Bayesian Inference Research Articles

Persistence of SARS-CoV-2 infection and viral intra- and inter-host evolution in COVID-19 hospitalized patients.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) persistence in COVID-19 patients could play a key role in the emergence of variants of concern. The rapid intra-host evolution of SARS-CoV-2 may result in an increased transmissibility, immune and therapeutic escape which could be a direct consequence of COVID-19 epidemic currents. In this context, a longitudinal retrospective study on eight consecutive COVID-19 patients with persistent SARS-CoV-2 infection, from January 2022 to March 2023, was conducted. To characterize the intra- and inter-host viral evolution, whole genome sequencing and phylogenetic analysis were performed on nasopharyngeal samples collected at different time points. Phylogenetic reconstruction revealed an accelerated SARS-CoV-2 intra-host evolution and emergence of antigenically divergent variants. The Bayesian inference and principal coordinate analysis analysis showed a host-based genomic structuring among antigenically divergent variants, that might reflect the positive effect of containment practices, within the critical hospital area. All longitudinal antigenically divergent isolates shared a wide range of amino acidic (aa) changes, particularly in the Spike (S) glycoprotein, that increased viral transmissibility (K417N, S477N, N501Y and Q498R), enhanced infectivity (R346T, S373P, R408S, T478K, Q498R, Y505H, D614G, H655Y, N679K and P681H), caused host immune escape (S371L, S375F, T376A, K417N, and K444T/R) and displayed partial or complete resistance to treatments (G339D, R346K/T, S371F/L, S375F, T376A, D405N, N440K, G446S, N460K, E484A, F486V, Q493R, G496S and Q498R). These results suggest that multiple novel variants which emerge in the patient during persistent infection, might spread to another individual and continue to evolve. A pro-active genomic surveillance of persistent SARS-CoV-2 infected patients is recommended to identify genetically divergent lineages before their diffusion.

How Trustworthy Is Your Tree? Bayesian Phylogenetic Effective Sample Size Through the Lens of Monte Carlo Error.

Bayesian inference is a popular and widely-used approach to infer phylogenies (evolutionary trees). However, despite decades of widespread application, it remains difficult to judge how well a given Bayesian Markov chain Monte Carlo (MCMC) run explores the space of phylogenetic trees. In this paper, we investigate the Monte Carlo error of phylogenies, focusing on high-dimensional summaries of the posterior distribution, including variability in estimated edge/branch (known in phylogenetics as "split") probabilities and tree probabilities, and variability in the estimated summary tree. Specifically, we ask if there is any measure of effective sample size (ESS) applicable to phylogenetic trees which is capable of capturing the Monte Carlo error of these three summary measures. We find that there are some ESS measures capable of capturing the error inherent in using MCMC samples to approximate the posterior distributions on phylogenies. We term these tree ESS measures, and identify a set of three which are useful in practice for assessing the Monte Carlo error. Lastly, we present visualization tools that can improve comparisons between multiple independent MCMC runs by accounting for the Monte Carlo error present in each chain. Our results indicate that common post-MCMC workflows are insufficient to capture the inherent Monte Carlo error of the tree, and highlight the need for both within-chain mixing and between-chain convergence assessments.

Non-obese lipedema patients show a distinctly altered quantitative sensory testing profile with high diagnostic potential.

Lipedema is a widespread severe chronic disease affecting mostly women. Characterized by painful bilateral fat accumulation in extremities sparing hands and feet, objective measurement-based diagnosis is currently missing. We tested for characteristic psychometric and/or sensory alterations including pain and for their potential for medical routine diagnosis. Pain psychometry was assessed using the German Pain Questionnaire. Sensory sensitivity toward painful and nonpainful stimuli was characterized in non-obese lipedema patients and matched controls using the validated quantitative sensory testing (QST) protocol of the German Research Network on Neuropathic Pain. Lipedema patients showed no overt psychometric abnormalities. Pain was reported as somatic rather than psychosomatic aversive. All QST measurements were normal, but the z-score of pressure pain thresholds (PPT) was twofold reduced and the z-score of vibration detection thresholds (VDT) was two and a half times increased. Both thresholds were selectively altered at the affected thigh but not the unaffected hand. Receiver operating characteristic analysis of the combination of PPT and VDT of thigh vs hand into a PVTH score (PPT, VDT, thigh, hand-score) shows high sensitivity and specificity, categorizing correctly 95.8% of the participants as lipedema patients or healthy controls. Bayesian inference analysis corroborated the diagnostic potential of such a combined PVTH score. We propose to assess PPT and VDT at the painful thigh and the pain-free hand. Combination in a PVTH score may allow a convenient lipedema diagnosis early during disease development.

Generalized Geographically Weighted Regression Model within a Modularized Bayesian Framework.

