Low Level Of Uncertainty Research Articles

Modelling historical landscape changes

ContextHistorical maps of land use/land cover (LULC) enable detection of landscape changes, and help to assess drivers and potential future trajectories. However, historical maps are often limited in their spatial and temporal coverage. There is a need to develop and test methods to improve re-construction of historical landscape change.ObjectivesTo implement a modelling method to accurately identify key land use changes over a rural landscape at multiple time points.MethodsWe used existing LULC maps at two time points for 1930 and 2015, along with a habitat time-series dataset, to construct two new, modelled LULC maps for Dorset in 1950 and 1980 to produce a four-step time-series. We used the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Scenario Generator tool to model new LULC maps.ResultsThe modelled 1950 and 1980 LULC maps were cross-validated against habitat survey data and demonstrated a high level of accuracy (87% and 84%, respectively) and low levels of model uncertainty. The LULC time-series revealed the timing of LULC changes in detail, with the greatest losses in neutral and calcareous grassland having occurred by 1950, the period when arable land expanded the most, whilst the expansion in agriculturally-improved grassland was greatest over the period 1950–1980.ConclusionsWe show that the modelling approach is a viable methodology for re-constructing historical landscapes. The time-series output can be useful for assessing patterns and changes in the landscape, such as fragmentation and ecosystem service delivery, which is important for informing future land management and conservation strategies.

Complete Data Inventory of a Geothermal Power Plant for Robust Cradle-to-Grave Life Cycle Assessment Results

Technologies to produce electric energy from renewable geothermal source are gaining increasing attention, due to their ability to provide a stable output suitable for baseload production. Performing life cycle assessment (LCA) of geothermal systems has become essential to evaluate their environmental performance. However, so far, no documented nor reliable information has been made available for developing robust LCA studies. This work provides a comprehensive inventory of the Italian Bagnore geothermal power plants system. The inventory is based exclusively on primary data, accounting for every life cycle stage of the system. Data quality was assessed by means of a pedigree matrix. The calculated LCA results showed, with an overall low level of uncertainty (2–3%), that the commissioning and operational phases accounted for more than 95% of the environmental profile. Direct emissions to atmosphere were shown to be the major environmental impact, particularly those released during the operational phase (84%). The environmental performances comparison with the average Italian electricity mix showed that the balance is always in favor of geothermal energy production, except in the climate change impact category. The overall outcome confirms the importance, for flash technology employing fluid with a high concentration of gas content, of using good quality primary data to obtain robust results.

Fuzzy decision-making in laser-assisted joining of polymer-metal hybrid structures

A fuzzy decision-making approach was developed to optimize laser-assisted joining process of polymer-metal hybrid structures. A systematic approach based on Design of Experiment was adopted to identify and explain the effect of each process parameter, i.e. laser energy and laser power, on the response variables, i.e. maximum operating temperature and shear strength. Then, a combined fuzzy-genetic algorithm model was developed to find the best input parameters’ combination capable to satisfy the requirement of the best processing performances, in terms of appropriate range of operating temperature and highest shear strength. The use of genetic algorithms involved the choice of the best nominal regression model and the optimisation of the fuzzy numbers. This enabled to account most of the experimental data in combination with the smallest uncertainty level. Experimental results showed the success of the joining process ensuring sufficient adhesion of the hybrid structure over time, with shear strength of almost 29 MPa by using a laser power of 150 W and energy of 2 kJ. Moreover, the fuzzy process maps enable the selection of the process parameters with the aim of achieving desired process output along with the lowest uncertainty level. In addition, it also provides the amount of uncertainty of the model. The results indicated that a low laser energy level of 2250 J and a laser power in the range 120–140 W would provide the optimal solution in terms of operating temperature (i.e. between 343 and 520 °C), shear strength (i.e. greater than 26 MPa) and uncertainty level (i.e. membership level greater than 0.9).

