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To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jpolmod.2007.04.004 Byline: Charles Harvie, Ali Salman Saleh Keywords: Budget deficits; Economic reconstruction; Donor assistance; Policy imperatives Abstract: Since the onset of the Civil War in 1975 Lebanon has experienced burgeoning fiscal deficits and an unsustainable public debt overhang. Much of this arose from the loss of revenues during the period of the Civil War 1975-1990 and attempts to maintain basic public expenditure, while from 1990 to 2006 this reflected post-Taif rebuilding and reconstruction of key infrastructure with limited revenue capacity. Considerable progress from the 1990s had been achieved in rebuilding the shattered economy from both public and private international and domestic sources, but its legacy is a huge public debt and a servicing requirement that currently absorbs alone almost 30 per cent of total government revenue and is the highest in the world on a per capita basis. While the need to reduce this debt to a sustainable level would be daunting enough in itself, Lebanon's fiscal predicament was further compounded by the outbreak of war with Israel during July-August 2006. The consequence of this 34-day war was the devastation of residential property, vital infrastructure, loss of agricultural production, industrial production, exports, environmental damage, the collapse of tourism and a further erosion of the influence and power of the central government. Estimates of the direct and indirect costs for Lebanon of this relatively brief but devastating war conservatively vary from US$ 10-15 billion. The implications of such reconstruction and rebuilding costs for the budget and public debt are potentially calamitous for Lebanon which is already struggling under the weight of debt overhang and its servicing. A key question is whether Lebanon can tackle this enormous task in insolation. This paper explores the background to the fiscal crisis, identifies from available literature the extent, nature and cost of the war damage, analyses the options available to the authorities in rebuilding the economy and highlights key policy issues and measures that will be required if a sustainable economic recovery is to be achieved. Despite its demonstrated and remarkable resilience to past trauma the paper concludes that the fiscal crisis makes it impossible for Lebanon to tackle the reconstruction and rebuilding task on its own and particularly in the wake of the events of summer 2006. The country will require substantial and ongoing financial support from international lenders and donors. The success of these efforts in the case of Lebanon is of particular interest as it could well be a microcosm of possible future outcomes for the region more generally. Author Affiliation: School of Economics, Faculty of Commerce, University of Wollongong, Wollongong, NSW 2522, Australia Article History: Received 1 September 2006; Revised 1 January 2007; Accepted 1 April 2007

• To implement an effectual healthcare data prediction model with big data. • To develop a Hybrid Flower Pollination Bumblebees Optimization Algorithm (HFPBOA). • To optimize the parameters of NN, Fuzzy, KNN with the help of HFPBOA algorithm. • To adopt a designed HFPBOA algorithm to improve the accuracy and precision values. • To adopt an ensemble-based prediction model with the fusion of NN, fuzzy, and KNN. As a result of the availability of healthcare data in sheer size, big data analytics has to grow regularly in this industry to ensure new and effective opportunities. This is helpful in providing early prevention, prediction, and detection of disease, thus helping in the enhancement of the overall life quality of the individuals. Likewise, in this paper, a machine learning-based big data analytics model is developed for predicting multi-diseases to provide a better decision support system for various healthcare applications. This developed framework utilizes the MapReduce framework, where the map phase performs feature extraction and the reduce phase performs feature selection for the purpose of handling and processing big data. The required healthcare data is collected from external web sources. In the map phase, the statistical features and the Principal Component Analysis (PCA) features are extracted. In the reduction phase, the optimal features are selected with the aid of the developed Hybrid Flower Pollination Bumblebees Optimization Algorithm (HFPBOA). Then, the Ensemble Learning (EL) model is developed to predict the multi-diseases. Moreover, the parameters present in the EL classifiers are optimized by using the same HFPBOA. The final prediction output is obtained by averaging the weight function between the outputs of the NN, KNN, and fuzzy classifier. Thus, the offered model attains 40.1%, 28.7%, 23.6%, and 10.5% improved than SSA-EL, DOA-EL, BOA-EL, and FA-EL respectively in terms of best value. The effectiveness computed for the developed multi-disease prediction framework is guaranteed by comparing the results among the recently developed prediction approaches. [ABSTRACT FROM AUTHOR]

