Areas Of High Probability Research Articles

Investigating and comparing the IRIS spectral lines Mg II, Si IV, and C II for flare precursor diagnostics

Context. Reliably predicting solar flares can mitigate the risks of technological damage and enhance scientific output by providing reliable pointings for observational campaigns. Flare precursors in the spectral line Mg II have been identified. Aims. We extend previous studies by examining the presence of flare precursors in additional spectral lines, such as Si IV and C II, over longer time windows, and for more observations. Methods. We trained neural networks and XGBoost decision trees to distinguish spectra observed from active regions that lead to a flare and those that did not. To enhance the information within each observation, we tested different masking methods to preprocess the data. Results. We find average classification true skill statistics (TSS) scores of 0.53 for Mg II, 0.44 for Si IV, and 0.42 for C II. We speculate that Mg II h&k performs best because it samples the highest formation height range, and is sensitive to heating and density changes in the mid- to upper chromosphere. The flaring area relative to the field of view has a large effect on the model classification score and needs to be accounted for. Combining spectral lines has proven difficult, due to the difference in areas of high probability for an imminent flare between different lines. Conclusions. Our models extract information from all three lines, independent of observational bias or GOES X-ray flux precursors, implying that the physics encoded in a combination of high resolution spectral data could be useful for flare forecasting.

Application of different earthquake-induced landslide hazard assessment models on the 2022 Ms 6.8 luding earthquake

Following the earthquake, prompt evaluation of the distribution of coseismic landslides and estimation of potential disaster losses are crucial for emergency response and resettlement planning. The Luding earthquake of 2022 offers a valuable opportunity to conduct a rapid assessment of coseismic landslides using various models. In this study, we utilize the Logistic Regression (LR)-based Xu2019 model, a new-generation model developed in China, alongside the Newmark model to perform the rapid hazard assessment of coseismic landslides. Assessing the accuracy and applicability of these two models based on the coseismic landslides from the Luding earthquake, we find that within intensity area of IX, the high probability area identified by the Newmark model aligns closely with the actual distribution of landslides. However, the Newmark model’s prediction is overestimated in the intensity area of VIII. For the Xu2019 model, the prediction results are in good agreement with the distribution of actual landslides. Most landslides are located in high probability areas, such as Detuo town, Wandong, and Xingfu villages, indicating that the model has a higher prediction accuracy. Overall, two models have good practical utility in emergency hazard assessment of coseismic landslides. However, the Newmark model requires multi-input parameters and the assignment of these parameters will increase the uncertainty and subjectivity in the practical application of the modeling assessment.

Navigating Visual Challenges: How Parkinson's Disease Alters Cognitive Priorities in Visual Search.

Parkinson's disease (PD) hampers visual search tasks such as reading, driving, and navigation. We examined expectations from past experiences, guiding cognition and contextual priors, on visual search in PD. We compared eye movements as PD and healthy participants searched for a hidden object (target) in cluttered real-world scenes. PD participants prolonged fixation on high-probability (high-prior) locations for the target, consistent across expected and unexpected scenario. Such emphasis on contextual visual priors, evidenced by high fixation duration on high-probability areas, was beneficial when the target was at the expected location but presented challenges when the target was situated in an unlikely place. This study contributes to understanding how PD impacts visual search behavior and cognitive processing. The findings indicate that PD alters attention allocation and visual processing by affecting the utilization of contextual visual priors. It provides insights for potential interventions targeting visuo-cognitive deficits in PD patients. Published 2024. This article is a U.S. Government work and is in the public domain in the USA.

Mapping high probability area for the Bacillus anthracis occurrence in wildlife protected area, South Omo, Ethiopia

Anthrax is a zoonotic disease caused by a spore-forming gram-positive bacterium, Bacillus anthracis. Increased anthropogenic factors inside wildlife-protected areas may worsen the spillover of the disease at the interface. Consequently, environmental suitability prediction for B. anthracis spore survival to locate a high-risk area is urgent. Here, we identified a potentially suitable habitat and a high-risk area for appropriate control measures. Our result revealed that a relatively largest segment of Omo National Park, about 23.7% (1,218 square kilometers) of the total area; 36.6% (711 square kilometers) of Mago National Park, and 29.4% (489 square kilometers) of Tama wildlife Reserve predicted as a high-risk area for the anthrax occurrence in the current situation. Therefore, the findings of this study provide the priority area to focus on and allocate resources for effective surveillance, prevention, and control of anthrax before it causes devastating effects on wildlife.

