Miracle-dz/UniFAR-FTUs huggingface dataset:
Applying Sparse Machine Learning Methods to Twitter: Analysis of the 2012 Change in Pap Smear Guidelines. A Sequential Mixed-Methods Study

Background It is difficult to synthesize the vast amount of textual data available from social media websites. Capturing real-world discussions via social media could provide insights into individuals’ opinions and the decision-making process. Objective We conducted a sequential mixed methods study to determine the utility of sparse machine learning techniques in summarizing Twitter dialogues. We chose a narrowly defined topic for this approach: cervical cancer discussions over a 6-month time period surrounding a change in Pap smear screening guidelines. Methods We applied statistical methodologies, known as sparse machine learning algorithms, to summarize Twitter messages about cervical cancer before and after the 2012 change in Pap smear screening guidelines by the US Preventive Services Task Force (USPSTF). All messages containing the search terms “cervical cancer,” “Pap smear,” and “Pap test” were analyzed during: (1) January 1–March 13, 2012, and (2) March 14–June 30, 2012. Topic modeling was used to discern the most common topics from each time period, and determine the singular value criterion for each topic. The results were then qualitatively coded from top 10 relevant topics to determine the efficiency of clustering method in grouping distinct ideas, and how the discussion differed before vs. after the change in guidelines . Results This machine learning method was effective in grouping the relevant discussion topics about cervical cancer during the respective time periods (~20% overall irrelevant content in both time periods). Qualitative analysis determined that a significant portion of the top discussion topics in the second time period directly reflected the USPSTF guideline change (eg, “New Screening Guidelines for Cervical Cancer”), and many topics in both time periods were addressing basic screening promotion and education (eg, “It is Cervical Cancer Awareness Month! Click the link to see where you can receive a free or low cost Pap test.”) Conclusions It was demonstrated that machine learning tools can be useful in cervical cancer prevention and screening discussions on Twitter. This method allowed us to prove that there is publicly available significant information about cervical cancer screening on social media sites. Moreover, we observed a direct impact of the guideline change within the Twitter messages.
Setting International Standards: Technological Rationality or Primacy of Power?

Standards have become one of the most important nontariff barriers to trade, especially national product standards that specify design or performance characteristics of manufactured goods. Divergent national standards often inhibit trade, whereas regional and international standards increasingly serve as instruments of trade liberalization. Consequently, the setting of international standards--seemingly technical and apolitical--is rapidly becoming an issue of economic and political salience. But who sets international standards? Who wins, who loses? This article offers a fresh analytical approach to the study of international standards, which the authors call the institutional complementarities approach. It builds on insights from realism and the "Battle of the Sexes" coordination game but emphasizes complementarities of historically conditioned standardization systems at the national level with the institutional structure of standardization at the international level. It posits that, after controlling for other factors that influence involvement in international standardization, differences in institutional complementarities play a critical though largely accidental role in placing firms from different countries or regions in a first- or second-mover position when standardization becomes global. The authors illustrate the insightfulness of this approach through statistical analyses of the first scientific set of data on standards use and standardization, collected by the authors through an international online survey.
PUBLIC HEALTH NUTRITION AND EPIDEMIOLOGY Low levels of food involvement and negative affect reduce the quality of diet in women of lower educational attainment

Background: Women of lower educational attainment tend to have poorer quality diets and lower food involvement (an indicator of the priority given to food) than women of higher educational attainment. The present study reports a study of the role of food involvement in the relationship between educational attainment and quality of diet in young women. Methods: The first phase uses six focus group discussions (n = 28) to explore the function of food involvement in shaping the food choices of women of lower and higher educational attainment with young children. The second phase is a survey that examines the relationship between educational attainment and quality of diet in women, and explores the role of mediating factors identified by the focus group discussions. Results: The focus groups suggested that lower food involvement in women of lower educational attainment might be associated with negative affect (i.e. an observable expression of negative emotion), and that this might mean that they did not place a high priority on eating a good quality diet. In support of this hypothesis, the survey of 1010 UK women found that 14% of the effect of educational attainment on food involvement was mediated through the woman’s affect (P £ 0.001), and that 9% of the effect of educational attainment on quality of diet was mediated through food involvement (P £ 0.001). Conclusions: Women who leave school with fewer qualifications may have poorer quality diets than women with more qualifications because they tend to have a lower level of food involvement, partly attributed to a more negative affect. Interventions to improve women’s mood may benefit their quality of
Prevalence of Musculoskeletal Pain in Adults with Developmental Disabilities

Purpose: The goal of our study was to assess the prevalence of specific musculoskeletal pain complaints in patients with developmental disabilities and how they compare to the general population. Method: A retrospective chart review was conducted at an outpatient hospital based primary care office serving patients with developmental disabilities in Northern New Jersey Results: Results showed an overall prevalence of 17.02% of musculoskeletal complaints in this population. This is similar to their peers without developmental disabilities. Of the patients seen, those with the diagnosis of cerebral palsy had the highest prevalence of musculoskeletal pain. Low back and knee pain were the most common complaints. Females were noted to be more than two times likely than males to have musculoskeletal pain, but no significant difference was found between age groups. Conclusions: We concluded that individuals with developmental diseases require equal vigilance for musculoskeletal pain because of similar prevalence as the general population and likelihood of communication deficits with intellectual disabilities
Fertilization of a native grassland in the 'Depresion del Rio Salado', Province of Buenos Aires: herbage dry matter accumulation and botanical composition.

