Development Of Risk Assessment Model Research Articles

Exposure to environmental pollutants selects for xenobiotic-degrading functions in the human gut microbiome

Environmental pollutants from different chemical families may reach the gut microbiome, where they can be metabolized and transformed. However, how our gut symbionts respond to the exposure to environmental pollution is still underexplored. In this observational, cohort study, we aim to investigate the influence of environmental pollution on the gut microbiome composition and potential activity by shotgun metagenomics. We select as a case study a population living in a highly polluted area in Campania region (Southern Italy), proposed as an ideal field for exposomic studies and we compare the fecal microbiome of 359 subjects living in areas with high, medium and low environmental pollution. We highlight changes in gut microbiome composition and functionality that were driven by pollution exposure. Subjects from highly polluted areas show higher blood concentrations of dioxin and heavy metals, as well as an increase in microbial genes related to degradation and/or resistance to these molecules. Here we demonstrate the dramatic effect that environmental xenobiotics have on gut microbial communities, shaping their composition and boosting the selection of strains with degrading capacity. The gut microbiome can be considered as a pivotal player in the environment-health interaction that may contribute to detoxifying toxic compounds and should be taken into account when developing risk assessment models. The study was registered at ClinicalTrials.gov with the identifier NCT05976126.

Advanced Bayesian study on inland navigational risk of remotely controlled autonomous ship

The arise of autonomous ships has necessitated the development of new risk assessment techniques and methods. This study proposes a new framework for navigational risk assessment of remotely controlled Maritime Autonomous Surface Ships (MASS). This framework establishes a set of risk influencing factors affecting safety of navigation of a remotely-controlled MASS. Next, model parameters are defined based on the risk factors, and the model structure is developed using Bayesian Networks. To this end, an extensive literature survey is conducted, enhanced with the domain knowledge elicited from the experts and improved by the experimental data obtained during representative MASS model trials carried out in an inland river. Conditional Probability Tables are generated using a new function employing expert feedback regarding Interval Type 2 Fuzzy Sets. The developed Bayesian model yields the expected utilities results representing an accident’s probability and consequence, with the results visualized on a dedicated diagram. Finally, the developed risk assessment model is validated by conducting three axiom tests, extreme scenarios analysis, and sensitivity analysis. Navigational environment, natural environment, traffic complexity, and shore-ship collaboration performance are critical from the probability and consequence perspective for inland navigational accidents to a remotely controlled MASS. Lastly, important nodes to Shore-Ship collaboration performance include autonomy of target ships, cyber risk, and transition from other remote control centers.

Facing the Wildfire Spread Risk Challenge: Where Are We Now and Where Are We Going?

Wildfire is a sudden and highly destructive natural disaster that poses significant challenges in terms of response and rescue efforts. Influenced by factors such as climate, combustible materials, and ignition sources, wildfires have been increasingly occurring worldwide on an annual basis. In recent years, researchers have shown growing interest in studying wildfires, leading to a substantial body of related research. These studies encompass various topics, including wildfire prediction and forecasting, the analysis of spatial and temporal patterns, the assessment of ecological impacts, the simulation of wildfire behavior, the identification of influencing factors, the development of risk assessment models, techniques for managing combustible materials, decision-making technologies for firefighting, and fire-retardant methods. Understanding the factors that affect wildfire spread behavior, employing simulation methods, and conducting risk assessments are vital for effective wildfire prevention, disaster mitigation, and emergency response. Consequently, it is imperative to comprehensively review and explore further research in this field. This article primarily focuses on elucidating and discussing wildfire spread behavior as a key aspect. It summarizes the driving factors of wildfire spread behavior and introduces a wildfire spread behavior simulation software and its main applications based on these factors. Furthermore, it presents the research progress in wildfire risk assessment based on wildfire spread behavior factors and simulation, and provides an overview of various methods used for wildfire risk assessment. Finally, the article proposes several prospects for future research on wildfire spread: strengthening the dynamic monitoring of wildfires and utilizing comprehensive data from multiple sources, further exploring the differential effects of key factors on wildfire spread, investigating differences in driving factors, improving wildfire models in China, developing applicable software, and conducting accurate and scientific assessments of wildfire risks to protect ecological resources.