A Study of Patient Satisfaction and Uncertainty in a Rapid Access Chest Pain Clinic

A number of Australian hospitals are piloting outpatient rapid access chest pain clinics (RACC) as a new clinical pathway. These clinics aim to provide an alternative to inpatient care for timely access to specialist cardiology assessment whilst improving quality of care and experience. An understanding of overall patient satisfaction can be considered a marker of overall clinical success, contributes to cost-effectiveness, and is needed to develop improvement strategies. We aimed to evaluate uncertainty, satisfaction and experience among patients treated in a RACC. All consecutive patients presenting with troponin negative intermediate risk chest pain treated in a new RACC in a public quaternary teaching hospital over a 12-month period, were invited to participate in two core components: A paper-based questionnaire, and a one-month follow-up telephone structured interview. Patient satisfaction and uncertainty were assessed by the short form of the Mishel Uncertainty in Illness Scale (MUIS) and Picker Dimensions of care. There was a 51.2% response rate (190/371). Patients ranged in age from 20 to 89 years (56.4±13.6 years) and 47% were female. Findings illustrated a global high level of satisfaction in all areas: care, comfort, communication and engagement, with low levels of uncertainty in illness experienced by patients. There were high levels of satisfaction and minimal uncertainty for patients related to their management and care in the RACC during the study period. These results have formed part of the formal cost-effectiveness assessment of the RACC used to support its transition from a pilot implementation to standard of care at the study centre.

Subjective Expectations, Social Security Benefits, and the Optimal Path to Retirement

Americans face the challenges of retirement with varying degrees of preparation. Evidence indicates that that many individuals may not be making the best possible choices with respect to their Social Security and retirement savings. We assess the subjective expectations of non-retirees and find that they have sizable biases and uncertainty about future retirement benefits. This uncertainty and the level of subjective expectations can affect workers’ wealth accumulation and retirement readiness. We build on these observations and combine unique survey data with a life-cycle optimization model to measure the role of Social Security literacy, subjective expectations about retirement benefits, and behavioral traits as determinants of life-cycle savings decisions and welfare. The goal of this project is to better understand the role of retirement expectations as determinants of savings decisions and retirement income. We forecast future benefits and measure the bias in expectations. We find heterogeneity in the direction of the expectation bias: Men and those with low levels of uncertainty about retirement benefits are less likely to overestimate their future retirement benefits, hence are more likely to save more and reach retirement better prepared. We find that these biases in subjective expectations translate into suboptimal asset accumulation and welfare losses.

The transit of external markers throughout the ruminant digestive tract: 1. The fitting quality of models to marker profiles in feces using an information-theoretic approach

We used the information-theoretic (I–T) approach to evaluate the quality of fit of nonlinear mixed-effects models fitted to fecal profiles of the particulate markers Cr and La, and Co-EDTA as a fluid marker pulse dosed to cattle and sheep. Experiments with dry and lactating dairy cows, steers, and sheep fed tropical forages including corn silage ad libitum or at multiples of maintenance were the sources of the fecal marker profiles. We fit nonlinear compartmental models of one (GN), two (GNG1), or multiple compartments (MC) to those markers by animal species/category. The animal was the random effect introduced in the nonlinear mixed-effects model. Variance and correlation functions accounted for heterogeneity of variance. The model averaging technique was necessary to summarize inferences from the fit of the several compartmental models according to the I–T approach. Some models presented an overall excellent fit, with model probabilities ≥0.9. This probability threshold leaves a low level of uncertainty for choosing such models to represent reality, given the data, and for those fits, there was no need for model averaging. For many profiles, however, there was a need for model averaging to improve predictive accuracy. The Cr-fiber and Co-EDTA travelled at different transit times (τ) in steers; however, the confidence intervals for τ overlapped for dry, lactating cows, and sheep. Chromium-labeled forage particles remained within retention segments during overlapping residence times for steers, sheep and dry cows, whereas Cr-labelled forage fiber and La-labelled concentrate fiber segregate with respect to residence times for sheep. The liquid phase Co-EDTA marker for lactating cows had the shortest residence times if compared to other markers fed to dry cows, steers, and sheep. The total mean residence times of Cr-labelled particles were shorter for lactating cows, intermediate for dry cows and sheep, and were longer for steers. The I–T approach was useful for selecting models within the nonlinear mixed models' framework, by combining the ability of different models to fit marker excretion profiles, and by combining the predictive powers of the fitted models by averaging solutions whenever necessary.

