Modelling welfare estimates in discrete choice experiments for seaweed-based renewable energy

We explore what researchers can gain or lose by using three widely used models for the analysis of discrete choice experiment data—the random parameter logit (RPL) with correlated parameters, the RPL with uncorrelated parameters and the hybrid choice model. Specifically, we analyze three data sets focused on measuring preferences to support a renewable energy programme to grow seaweed for biogas production. In spite of the fact that all three models can converge to very similar median WTP values, they cannot be used indistinguishably. Each model is based on different assumptions, which should be tested before their use. The fact that standard sample sizes usually applied in environmental valuation are generally unable to capture the outcome differences between the models cannot be used as a justification for their indistinct application.

Determination of physico-chemical parameters of coal mines water samples and their correlation establishment with WQI using python programming

The quality assessment of water is the need of the hour as water pollution has reached to an alarming level. The pollution of natural water bodies due to mine drainage system and mining activities is a major environmental concern worldwide. There are many potential reasons of water pollution such as agricultural, sewage, oil, radioactive materials, dumping & mining activities etc. Mining activities are responsible for the contamination of watercourses with metal and increment of sediment levels in it, however acid mine drainage can be viewed as the most lethal means of polluting watercourse. In this study an analysis was done on the water samples collected from different coal mines of Jharkhand and Telangana State. The WQI for each sample were calculated and correlated with their physico-chemical parameters. The lowest grades of the water samples are mainly due to the presence of the strongest correlated parameters. It was observed that the iron content in the samples has the strongest correlation with a Pearson coefficient of 0.9977 and highest significance with a P value lower than 0.001.

Key Factors for Improving the Carcinogenic Risk Assessment of PAH Inhalation Exposure by Monte Carlo Simulation

Monte Carlo simulation (MCS) is a computational technique widely used in exposure and risk assessment. However, the result of traditional health risk assessment based on the MCS method has always been questioned due to the uncertainty introduced in parameter estimation and the difficulty in result validation. Herein, data from a large-scale investigation of individual polycyclic aromatic hydrocarbon (PAH) exposure was used to explore the key factors for improving the MCS method. Research participants were selected using a statistical sampling method in a typical PAH polluted city. Atmospheric PAH concentrations from 25 sampling sites in the area were detected by GC-MS and exposure parameters of participants were collected by field measurement. The incremental lifetime cancer risk (ILCR) of participants was calculated based on the measured data and considered to be the actual carcinogenic risk of the population. Predicted risks were evaluated by traditional assessment method based on MCS and three improved models including concentration-adjusted, age-stratified, and correlated-parameter-adjusted Monte Carlo methods. The goodness of fit of the models was evaluated quantitatively by comparing with the actual risk. The results showed that the average risk derived by traditional and age-stratified Monte Carlo simulation was 2.6 times higher, and the standard deviation was 3.7 times higher than the actual values. In contrast, the predicted risks of concentration- and correlated-parameter-adjusted models were in good agreement with the actual ILCR. The results of the comparison suggested that accurate simulation of exposure concentration and adjustment of correlated parameters could greatly improve the MCS. The research also reveals that the social factors related to exposure and potential relationship between variables are important issues affecting risk assessment, which require full consideration in assessment and further study in future research.

PSV-14 Using Bayesian Hamiltonian Monte Carlo and a nonlinear model for the estimation of genetic parameters for lactation curves in goats