The objective of this study was the evaluation of herbage production and botanical composition of a native grassland, (“flechilla? ‘), of the Salado River Basin fertilized, during 3 years, with 0, 381, or 762 kg&a-‘*yr-’ of ammonium sulphate alone or combined with 0 or 208 kg-ha-‘*yr-’ of triple superphosphate. Annual dry matter accumulation of herbage, (ADMA), was restricted by a negative water-balance in the soil during the first experimental year. A linear response to ammonium sulphate rate, irrespective of triple superphosphate, was observed. In the following years the relationship between ADMA and ammonium sulphate became progressively quadratic, and it was manifest firstly in the plots fertilized with superphosphate. The response to superphosphate seemed to be due to an environmentally stimulated growth demand more than to a phosphorus deficiency in the soil. Ammonium sulphate promoted the growth of gramhtoids and decreased that of legumes and forbs. Superphosphate increased the proportion of legumes and ameliorated the detrimental effect of ammonium sulphate, but only few species reflected the effect of fertilization. Because (a) the sward reacted remarkably to the addition of a nitrogenous fertilizer, and (b) its main legume species, Medicogo polymorpha, (annual), was scarce, it is suggested that the sward’s herbage production could be substantially increased by its enrichment with perennial legumes provided that their growth and expansion were assisted by periodic additions of a phosphorous fertilizer.
Exhaust Gas Recirculation Control based on Artificial Neural Network

In this paper, the new exhaust gas recirculation (EGR) control scheme for Diesel engine is proposed. EGR is so far the most promising approach to reduce in-cylinder NOx formation. The design of EGR control system for modern Diesel engine is based on MAP which is obtained from experiments. The many experiments with different engine speed and loads are necessary. MAP from experiments is not accurate, cannot be used for all the range exactly in case of engine running conditions. Therefore, it is very difficult to design PID controller based on MAP. Moreover, the robust performance cannot be achieved. To overcome these problems, the control scheme based on artificial neural network (ANN) is proposed. This controller has good robust performance and rapid adjustment. From experiments, the system using this controller can achieve good performance.
Prevalence of Cardiovascular Risk Factors Among Military Personnel in Southern Iran

Background: The incidence of cardiovascular disease (CVD) is rapidly increasing at an alarming rate worldwide and is currently considered as the leading cause of death in both developing and developed countries. The aim of the present study is to determine the prevalence and clear analysis of cardiovascular risk factors among army population and provide a guideline for improving the health status of army personnel. Methods: This is a cross-sectional study on the prevalence of cardiovascular risk factors among 341 male subjects from a military population in southern Iran. Each eligible participant was evaluated in the military clinic in Shiraz, Southern Iran. Information regarding demographic and life style were obtained from each subject. Arterial blood pressure, weight, height, body mass index (BMI), waist circumference (WC) and hip circumference (HC), fasting blood glucose, lipid profile consisting of total cholesterol, LDL, HDL and triglyceride were measured by standard methods. Results: Mean age of the population under study was 35.1±7.5 years. Twenty-nine (8.8%) individuals were hypertensive whereas 108 (32.9%) had blood pressure in the range of pre-hypertension. According to laboratory investigations, 29 (8.5%) participants had glucose intolerance while 6 (1.8%) of them had diabetes mellitus. On the other hand, prevalence of hypertriglyceridemia and hypercholesterolemia were 104 (30.5%) and 114 (33.4%) respectively. Twenty-eight (8.2%) subjects had criteria for metabolic syndrome. Conclusions: Clinical and Para-clinical data indicated that army population in southern Iran had a low level of CVD risk factors that may be related to their life styles.
SimTeG: A Frustratingly Simple Approach Improves Textual Graph Learning

