Using the kurtosis correction method to design quality control limits for asymmetrical distributions: A comparative study in Kirkuk city

In this paper, the limits of the mean and the range are constructed when the normal distribution condition is not available for the measured quality (relative humidity in Kirkuk city). By using kurtosis correction as well as Johnson's transform to convert the data to normal distribution, it is appeared to be subjected to the smallest extreme value distribution and then compared the new charts with Shewhart chart using process capability. The results show that the new charts are more efficient and sensitive to changes in the production process, which makes the proposed method of dealing with asymmetric data more benefit

Trends in utilization of deceased donor kidneys based on hepatitis C virus status and impact of public health service labeling on discard

BackgroundKidneys from deceased donors infected with hepatitis C virus (HCV) are underutilized. Most HCV virus‐infected donors are designated as Public Health Service increased donors (PHS‐IR). Impact of PHS and HCV designations on discard is not well studied.MethodsWe queried the UNOS data set for all deceased donor kidneys between January 2015 and December 2018. The final study cohort donors (n = 38 702) were stratified into three groups based on HCV antibody (Ab) and NAT status: (a) Ab−/NAT− (n = 35 861); (b) Ab+/NAT− (n = 973); and (c) Ab±/NAT+ (n = 1868). We analyzed utilization/discard rates of these organs, the impact of PHS‐IR and HCV designations on discard using multivariable two‐level hierarchical logistic regression models, forecasted number of HCV viremic donors/kidneys by 2023.ResultsDuring the study period, (a) the number of viremic donor kidneys increased 2 folds; (b) the multilevel mixed‐effects logistic regression models showed that, overall, the PHS labeling (OR 1.20, CI 95% CI 1.15‐1.29) and HCV designation (OR 2.29; 95% CI 2.15‐2.43) were independently associated with increased risk of discard; (c) contrary to the general perception, PHS‐IR kidneys across all HCV groups, compared to PHS‐IR kidneys were more likely to be discarded; (d) we forecasted that the number of kidneys from HCV viremic donor kidneys might increase from 1376 in 2019 to 2092 in 2023.ConclusionHepatitis C virus viremic kidneys might represent 10%‐15% of deceased donor organ pool soon with the current rate of the opioid epidemic. PHS labeling effect on discard requires further discussion of the utility of this classification.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154409/1/tid13204_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154409/2/tid13204.pd

Physical And Mechanical Characteristics Of Charcoal, Sawdust And Sugarcane Bagasse As Solid Fuel Materials

This paper reports on the physical and mechanical characteristics of briquettes produced from charcoal, sawdust and sugarcane bagasse using molasses with sodium silicate as binders. Charcoal, sawdust and sugarcane bagasse were mixed in respective ratio of 20:20:60, 20:30:50, 20:40:40, 20:50:30 and 20:60:20. The briquettes were produced using Budenberg dial gauge compression machine, with pressure of 64 MPa at 120 seconds dwell time. Physical properties (relaxation ratio, compaction ratio and shattering index) and mechanical property (compressive strength) of the produced briquettes were investigated. Results show that briquette with sample composition of 20:30:50 has better physical properties with relaxation ratio of 1.562, compaction ratio of 7.573 and shatter index of 99.6%, while sample with ratio 20:40:40 has highest compressive strength of 55.43 kN/m2

Fixed-wing MAV attitude stability in atmospheric turbulence-part 2: Investigating biologically-inspired sensors

Challenges associated with flight control of agile fixed-wing Micro Air Vehicles (MAVs) operating in complex environments is significantly different to any larger scale vehicle. The micro-scale of MAVs can make them particularly sensitive to atmospheric disturbances thus limiting their operation. As described in Part 1, current conventional reactive attitude sensing systems lack the necessary response times for attitude control in high turbulence environments. This paper reviews in greater detail novel and emerging biologically inspired sensors, which can sense the disturbances before a perturbation is induced. A number of biological mechanoreceptors used by flying animals are explored for their utility in MAVs. Man-made attempts of replicating mechanoreceptors have thus been reviewed. Bio-inspired flow and pressure-based sensors were found to be the most promising for complementing or replacing current inertial-based reactive attitude sensors. Achieving practical implementations that meet the size, weight and power constraints of MAVs remains a significant challenge. Biological systems were found to rely on multiple sensors, potentially implying a number of research opportunities in the exploration of heterogeneous bio-inspired sensing solution

A novel locus for restless legs syndrome maps to chromosome 19p in an Irish pedigree

Restless legs syndrome (RLS) is a common, sleep-related movement disorder. The symptoms follow a circadian pattern, worsening in the evening or night, leading to sleep disruption and daytime somnolence. Familial forms of RLS have been described and usually display an autosomal dominant pattern of inheritance. To date, linkage analysis has identified nine RLS loci, but no specific causative gene has been reported. Association mapping has highlighted a further four genomic areas of interest. We have conducted a genome-wide linkage analysis in an Irish autosomal dominant RLS pedigree with 11 affected members. Significant linkage was found on chromosome 19p for a series of microsatellite markers, with a maximum two-point LOD score of 3.59 at θ = 0.0 for marker D19S878. Recombination events, identified by haplotype analysis, define a genetic region of 6.57 cM on chromosome 19p13.3, corresponding to an interval of 2.5 Mb. This study provides evidence of a novel RLS locus and provides further evidence that RLS is a genetically heterogenous disorder

