Can twice weekly hemodialysis expand patient access under resource constraints?

The convention of prescribing hemodialysis on a thrice weekly schedule began empirically when it seemed that this frequency was convenient and likely to treat symptoms for a majority of patients. Later, when urea was identified as the main target and marker of clearance, studies supported the prevailing notion that thrice weekly dialysis provided appropriate clearance of urea. Today, national guidelines on hemodialysis from most countries recommend patients receive at least thrice weekly therapy. However, resource constraints in low‐ and middle‐income countries (LMIC) have resulted in a substantial proportion of patients using less frequent hemodialysis in these settings. Observational studies of patients on twice weekly dialysis show that twice weekly therapy has noninferior survival rates compared with thrice weekly therapy. In fact, models of urea clearance also show that twice weekly therapy can meet urea clearance “targets” if patients have significant residual function or if they follow a protein‐restricted diet, as may be common in LMIC. Greater reliance on twice weekly therapy, at least at the start of hemodialysis, therefore has potential to reduce health care costs and increase access to renal replacement therapy in low‐resource settings; however, randomized control trials are needed to better understand long‐term outcomes of twice versus thrice weekly therapy.     

Characterization of Cementitious Materials Exposed to Freezing and Thawing in Combination with Deicing Salts Using 3D Scans

Surface deterioration of concrete subjected to freezing and thawing in combination with deicing salts is one of the most important factors determining the durability of concrete infrastructure in cold climates. The freeze–thaw deicing salt (FTDS) resistance of cementitious materials can be determined by the capillary suction of de-icing chemicals and freeze–thaw (CDF) test. Specimens are subjected to repeated freeze–thaw cycles with simultaneous addition of deicing salt and the amount of material scaled off near the surface is determined. For concretes with adequate FTDS resistance, this test method works very well. However, specimens with unknown performance often experience increased edge scaling. This leads to a falsification of results and consequently to an underestimation of the actual freeze–thaw resistance. In materials research, however, concretes with high levels of surface deterioration are studied in order to investigate various factors of influence on the freeze–thaw resistance of concretes in a targeted manner. This article presents a novel methodology that delivers new information regarding surface deterioration of CDF samples using high-resolution 3D scan data. Change of volume is used to support deterioration results of the standard CDF methodology. Increase of surface area is used to estimate change in roughness of samples.     

TWC LOGD: A portal for linked open government data ecosystems

International open government initiatives are releasing an increasing volume of raw government datasets directly to citizens via the Web. The transparency resulting from these releases not only creates new application opportunities but also imposes new burdens inherent to large-scale distributed data integration, collaborative data manipulation and transparent data consumption. The Tetherless World Constellation (TWC) at Rensselaer Polytechnic Institute (RPI) has developed the Semantic Web-based TWC LOGD portal to support the deployment of linked open government data (LOGD). The portal is both an open source infrastructure supporting linked open government data production and consumption and a vibrant community portal that educates and serves the growing international open government community of developers, data curators and end users. This paper motivates and introduces the TWC LOGD Portal and highlights innovative aspects and lessons learned.     

A Two-Step Filtering approach for detecting maize and soybean phenology with time-series MODIS data

The crop developmental stage represents essential information for irrigation scheduling/fertilizer management, understanding seasonal ecosystem carbon dioxide (CO₂) exchange, and evaluating crop productivity. In this study, we devised an approach called the Two-Step Filtering (TSF) for detecting the phenological stages of maize and soybean from time-series Wide Dynamic Range Vegetation Index (WDRVI) data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m observations. The TSF method consists of a Two-Step Filtering scheme that includes: (i) smoothing the temporal WDRVI data with a wavelet-based filter and (ii) deriving the optimum scaling parameters from shape-model fitting procedure. The date of key crop development stages are then estimated by using the optimum scaling parameters and an initial value of the specific phenological date on the shape model, which are preliminary defined in reference to ground-based crop growth stage observations. The shape model is a crop-specific WDRVI curve with typical seasonal features, which were defined by averaging smoothed, multi-year WDRVI profiles from MODIS 250-m data collected over irrigated maize and soybean study sites.In this study, the TSF method was applied to MODIS-derived WDRVI data over a 6-year period (2003 to 2008) for two irrigated sites and one rainfed site planted to either maize or soybean as part of the Carbon Sequestration Program (CSP) at the University of Nebraska-Lincoln. A comparison of satellite-based retrievals with ground-based crop growth stage observations collected by the CSP over the six growing seasons for these three sites showed that the TSF method can accurately estimate the date of four key phenological stages of maize (V2.5: early vegetative stage, R1: silking stage, R5: dent stage and R6: maturity) and soybean (V1: early vegetative stage, R5: beginning seed, R6: full seed and R7: beginning maturity). The root mean square error (RMSE) of phenological-stage estimation for maize ranged from 2.9 [R1] to 7.0 [R5] days and from 3.2 [R6] to 6.9 [R7] days for soybean, respectively. In addition, the TSF method was also applied for two years (2001 and 2002) over eastern Nebraska to test its ability to characterize the spatio-temporal patterns of these key phenological stages over a larger geographic area. The MODIS-derived crop phenological stage dates agreed well with the statistical crop progress data reported by the United State Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) for eastern Nebraska's three crop agricultural statistic districts (ASDs). At the ASD-level, the RMSE of phenological-stage estimation ranged from 1.6 [R1] to 5.6 [R5] days for maize and from 2.5 [R7] to 5.3 [R5] days for soybean.     