Spatial heterogeneity facilitates coexistence between striped hyaenas and sympatric carnivores

AbstractAimInterspecific competition plays a key role in shaping carnivore communities. Top‐down effects can impact the coexistence of superordinate and subordinate competitors and prey across a shared landscape. Limited resources, their abundance and diminishing habitats can all exacerbate interspecific competition. Sometimes, behavioural mechanisms or adaptations are critical for subordinate carnivores to coexist with larger species. Here, we investigated the distribution pattern of the striped hyaena (Hyaena hyaena) population with respect to habitat features, sympatric carnivores and their prey species across a large landscape.LocationWestern and Eastern Ghats, India.TaxonStriped hyaena.MethodsWe collected presence–absence data for hyaenas, tigers, leopards, dholes and their prey using camera‐trapping and indirect sign surveys. Using a random forest algorithm, we modelled the local distribution of hyaenas as a function of important ecological variables, including climate, domestic prey abundance and the predicted probabilities of occurrence of other large carnivores and their prey.ResultsWe found that striped hyaenas were confined to relatively small, discrete areas compared to the three other predators, likely a mechanism for achieving coexistence in the landscape. Our empirical results suggested that hyaenas tend to avoid high occurrence probability areas of their co‐predators at a landscape level but foothold opportunities such as, arid climatic conditions, and prey abundance primarily determined spatial incumbency. Coexistence in an area with low to medium relative abundance of co‐predators offsets the interplay between carcass acquisition and risk associated. The coexistence of species was favoured by altitudinal heterogeneity probably providing denning refugia and also by their preference towards suboptimal thorn forests that sustain a low density of co‐predators.Main ConclusionsOur study showed a desisting impact of large predators on the distribution of the striped hyaena population. Furthermore, we suggest preventing habitat alteration in suboptimal habitats of large carnivores to facilitate coexistence in a shared landscape.

Post-Prostatectomy Risk Stratification of Biochemical Recurrence Using Transfer Learning-Based Multi-Modal Artificial Intelligence

For patients undergoing radical prostatectomy for prostate cancer (PCa), accurate risk stratification is essential to guide post-prostatectomy therapeutic decision making. Recently, there has been success in the use of multi-modal artificial intelligence models for men after prostate biopsy to aid in risk stratification. Herein, we trained and tested a TRansfer learning-based multi-modal Artificial InteLligence model (TRAIL) for biochemical recurrence (BCR) risk stratification following radical prostatectomy. Patients contained within a prospective PCa registry at a single institution were utilized. Digital pathology slides from the diagnostic biopsies prior to radical prostatectomy for patients with clinically localized PCa were scanned at 20x resolution. Features were extracted for the TRAIL model from pathology slides via two transfer learning steps: (1) InceptionResNetv2 that first determines a heatmap of tumor areas, and (2) A ResNet18 that extracts representative features from the high tumor probability areas. Least Absolute Shrinkage and Selection Operator (LASSO) was used for feature selection from the pathology-extracted features. Finally, TRAIL combines the clinical and pathology-extracted features via a classification ensemble model based on weak tree learners to predict 2- and 5-year BCR defined as two consecutive serum PSA levels ≥0.2 ng/mL. TRAIL training was performed on 250 patients and was then locked and applied to the test set of 125 patients. Accuracy and the area under the curve (AUC) were calculated. Comparison to CAPRA-S and to clinical-only features were assessed. A total of 818 digital whole pathology biopsy slides from 375 patients treated with subsequent radical prostatectomy were included. Surgical margins were positive in 29% of the patients, and 41% had extra-prostatic extension. The median follow-up was 48 months (Range: 1-132 months). The rates of 2-and 5-year BCR were 11% and 18% respectively. A total of 19 digital pathology-driven features were included in TRAIL. Clinical factors included age, ISUPG, Gleason score, PSA, pathological T and N stages, surgical margin involvement, and the presence of extra-prostatic extension. On the testing set, TRAIL achieved a 2-year BCR AUC of 0.76 and accuracy of 0.87, and was superior to CAPRA-S (AUC = 0.57) and clinical-only features (AUC 0.50, accuracy 0.14). For 5-year BCR, TRAIL achieved an AUC of 0.69 and accuracy of 0.78, and performed better than CAPRA-S (AUC = 0.58), and clinical only features (AUC = 0.50, accuracy = 0.23). Through a combination of deep and ensemble learning, TRAIL incorporates clinical and histopathology features, enabling an improved BCR risk stratification post-prostatectomy when compared to the currently used clinicopathologic models. Future work with larger datasets with metastatic events is warranted to further optimize the model for clinical use.