Ankle fractures are extremely common, with isolated distal fibular fractures being the most common variant. The current gold standard in treating unstable distal fibular fractures is open reduction internal fixation. However, with potential risk of wound complications, minimally invasive techniques have been introduced. This systematic review was performed to evaluate the clinical and functional outcomes of varying minimally invasive techniques including minimally invasive plate osteosynthesis, intramedullary (IM) nailing, and IM screw fixation. A comprehensive English literature search on PubMed was performed yielding 543 studies. With specific study selection criteria, a total of 13 articles were selected. After studying the reference of each of the 13 studies, an additional 7 articles were included, resulting in a total of 20 studies reviewed. A total of 8 articles reviewed used IM nailing as the fixation of choice with a total of 211 patients. Of the 211 patients, 33 experienced complications. Six articles using minimally invasive plate osteosynthesis were reviewed with a total of 264 patients. Of the 264 patients, 39 experienced complications. IM screw fixation was used in 6 articles reviewed with a total of 219 patients. There were 30 cases of complications from the 132 patients. Additionally, mean American Orthopedic Foot and Ankle Society hindfoot-ankle scores among all fixation types was 88.4 ± 3.40 whereas the mean Olerud and Molander Score among all fixation types was 76.7 ± 16.58. The results of this study indicate that minimally invasive techniques for fixation of distal fibular fractures can provide excellent functional results with low complication rates compared with traditional open reduction internal fixation. [ABSTRACT FROM AUTHOR]

The maintenance strategy in railway transportation is crucial in ensuring safety, availability, and reducing operating costs. However, finding the optimal maintenance plan that takes into account the complex relationships between railway assets can be a challenging task. To address this challenge, this study introduces an Intelligent Petri Net (i PN) model to effectively consider the maintenance and operation of railway sections with a focus on optimizing ballast maintenance. The i PN model merges Petri net (PN) with Reinforcement Learning (RL) to create a model that is able to simulate and learn at the same time. The model is able to use diverse information, including usage, degradation rates, maintenance effectiveness, fault probabilities, and maintenance time, to simulate and learn at the same time. By considering the interconnections between these factors, the model found that reducing unnecessary maintenance actions increases the age of railway sections and leads to higher net profits. The study also introduced a method to reduce computational effort by dividing the PN into subnets and another method to make learning faster by using multiple RL environments. In conclusion, the developed i PN model presents a promising solution for optimizing ballast maintenance within railway operation. • An Intelligent Petri net model was created to optimize railway maintenance and operation. • Proposed a Petri net decomposition method that cuts computational costs. • Presented a novel approach: Multiple Reinforcement learning environments for efficient optimization. • The maintenance policy was optimized to lower risks, reduce costs, and boost revenues. [ABSTRACT FROM AUTHOR]

Abstract Design computations of industrial storage racks in accordance with current industry standards rely in part on laboratory testing. One of these tests is for determining the bending strength of upright sections. When testing the bending strength about the axis of symmetry of the upright, a four-point bending test of the assembled upright frame is mandated. The test arrangement prescribed by the standard must permit free twisting of the section at the supports, while the applied loads and their reactions for each upright may be applied in the plane of the section's shear centre. A test arrangement that provides free twisting of the upright section at the supports is more complex and difficult to set up compared with a simple support. This paper examines if the condition of free twisting at supports is necessary in the case of shear centre loading, especially that relaxing this particular code requirement would lead to a simpler test arrangement. Laboratory testing of two sets of upright frames, loaded through the upright's shear centre but with each set having a different support condition indicated that free twisting at the supports had no effect on the bending capacity of the upright members tested. The paper outlines the test setup and reports the results in form of characteristic load deformation curves of the tested specimen. Highlights • For determining the bending strength of storage frames, four-point bending test of the assembled frame is mandated. • The test arrangement prescribed by the standard must permit free twisting of the section at the supports. • Providing free twisting of upright sections at the supports is more complex compared with providing simple supports. • The outcomes clearly imply that free twisting at the supports had no effect on the bending capacity of the tested uprighs. • It is recommended to consider dropping the free twisting code requirement. [ABSTRACT FROM AUTHOR]