Modular architecture of the microfactories for automatic micro-assembly

The construction of a new generation of MEMS which includes micro-assembly steps in the current microfabrication process is a big challenge. It is necessary to develop new production means named micromanufacturing systems in order to perform these new assembly steps. The classical approach called “top-down” which consists in a functional analysis and a definition of the tasks sequences is insufficient for micromanufacturing systems. Indeed, the technical and physical constraints of the microworld (e.g. the adhesion phenomenon) must be taken into account in order to design reliable micromanufacturing systems. A new method of designing micromanufacturing systems is presented in this paper. Our approach combines the general “top-down” approach with a “bottom-up” approach which takes into account technical constraints. The method enables to build a modular architecture for micromanufacturing systems. In order to obtain this modular architecture, we have devised an original identification technique of modules and an association technique of modules. This work has been used to design the controller of an experimental robotic micro-assembly station.     

Comparison of paper-and-pencil versus Web administration of the Youth Risk Behavior Survey (YRBS): Participation,data quality,and perceived privacy and anonymity

The Youth Risk Behavior Surveillance System (YRBSS) monitors priority health-risk behaviors among US high school students. To better understand the ramifications of changing the YRBSS from paper-and-pencil to Web administration, in 2008 the Centers for Disease Control and Prevention conducted a study comparing these two modes of administration. Eighty-five schools in 15 states agreed to participate in the study. Within each participating school, four classrooms of students in grades 9 or 10 were randomly assigned to complete the Youth Risk Behavior Survey questionnaire in one of four conditions (in-class paper-and-pencil, in-class Web without programmed skip patterns, in-class Web with programmed skip patterns, and “on your own” Web without programmed skip patterns). Findings included less missing data for the paper-and-pencil condition (1.5% vs. 5.3%, 4.4%, 6.4%; p < .001), less perceived privacy and anonymity among respondents for the in-class Web conditions, and a lower response rate for the “on your own” Web condition than for in-class administration by either mode (28.0% vs. 91.2%, 90.1%, 91.4%; p < .001). Although Web administration might be useful for some surveys, these findings do not favor the use of a Web survey for the YRBSS.     

Factors influencing teachers’ intention to use technology: Model development and test

Among the key players in any effective integration of technology in teaching and learning is the teacher. Despite the research that has been conducted to examine the factors that explain teachers’ intention to use technology, few have developed a model to statistically explain the interactions among these factors and how they influence teachers’ intention to use technology. Five variables (perceived usefulness, perceived ease of use, subjective norm, facilitating conditions, and attitude towards use) and behavioural intention to use technology were used to build a research model in this study and structural equation modelling was used for parameter estimation and model testing. Self-reported data were gathered from 592 teachers from schools in Singapore. Results revealed a good model fit and of the nine hypotheses formulated in this study, eight were supported. Subjective norm was not found to be a significant influence on teachers’ intention to use technology while the other four variables were.     

An assessment of the influence of perceived enjoyment and attitude on the intention to use technology among pre-service teachers: A structural equation modeling approach

This study examined the influence of perceived enjoyment on pre-service teachers’ intention to use technology, in addition to re-appraising the role of attitude toward use in the Technology Acceptance Model (TAM; Davis, Bagozzi, &; Warshaw, 1989). One hundred and fifty-three participants in Singapore completed a survey questionnaire measuring their responses to five constructs from a research model that was developed specifically for the study. Structural Equation Modeling (SEM) showed that perceived enjoyment was a significant predictor of perceived usefulness, perceived ease of use, and intention to use technology. The findings of this study support the view of Davis et al. (1989) from over 30 years ago that attitude toward use contributes only modestly to the TAM.     

Dynamic Connectivity for Axis-Parallel Rectangles

In this paper we give a fully dynamic data structure to maintain the connectivity of the intersection graph of n axis-parallel rectangles. The amortized update time (insertion and deletion of rectangles) is and the query time (deciding whether two given rectangles are connected) is O(1). It slightly improves the update time (O(n ^0.94)) of the previous method while drastically reducing the query time (near O(n ^1/3)). In addition, our method does not use fast matrix multiplication results and supports a wider range of queries. This work has been supported by an NSERC grant.