Coursing the mottled mosaic: Generalist predators track pulses in availability of neonatal ungulates.

The density and distribution of resources shape animal movement and behavior and have direct implications for population dynamics. Resource availability often is "pulsed" in space and time, and individuals should cue in on resource pulses when the energetic gain of doing so exceeds that of stable resources. Birth pulses of prey represent a profitable but ephemeral resource and should thereby result in shifting functional responses by predators. We evaluated movements and resource selection of coyotes (Canis latrans) across a gradient of reproductive stages ranging from late gestation to peak lactation of female mule deer (Odocoileus hemionus) in southwest Wyoming, USA, to test whether coyotes exhibited shifts in selection and movement behavior relative to the availability and vulnerability of neonatal mule deer. We expected coyotes to track pulses in availability of neonatal mule deer, and such behavior would be represented by shifts in resource selection and search behavior of coyotes that would be strongest during peak parturition of mule deer. Coyotes selected areas of high relative probability of use by female mule deer and did so most strongly during peak parturition. Furthermore, searching behavior of coyotes intensified during pulses of availability of deer neonates. Our findings support the notion that coyotes exploit pulses of neonatal deer, presumably as an attempt to capitalize on a vulnerable, energy-rich resource. Our work quantifies the behavioral mechanisms by which coyotes consume ungulate neonates and provides one of the first examples of a mammalian predator-prey system centered on a pulsed resource.

Gene regulatory network inference based on a nonhomogeneous dynamic Bayesian network model with an improved Markov Monte Carlo sampling

A nonhomogeneous dynamic Bayesian network model, which combines the dynamic Bayesian network and the multi-change point process, solves the limitations of the dynamic Bayesian network in modeling non-stationary gene expression data to a certain extent. However, certain problems persist, such as the low network reconstruction accuracy and poor model convergence. Therefore, we propose an MD-birth move based on the Manhattan distance of the data points to increase the rationality of the multi-change point process. The underlying concept of the MD-birth move is that the direction of movement of the change point is assumed to have a larger Manhattan distance between the variance and the mean of its left and right data points. Considering the data instability characteristics, we propose a Markov chain Monte Carlo sampling method based on node-dependent particle filtering in addition to the multi-change point process. The candidate parent nodes to be sampled, which are close to the real state, are pushed to the high probability area through the particle filter, and the candidate parent node set to be sampled that is far from the real state is pushed to the low probability area and then sampled. In terms of reconstructing the gene regulatory network, the model proposed in this paper (FC-DBN) has better network reconstruction accuracy and model convergence speed than other corresponding models on the Saccharomyces cerevisiae data and RAF data.

Convex combination multiple populations competitive swarm optimization for moving target search using UAVs

Unmanned aerial vehicles (UAVs) searching for a moving target is a complex search optimization problem that aims to find a path with maximum probability of observing the target. However, as the area where the target may be located increases in size, existing methods tend to cause easy entrapment into local optima, resulting in poor performance. Hence, we propose a Convex Combination Multiple Populations Competitive Swarm Optimization algorithm (CDCSO) in this paper. First, we suggest applying the multiple population strategy to enhance the search ability of the algorithm. The population of interest is divided into two subgroups that each conduct the CSO. Second, we propose a novel convex combination update strategy to coordinate the two subgroups to search jointly for the global optimal solution. At each iteration, this strategy allows the two subgroups to compete and learn from each other, which prevents the population from falling into the local optimum. We then designed new scenarios containing 3 to 6 probability areas to investigate the performance of our proposed algorithm in multiple and complex scenarios.Results: To demonstrate the effectiveness and stability of the proposed algorithm, we compared it with several swarm optimization algorithms from different perspectives. Experimental results showed that the proposed algorithm has superior performance in following and intercepting a high-probability area. To further demonstrate the generality of the convex combination update strategy, we evaluated the strategy in several developed particle swarm optimization algorithms. The results demonstrated that the convex combination update strategy can be integrated into other algorithms to better address the moving target search problem.

