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Research on the link between investments in ERP systems and organizational performance has often led to mixed results. Besides internal organizational factors, many external contextual factors come into play. This study examined the role of firm size, industry, and duration of ERP system's use in influencing the performance impact of ERP systems through moderating the relationships between antecedent variables, ERP-induced benefits, and improvement in overall organizational performance. Using a sample of 200 participant firms, and structural equation modeling (SEM) analysis, the author confirmed the significant role of business process re-engineering and organizational fit and alignment as antecedents to ERP-induced benefits in information quality, and coordination/integration. Data and information quality was in turn confirmed as a significant predictor of organizational performance. Furthermore, the roles of industry, firm size, and time elapsed were also confirmed as significant moderators to the influence of the antecedent variables on ERP benefits and organizational performance.

Exposures from diagnostic medical X-rays are the most important synthetic source of exposure to ionizing radiation in several countries. However information on medical exposure of diagnostic X-rays is integrated into international legislative repertories. In Sebha city, there is a lack of data on the assessment of patient's entrance surface dose (ESD) and the health risk from conventional radiography in daily routine of diagnostic medical examinations. In this research, the Entrance Surface Dose (ESD) was estimated for adult patients underwent diagnosis X-ray examinations in one of radiographic center in Sebha city. The ESD has been estimated indirectly using exposure factors for patents. The results showed that the mean patient entrance surface doses (ESD) were 41.73±5.84 mGy, 7.43±2.58 mGy, 103.7±125.53 mGy, 7.25±4.32 mGy and 11.24±16.18 mGy respectively for Pelvis (AP), Chest (AP), Lumbar Spine (AP), Cervical Spine (AP) and Skull (AP). The mean ESD values are found to be higher than mean ESD reference values. This indicates that the necessity for reducing the patient doses to the acceptable levels recommended by the international radiation protection commissions to protect patients from risk of higher exposures to diagnostic X-rays.

Aims: This study was conducted to determine the physicochemical characteristics of fermented milk "roub" during the storage period. Methodology: Thirty roub samples were collected (10 samples from each of Khartoum, Khartoum North and Omdurman areas) in sterile polyethylene bags, preserved in sample containers in ice (4°C) and transported to the laboratory for analysis. Physicochemical characteristics [fat, protein, total solids (TS), solids-non-fat (SNF), ash, acidity] were determined at 1, 7, 14 and 20-day intervals. Results: The results showed that area from which samples were collected had a significant effect on all physicochemical characteristics of roub except solids-non-fat and acidity. During the storage period, there was no significant variation in all physicochemical characteristics of samples collected from Khartoum except the protein content, while in samples collected from Khartoum North there was a significant effect of the storage period on the protein and total solids contents only, and in samples collected from Omdurman the storage period significantly affected the protein and ash contents and acidity of roub. Roub samples were good up to the end of the storage period. Conclusion: The significant variation in physicochemical characteristics of the product indicates the differences in the source of milk rather than and/or the processing methods. Therefore, the authorities are encouraged to control this process by implementing legislations or prohibit the manufacture of this product in such prevailing conditions.

IntroductionReliable information about the time spent waiting for health care services is a critical metric for measuring health system performance. Wait times are a useful measure of access to various health care sectors. Alongside the increased adoption of electronic medical records (EMR) by Canadian family physicians (FP), is the secondary use of FP EMR data for research. However, using FP EMR data can be challenging in its unstructured, free-text format.
Objectives and ApproachOur objective was to identify the target specialist physician type from the EMR FP referral note and then calculate wait times from a FP referral to a specialist physician visit. We used FP EMR data and linked to Ontario, Canada health administrative data (called EMRPC). EMRPC collects the entire clinical record from patients including the content of FP referral notes. We used machine learning (ML) methods to identify the type of specialist physician in which the referral was intended. Labels to test the ML methods were created from physicians' claims data. Wait times were calculated from the FP EMR referral note date to the specialist physician claim date in administrative data.
ResultsOur ML models' ability to classify 2016 FP EMR referral notes to selected medical and surgical specialists achieved sensitivity and positive predictive values ranging from the high 70s to low 80s.Compared to earlier analyses from 2008, we observed a similar relative ordering to see specific specialist physicians. Overall, the median wait times have increased by 14 days on average, with a maximum increase of 28 days to see a gastroenterologist.
Conclusion / ImplicationsThe accuracy of ML on unstructured FP EMR data is high, thereby providing a mechanism to "codifying" information in a timely manner. This information can help inform decision makers and providers about which patients or FP practices are experiencing long wait times in seeing specialist physicians.

The senior author gratefully acknowledges the funding by the Science, Technology, Research and Innovation for the Environment (STRIVE) Programme of the Irish Government under the National Development Plan 2007-2013 and the Department of the Environment, Heritage and Local Government. The authors would like to thanks Phillip O'Brien (EPA) for extending technical and relevant support; Mike Williams, Mike Jones and Matt Saunders (TCD), Komsan Rueangritsarakul and Mohamed Helmy (UCD) for supplying experimental data for modelling work; as well as Tom Bolger and Tommy Gallagher (UCD) for providing administrative support. ; Peer reviewed ; Publisher PDF

