasingly shifting from the professional to the public spheres. In this work, we apply linguistic analytics to restaurant reviews, from Yelp, in order to automatically predict official health inspection reports. We consider two types of feature sets, i.e., keyword detection and topic model features, and use these in several classification methods. Our empirical analysis shows that these extracted features can predict public health inspection reports with over 90% accuracy using simple support vector machines.
Demand-side management: Lighting system based on microcontroller and solar cell supply

The big demand on energy does not go hand in hand with the availability and supply of the required ones. Renewable energy could fills this gap. Utilization of solar cell, to exploit the biggest energy source, for household electricity needs is one of the main alternative energy solution. A maximal power output from solar panels can be obtained if the panels is kept aligned with the sun. To do this tracking process, a feedback structure containing of sensors and microcontroller is used. The design of the solar cell system is a dual axis sun tracker based microcontroller Atmega 32 for use in household Intelligent Lighting provides lights for home automation that makes it easy for homeowners, as well as the implementation of Demand Side Management Peak electricity use at the time can be reduced by methods Peak the clipping. Measurement gives data which reveals that solar cell energy supply with dual-axis sun tracker on peak hours of energy gives success in energy efficiency Demand Side Management. As well as using the 4 sides of the LDR sensor that communicates with a dc motor as the driving on a sun tracker also provides maximum input voltage.
Protein PEGylation Decreases Observed Target Association Rates via a Dual Blocking Mechanisms⃞

PEGylation is an attractive strategy to enhance the therapeutic efficacy of proteins with a short serum half-life. It can be used to extend the serum persistence and to reduce the immunogenicity of proteins. However, PEGylation can also lead to a decrease in the functional activity of the molecule to which it is applied. We constructed site-specifically PEGylated variants of anti-p185HER-2 antibody fragments in the format of a monovalent single-chain variable fragment and a divalent miniantibody and characterized the antigen binding properties in detail. Mass-transport limited BIAcore measurements and binding assays on HER-2-overexpressing cells demonstrated that the immunoreactivity of the antibody fragments is fully maintained after PEGylation. Nevertheless, we found that the attachment of a 20-kDa polyethylene glycol (PEG) moiety led to a reduction in apparent affinity of approximately 5-fold, although in both formats, the attachment site was most distal to the antigen binding regions. This decrease in affinity was observed in kinetic BIAcore measurements as well as in equilibrium binding assays on whole cells. By analysis of the binding kinetics, we could pinpoint this reduction exclusively to slower apparent on rates. Through both experimental and computational analyses, we demonstrate that these reduced on-rates do not arise from diffusion limitations. We show that a mathematical model accounting for both intramolecular and intermolecular blocking mechanisms of the PEG moiety can robustly explain the observed binding kinetics. The results suggest that PEGylation can significantly alter the binding-competent fraction of both ligands and receptors and may help to explain some of the beneficial effects of PEGylation in vivo.