Species, Habitat Characteristics, and Screening Suggestions of Cadmium Hyperaccumulators in China

Phytoremediation, as a green and effective in-situ remediation technology for heavy metal-contaminated soil, has attracted the attention of Chinese scholars and has resulted in a series of achievements over the past 20 years. In this study, the species characteristics, distribution of field discovery sites in various vegetation zones, habitat characteristics, habitat geological characteristics, and geochemistry of cadmium (Cd) of the Cd hyperaccumulators in China reported in the relevant literature from the past 20 years (from 2002 to 2021) were summarized by searching for related keywords. Finally, suggestions were proposed for the screening of new Cd hyperaccumulators. The results showed that a total of 45 species of Cd hyperaccumulators in China have been reported so far. In terms of plant species, they belonged to 22 families and 36 genera, among which Compositae with 14 species was the most abundant. There were 25 species discovered through the field investigation, which were mainly distributed in the subtropical broadleaf evergreen forest region of southern China. Additionally, the Cd hyperaccumulators discovered by field surveys were mainly found in high Cd-concentrated soils surrounding lead-zinc mines. In conclusion, abundant plant resources, high concentrations of heavy metal soils, and long-term domestication jointly promoted the formation of hyperaccumulators. Therefore, the region with these three points could be considered a high probability region for the presence of hyperaccumulators, and the screening of hyperaccumulators could be carried out around this. We proposed that the screening of new hyperaccumulators can be carried out through the following six steps:the identification and investigation of high probability areas, the enrichment capability test, the enrichment capability test in low concentration levels, the enrichment capability test between different ecotypes, the succession of enrichment capability, and the test of remediation proficiency.

Development of an Effective Oral Vaccine Dissemination Strategy against Classical Swine Fever for Wild Boar in Gifu Prefecture, Japan

In September 2018, classical swine fever (CSF) reemerged in Japan after more than a quarter of a century. After the first notification on a pig farm, wild boars positive for CSF were found continuously in the surrounding area. Gifu was the first prefecture in Japan to disseminate oral vaccines to wild boars in March 2019, with vaccines spread to approximately 14,000 sites between 2019 and 2020. While these diligent measures seemed to have shown some effectiveness, several vaccine spray sites remained without wild boar emergence. Based on the vaccine dissemination records from these periods, this study conducted a statistical analysis to propose more effective vaccine dissemination sites. First, a generalized linear mixed model was used to identify factors correlated with wild boar emergence. Then, two spatial interpolation methods, inverse distance weighted (IDW) and Kriging, were adopted to create a probability map of wild boar emergence for the entire Gifu Prefecture. The analysis showed a positive correlation between wild boar emergence and the appearance of raccoons, raccoon dogs, and crows as well as road density and wild boar distribution index. In particular, raccoon (OR: 1.83, 95%CI: 1.25–2.68, p < 0.001), raccoon dog (OR: 1.81, 95%CI: 1.25–2.66, p < 0.001), and medium level road density (OR: 1.56, 95%CI: 1.04–2.39, p = 0.04) were strongly correlated with wild boar emergence. The spatial interpolation approach resulted in better prediction accuracy for the Kriging method than for IDW by the root mean square error, but both approaches identified a high wild boar appearance probability area in southeastern Gifu and a low appearance probability area in central Gifu. Here we have demonstrated a tool to effectively disperse oral vaccine to wildlife.