Prospective Feasibility Trial of a Novel Strategy of Facilitated Cascade Genetic Testing Using Telephone Counseling.

Probands with newly diagnosed cancer-associated pathogenic variants were offered facilitated cascade testing whereby the genetics team identified and contacted ARRs by telephone to disclose the familial pathogenic variant and offer telephone counseling and mailed saliva testing. Results and guideline-based recommendations were reviewed by telephone and shared with the primary care physician. Thirty probands were enrolled, and 114 ARRs were identified. Twelve ARRs were excluded (lived outside of the United States, n = 5; proband did not approve of contact, n = 7). Among 102 ARRs telephoned, contact was established with 95 (93%). Among 114 identified ARRs, 66 (58%) completed genetic testing. Among those completing testing, 27 (41%) carried the familial pathogenic variant. Surveys of ARRs at the time of genetic testing and 6 months later demonstrated low levels of anxiety, depression, distress, and uncertainty and high levels of satisfaction with testing. At 6 months, 7 ARRs with pathogenic variants had undergone cancer surveillance interventions and 4 had undergone cancer risk-reducing surgery. Facilitated cascade testing with telephone genetic counseling and mailed saliva kits resulted in high testing uptake among ARRs. Positive genetic testing resulted in utilization of genetically targeted primary disease prevention at short-term follow-up. Facilitated cascade testing is a straightforward, low-cost, easily implemented strategy with significant potential to promote early detection for affected ARRs and reduce cancer mortality and should be evaluated in larger scale clinical trials.

Combined Fuzzy and Genetic Algorithm for the Optimisation of Hybrid Composite-Polymer Joints Obtained by Two-Step Laser Joining Process.

In the present work, genetic algorithms and fuzzy logic were combined to model and optimise the shear strength of hybrid composite-polymer joints obtained by two step laser joining process. The first step of the process consists of a surface treatment (cleaning) of the carbon fibre-reinforced polymer (CFRP) laminate, by way of a 30 W nanosecond laser. This phase allows removing the first matrix layer from the CFRP and was performed under fixed process parameters condition. In the second step, a diode laser was adopted to join the CFRP to polycarbonate (PC) sheet by laser-assisted direct joining (LADJ). The experimentation was performed adopting an experimental plan developed according to the design of experiment (DOE) methodology, changing the laser power and the laser energy. In order to verify the cleaning effect, untreated laminated were also joined and tested adopting the same process conditions. Analysis of variance (ANOVA) was adopted to detect the statistical influence of the process parameters. Results showed that both the laser treatment and the process parameters strongly influence the joint performances. Then, an uncertain model based on the combination of fuzzy logic and genetic algorithms was developed and adopted to find the best process parameters’ set able to give the maximum joint strength against the lowest uncertainty level. This type of approach is especially useful to provide information about how much the precision of the model and the process varies by changing the process parameters.

Local land subsidence in Miami Beach (FL) and Norfolk (VA) and its contribution to flooding hazard in coastal communities along the U.S. Atlantic coast