The shear stiffness of braced frames of thin-walled cold-formed steel storage racks was experimentally and numerically investigated in order to establish the effect of connection flexibility on the accuracy of different analysis methods. The analyses which included a detailed 3D Finite Element model, a 2D frame analysis with beam elements and a simple hand calculation indicated significant variation of results compared with experimental values. A simplified modelling approach for 2D elastic analysis of braced frames was proposed. The approach is aimed at practical applications to account for the flexibility in bolted connections and leads to better approximation of the shear stiffness. [ABSTRACT FROM AUTHOR]

With the emerging monitoring technologies, condition-based maintenance is nowadays a reality for the wind energy industry. This is important to avoid unnecessary maintenance actions, which increase the operation and maintenance costs, along with the costs associated with downtime. However, condition-based maintenance requires a policy to transform system conditions into decision-making while considering monetary restrictions and energy productivity objectives. To address this challenge, an intelligent Petri net algorithm has been created and applied to model and optimize offshore wind turbines' operation and maintenance. The proposed method combines advanced Petri net modelling with Reinforcement Learning and is formulated in a general manner so it can be applied to optimize any Petri net model. The resulting methodology is applied to a case study considering the operation and maintenance of a wind turbine using operation and degradation data. The results show that the proposed method is capable to reach optimal condition-based maintenance policy considering maximum availability (equal to 99.4%) and minimal operational costs. • An intelligent Petri net (i PN) method by integrating Petri nets with Reinforcement Learning. • i PNs can maximize the reliability and availability by finding an optimal O&M policy. • Reinforcement learning is adapted to incorporate multiple and simultaneous actions. • The method is demonstrated on an optimal O&M model for an offshore wind turbine. • The resulting method works as an optimal self-adaptive expert system. [ABSTRACT FROM AUTHOR]

Tin oxide (SnO 2 ) nanoparticles exhibit an intense luminescent behavior under UV-light in contrast to the bulk tin oxide and therefore have become focus of many investigations. SnO 2 nano-agglomerates were successfully prepared by continuous microwave flow synthesis (CMFS) method using tin chloride pentahydrate as a tin precursor. The effect of concentration of reacting species on the degree of crystallinity, particle size, lattice parameters, morphology, and photocatalytic behavior was probed. Structural and morphological features of the resulting SnO 2 nano-structures were examined by using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), Brunar Emmett Tellar (BET), Transmission electron microscopy (TEM) and ultra-violet (UV/Visible) spectroscopy. From the XRD spectra the crystal structure of the synthesized product was confirmed as phase pure tetragonal cassiterite type with particle size of 4.43 nm. TEM images further confirmed the formation of highly agglomerated nanoparticles, whereas the change in concentration had no appreciable effect on the particle morphology. BET surface area measurements confirmed that the surface area of the SnO 2 nanoparticles decreased with increase in Sn precursor concentration. The optical band gap values of SnO 2 nanoparticles were calculated to be 3.19 eV, which is a red-shift compared with that of the bulk SnO 2 (3.6 eV). The nano-agglomerates were efficient catalyst for the photodegradation of methylene blue (MB) dye. Our results indicate that the synthesized SnO 2 nanoparticles can have potential applications in liquid photovoltaic, photocatalysis and sensors. [ABSTRACT FROM AUTHOR]