Uncertainty assessment of spatiotemporal distribution and variation in regional soil heavy metals based on spatiotemporal sequential Gaussian simulation

Revealing the spatiotemporal (ST) distribution and changes in regional soil heavy metals is significant to soil pollution control and management. However, most of the ST analysis models in the existing studies ignore the uncertainty of ST changes in soil heavy metals, making their results unreliable. In this study, using soil Pb collected from 2016 to 2019 in a mining city in China as case data, an ST sequential Gaussian simulation (STSGS) is proposed to reveal the ST distribution and variation in heavy metals in regional soils and their uncertainties. Firstly, the ST variogram was analysed and fitted using a theoretical variogram model integrating the experimental variations at the ST scale. Secondly, 500 simulation realisations with random access path were generated by the ST Kriging method. Considering the obtained 500 simulation realisations, a series of ST analysis methods was proposed and employed to reveal the ST distribution and changes with uncertainty assessment of regional soil heavy metals. The main results are as follows. (1) For the whole study region, soil Pb content initially increased and then decreased from 2016 to 2019. The average probability of soil Pb exceeding 90 mg/kg was 0.121, 0.214, 0.312 and 0.291 in 2016, 2017, 2018 and 2019, respectively, whereas the average probability of always exceeding 90 mg/kg in the 4 years was only 0.032. (2) From 2016 to 2019, the area proportions of the increase and decrease of soil Pb content in the study area were 87.2% and 12.8%, respectively. However, according to the standardised statistic, only 0.161% and 8.72% of the total areas were determined to have a significant decrease and increase in soil Pb content from 2016 to 2019. (3) From 2016 to 2019, the areas with a greater than 0.6 probability of soil Pb concentration decreasing by more than 5 mg/kg and increasing by more than 20, 40 and 80 mg/kg accounted for 4.96%, 32.2%, 11.5% and only 1.91% of the total study region, respectively. The incremental high-probability areas were primarily those where Pb pollution was already serious. Finally, the advantages of the proposed STSGS method were summarised.

Region Probability Map-Guided Fast Wide-Area Multiobject Detection

Detecting multiobjects in a wide-area scenario efficiently is a critical technology for industry and security applications. The detection performance has benefited enormously from the probability estimation of the unknown environment. Representative methods, such as particle filter (PF), construct the probability distribution model and iteratively locate target objects. However, randomly sampling and detecting image patches in the large covering field-of-view (FOV) make these methods inefficient and computation costly. To address these issues, we propose a region probability map (RPM)-guided fast wide-area detection system that can simultaneously detect multiobjects from a large FOV at 300 frames per second (fps) through a coarse-to-fine-grained detection paradigm. Specifically, a segmentation-based RPM generation module is introduced to assign probability measurements to different regions of the panoramic image, which models how likely the desired objects will occur in these regions. Then, based on the generated probability map of the whole scene, a novel RPM-guided PF framework is proposed to speed up the detection process by concentrating the detection on high-probability areas. Finally, a rapid and low-latency active detection system based on a wide-angle camera, a high-speed camera, and an ultrafast galvano mirror is implemented, which gains a 15.38% efficiency improvement while achieving more accurate detection compared with existing methods. Extensive experimental results verify the robustness and effectiveness of our proposed system.

Mapping the probability of wildland fire occurrence in Central America, and identifying the key factors

Background Wildland fires are part of the ecology of forests in Central America. Nevertheless, limited understanding of fire probability and the factors that influence it hinder the planning of intervention strategies. Aims This research combined climatic, anthropogenic and vegetation factors to identify wildland fire probability and determine the most relevant factors. Methods We performed an exploratory analysis to identify important factors and integrated them with fire observations using random forest. We then used the most relevant factors to predict wildland fire occurrence probability and validated our results using different measures. The results demonstrated satisfactory agreement with the independent data. Key results Central regions of Honduras, northern Guatemala and Belize have a very high probability of wildland fire occurrence. Human imprint and extreme climatic conditions influence wildland fire probability in Central America. Conclusions Using random forest, we identified the major influencing factors and areas with a high probability of wildland fire occurence in Central America. Implications Results from this research can support regional organisations in applying enhanced strategies to minimise wildland fires in high-probability areas. Additional efforts may also include using future climate change scenarios and increasing the time frame to evaluate the influence of teleconnection patterns.