Increasing rate of sea level rise (SLR) along the US Atlantic coast has resulted in increasing flooding hazard in several coastal communities, including Boston (MA), Norfolk (VA), and Miami Beach (FL). Here, we evaluate the contribution of local land subsidence to coastal flooding hazard in two communities, Miami Beach and Norfolk, using Interferometric Synthetic Aperture Radar (InSAR) time series observations. The InSAR analysis relies on two data sets of ERS-1/2 scenes that were acquired during 1992–1999. The long period covered by the data sets and the large number of available scenes (>20), allowed us to detect movements with lower uncertainty levels (up to 2.4 mm/yr) compared to previous studies. Our results revealed the occurrence of localized subsidence in both communities. In Miami Beach, subsidence at rates of 1–3 mm/yr occurred in a small portion of the territory, mainly in parts of the city built on reclaimed wetlands. In Norfolk, relative subsidence occurred in several localized areas, some along the shore and some inland, at rates of 1–3 mm/yr, while only few sectors show subsidence up to 6 mm/yr. In these areas, the subsidence is higher and reaches ~8 mm/yr if the combined effects of regional-scale (~1.7 mm/yr) and InSAR-derived subsidence is considered. The subsidence observed in this study indicates localized areas of relative higher rate of SLR and a potential increased coastal flooding hazard for both communities.

Motivational effects on brain activity: need for closure moderates the impact of task uncertainty on engagement-related P3b.

Need for cognitive closure (NFC), that is, individual's aversion toward uncertainty and the desire to reduce it quickly usually manifests in effort minimizing cognitive strategies. Recent studies, however, demonstrated that it is also linked with laborious processing when the task itself induces uncertainty. Although this pattern was observed when testing behaviors and cardiovascular activity, it has never been tested on a neurocognitive level. To fill this gap, we investigate whether NFC moderates the impact of task uncertainty on engagement-related P3b component of brain activity. In the experiment, we recorded the electroencephalographic activity of the brain while participants performed a sampling task which provides uncertainty manipulation within participants. We also manipulated NFC between participants. As predicted, we did not find any differences between high and low NFC participants in the P3b component at lower levels of uncertainty. However, at the highest level of uncertainty, the P3b component decreases significantly among low but not high NFC participants. That is because gaining certainty and achieving closure is not a critical epistemic goal for low NFC and thus, exerting extra effort to gain certainty is not justified.

Penalising on the Basis of the Severity of the Offence: A Sophisticated Revenue-Based Cartel Penalty

We propose a new penalty regime for cartels in which the penalty base is the revenue of the cartel but the penalty rate increases in a systematic and transparent way with the cartel overcharge. The proposed regime formalises how revenue can be used as the base while taking into account the severity of the offence. We show that this regime has better welfare properties than the simple revenue-based regime under which the penalty rate is fixed, while having relatively low levels of implementation costs and uncertainty. We conclude that the proposed penalty regime deserves serious consideration by Competition Authorities.

Soft‐decision schemes for radar estimation of elevation at low grazing angles

In modern radars, the problem of estimating elevation angle at low grazing angles is typically solved using super-resolution techniques. These techniques often require one to provide an estimate of the number of waveforms impinging the array, which one can accomplish using model selection techniques. In this study, the authors investigate the performance of an alternative approach, based on the Bayesian-like model averaging. The Bayesian approach exploits the fact that the parameters of the model related to multipath signals are nuisance ones, which allows one to avoid the estimation of the number of waveforms and improves estimation performance. The method is introduced for the classical conditional maximum-likelihood estimator and extended to its, recently proposed, robustified version. The authors find, however, that the robustified estimator includes its own soft-decision mechanism and benefits from the averaging only for low levels of model uncertainty.

Comprehensive Uncertainty Quantification and Sensitivity Analysis for Cardiac Action Potential Models.