Hamiltonian Monte Carlo (HMC) is an algorithm of the Markov Chain Monte Carlo (MCMC) method that uses dynamics to propose samples that follow a target distribution. This algorithm enables more effective and consistent exploration of the probability interval and is more sensitive to correlated parameters. Therefore, Bayesian-HMC is a promising alternative to estimate individual parameters of complex functions such as nonlinear models, especially when using small datasets. Our objective was to estimate genetic parameters for milk traits defined based on nonlinear model parameters predicted using the Bayesian-HMC algorithm. A total of 64,680 milk yield test-day records from 2,624 first, second, and third lactations of Saanen and Alpine goats were used. First, the Wood model was fitted to the data. Second, lactation persistency (LP), peak time (PT), peak yield (PY), and total milk yield [estimated from zero to 50 (TMY50), 100(TMY100), 150(TMY150), 200(TMY200), 250(TMY250), and 300(TMY300) days-in-milk] were predicted for each animal and parity based on the output of the first step (the individual phenotypic parameters of the Wood model). Thereafter, these predicted phenotypes were used for estimating genetic parameters for each trait. In general, the heritability estimates across lactations ranged from 0.10 to 0.20 for LP, 0.04 to 0.07 for PT, 0.26 to 0.27 for PY, and 0.21 to 0.28 for TMY (considering the different intervals). Lower heritabilities were obtained for the nonlinear function parameters (A, b and l) compared to its predicted traits (except PT), especially for the first and second lactations (range: 0.09 to 0.18). Higher heritability estimates were obtained for the third lactation traits. To our best knowledge, this study is the first attempt to use the HMC algorithm to fit a nonlinear model in animal breeding. The two-step method proposed here allowed us to estimate genetic parameters for all traits evaluated.

Date of introduction and epidemiologic patterns of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Mogadishu, Somalia: estimates from transmission modelling of satellite-based excess mortality data in 2020

Background: In countries with weak surveillance systems, confirmed coronavirus disease 2019 (COVID-19) deaths are likely to underestimate the pandemic’s death toll. Many countries also have incomplete vital registration systems, hampering excess mortality estimation. Here, we fitted a dynamic transmission model to satellite imagery data of cemeteries in Mogadishu, Somalia during 2020 to estimate the date of introduction and other epidemiologic parameters of the early spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in this low-income, crisis-affected setting. Methods: We performed Markov chain Monte Carlo (MCMC) fitting with an age-structured compartmental COVID-19 model to provide median estimates and credible intervals for the date of introduction, the basic reproduction number (R0) and the effect of non-pharmaceutical interventions (NPIs) up to August 2020. Results: Under the assumption that excess deaths in Mogadishu March-August 2020 were attributable to SARS-CoV-2 infections, we arrived at median estimates of November-December 2019 for the date of introduction and low R0 estimates (1.4-1.7) reflecting the slow and early rise and long plateau of excess deaths. The date of introduction, the amount of external seeding, the infection fatality rate (IFR) and the effectiveness of NPIs are correlated parameters and not separately identifiable in a narrow range from deaths data. Nevertheless, to obtain introduction dates no earlier than November 2019 a higher population-wide IFR (≥0.7%) had to be assumed than obtained by applying age-specific IFRs from high-income countries to Somalia’s age structure. Conclusions: Model fitting of excess mortality data across a range of plausible values of the IFR and the amount of external seeding suggests an early SARS-CoV-2 introduction event may have occurred in Somalia in November-December 2019. Transmissibility in the first epidemic wave was estimated to be lower than in European settings. Alternatively, there was another, unidentified source of sustained excess mortality in Mogadishu from March to August 2020.

Multicriteria assessment of optimal forecasting models in decision support systems to ensure the navigation safety

When developing the Decision Support System (DSS), the operability of the internal motion and maneuvering control systems of the vessel is characterized by a large number of parameters, which should be monitored in order to achieve the expected results. The task is to develop an appropriate methodology for automatic monitoring of these systems, which, using a minimum set of sensors, makes it possible to predict the state of the vessel and change the dangerous state to the safe one. To implement the method of automatic monitoring, a set of statistical control tools is selected depending on the alleged violations and the level of correlation of parameters. The uncorrelated parameters are monitored by instruments based on the Shewhart map [1], the correlated parameters are monitored on the basis of Hotelling statistics [2]. This approach makes it possible to diagnose the pre-emergency and emergency states of ship control systems in on-line mode. The method used for multi-level integrated monitoring of the technical state of control systems in on-line mode can improve the reliability of of identification of the technical state of vessel subsystems and expandthe scope of application of monitoring and diagnostics tools. The data obtained can become the basis for the development of rational decisions in the DSS at the level of control subsystems for the vessel transfer from the dangerous to safe state.