Development and Validation of a Risk Assessment Model for Venous Thromboembolism in Patients With Invasive Mechanical Ventilation.

BackgroundPatients with invasive mechanical ventilation may be at high risk of acquiring venous thromboembolism (VTE). We aim to develop risk assessment models for predicting the improvement of VTE in invasively ventilated patients.MethodologyA total of 6,734 invasively ventilated patients enrolled from the Medical Information Mart for Intensive Care-III (MIMIC-III) database were used as input for model development and internal validation, while data from 168 patients from Nanfang Hospital were used for external validation. Logistic regression was performed based on predictive factors derived from least absolute shrinkage and selection operator (LASSO) regression analysis and logistic regression with backward selection to develop two Risk Assessment Models (RAM), namely, I and II, for the prediction of VTE, respectively. Model selection was performed by evaluation of the area under the receiver operating characteristic curve (AUC), the goodness of fit with calibration curves, and decision curve analyses (DCA).ResultsRAM-I included prior history of VTE, in-hospital immobilization, infection, glucose, the use of antiplatelet, and activated partial thromboplastin time (APTT) as variables, while RAM-II included prior history of VTE, in-hospital immobilization, infection, ischemic stroke, glucose, the use of antiplatelet and APTT as variables. Compared with RAM-I and ICU-Venous Thromboembolism Score, RAM-II exhibited better discrimination in the training dataset (AUC = 0.826), internal validation dataset (AUC = 0.771), and external validation dataset (AUC = 0.770). Additionally, DCA demonstrated that RAM-II was clinically beneficial. Inspection of the calibration curves revealed good agreement between the predictions and observations.ConclusionsA RAM for VTE in invasively ventilated patients was developed with reasonable performance.

A method for correcting underestimation of enteric pathogen genome quantities in environmental samples

Exposure to enteric pathogens in the environment poses a serious risk for infection and disease. The accurate detection and quantification of enteric pathogens in environmental samples is critical for understanding pathogen transport and fate and developing risk assessment models. In this study, we successfully applied TaqMan real-time PCR assays to quantitatively detect five human-specific pathogens (Shigella/EIEC, Salmonella Typhi, Vibrio cholera, Norovirus, and Giardia) in samples from open drains, canals, floodwater, septic tanks, and anaerobic baffled reactors (ABR) collected in Mirpur, Dhaka, Bangladesh from April to October 2019. Overall, the grab and sediment samples showed low inhibition but the ultrafiltration samples collected from open drain had significantly higher (P = 0.0049) degree of PCR inhibition (median Ct = 31.06) compared to the extraction controls (Ct = 28.54). We developed a two-step method to adjust underestimation of pathogen quantities due to PCR inhibition and non-optimum PCR efficiency. Compared to other sample types, ultrafiltration samples demonstrated a wide range of concentration increase (1.0%–182.5%) by pathogens after adjusting for PCR inhibition and non-optimum efficiencies. These quantitative qPCR assays are successful in quantifying multiple enteric pathogens in environmental samples, and the adjustment method would be useful for correcting underestimates of pathogen quantities due to partial PCR inhibition and non-optimum efficiency.

Consumer Knowledge and Behaviors Regarding Food Safety Risks Associated with Wheat Flour