Electric Grid Vulnerability Analysis to Identify Communities Prone to Wildfires

Natural hazards, like wildfires, present various challenges to the electric grid that can leave many communities without power. To identify vulnerabilities in the grid and the corresponding at-risk communities, this work considers the implementation of two topological assessment approaches, namely betweenness centrality and minimum cut, and combines the results from each with spatial fire probability data. The results from a betweenness centrality analysis identified at-risk communities whose critical lines, necessary for routing power to the community from the numerous generators, were found to be at-risk if they were located within high probability burn zones. Communities at-risk of separation from the grid with one cut (or electrical shorting) of a transmission line due it’s proximity to a high burn probability (BP) area were also identified using the minimum cut Graph Theory algorithm. When the methodologies were applied to a demonstration transmission grid, the results found that about about one third of the 585 substations had centrally located lines in high BP areas. About 46% of the substations require just one cut to be removed from the grid, and the average length of these one-cut segments were 37 km with the longest at 188 km.

Habitat use of Guiana dolphin Sotalia guianensis in a heavily urbanized embayment

Understanding environmental and anthropogenic variables that influence the presence of dolphins in coastal areas is fundamental for conservation planning and forecasting. In this study, we applied generalized additive models to identify areas of high probability of occurrence of Guiana dolphins in Mucuripe embayment, a heavily urbanized area in northeastern Brazil. Sighting and effort data were collected during systematic, boat-based surveys between 2009 and 2011. Models were built using 70% of the data to test the model and 30% to evaluate its predictive performance. Variables investigated included depth, slope, seabed complexity, and distance to the coast, breakwaters, and the fishing grounds. The best model explained 40.8% of the total deviance. Seabed complexity, distance to the breakwaters and distance to the fishing grounds were the most important variables, with dolphins showing a preference for areas with a less complex seabed immediately adjacent to the breakwaters (<500 m, decreasing with distance) as well as a preference for fishing grounds (again decreasing with distance). Using the validation data, the model showed excellent performance. The habitat use and preference of Guiana dolphins in the study area seem to be mainly influenced by foraging opportunities, with dolphins concentrating in areas with higher prey abundance and where foraging success is higher because of a strategy called ‘barrier-feeding’, in which animals herd fish against piers, breakwaters, and the coast.

Convolutional Neural Networks for Classifying Cervical Cancer Types Using Histological Images.

Cervical cancer is the most common cancer among women worldwide. The diagnosis and classification of cancer are extremely important, as it influences the optimal treatment and length of survival. The objective was to develop and validate a diagnosis system based on convolutional neural networks (CNN) that identifies cervical malignancies and provides diagnostic interpretability. A total of 8496 labeled histology images were extracted from 229 cervical specimens (cervical squamous cell carcinoma, SCC, n = 37; cervical adenocarcinoma, AC, n = 8; nonmalignant cervical tissues, n = 184). AlexNet, VGG-19, Xception, and ResNet-50 with five-fold cross-validation were constructed to distinguish cervical cancer images from nonmalignant images. The performance of CNNs was quantified in terms of accuracy, precision, recall, and the area under the receiver operating curve (AUC). Six pathologists were recruited to make a comparison with the performance of CNNs. Guided Backpropagation and Gradient-weighted Class Activation Mapping (Grad-CAM) were deployed to highlight the area of high malignant probability. The Xception model had excellent performance in identifying cervical SCC and AC in test sets. For cervical SCC, AUC was 0.98 (internal validation) and 0.974 (external validation). For cervical AC, AUC was 0.966 (internal validation) and 0.958 (external validation). The performance of CNNs falls between experienced and inexperienced pathologists. Grad-CAM and Guided Gard-CAM ensured diagnoses interpretability by highlighting morphological features of malignant changes. CNN is efficient for histological image classification tasks of distinguishing cervical malignancies from benign tissues and could highlight the specific areas of concern. All these findings suggest that CNNs could serve as a diagnostic tool to aid pathologic diagnosis.

Intelligent collision risk detection in medium-sized cities of developing countries, using naturalistic driving: A review