Background ACS management aims for early coronary reperfusion, which should be within one hour from symptoms onset. This time was found to be relatively long, and many patients died before hospital arrival. In Jordan, this phenomenon is not clearly understood with a discrepancy between the reported durations of delay time. Aims To evaluate Jordanian ACS patients' delay time in seeking medical care, along with predictors of delay. Methods A descriptive, cross-sectional design was utilized to conveniently recruit 160 Jordanian ACS patients. Data were collected using chart review and the Modified ACS Response Questionnaire. Results The mean delay time was 7.8 hours ( SD  =   3.5), with none of participants presenting within one hour. Delay time correlated negatively with ACS history, knowledge, attitudes, beliefs, and perceived risk ( r  = −0.448, r  = −0.400, r  = −0.408, r  = −0.261, r  = −0.411, respectively) and positively with health perception ( r  = 0.469). A 4-predictor model (history, beliefs, health perception, STEMI diagnosis) was revealed explaining 40% of variance in delay time ( R 2   =   .400, F (14,145) = 6.908, P  < .001). Conclusion Improving ACS patients' health seeking behaviors can be achieved when all components of care are considered together. [ABSTRACT FROM AUTHOR]

The accurate modeling of engineering systems and processes using Petri nets often results in complex graph representations that are computationally intensive, limiting the potential of this modeling tool in real life applications. This paper presents a methodology to properly define the optimal structure and properties of a reduced Petri net that mimic the output of a reference Petri net model. The methodology is based on Approximate Bayesian Computation to infer the plausible values of the model parameters of the reduced model in a rigorous probabilistic way. Also, the method provides a numerical measure of the level of approximation of the reduced model structure, thus allowing the selection of the optimal reduced structure among a set of potential candidates. The suitability of the proposed methodology is illustrated using a simple illustrative example and a system reliability engineering case study, showing satisfactory results. The results also show that the method allows flexible reduction of the structure of the complex Petri net model taken as reference, and provides numerical justification for the choice of the reduced model structure. • A method to mimic a reduced Petri net with respect to a reference one, is proposed. • The approach is based on Approximate Bayesian Computation algorithms. • An improved Markov Chain Monte Carlo sampling is also proposed for the (ABC) algorithm. • The method is demonstrated on a maintenance model for a lube oil system case study. • The method allows the selection of the optimal reduced graph structure. [ABSTRACT FROM AUTHOR]

Highlights: [•] The experimental analysis of a static 3-D EHC prototype has been presented. [•] The performance of EHC studied for different experimental conditions. [•] The experimental study has also been carried out to estimate outlet temperature. [•] The flux distribution on the receiver for the EHC configuration has been presented. [Copyright &y& Elsevier]

Abstract: Pain is still undertreated among hospitalized patients. Recently, patient satisfaction with pain management has received significant attention. This field has not yet been explored among Jordanian patients. The purpose of this study was to determine the knowledge regarding pain characteristics, beliefs, and satisfaction that can be included in planning pain management strategies and protocols within Jordanian hospitals. Using descriptive cross-sectional methodology, the American Pain Society Patient Outcome Questionnaire (APS-POQ) was used to survey 375 inpatients from Jordanian hospitals. Participants reported relatively severe pain and pain interferences while being hospitalized and seemed to be well informed regarding pain and pain management. Participants reported high levels of pain management satisfaction. Also, the Arabic version of the APS-POQ was found to be reliable among the Jordanian population. Findings of this study are similar to those reported by earlier studies in other countries and support the need for applying the caring attitude in managing patients' reports of having pain. This study is the first in Jordan, opening the door for future studies to be conducted in this important field. [Copyright &y& Elsevier]

Abstract: As with many cities all over the world with active industrial developments, the city of Penang in Malaysia has also the potential of being exposed to industrial pollution. Such exposure would certainly have a detrimental impact on the environment and the people. The determination of trace elemental levels in hair which is well known as a method for environmental exposure monitoring, evaluation of heavy metal poisoning, assessment of nutrient levels and disease diagnoses. In this study, it is selected as the method to determine the possible exposure to pollutants in the form of unwanted trace elements. The natural levels of trace elements in hair are hence monitored first as reference values for the assessment of the possible human contamination levels. In this work the concentrations of As and Hg in the human scalp hair of 100 residents of Penang were determined using XRF. The results of this study are compared with the results obtained in other cities where such measurements have also been carried out. [Copyright &y& Elsevier]