Consumers do not consider flour, a low-moisture food product, a high risk for microbial contamination. In the past 10 years, however, flour has been identified as a source of pathogenic bacteria, including Salmonella and Escherichia coli. Online surveys were conducted to study consumers' flour handling practices and knowledge about food safety risks related to flour. The survey also evaluated message impact on three food safety messages in communicating information and convincing consumers to adopt safe flour handling practices. Flour-using consumers (n = 1,045) from the United States reported they used flour to make cakes, cookies, and bread. Most consumers stored flour in sealed containers. Less than 1% kept a record of product identification numbers, such as lot numbers, and less than 11% kept brand and use-by date information. Many consumers (85%) were unaware of flour recalls, or outbreaks, and few (17%) believed they would be affected by flour recalls or outbreaks. If the recall affected the flour they bought, nearly half of the consumers (47%) would buy the same product from a different brand for a few months before they returned to the recalled brand. Among consumers who use flour to bake, 66% said they ate raw cookie dough or batter. Raw dough "eaters" were more difficult to convince to avoid eating and playing with raw flour than "noneaters." Food safety messages were less impactful on those raw dough eaters than noneaters. Compared with the food safety message with only recommendations, those messages with recommendations and an explanation as to the benefits of the practice were more effective in convincing consumers to change their practices. These findings provide insight into effective consumer education about safe flour handling practices and could assist in the accurate development of risk assessment models related to flour handling.

Assessing the Risk of Salmonellosis from Consumption of Conventionally and Alternatively Produced Broiler Meat Prepared In-Home in the United States

Salmonella has long presented a major problem for the food safety of broiler meat. As the popularity of alternatively produced (e.g. organic) broiler meat increases, an understanding of the food safety risks associated with these types of products is needed. The purpose of this study was to develop a retail-to-consumption quantitative microbial risk assessment model that could be used to estimate the differences in risk of salmonellosis acquired from the consumption of conventionally and alternatively produced broiler meat in the United States annually. Data were extracted and used to define distributions that could be used to estimate Salmonella growth during retail storage, transportation, and home storage, as well as concentration changes during preparation and due to cross-contamination. A Monte Carlo simulation with 100,000 iterations was performed to estimate the risk of infection per serving and total number of infections in the United States annually from both meat types. Sensitivity analyses were performed to determine the factors that were highly correlated with increased risk of salmonellosis. Conventionally produced chicken meat was estimated to have a median risk of infection per serving of 6.4 × 10−8 and cause an average of approximately 3,880,000 infections annually compared with a median risk of infection per serving of 7.7 × 10−8 and average of approximately 641,000 estimated infections for alternatively produced chicken. The sensitivity analysis identified cross-contamination of hands during meal preparation as the most important factor linked to risk. The ‘what-if’ scenario analysis estimated that using antimicrobial soap during hand washing after handling raw chicken can reduce the risk considerably. The developed risk assessment model provides information on the public health risk of conventionally and alternatively produced broiler meat. These results will be useful in determining the key intervention strategies to mitigate the food safety risks associated with the consumption of contaminated chicken products.

Development of risk assessment model for biomass plant boiler using bayesian network

Malaysia as the second-largest producer of crude palm oil has abundance of biomass residues from palm oil industries which can be converted to bio-chemicals to generate electricity. However, despite institutional arrangements of the biomass industry, there are several risks which may prone to reduce efficiency of biopower boiler especially empty fruit bunch as the fuel. Boiler is one of the primary equipment of power generation plants, in a significant role in converting biofuel to electricity. The main risk areas in biopower boiler are dearator, economizer, fuel preparation, and water cooling system. Available risk methodologies are not able to provide accurate results for a combination of risks. In this work, Bayesian network approach is introduced to determine and predict risk associated with biopower boiler. The predictive and diagnosis analyses of the Bayesian Network were performed to find the casual links which cause the failure and make a prediction of the control measures to reduce the rate of mistakes. Results revealed that dearator showed a significant effect when the system operates beyond the limits of its design. In conclusion, Bayesian Networks appear to be an assist for decision makers to decide when and where to take preventive or mitigate measures.