Recent efforts to ensure the reliability of computational model-based predictions in healthcare, such as the ASME V&V40 Standard, emphasize the importance of uncertainty quantification (UQ) and sensitivity analysis (SA) when evaluating computational models. UQ involves empirically determining the uncertainty in model inputs—typically resulting from natural variability or measurement error—and then calculating the resultant uncertainty in model outputs. SA involves calculating how uncertainty in model outputs can be apportioned to input uncertainty. Rigorous comprehensive UQ/SA provides confidence that model-based decisions are robust to underlying uncertainties. However, comprehensive UQ/SA is not currently feasible for whole heart models, due to numerous factors including model complexity and difficulty in measuring variability in the many parameters. Here, we present a significant step to developing a framework to overcome these limitations. We: (i) developed a novel action potential (AP) model of moderate complexity (six currents, seven variables, 36 parameters); (ii) prescribed input variability for all parameters (not empirically derived); (iii) used a single “hyper-parameter” to study increasing levels of parameter uncertainty; (iv) performed UQ and SA for a range of model-derived quantities with physiological relevance; and (v) present quantitative and qualitative ways to analyze different behaviors that occur under parameter uncertainty, including “model failure”. This is the first time uncertainty in every parameter (including conductances, steady-state parameters, and time constant parameters) of every ionic current in a cardiac model has been studied. This approach allowed us to demonstrate that, for this model, the simulated AP is fully robust to low levels of parameter uncertainty — to our knowledge the first time this has been shown of any cardiac model. A range of dynamics was observed at larger parameter uncertainty (e.g., oscillatory dynamics); analysis revealed that five parameters were highly influential in these dynamics. Overall, we demonstrate feasibility of performing comprehensive UQ/SA for cardiac cell models and demonstrate how to assess robustness and overcome model failure when performing cardiac UQ analyses. The approach presented here represents an important and significant step toward the development of model-based clinical tools which are demonstrably robust to all underlying uncertainties and therefore more reliable in safety-critical decision-making.

Flow rate measurements in hydropower plants using the pressure-time method – Experiences and improvements

One of the most difficult tasks in the performance tests of hydraulic machines (turbines and pumps) is related to measuring the flow rate (discharge). Particularly, this problem concerns the large-size structures, which can be found in hydropower plants. In such cases, flow rate measurement is difficult and expensive due to the need to use special measuring devices assuring sufficiently low uncertainty level of measurement results in structures of such sizes, complex geometry, difficult access to the measurement cross-sections, etc. Only a few methods fulfilling this requirement are available under the conditions occurring in hydropower plants. One of them is the pressure-time method (known as the Gibson method) which is primary method of flow rate measurement particularly used in hydropower plants equipped with large-scale derivation.The Authors present some own experiences on flow rate measurements carried out by means of the pressure-time method gathered during tests performed in hydropower plants. Presented issues are related to instrumentation, measurement methodology and computation of the flow rate based on the recorded time course of pressure difference changes in penstocks of hydraulic machines installed in hydropower plants.Improvements in the pressure-time method developed on the basis of the aforementioned experiments allow to extend the applicability of the method, e.g. to measure flow in pump mode of hydraulic machines and reduce the uncertainty of flow measurement by applying elastic waterhammer theory based approach or CFD analysis in pipelines with complex geometry.

Characterization of uncertainty contributions in a high-accuracy PMU validation system

The effective deployment of Phasor Measurement Units (PMUs) in Distribution Networks (DNs) requires an enhancement in terms of estimation accuracy beyond the limits of IEEE Std C37.118.1 (IEEE Std), aiming at a Total Vector Error (TVE) in the order of 0.0x% in steady-state test conditions. As a consequence, a rigorous metrological characterization of PMU performance requires a validation system whose accuracy is at least one order of magnitude better than the one of the device under test, i.e. it requires a TVE in the order of 0.00x% in steady-state test conditions and 0.0x% in distorted or dynamic test conditions. In this paper, we consider the hardware and software architecture of a previously published PMU validation system specifically designed for PMUs operating in DNs. In this context, we evaluate the quality of the generated test waveforms, and we carry out a thorough metrological characterization of the uncertainty contributions due to generation, acquisition and synchronization stages. In this sense, the proposed analysis provides a rigorous methodology for the evaluation of such low uncertainty levels, and enables us to identify and discuss the most challenging aspects in the implementation of a high-accuracy PMU validation system.