Traffic accidents are one of the most serious problems worldwide, being one of the leading causes of death and economic loss in the world. Low-and middle-income countries, mainly their medium-sized cities, are among the most affected by this problem. 93% of traffic accidents occur in low and middle-income countries, even though these countries have approximately 60% of the world's vehicles. This occurs mainly because in these types of countries, especially in medium-sized cities (target context), there are no ideal conditions for driving, such as adequate road infrastructure, good condition of vehicles, and rigorous safety policies. Advanced data analysis techniques including machine learning (ML) have increasingly been used to solve this problem. Naturalistic driving (ND) can be applied as a data collection method that provides information on traffic accidents. ND commonly uses a vehicle's kinematic data to detect high-risk driving behaviors that could cause an accident. The objectives of this document are to present a review of different alternatives that help in data collection and creation of intelligent solutions related to detection of possible traffic accidents, principally using ND; and to propose an intelligent collision risk detection system (ICRDS) for identification of areas with a high probability of TA in the target context. Through the review, it was possible to analyze and evaluate the devices, variables and algorithms that help characterize a risk event in driving, considering the target context. The development of a prototype of an ICRDS for a medium-sized city in a developing country is considered viable, considering the identified components, with the aim of identifying risk events in driving, and areas of high probability of accidents in the city. • A systematic review of analyses and detection of near-crashes and vehicle data collection. • A comparative list of the best devices for naturalistic driving studies. • A comparative list of Machine Learning algorithms for predicting, classifying or analyzing driving events. • Provide a guidance for future research in naturalistic driving study approaches.

Cyclical Variational Bayes Monte Carlo for efficient multi-modal posterior distributions evaluation

Multi-modal distributions of some physics-based model parameters are often encountered in engineering due to different situations such as a change in some environmental conditions, and the presence of some types of damage and non-linearity. In statistical model updating, for locally identifiable parameters, it can be anticipated that multi-modal posterior distributions would be found. The full characterization of these multi-modal distributions is important as methodologies for structural condition monitoring in structures are frequently based in the comparison of the damaged and healthy models of the structure. The characterization of posterior multi-modal distributions using state-of-the-art sampling techniques would require a large number of simulations of expensive-to-run physics-based models. Therefore, when a limited number of simulations can be run, as it often occurs in engineering, the traditional sampling techniques would not be able to capture accurately the multi-modal distributions. This could potentially lead to large numerical errors when assessing the performance of an engineering structure under uncertainty.Therefore, an approach is proposed for drastically reducing the number of models runs while yielding accurate estimates of highly multi-modal posterior distributions. This approach introduces a cyclical annealing schedule into the Variational Bayes Monte Carlo (VBMC) method to improve the algorithm’s phase of exploration and the finding of high probability areas in the multi-modal posteriors throughout the different cycles.Three numerical and one experimental investigations are used to compare the proposed cyclical VBMC with the standard VBMC algorithm, the monotonic VBMC and the Transitional Ensemble Markov Chain Monte Carlo (TEMCMC). It is shown that the standard VBMC fails in capturing multi-modal posteriors as it is unable to escape already found regions of high posterior density. In the presence of highly multi-modal posteriors, the proposed cyclical VBMC algorithm outperforms all the other approaches in terms of accuracy of the resulting posterior, and number of model runs required.

Species distribution models: Administrative boundary centroid occurrences require careful interpretation

Describing and understanding species distributions and the factors driving them is fundamental to ecology and biogeography. Species distribution models (SDMs) allow one to investigate objectives of identifying ecologically important factors to the distribution, estimating species-environment responses, predicting the probability of species occurrence, and predicting species presence or absence. Mosquito occurrence records used in SDMs are often imprecise and represented as a centroid of a geopolitical/administrative boundary. Using a virtual species, we investigated the effect of centroids on SDMs and determined which methodology was best suited to provide accurate and applicable conclusions for each of the objectives. We compared 12 distinct algorithms, four levels of pseudo-absences, and three predictor sets to determine the optimal SDM methodology for each objective. The ability of methodology considerations to account for the effects of centroids varied for each objective. Ecologically important predictors were misidentified but could be best approximated by generalized additive models with 10,000 pseudo-absences. Response curves only captured the expected positive or negative trends. Centroids limited SDMs’ ability to differentiate expected probabilities, resulting in overprediction of high probability areas. Response curves and occurrence probabilities were best estimated by generalized boosting regression models. Species presence was largely over-estimated within southern regions, but underpredicted in northern regions, and was best estimated by weighted mean ensembles. Overall, generalized boosting regression methods and (weighted) mean ensembles provided the most reliable conclusions across all four objectives. Further, the most reliable conclusions were consistently observed with equal pseudo-absences when considered with the removal of low-contributing predictors, except for predictor identification.