Development and Validation of a Computable Phenotype for Bleeding in Hospitalized Patients: The Medical Inpatients Thrombosis and Hemostasis (MITH) Study

Introduction: Bleeding is an uncommon event but it is causes significant increase in morbidity and mortality. Identifying bleeding events using electronic health record data (both resulting from hospitalization and causing hospitalization) would allow the development of risk assessment models (RAM) to identify those at most risk. Traditional prospective cohorts for rare events are time consuming and expensive. We suggest a more efficient method using the electronic health record (EHR) data by developing and validating an algorithm to detect bleeding in hospitalized patients, ie, a "computable phenotype". Methods: We captured all admissions to the University of Vermont (UVM) Medical Center between 2010-19, a tertiary care medical center in northwest Vermont. Using International Classification of Disease (ICD) 9 and 10 discharge diagnoses, "present on admission" flags, problem lists, laboratory values, vital signs, current procedure terminology (CPT) codes, medication administration, and flowsheet data for transfusion support, we developed computable phenotypes for bleeding. Classification was based on the gold standard International Society of Thrombosis and Haemostasis definitions for clinically relevant non-major bleeding (CRNMB) and major bleeding (MB) and validated by medical record review. To improve sensitivity and specificity, algorithms were developed by bleeding site (intracerebral, intraspinal, pericardial, retroperitoneal, orbital, intramuscular, gastrointestinal, genitourinary, gynecologic, pulmonary, nasal, post-procedure, or miscellaneous). We preliminary validated the computable phenotype by randomly abstracting 10 medical records from each bleeding site. Results: Among 62,468 admissions, our computable phenotype for bleeding identified 10,202 bleeding events associated with hospitalization; 4,650 were CRNMB and 5,552 were MB. On chart abstraction, 135 of 153 hospitalizations had either a MB or CRNMB (88%, Figure). For MB, 95 of 119 (80%) of the computed MB phenytope events were validated. Of the 24 of 119 (20%) not validated, 14% (16) were CRNMB and 7% (8) the bleeding was present on coding but was not detected by chart review. Only 29%(10/34) of the CRNMB were validated. The most common error in the CRNMB computable phenotype was misclassification of 14 MB as CRNMB (41% of CRNMB. For individual bleeding sites, (figure), the algorithms performed well for most sites including intracerebral hemorrhage, gastrointestinal, and intramuscular bleeding, but performed less well for unusual and rarer bleeding sites (i.e. nasal). Conclusion: We developed a computable phenotype for bleeding which can be applied to our EHR system. The computable phenotype was specific for MB, but underestimated the severity of potential CRNMB. Importantly, we correctly classified specific important bleeding sites such as intracerebral, gastrointestinal, and retroperitoneal. This computable phenotype forms the basis for further refinement, and provides a road map for future studies on epidemiology of hospital-acquired bleeding and hospitalization for bleeding. Figure: Major and Clinically relevant non-major bleeding as detected by Electronic Health Record compared to the chart validation Figure 1 Disclosures No relevant conflicts of interest to declare.

Overview of risk assessment models for venous thromboembolism in ambulatory patients with cancer

A B S T R A C T Important progress has been made in the development of risk assessment models (RAM) for the identification of outpatients on anticancer treatment at risk of venous thromboembolism (VTE). Since the breakthrough publication of the original Khorana risk score (KRS) more than 10 years ago, a new generation of KRS-based scores have been developed, including the Vienna Cancer and Thrombosis Study, PROTECHT, CONKO, ONCOTEV, TicOnco and the CATS/MICA score. Among these the CATS/MICA score showed that a simplified score composed of only two calibrated predictors, the type of cancer and the D-dimer levels, offers a user-friendly tool for the evaluation of cancer-associated thrombosis (CAT) risk. The COMPASS-CAT score is the first that introduced a more synthetic approach of risk evaluation by combining cancer-related predictors with patient comorbidity in a score which is designed for the types of cancer frequently seen in the community (i.e. breast, lung colon or ovarian cancers) and has been externally validated in independent studies. The Throly score is registered as part of the same group as it has a similar structure to the COMPASS-CAT score and is applicable in patients with lymphoma. The incorporation of specific biomarkers of hypercoagulability to the RAM for CAT offers the possibility to perform a precision medicine approach in the prevention of CAT. The improvement of RAM for CAT with artificial intelligence methodologies and deep learning techniques is the challenge in the near future.