Abstract: Concentrating technology is long established in the field of solar thermal applications. However, there is still scope for improvement due to innovation in design, materials and manufacturing methods. The optical efficiency of a solar concentrator depends largely on the geometry of the concentrator profile. This paper evaluates the optical performance of a static 3-D Elliptical Hyperboloid Concentrator (EHC) using ray tracing software. Ray tracing has been used extensively to calculate the optical efficiency of the static 3-D EHC. Performance parameters such as effective concentration ratio, optical efficiency and geometric concentration ratio are also evaluated for different aspect ratios of the elliptical profile. Optimization of the concentrator profile and geometry is also carried out to improve the overall performance; this parametric study includes the concentrator height, solar incidence angle and aspect ratio of the ellipse. The overall performance of the concentrator was assessed based on the acceptance angle, effective concentration ratio and optical efficiency. Finally, the flux distribution on the receiver area for different concentrator heights is also presented. [Copyright &y& Elsevier]

Abstract: Diabetes causes mitochondrial dysfunction in sensory neurons that may contribute to peripheral neuropathy. Ciliary neurotrophic factor (CNTF) promotes sensory neuron survival and axon regeneration and prevents axonal dwindling, nerve conduction deficits and thermal hypoalgesia in diabetic rats. In this study, we tested the hypothesis that CNTF protects sensory neuron function during diabetes through normalization of impaired mitochondrial bioenergetics. In addition, we investigated whether the NF-κB signal transduction pathway was mobilized by CNTF. Neurite outgrowth of sensory neurons derived from streptozotocin (STZ)-induced diabetic rats was reduced compared to neurons from control rats and exposure to CNTF for 24 h enhanced neurite outgrowth. CNTF also activated NF-κB, as assessed by Western blotting for the NF-κB p50 subunit and reporter assays for NF-κB promoter activity. Conversely, blockade of NF-κB signaling using SN50 peptide inhibited CNTF-mediated neurite outgrowth. Studies in mice with STZ-induced diabetes demonstrated that systemic therapy with CNTF prevented functional indices of peripheral neuropathy along with deficiencies in dorsal root ganglion (DRG) NF-κB p50 expression and DNA binding activity. DRG neurons derived from STZ-diabetic mice also exhibited deficiencies in maximal oxygen consumption rate and associated spare respiratory capacity that were corrected by exposure to CNTF for 24 h in an NF-κB-dependent manner. We propose that the ability of CNTF to enhance axon regeneration and protect peripheral nerve from structural and functional indices of diabetic peripheral neuropathy is associated with targeting of mitochondrial function, in part via NF-κB activation, and improvement of cellular bioenergetics. [Copyright &y& Elsevier]

Abstract: The presence of a proinflammatory environment in the sensory neuron axis in diabetes was tested by measuring levels of proinflammatory cytokines in lumbar dorsal root ganglia (DRG) and peripheral nerve from age matched control and streptozotocin (STZ)-induced diabetic rats. The levels of tumor necrosis factor-α (TNFα) and other cytokines were diminished in lumbar DRG from diabetic animals. Consequently, we tested the hypothesis that TNFα modulated axonal plasticity in adult sensory neurons and posited that impairments in this signal transduction pathway may underlie degeneration in diabetic sensory neuropathy. Cultured adult rat sensory neurons were grown under defined conditions and TNFα caused a dose-dependent 2-fold (P<0.05) elevation in neurite outgrowth. Neurons derived from 3 to 5month STZ-induced diabetic rats exhibited significantly reduced levels of neurite outgrowth in response to TNFα. TNFα enhanced NF-κB activity as assessed using Western blotting and plasmid reporter technology. Blockade of TNFα-induction of NF-κB activation caused inhibition of neurite outgrowth in cultured neurons. Immunofluorescent staining for NF-κB subunit p50 within neuronal nuclei revealed that medium to large diameter neurons were most susceptible to NF-κB inhibition and was associated with decreased neurite outgrowth. The results demonstrating reduced cytokine expression in DRG confirm that diabetic sensory neuropathy does not involve a neuroinflammatory component at this stage of the disease in experimental animal models. In addition, it is hypothesized that reduced TNFα expression in the DRG and possibly associated deficits in anterograde transport may contribute to impaired collatoral sprouting and regeneration in target tissue in type 1 diabetes. [Copyright &y& Elsevier]