Investigation of Thermal Insulation Properties of Biomass Composites

This paper reports on the investigation of thermal properties of Kapok, Coconut fibre and Sugarcane bagasse composite materials using molasses as a binder. The composite materials were moulded into 12 cylindrical samples using Kapok, Bagasse, Coconut fibre, Kapok and Bagasse in the ratios of (70:30; 50:50 and 30:70), Kapok and Coconut fibre in the ratios of (70:30; 50:50 and 30:70), as well as a combination of Kapok, Bagasse and Coconut fibre in ratios of (50:10:40; 50:40:10 and 50:30:20). The sample size is a 60mm diameter with 10mm – 22mm thickness compressed at a constant load of 180N using a Budenberg compression machine. Thermal conductivity and diffusivity tests were carried out using thermocouples and the results were read out on a Digital Multimeter MY64 (Model: MBEB094816), while a Digital fluke K/J thermocouple meter PRD-011 (S/NO 6835050) was used to obtain the temperature measurement for diffusivity. It was observed that of all the twelve samples moulded, Bagasse, Kapok plus Bagasse (50:50), Kapok plus Coconut fibre (50:50) and Kapok plus Bagasse plus Coconut fibre (50:40:10) has the lowest thermal conductivity of 0.0074, 0.0106, 0.0132, and 0.0127 W/(m-K) respectively and the highest thermal resistivity. In this regard, Bagasse has the lowest thermal conductivity followed by Kapok plus Bagasse (50:50), Kapok plus Bagasse plus Coconut fibre (50:40:10) and Kapok plus Coconut fibre (50:50)

Harnessing the NEON data revolution to advance open environmental science with a diverse and data-capable community

It is a critical time to reflect on the National Ecological Observatory Network (NEON) science to date as well as envision what research can be done right now with NEON (and other) data and what training is needed to enable a diverse user community. NEON became fully operational in May 2019 and has pivoted from planning and construction to operation and maintenance. In this overview, the history of and foundational thinking around NEON are discussed. A framework of open science is described with a discussion of how NEON can be situated as part of a larger data constellation—across existing networks and different suites of ecological measurements and sensors. Next, a synthesis of early NEON science, based on \u3e100 existing publications, funded proposal efforts, and emergent science at the very first NEON Science Summit (hosted by Earth Lab at the University of Colorado Boulder in October 2019) is provided. Key questions that the ecology community will address with NEON data in the next 10 yr are outlined, from understanding drivers of biodiversity across spatial and temporal scales to defining complex feedback mechanisms in human–environmental systems. Last, the essential elements needed to engage and support a diverse and inclusive NEON user community are highlighted: training resources and tools that are openly available, funding for broad community engagement initiatives, and a mechanism to share and advertise those opportunities. NEON users require both the skills to work with NEON data and the ecological or environmental science domain knowledge to understand and interpret them. This paper synthesizes early directions in the community’s use of NEON data, and opportunities for the next 10 yr of NEON operations in emergent science themes, open science best practices, education and training, and community building

Phenogrouping heart failure with preserved or mildly reduced ejection fraction using electronic health record data

Background: Heart failure (HF) with preserved or mildly reduced ejection fraction includes a heterogenous group of patients. Reclassification into distinct phenogroups to enable targeted interventions is a priority. This study aimed to identify distinct phenogroups, and compare phenogroup characteristics and outcomes, from electronic health record data. Methods: 2,187 patients admitted to five UK hospitals with a diagnosis of HF and a left ventricular ejection fraction ≥ 40% were identified from the NIHR Health Informatics Collaborative database. Partition-based, model-based, and density-based machine learning clustering techniques were applied. Cox Proportional Hazards and Fine-Gray competing risks models were used to compare outcomes (all-cause mortality and hospitalisation for HF) across phenogroups. Results: Three phenogroups were identified: (1) Younger, predominantly female patients with high prevalence of cardiometabolic and coronary disease; (2) More frail patients, with higher rates of lung disease and atrial fibrillation; (3) Patients characterised by systemic inflammation and high rates of diabetes and renal dysfunction. Survival profiles were distinct, with an increasing risk of all-cause mortality from phenogroups 1 to 3 (p < 0.001). Phenogroup membership significantly improved survival prediction compared to conventional factors. Phenogroups were not predictive of hospitalisation for HF. Conclusions: Applying unsupervised machine learning to routinely collected electronic health record data identified phenogroups with distinct clinical characteristics and unique survival profiles

Optimization of a Morphing Wing Based on Coupled Aerodynamic and Structural Constraints

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/77091/1/AIAA-39016-101.pd