Model development of risk assessment of risk-taking relationship with integrated approach

Each organization or company must always be dealing with risk. The decision-makers strive to be able to avoid the impact of such risks. Those generated risks may affect the achievement of the objectives, therefore risk management is needed. Risk management is a solution to avoid or to minimize the occurrence of the risk. Risk management consists of several steps, one of them is risk assessment. An appropriate risk assessment would support the achievement of the objectives. In this study, a risk assessment model is developed with PLTSa Kota Surabaya as a (research) object, which was selected because there were still constraints that affect the energy produced and waste volume used as fuel in this PLTSa. This study aims to develop risk assessment models integrated with the Delphi method, Fuzzy Cognitive Maps, and Fuzzy Synthetic Evaluation, then implementing these models in real cases and proposing risk mitigation steps by considering the relationship of the risks. The Delphi method was used to identify risks, FCM was used to identify the relationship among risks and the relationship between risks with the objectives of PLTSa, and FSE was used to assess the level of risks to find risks with the critical category. Delphi method identified 15 risks as a result and reached the consensus based on statistical analysis, FCM generated positive value of the relationship among all of the identified risks and negative value of the relationship between identified risks with the objectives of PLTSa. Meanwhile, FSE method obtained six types of critical risk, which were the machine brake all of a sudden (RC = 3,082), the blower did not work (RC = 3,041), congestion in fuel flow (RC = 2,954), clogged filter (RC = 2.915), lack of machine maintenance knowledge by the operator (RC = 2,872), and the energy produced had not maximum. (RC = 2.828). Those six types of risk used a scenario to run the FCM simulation and meet the dynamic behavior of the risk relationship value. The mitigation proposals are to perform maintenance activities programmatically, to improve the management system related to human resources, and to do the fuel examination according to the standard.

Machine Learning and Deep Neural Networks Applications in Computed Tomography for Coronary Artery Disease and Myocardial Perfusion.

During the latest years, artificial intelligence, and especially machine learning (ML), have experienced a growth in popularity due to their versatility and potential in solving complex problems. In fact, ML allows the efficient handling of big volumes of data, allowing to tackle issues that were unfeasible before, especially with deep learning, which utilizes multilayered neural networks. Cardiac computed tomography (CT) is also experiencing a rise in examination numbers, and ML might help handle the increasing derived information. Moreover, cardiac CT presents some fields wherein ML may be pivotal, such as coronary calcium scoring, CT angiography, and perfusion. In particular, the main applications of ML involve image preprocessing and postprocessing, and the development of risk assessment models based on imaging findings. Concerning image preprocessing, ML can help improve image quality by optimizing acquisition protocols or removing artifacts that may hinder image analysis and interpretation. ML in image postprocessing might help perform automatic segmentations and shorten examination processing times, also providing tools for tissue characterization, especially concerning plaques. The development of risk assessment models from ML using data from cardiac CT could aid in the stratification of patients who undergo cardiac CT in different risk classes and better tailor their treatment to individual conditions. While ML is a powerful tool with great potential, applications in the field of cardiac CT are still expanding, and not yet routinely available in clinical practice due to the need for extensive validation. Nevertheless, ML is expected to have a big impact on cardiac CT in the near future.

Developing a systematic methodology to build a systems dynamics model for assessment of non-technical risks in power plants

In a dynamic business environment like the energy sector, power plants face several complex risks, including both technical and non-technical risks. These risks are not isolated, as their impact may affect a series of interrelated risks. Those risks may change with time, which in turn, makes the strategic decision-making process less effective. Understanding the dynamic behaviour of a complex system is very important to achieve a more sustainable overall performance of the power plants. Thus, it is important to further develop a systematic risk assessment methodology that could help to identify and analyse the interdependencies among risks and to understand the dynamics of these risks in complex systems. This paper develops a system dynamics (SD) methodology to support the development of risk assessment models. This paper highlights the environmental perspective. The first step to develop a SD model will be applied, while the final SD model will be discussed in another paper.

Detection and Quantification of Spongospora subterranea Sporosori in Soil by Quantitative Real-Time PCR.