The Future of Educational Leadership in Light of the Crisis Management Aspects: مستقبل القيادة التعليمية في ضوء أبعاد إدارة الأزمات والكوارث

The current descriptive survey study aims to address the problem of the apparent inadequacy of most school leaders in managing crises and disasters and to anticipate future scenarios for such crises. To achieve this, the researcher designed a questionnaire consisting of thirty-one (31) items distributed on three axes: The reality of school leadership, potential crises, and the anticipated future of the leadership in light of crisis management. was validated by calculating the correlated parameters statistically, and its stability by (test-retest) method on an external sample and then the Pearson correlation coefficient between their estimates in both times, and the questionnaires was distributed to the study's sample consisting of one hundred eighty (180) general education leaders in Wadi Al-Dawasir. The study's population is two hundred forty-six (246) male and female leaders, agents, and assistants. The study revealed that the leaders’ abilities in crisis management in the current reality and in anticipating the potential crises were weak while the expected scenario for the future of school leadership in light of crisis management was strongly positive. With a statistically significant difference at the level of (α = 0.05) due to the gender variable in favor of female leaders in the “Potential crises” axis, and statistically significant differences due to the variable of experience in leadership in all axes in favor of “Five years or more”. The researcher recommends that the Education Department should ensure that school leaders possess the skills of crisis management, the need to adopt one of the contemporary models in crisis management, and the need to establish an active unit for crisis management in schools.

Syndecan-1 and Free Indoxyl Sulfate Levels Are Associated with miR-126 in Chronic Kidney Disease

Chronic kidney disease (CKD) is a major cause of death worldwide and is associated with a high risk for cardiovascular and all-cause mortality. In CKD, endothelial dysfunction occurs and uremic toxins accumulate in the blood. miR-126 is a regulator of endothelial dysfunction and its blood level is decreased in CKD patients. In order to obtain a better understanding of the physiopathology of the disease, we correlated the levels of miR-126 with several markers of endothelial dysfunction, as well as the representative uremic toxins, in a large cohort of CKD patients at all stages of the disease. Using a univariate analysis, we found a correlation between eGFR and most markers of endothelial dysfunction markers evaluated in this study. An association of miR-126 with all the evaluated uremic toxins was also found, while uremic toxins were not associated with the internal control, specifically cel-miR-39. The correlation between the expression of endothelial dysfunction biomarker Syndecan-1, free indoxyl sulfate, and total p-cresyl glucuronide on one side, and miR-126 on the other side was confirmed using multivariate analysis. As CKD is associated with reduced endothelial glycocalyx (eGC), our results justify further evaluation of the role of correlated parameters in the pathophysiology of CKD.

Efficient ensemble generation for uncertain correlated parameters in atmospheric chemical models: a case study for biogenic emissions from EURAD-IM version 5

Atmospheric chemical forecasts heavily rely on various model parameters, which are often insufficiently known, such as emission rates and deposition velocities. However, a reliable estimation of resulting uncertainties with an ensemble of forecasts is impaired by the high dimensionality of the system. This study presents a novel approach, which substitutes the problem into a low-dimensional subspace spanned by the leading uncertainties. It is based on the idea that the forecast model acts as a dynamical system inducing multivariate correlations of model uncertainties. This enables an efficient perturbation of high-dimensional model parameters according to their leading coupled uncertainties. The specific algorithm presented in this study is designed for parameters that depend on local environmental conditions and consists of three major steps: (1) an efficient assessment of various sources of model uncertainties spanned by independent sensitivities, (2) an efficient extraction of leading coupled uncertainties using eigenmode decomposition, and (3) an efficient generation of perturbations for high-dimensional parameter fields by the Karhunen–Loéve expansion. Due to their perceived simulation challenge, the method has been applied to biogenic emissions of five trace gases, considering state-dependent sensitivities to local atmospheric and terrestrial conditions. Rapidly decreasing eigenvalues state that highly correlated uncertainties of regional biogenic emissions can be represented by a low number of dominant components. Depending on the required level of detail, leading parameter uncertainties with dimensions of 𝒪(106) can be represented by a low number of about 10 ensemble members. This demonstrates the suitability of the algorithm for efficient ensemble generation for high-dimensional atmospheric chemical parameters.