Robotic-cell scheduling with pick-up constraints and uncertain processing times

Technological developments have propelled the deployment of robots in many applications, which has led to the trend to integrate an increasing number of uncertain processes into robotic and automated equipment. We contribute to this domain by considering the scheduling of a dual-gripper robotic cell. For systems with one potential bottleneck, we determine conditions under which the widely used swap sequence does not guarantee optimality or even feasibility and prove that optimal schedules can be derived under certain conditions when building on two types of slack we introduce. With the addition of a third type of slack and the concept of fixed partial schedules, we develop an offline-online scheduling approach that, in contrast with previous work, is able to deal with uncertainty in all process steps and robot handling tasks, even under pick-up constraints. The approach can deal with single- or multiple-bottleneck systems, and is the first approach that is not restricted to a single predefined sequence such as the swap sequence. Our approach is well suited for real-world applications, since it generates cyclic schedules and allows integration into commonly-used frameworks for robotic-cell scheduling and control.We demonstrate the applicability of our approach to cluster tools in semiconductor manufacturing, showing that our approach generates feasible results for all tested levels of uncertainty and optimal or near-optimal results for low levels of uncertainty. With additional symmetry-breaking constraints, the model can be efficiently applied to industrial-scale test instances. We show that reducing uncertainty to below 10% of the processing time would yield significantly improved cycle lengths and throughput. We also demonstrate that the widely used swap sequence only finds solutions for less than 1% of the instances when strict pick-up constraints are enforced and processing times are heterogeneous. As our approach finds feasible solutions to all of these instances, it enables the application of robotic cells to a significantly broader application environment.

Prenatal Coping Patterns Mediate The Effect of Uncertainty and Perceived Social Support on The Psychological Well-Being of High-Risk Pregnant Women

Background: High risk pregnancy can introduce additional psychological stress on the pregnant women. Also, poor coping skills during pregnancy are associated with negative outcomes. The effects of perceived social support and uncertainty on the psychological well-being of high risk pregnant women can be mediated by their prenatal coping patterns. Aim: This study aimed to assess the mediating effects of prenatal coping patterns of the perceived social support, and uncertainty on the psychological well-being of high-risk pregnant women. Design: A cross sectional, correlational study design was utilized on 162 high-risk pregnant women according to certain inclusion criteria and who were attending outpatient clinic in Obstetrics and Gynecology Hospital affiliated to Ain Shams University Hospitals. Tools of data collection: The data was collected using an interview questionnaire that included five sections as the following: 1) patient's demographic characteristics and obstetric history 2) The Mishel Uncertainty in Illness Scale; 3) The Medical Outcomes Study (MOS) Social Support Survey; 4) The Prenatal Coping Inventory; 5) and The Psychological General Well-Being Index. Results: data and path analysis revealed that, planning/preparation for motherhood negatively mediated the effect of uncertainty on psychological wellbeing of high-risk pregnant women; planning/preparation for motherhood mediated the effect of perceived social support on psychological wellbeing of them; and perceived social support had direct effect on psychological wellbeing of the studied subject. Regarding the psychological well-being, perceived social support, uncertainty, and prenatal coping pattern of the studied high-risk pregnant women, less than half of them had moderate psychological distress, less than two third of them had high perceived social support, more than two third of them reported low level of uncertainty, the majority of them reported less use of avoidance, less than two third of them reported less use of spiritual coping, whereas more than three quarters of them reported frequent use of planning/preparation for motherhood. Conclusion: The study concluded that prenatal coping patterns (problem focused coping) mediated the effects of uncertainty and perceived social support on the psychological well-being of the studied high-risk pregnant women. Recommendation: Incorporating psychosocial assessment in routine prenatal screening for women experiencing medically complex pregnancies with continuous and routine screening for depression and anxiety

Bullwhip severity in conditions of uncertainty: regional vs global supply chain strategies