Powdery scab on potato tubers is caused by the obligate soilborne biotroph Spongospora subterranea and is known to cause substantial losses in potato production. The pathogen also infects roots of susceptible hosts, forming galls which can negatively affect root function. S. subterranea is also the vector of Potato mop-top virus, which causes a tuber necrosis disease that can, depending on temperature and cultivar, render potato tubers unmarketable. In this study, we adapted a published protocol to develop a sensitive and robust quantitative real-time PCR (qPCR) assay using specific primers and probes for detecting and quantifying S. subterranea sporosori in soil types that differ in physical properties, including organic matter content and soil pH. For the first time, an external control was utilized and applied directly to the soil prior to DNA extraction, which facilitated normalization of S. subterranea sporosori soil levels from sample to sample. The duplex qPCR protocol was demonstrated to be highly sensitive, capable of detecting and quantifying as few as 1 sporosorus/g of soil, with consistently high qPCR efficiency and the coefficient of determination (R2) values ranging from 94 to 99% and 0.98 to 0.99, respectively. The protocol was successfully implemented in enumerating S. subterranea sporosori in naturally infested field soil collected from several states and in artificial potting mixes with high organic matter content ranging from 64 to 71%. The qPCR method developed can be useful for potato growers to avoid agricultural soils highly infested with S. subterranea and in the development of risk assessment models in the future that incorporate cultivar susceptibility to powdery scab and soil infestation levels.

Development of food safety risk assessment tools based on molecular typing and WGS of Campylobacterjejuni genome.

The ‘learning‐by‐doing’ EU‐FORA fellowship programme in the development of risk assessment tools based on molecular typing and WGS of Campylobacter jejuni genome was structured into two main activities: the primary one focused on training on risk assessment methodology and the secondary one in starting and enhancing the cooperation between the hosting and home organisations, or other joint activities. The primary activities had three subsequent work packages (WPs): WP1 data organisation, WP2 cluster and association analyses, and WP3 development of risk assessment models. The secondary activities have branched into one workshop and the initiation of a cooperation programme between the hosting and home organisations. In the last quarter, the fellow had contributed to the characterisation of some pathogens in possible response to a changing climate, part of the CLEFSA project. The fellow attended various forms of training: online and on‐site courses, and also participated at several conferences and meetings for improving his knowledge and skills, contributing to performing the Campylobacter risk assessment and source attribution.

Evaluation of the effect of three constituent metals of monazita on the radiosensibility of human osteoblasts

Thorium has gained notoriety in recent years, as a potential source of nuclear energy, substituting uranium in power plants. Monazite is an important source of thorium, as well of uranium and rare earths elements. Workers involved in the extraction and manipulation of this mineral are occupationally exposed to a range of metal mixtures containing thorium and to ionizing radiation. As an osteotropic substance, thorium is mostly deposited in bone tissue and may interfere in cellular radiosensitivity. A human osteoblast cell line was used to evaluate the effects of thorium (Th), cerium (Ce) and lanthanum (La) on cell radiosensivity, using proliferation as indicator. Assays were performed using cell cultures exposed to metals alone and metals combined with ionizing radiation. No stimulus of proliferation was observed when samples were exposed to metals or radiation alone. On the other hand, the metals were able to influence cell radiosensivity, in a concentration-dependent manner when metals and radiation were applied simultaneously. Samples irradiated and exposed to metals combinations revealed an interaction between them in all the tested arrangements (Th-Ce, Th-La, Th-Ce-La). All interactions proved to be of the antagonist type relative to the proliferation indicator, with a higher degree seen for the Th-Ce association. Such results showed the possibility that metal mixtures together with radiation may produce combined effects on osteoblasts, through modifications on the degree of radiosensivity. The results indicate the possibility of an enhancement in occupational risk for workers that manipulate monazite byproducts. Thus, the development of risk assessment models that include the evaluation of mixtures and their cytotoxic and radiotoxic effects on tissues and organs must be highlighted.