Textual graphs (TGs) are graphs whose nodes correspond to text (sentences or documents), which are widely prevalent. The representation learning of TGs involves two stages: (i) unsupervised feature extraction and (ii) supervised graph representation learning. In recent years, extensive efforts have been devoted to the latter stage, where Graph Neural Networks (GNNs) have dominated. However, the former stage for most existing graph benchmarks still relies on traditional feature engineering techniques. More recently, with the rapid development of language models (LMs), researchers have focused on leveraging LMs to facilitate the learning of TGs, either by jointly training them in a computationally intensive framework (merging the two stages), or designing complex self-supervised training tasks for feature extraction (enhancing the first stage). In this work, we present SimTeG, a frustratingly Simple approach for Textual Graph learning that does not innovate in frameworks, models, and tasks. Instead, we first perform supervised parameter-efficient fine-tuning (PEFT) on a pre-trained LM on the downstream task, such as node classification. We then generate node embeddings using the last hidden states of finetuned LM. These derived features can be further utilized by any GNN for training on the same task. We evaluate our approach on two fundamental graph representation learning tasks: node classification and link prediction. Through extensive experiments, we show that our approach significantly improves the performance of various GNNs on multiple graph benchmarks.
Alcoholism causes alveolar macrophage zinc deficiency and immune dysfunction.

RATIONALE Alcohol use disorders cause oxidative stress in the lower airways and increase susceptibility to pneumonia and lung injury. Currently, no therapeutic options exist to mitigate the pulmonary consequences of alcoholism. OBJECTIVES We recently determined in an animal model that alcohol ingestion impairs pulmonary zinc metabolism and causes alveolar macrophage immune dysfunction. The objective of this research is to determine the effects of alcoholism on zinc bioavailability and alveolar macrophage function in human subjects. METHODS We recruited otherwise healthy alcoholics (n = 17) and matched control subjects (n = 17) who underwent bronchoscopy for isolation of alveolar macrophages, which were analyzed for intracellular zinc, phagocytic function, and surface expression of granulocyte-macrophage colony-stimulating factor receptor; all three of these indices are decreased in experimental models. MEASUREMENTS AND MAIN RESULTS Alcoholic subjects had normal serum zinc, but significantly decreased alveolar macrophage intracellular zinc levels (adjusted means [SE], 718 [41] vs. 948 [25] RFU/cell; P < 0.0001); bacterial phagocytosis (adjusted means [SE], 1,027 [48] vs. 1,509 [76] RFU/cell; P < 0.0001); and expression of granulocyte-macrophage colony-stimulating factor receptor β subunit (adjusted means [SE], 1,471 [42] vs. 2,114 [35] RFU/cell; P < 0.0001]. Treating alveolar macrophages with zinc acetate and glutathione in vitro increased intracellular zinc levels and improved their phagocytic function. CONCLUSIONS These novel clinical findings provide evidence that alcohol abuse is associated with significant zinc deficiency and immune dysfunction within the alveolar space and suggest that dietary supplementation with zinc and glutathione precursors could enhance airway innate immunity and decrease the risk for pneumonia or lung injury in these vulnerable individuals.
False Drop Analysis of Set Retrieval with Signature Files

Modern database systems have to support complex data objects, which appear in advanced data models such as object-oriented data models and nested relational data models. Set-valued objects are basic constructs to build complex structures in those models. Therefore, e cient processing of setvalued object retrieval (simply, set retrieval) is an important feature required of advanced database systems. Our previous work proposed a basic scheme to apply superimposed coded signature les to set retrieval and showed its potential advantages over the B-tree index based approach using a performance analysis model. Retrieval with signature les is always accompanied by mismatches called false drops, and proper control of the false drops is indispensable in the signature le design. This study intensively analyzes the false drops in set retrieval with signature les. First, schemes to use signature les are presented to process set retrieval involving \has-subset," \is-subset," \hasintersection," and \is-equal" predicates, and generic formulas estimating the false drops are derived. Then, three sets of concrete formulas are derived in three ways to estimate the false drops in the four types of set retrieval. Finally, their estimates are validated with computer simulations, and advantages and disadvantages of each set of the false drop estimation formulas are discussed. The analysis shows that proper choice of estimation formulas gives quite accurate estimates of the false drops in set retrieval with signature les. key words: access method, complex object, advanced data models, signature le, false drop, set retrieval
Power-optimized scheduling server for real-time tasks

In this paper we propose a novel scheduling framework for a dynamic real-time environment with energy constraints. This framework dynamically adjusts the CPU voltage/frequency so that no task in the system misses its deadline and the total energy savings of the system are maximized. In this paper we consider only realistic, discrete-level speeds. Each task in the system consumes a certain amount of energy, which depends on a speed chosen for execution. The process of selecting speeds for execution while maximizing the energy savings of the system requires the exploration of a large number of combinations, which is too time consuming to be computed online. Thus, we propose an integrated heuristic methodology which executes an optimization procedure in a. low computation time. This scheme allows the scheduler to handle power-aware real-time tasks with low cost while maximizing the use of the available resources and without jeopardizing the temporal constraints of the system. Simulation results show that our heuristic methodology is able to generate power-aware scheduling solutions with near-optimal performance.
Towards Vulnerability Assessment as a Service in OpenStack Clouds