The Welsh Government is committed to reduce greenhouse gas (GHG) emissions from agricultural systems and combat the effects of future climate change. In this study, the ECOSSE model was applied spatially to estimate GHG and soil organic carbon (SOC) fluxes from three major land uses (grass, arable and forest) in Wales. The aims of the simulations were: (1) to estimate the annual net GHG balance for Wales; (2) to investigate the efficiency of the reduced nitrogen (N) fertilizer goal of the sustainable land management scheme (Glastir), through which the Welsh Government offers financial support to farmers and land managers on GHG flux reduction; and (3) to investigate the effects of future climate change on the emissions of GHG and plant net primary production (NPP). Three climate scenarios were studied: baseline (1961–1990) and low and high emission climate scenarios (2015–2050). Results reveal that grassland and cropland are the major nitrous oxide (N2O) emitters and consequently emit more GHG to the atmosphere than forests. The overall average simulated annual net GHG balance for Wales under baseline climate (1961–1990) is equivalent to 0.2 t CO2e ha-1 y-1 which gives an estimate of total annual net flux for Wales of 0.34 Mt CO2e y-1. Reducing N fertilizer by 20 and 40 % could reduce annual net GHG fluxes by 7 and 25 %, respectively. If the current N fertilizer application rate continues, predicted climate change by the year 2050 would not significantly affect GHG emissions or NPP from soils in Wales.

Abstract
5-fluorouracil (5-FU) is a widely used chemotherapeutic drug, but its hepatotoxicity challenges its clinical use. Thus, searching for a hepatoprotective agent is highly required to prevent the accompanied hepatic hazards. The current study aimed to investigate the potential benefit and mechanisms of action of rupatadine (RU), a Platelet-activating factor (PAF) antagonist, in the prevention of 5-FU-related hepatotoxicity in rats. Hepatotoxicity was developed in male albino rats by a single 5-FU (150 mg/kg) intra-peritoneal injection on the 7th day of the experiment. RU (3 mg/kg/day) was orally administrated to the rodents for 10 days. Hepatic toxicity was assessed by measuring both liver and body weights, serum alanine aminotransferase and aspartate aminotransferase (ALT and AST), hepatic oxidative stress parameters (malondialdehyde (MDA), nitric oxide levels (NOx), reduced glutathione (GSH), superoxide dismutase (SOD)), and heme oxygenase-1 (HO-1). Inflammatory markers expressions (inducible nitric oxide synthase (iNOS), tumor necrosis factor-alpha (TNFα), interleukins; IL-1B, IL-6), the apoptotic marker (caspase-3), and PAF were measured in the hepatic tissue. 5-FU-induced hepatotoxicity was proved by the biochemical along with histopathological assessments. RU ameliorated 5-FU-induced liver damage as proved by the improved serum ALT, AST, and hepatic oxidative stress parameters, the attenuated expression of hepatic pro-inflammatory cytokines and PAF, and the up-regulation of HO-1. Therefore, it can be concluded that RU pretreatment exerted a hepatoprotective effect against 5-FU-induced liver damage through both its powerful anti-inflammatory, antioxidant, and anti-apoptotic effect.

Acknowledgements Funding for this publication was provided by the New Zealand Government to support the objectives of the Livestock Research Group of the Global Research Alliance on Agricultural Greenhouse Gases. Individual authors work contribute to the following projects for which support has been received: Climate smart use of Norwegian organic soils (MYR, 2017-2022) project funded by the Research Council of Norway (decision no. 281109); Scottish Government's Strategic Research Programme, SuperG (under EU Horizon 2020 programme); DEVIL (NE/M021327/1), Soils-R-GRREAT (NE/P019455/1) and the EU H2020 project under Grant Agreement 774378—Coordination of International Research Cooperation on Soil Carbon Sequestration in Agriculture (CIRCASA); to project J-001793, Science and Technology Branch, Agriculture and Agri-Food Canada; and New Zealand Ministry of Business, Innovation and Employment (MBIE) core funding. Thanks to Alasdair Noble and the anonymous reviewers for helpful comments on a draft of this paper and to Anne Austin for editing services. ; Peer reviewed ; Publisher PDF

ObjectivesIndividual risk factors for dementia are well known, but the influence of co-occurring chronic conditions has not been considered. We identified clusters of chronic conditions using an unsupervised machine learning approach and examined associations with incident dementia.
ApproachUsing linked population-based administrative databases, we followed all community-dwelling adults aged 40-54 years in Ontario, Canada from April 2002 until March 2019 for incident dementia. We estimated the prevalence of 29 chronic conditions using validated algorithms and/or diagnosis codes. We reduced dataset dimensionality using multiple correspondence analysis and a fuzzy c-means clustering algorithm identified the optimal number of clusters (between 3-6 tested). Associations between clusters and incident dementia were examined using a cause-specific hazard model adjusted for sociodemographic characteristics and accounting for the competing risk of death.
ResultsWe identified 82,359 eligible individuals (random 3% sample of total eligible individuals; mean age 46.5 years; 50.4% female). Regression analyses were based on 5 comorbidity clusters (fuzzy silhouette index:0.69). Compared to the low comorbidity cluster, persons in the cerebrovascular disease/metabolic (HRadj=3.06, 95%CI[2.42,3.86]) and neuro-related/mental health clusters (HRadj=2.51, 95%CI[2.05,3.07]) had the highest rates of incident dementia, followed by the cardiovascular risk factor cluster (HRadj=1.66,95%CI[1.32,2.09]). Persons in the cancer cluster did not have an increased incidence of dementia (HRadj=0.96,95%CI[0.77,1.20]).
ConclusionsWe found significant associations between machine learning-derived clusters of chronic conditions and dementia.
ImplicationsUnsupervised machine learning approaches to identify clusters of chronic conditions may be a useful tool for considering the impact of multimorbidity on dementia risk.