PurposeUsing a phenomenon known as the bullwhip effect, the authors explore why additional uncertainty in the marketplace can create severe disruptions in global supply chains (GSCs). The purpose of this paper is to analyze related risks in regional vs GSCs during low and high levels of uncertainty. The authors propose and discuss a number of potential implications alongside some tactics that may help mitigate disruptions in some cases before they become terminal problems for the supply chain sustainability.Design/methodology/approachMonte Carlo simulation is used to generate the conditions of uncertainty and various scenarios that may emerge to challenge GSCs. Vensim software is utilized as a tool for simulation purposes. The authors considered scenarios applicable to manufacturing and retail sectors specifically because of storability property of goods.FindingsRegional supply chains, as opposed to global ones, are more stable and reliable (less risk of disruption) during low and high levels of uncertainty. During uncertain times, upstream suppliers are at greater risk in GSCs. Firms must make strategic decisions that will secure its supply chain functionality and assess the likelihood of such events since many firms entered emerging markets.Originality/valueBuilding on internalization theory, it shows that risk and survival are components of decision making that are further complicated by supply chains now operating globally in emerging markets. The paper demonstrates with simulation that GSCs are riskier than regional supply chains in low and high levels of uncertainty, particular as it relates to the bullwhip effect. It also provides recommendations about supply chain restructure and investments in communication improvements to reduce the bullwhip effect in the supply chain.

Level of uncertainty in people living with HIV

Introduction: Worldwide, human immunodeficiency virus (HIV) infection continues to be an emerging public health problem. With the positive screening of this chronic degenerative disease, the virus generates physical, psychological, social and spiritual changes in people, that lead to uncertainty in the face of an uncertain future. Identifying the uncertainty in the patients, allows the care professional to plan integral interventions to favour the reduction of this process and can integrate well-being in the experience with their chronic condition and thus, have quality of life. Objective: To determine the level of uncertainty in a group of people living with HIV. Method: Quantitative, descriptive and transversal study. Non-probabilistic sampling of 30 people living with HIV, from the CONVIHVE Civil Association of Morelia, Michoacán. An identification card and the scale “Uncertainty in front of the disease” (MUIS) of Merle Mishel were applied, with a reliability of (.904). The statistical package SPSS V was used to analyze the data. 22. Results: 83.3% of the total population are men and 16.7% are women, 32-39 years of age (36.6%) of whom 70% are men. Being single, 53.3% have a bachelor’s degree. 60% report being employed. 30% of the population refers to having more than 10 years of diagnosis and less than 5 years in treatment 53.3%. 76.7% are Catholic. From the total of participants, 23.3% showed a low level of uncertainty, followed by a level of regular uncertainty with 26.7%, and 50% showed a high level of uncertainty. Conclusion: The level of uncertainty in people living with HIV is high. It is necessary to carry out comprehensive interventions that favour the reduction of the level of uncertainty and thus improve the well-being and quality of life of people living with HIV.

Low efficiency of large volcanic eruptions in transporting very fine ash into the atmosphere

Volcanic ash clouds are common, often unpredictable, phenomena generated during explosive eruptions. Mainly composed of very fine ash particles, they can be transported in the atmosphere at great distances from the source, having detrimental socio-economic impacts. However, proximal settling processes controlling the proportion (ε) of the very fine ash fraction distally transported in the atmosphere are still poorly understood. Yet, for the past two decades, some operational meteorological agencies have used a default value of ε = 5% as input for forecast models of atmospheric ash cloud concentration. Here we show from combined satellite and field data of sustained eruptions that ε actually varies by two orders of magnitude with respect to the mass eruption rate. Unexpectedly, we demonstrate that the most intense eruptions are in fact the least efficient (with ε = 0.1%) in transporting very fine ash through the atmosphere. This implies that the amount of very fine ash distally transported in the atmosphere is up to 50 times lower than previously anticipated. We explain this finding by the efficiency of collective particle settling in ash-rich clouds which enhance early and en masse fallout of very fine ash. This suggests that proximal sedimentation during powerful eruptions is more controlled by the concentration of ash than by the grain size. This has major consequences for decision-makers in charge of air traffic safety regulation, as well as for the understanding of proximal settling processes. Finally, we propose a new statistical model for predicting the source mass eruption rate with an unprecedentedly low level of uncertainty.