Efforts towards improving security in cloud infrastructures recommend regulatory compliance approaches such as HIPAA and PCI DSS. Similarly, vulnerability assessments are imperatives for fulfilling these regulatory compliance requirements. Nevertheless, conducting vulnerability assessments in cloud environments requires approaches different from those found in traditional computing. Factors such as multi-tenancy, elasticity, self-service and cloud-specific vulnerabilities must be considered. Furthermore, the Anything-as-a-Service model of the cloud stimulates security automation and user-intuitive services. In this paper, we tackle the challenge of efficient vulnerability assessments at the system level, in particular for core cloud applications. Within this scope, we focus on the use case of a cloud administrator. We believe the security of the underlying cloud software is crucial to the overall health of a cloud infrastructure since these are the foundations upon which other applications within the cloud function. We demonstrate our approach using OpenStack and through our experiments prove that our prototype implementation is effective at identifying "OpenStack-native" vulnerabilities. We also automate the process of identifying insecure configurations in the cloud and initiate steps for deploying Vulnerability Assessment-as-a-Service in OpenStack.
Support vector machines (SVMs) for monitoring network design

In this paper we present a hydrologic application of a new statistical learning methodology called support vector machines (SVMs). SVMs are based on minimization of a bound on the generalized error (risk) model, rather than just the mean square error over a training set. Due to Mercer's conditions on the kernels, the corresponding optimization problems are convex and hence have no local minima. In this paper, SVMs are illustratively used to reproduce the behavior of Monte Carlo–based flow and transport models that are in turn used in the design of a ground water contamination detection monitoring system. The traditional approach, which is based on solving transient transport equations for each new configuration of a conductivity field, is too time consuming in practical applications. Thus, there is a need to capture the behavior of the transport phenomenon in random media in a relatively simple manner. The objective of the exercise is to maximize the probability of detecting contaminants that exceed some regulatory standard before they reach a compliance boundary, while minimizing cost (i.e., number of monitoring wells). Application of the method at a generic site showed a rather promising performance, which leads us to believe that SVMs could be successfully employed in other areas of hydrology. The SVM was trained using 510 monitoring configuration samples generated from 200 Monte Carlo flow and transport realizations. The best configurations of well networks selected by the SVM were identical with the ones obtained from the physical model, but the reliabilities provided by the respective networks differ slightly.
Counterfactual Graphical Models for Longitudinal Mediation Analysis With Unobserved Confounding

Questions concerning mediated causal effects are of great interest in psychology, cognitive science, medicine, social science, public health, and many other disciplines. For instance, about 60% of recent papers published in leading journals in social psychology contain at least one mediation test (Rucker, Preacher, Tormala, & Petty, 2011). Standard parametric approaches to mediation analysis employ regression models, and either the "difference method" (Judd & Kenny, 1981), more common in epidemiology, or the "product method" (Baron & Kenny, 1986), more common in the social sciences. In this article, we first discuss a known, but perhaps often unappreciated, fact that these parametric approaches are a special case of a general counterfactual framework for reasoning about causality first described by Neyman (1923) and Rubin (1924) and linked to causal graphical models by Robins (1986) and Pearl (2006). We then show a number of advantages of this framework. First, it makes the strong assumptions underlying mediation analysis explicit. Second, it avoids a number of problems present in the product and difference methods, such as biased estimates of effects in certain cases. Finally, we show the generality of this framework by proving a novel result which allows mediation analysis to be applied to longitudinal settings with unobserved confounders.
Explaining Africa’s Export Performance – Taking a New Look

Africa’s export performance over recent decades is typically portrayed as being poor. This paper takes a new look at the record, using data on the volume rather than the value of African exports. When analysed in volume terms a quite different picture of African export performance emerges. According to UNCTAD data, between 1990-2002 for non-oil exporters, export volumes actually increased by over 130%. This impressive supply-side performance has not been properly documented. Previous studies have fixed too much attention to the value of African exports, something which, as primary commodity exporters, is largely beyond their control. The study uses estimates of volume of exports, available from UNCTAD, to explain African trade performance. Using a dynamic panel data analysis for 48 African countries over the period 1987-2002, the key determinants of export performance are ascertained. It is shown that there has been a highly significant negative relationship between fluctuations in prices and export volumes. This is contrary to standard economic theory, and shows that whereas in well-functioning markets exports are supposed to respond positively to changes in relative prices (i.e. higher prices lead to higher exports), in Africa the reverse relationship has held (i.e. lower prices have lead to higher exports). In the light of the regression results, other fundamental determinants of export performance are analysed, and the implications of the findings for policy makers are subsequently discussed.