Poor obesity control is a risk factor for cardiovascular diseases and type 2 diabetes complications. Reducing body weight with pharmacotherapy can be challenging. This study aims to explore the effect of energy restriction diet (ERD) and Phenolic acids (PAs) Supplementation on Cardiometabolic Risk Factors in Overweight and Obese Adults. A prospective, 12-week randomized, double blind, placebo-controlled, parallel group study conducted at University Hospital in Yemen between January 12, 2020, and May 28, 2020. In total 87 healthy non-athlete overweight and obese subjects (58% women; mean age, 42 years; with body mass index (BMI) ≥ 27 kg/m2 were assigned randomly to treated group 47 to receive PAs, 25mg/d after energy restriction diet daily (≤ 100k kcal/day, and 40 to receive placebo as control. Additionally, anthropometric measurements, blood pressure, body composition, plasma lipoproteins, lipids, fasting blood sugar (FBS), insulin sensitivity and hormone peptides of both groups were measured before the beginning of treatment and 12 weeks after treatment. Compared to the control group, body weight, body fat %, body mass index (BMI), triglyceride, low-density lipoprotein(LDL), blood pressure, and insulin sensitivity significantly decreased in the treated groups while insulin sensitivity and adiponectin hormone level were increased (P < 0.05). However, no significant changes occurred in FBS, high-density lipoprotein (HDL), total cholesterol (TC), and waist circumference (WC) (P > 0.05). Conclusions: The study shows that ERDs and PAs treatment was effective in attenuating cardiovascular risk factors such as blood pressure, glycemic control and lipid management, and it may provide a novel treatment for improving dual cardiovascular risk factors. [ABSTRACT FROM AUTHOR]

Congenital abnormalities of the tricuspid valve without downward displacement of the septal and posterior tricuspid leaflets are uncommon causes of tricuspid regurgitation. Progressive tricuspid regurgitation can lead to right heart dilatation, arrhythmia, and irreversible deterioration of right ventricular function. Tricuspid valve repair is an important method to stabilize symptomatic children because tricuspid valve replacement has a poor prognosis. We report the successful use of right ventricular trabecula as a “neopapillary muscle” for treating severe low-pressure tricuspid regurgitation in children. [Copyright &y& Elsevier]

Abstract Much of the current literature that deals with the effect of urbanization on energy demand focuses only on a specific region or a single country. This research extends the current literature by concentrating on a sample of countries containing developed and emerging countries. Our study inspect for those countries the impacts of urbanization and other key determinants on demand of non-renewable energy during the period 1980–2014. In addition, this empirical research employs an advanced heterogeneous panel techniques such as Augmented Mean Groups (AMG). Our empirical results suggest that a one percent rises in urbanization rises the consumption of non-renewable energy by 0.72%. We find that (as compared to the effect of factors such as the GDP and the price of oil) urbanization has the largest effect on non-renewable energy demand. Highlights • Examining the impact of urbanization on non-renewable energy demand. • Results suggest the presence of cointegration with cross-sectional dependence. • Urbanization has the largest influence on non-renewable energy demand. • Urbanization has a higher impact in developed countries compared to emerging ones. • A bidirectional causality between urbanization and non-renewable energy consumption. [ABSTRACT FROM AUTHOR]