Risk assessment models of integrative medicine indicators for malignant transformation of chronic atrophic gastritis: A registry study protocol

Abstract Objective Traditional Chinese medicine (TCM) and modern medicine have both been used in arresting malignant transformation of chronic atrophic gastritis (CAG) in China with good therapeutic effect. However, no studies have been undertaken to assess the risk of malignant transformation in CAG patients using both modern medicine and TCM features. Our study aimed to develop risk assessment models for malignant transformation of CAG combining indicators of both TCM and modern medicine. These models will facilitate early warning and control of malignant transformation of CAG from the perspective of evidence-based integrative medicine. Methods In the proposed registry study, a total of 1000 eligible CAG patients will be recruited from four hospitals in China. A 10-year follow-up study will be conducted both on-site and off-site to track the events of malignant transformation. Frequency analysis and chi-squared tests will be used to perform the comparative analysis on the prevalence of malignant transformation events and indicators in TCM or modern medicine in different groups. A multivariate Cox proportional hazard model will be used to perform correlation analyses of malignant transformation events and factors of TCM or modern medicine. Conclusion The proposed study has been designed with a large sample size and long follow-up period, in which wide-ranging modern medicine and TCM indicators can be gathered over the whole process of malignant transformation of CAG. Based on this study, risk assessment models for malignant transformation of CAG may be constructed from the perspective of integrative medicine. This may provide clinicians and patients with an optimized early warning system as well as prevention strategies for malignant transformation of CAG.

Application Development Risk Assessment Model Based on Bayesian Network

This paper describes a new risk assessment model for application development and its implementation. The model is developed using a Bayesian network and Boehm’s software risk principles. The Bayesian network is created after mapping top twenty risks in software projects with interrelationship digraph of risk area category. The probability of risk on the network is analyzed and validated using both numerical simulation and subjective probability from several experts in the field and a team of application developers. After obtaining the Bayesian network model, risk exposure is calculated using Boehm's risk principles. Finally, the implementation of the proposed model in a government institution is shown as a real case illustration.

Impact of Traditional Education and Tablet-Assisted Education on Students: A Comparative Analysis

With the recent technological advances, there is a shift from traditional education to tablet-assisted learning environment. This study compares the problems experienced by students in traditional education and tablet-assisted educational activities. The impact of traditional education on students has been discussed by a comprehensive analysis of the literature. The findings showed that the physical discomforts due to tablet-use are intensively experienced at neck, upper back, lower back, and shoulder regions, which are very similar to those experienced in traditional education. Reading and writing activities have impact on the shoulders, upper back, and the left upper arm. The developed risk assessment model has shown that both educational and extra-curricular activities result in significant risk factors of physical discomfort. Analysis of the muscular activity data implied that there were significant differences among muscle groups for each respondent, which validates and verifies our risk assessment model.

Movement and contact patterns of long-distance free-grazing ducks and avian influenza persistence in Vietnam.

Presence of ducks, and in particular of free-grazing ducks, has consistently been shown to be one of the most important risk factors for highly pathogenic avian influenza outbreaks which has compromised poultry production in South-East Asia since the early 2000s and continues to threaten public health, farmers’ livelihood and food security. Although free-grazing duck production has been practised for decades in South-East Asia, there are few published studies describing this production system, which is suspected to play an important role in the maintenance of avian influenza viruses. This study aimed at describing quantitatively the long-distance free-grazing duck production system in South Vietnam, characterising the movement and contact patterns of the duck flocks, and identifying potential associations between farming practices, movement and contact patterns and the circulation of avian influenza viruses. We conducted interviews among stakeholders involved in the free-grazing duck production system (duck farmers, transporters and rice paddy owners) in combination with a virological cross-sectional survey in South Vietnam. Results show that both direct and indirect contacts between free-grazing duck flocks were frequent and diverse. The flocks were transported extensively across district and province boundaries, mainly by boat but also by truck or on foot. A third of the investigated flocks had a positive influenza A virology test, indicating current circulation of avian influenza viruses, but none were positive for H5 subtypes. The age and size of the flock as well as its location at the time of sampling were associated with the risk of influenza A circulation in the flocks. These findings should be considered when developing risk assessment models of influenza virus spread aimed at informing the development of improved biosecurity practices leading to enhanced animal health, sustainable animal production and reliable income for farmers.