By incorporating information about asset condition from a monitoring system, engineers can utilize asset management models to manage maintenance activities on wind turbine blades throughout their lifespan. This can lower operating and maintenance costs and increase the life of the blades. The asset management model relies on the monitoring system as a source of information, however, commonly the reliability of the monitoring system is not considered. This paper presents a wind turbine blade asset management Petri net (PN) model that covers the blade asset management process, including degradation, inspection, condition monitoring (CM), and maintenance processes. The paper proposes two contributions. Firstly, while taking into account detailed industry guidelines, the developed model can forecast the future blade condition for a given asset management strategy. Secondly, it investigates the impact of the reliability of the monitoring system on the asset management modelling results. With the aid of the developed model, the number of repair actions and probability distributions of blade condition discovery time are obtained. In addition, the PN gives an indication of how misreporting (underestimation and overestimation) occurs and the extent of the misreporting. The simulation results illustrate the degree of uncertainty introduced into the monitoring results by the reliability of the monitoring system and, consequently, the extent to which this factor influences the maintenance strategies. The proposed model can be used to support asset management decisions when monitoring system performance degrades. [ABSTRACT FROM AUTHOR]

Agricultural field- and watershed-scale water quality models are used to assess the potential impact of management practices to reduce nutrient and sediment exports. However, observed data are often not available to calibrate and verify these models. Three years of data from the U.S. Department of Agriculture-Agricultural Research Service's 12 paired edge-of-field sites in northwest Ohio were used to calibrate and validate the Nutrient Tracking Tool. The goal of this study was to identify a single optimal parameter set for the Nutrient Tracking Tool in simulating annual crop yields, water balance, and nutrient loads across the Western Lake Erie Basin. A multi-site and multi-objective auto-calibration subroutine was developed in R to perform model calibration across the edge-of-field sites. The statistical metrics and evaluation criteria used in comparing the simulated results with the observed data were: Cohen's D Effect Size (Cohen's D < 0.20) and Percent bias (PBIAS ± 10% for crop yields, subsurface (tile) discharge, and surface runoff and ± 25% for dissolved reactive phosphorus (DRP) and nitrate‑nitrogen (nitrate-N) in tile discharge, and DRP, particulate phosphorus, and nitrate-N in surface runoff). In both calibration and validation, the Cohen's D and PBIAS for annual crop yields, tile discharge, surface runoff, DRP, particulate P, and nitrate-N showed that the average simulated results were similar to the average observed values for each variable. The calibrated model simulated well the annual averages of crop yields, flows, and nutrient losses across fields. The tile drainage and phosphorus transport subroutines in the Nutrient Tracking Tool should be further improved to better simulate the dynamics of discharge and phosphorus transport through subsurface drainage. Stakeholders can use the verified model to evaluate the effectiveness of conservation practices in improving the water quality across the Western Lake Erie Basin. Unlabelled Image • We developed a new tool in R to perform multi-site and multi-variable calibration. • APEX/NTT calibration across Edge-of-Field (EOF) sites in the Western Lake Erie Basin • The calibrated APEX/NTT could simulate annual average values for all the EOF sites. • APEX/NTT's tile discharge and phosphorus transport processes need improvement. • Resulting model can assess the impacts of conservation practices in the region. [ABSTRACT FROM AUTHOR]

Abstract: The hepatitis B virus X protein (HBxAg) is responsible for severe complications of HBV infections including primary hepatocellular carcinoma. A sandwich type ELISA and a flow cytometric microbead assay for quantitative determination of serum levels of Hbx-Ag are introduced. We have previously developed monoclonal antibody families against well-conserved epitopes on HbxAg, characterized by different immunohistochemical and immunoserological techniques. Special selection of the antibody pairs provided highly sensitive and highly specific tools for quantitative immunoassay development. The resulting assays were tested on human sera (208 samples) collected from patients suffering from different clinical forms of HBV infection. The sensitivity range of the sandwich type ELISA was between 4 and 2000 ng/ml as measured on both the recombinant antigen and the sera of chronic hepatitis patients. A further flow cytometric microbead assay was established and tested in parallel with the ELISA. The quantitative results of these two immunoserological techniques were in strong correlation and they were found to be highly specific and sensitive on clinical samples. The HBxAg ELISA technique is applicable for routine clinical laboratory measurements, and our HBxAg microbead technique is recommended for complex multiparametric measurements combined with other markers. [Copyright &y& Elsevier]