Geographically weighted regression (GWR) models handle geographical dependence through a spatially varying coefficient model and have been widely used in applied science, but its general Bayesian extension is unclear because it involves a weighted log-likelihood which does not imply a probability distribution on data. We present a Bayesian GWR model and show that its essence is dealing with partial misspecification of the model. Current modularized Bayesian inference models accommodate partial misspecification from a single component of the model. We extend these models to handle partial misspecification in more than one component of the model, as required for our Bayesian GWR model. Information from the various spatial locations is manipulated via a geographically weighted kernel and the optimal manipulation is chosen according to a Kullback-Leibler (KL) divergence. We justify the model via an information risk minimization approach and show the consistency of the proposed estimator in terms of a geographically weighted KL divergence.

Objective Bayesian Meta-Analysis Based on Generalized Marginal Multivariate Random Effects Model

Objective Bayesian inference procedures are derived for the parameters of the multivariate random effects model generalized to elliptically contoured distributions. The posterior for the overall mean vector and the between-study covariance matrix is deduced by assigning two noninformative priors to the model parameter, namely the Berger and Bernardo reference prior and the Jeffreys prior, whose analytical expressions are obtained under weak distributional assumptions. It is shown that the only condition needed for the posterior to be proper is that the sample size is larger than the dimension of the data-generating model, independently of the class of elliptically contoured distributions used in the definition of the generalized multivariate random effects model. The theoretical findings of the paper are applied to real data consisting of ten studies about the effectiveness of hypertension treatment for reducing blood pressure where the treatment effects on both the systolic blood pressure and diastolic blood pressure are investigated.

Fast Bayesian Functional Regression for Non-Gaussian Spatial Data

Functional generalized linear models (FGLM) have been widely used to study the relations between non-Gaussian response and functional covariates. However, most existing works for FGLM assume independence among observations and therefore they are of limited applicability for correlated data. A particularly important example is spatial functional data, where we observe functions over spatial domains, such as the age population curve or temperature curve at each areal unit. In this paper, we extend FGLM by incorporating spatial random effects. Especially, we study the relationship between the non-Gaussian response variable and functional covariates that are spatially observed. However, such model has computational and inferential challenges. The high-dimensional spatial random effects cause the slow mixing of Markov chain Monte Carlo (MCMC) algorithms. Furthermore, spatial confounding can lead to bias in parameter estimates and inflate their variances. To address these issues, we propose an efficient Bayesian method using a sparse reparameterization of high-dimensional random effects. We also study the average coverage probabilities of the credible intervals of functional parameters. We apply our methods to simulated and real data examples, including malaria incidence data and US COVID-19 data. The proposed method is fast while providing accurate functional estimates.

Enhancing Contextual Masking in Reversible Linguistic Steganography with Ensemble Methods

Various cybercrimes can be prevented by text authentication that is responsible for preserving digital identities and contents. Digital signatures come in handy as a way of authenticating texts, which is an extensively used method. One approach to this problem is linguistic steganography, which allows hiding the signature in other words within the text and thereby facilitating efficient data management. However, it should be noted that there is a danger that these kinds of changes may result in inappropriate decisions being taken by automated computing systems not to mention change their final outputs (unseen). As such, many people are becoming more concerned with the possibility of reversing steganography so that it becomes possible to eliminate any distortions made during the process. This paper uses Contextual masking instead of masking randomly with BERT model. The goal behind this research was developing a natural language text specific Reversible Steganographic System. Our model uses pre-trained BERT as a transformer based masked language model and reversibly embeds messages through predictive word substitution. To quantify predictive uncertainty, we introduce an adaptive steganographic technique using Bayesian deep learning. This experiment shows us how our proposed system balances imperceptibility with capacity while maintaining near semantics at all times. Also, we integrate ensemble methods instead of Monte Carlo to balance the imperceptibility.

Ultraconserved elements from transcriptome and genome dataprovide insight into the phylogenomics of Sternorrhyncha (Insecta: Hemiptera).

Sternorrhyncha, one of the four major suborders of Hemiptera, is a phytophagous taxon inclusive of nearly 18 000 described species. The phylogenetic relationships within the taxon and the earliest-branching lineage of its infraorders remain incompletely understood. This study attempted to illuminate the phylogenetic relationships within Sternorrhyncha through the use of maximum likelihood, Bayesian inference and maximum parsimony analyses, employing ultraconserved element (UCE) data from 39 genomic and 62 transcriptomic datasets and thereby representing most families within the taxon. The probe set Hemiptera 2.7Kv1 was used to recover a total of 2731 UCE loci: from 547 to 1699 (with an average of 1084) across all genomic datasets and from 108 to 849 (with an average of 329) across all transcriptomic datasets. All three types of phylogenetic analyses employed in this study produced robust statistical support for Sternorrhyncha being a monophyletic group. The different methods of phylogenetic analysis produced inconsistent descriptions of topological structure at the infraorder level: while maximum likelihood and Bayesian inference analyses produced strong statistical evidence (100%) indicating the clade Psylloidea + Aleyrodoidea to be a sister of the clade Aphidoidea (Aphidomorpha) + Coccoidea (Coccomorpha), the maximum parsimony analysis failed to recover a similar result. Our results also provide detail on the phylogenetic relationships within each infraorder. This study presents the first use of UCE data to investigate the phylogeny of Sternorrhyncha. It also shows the viability of amalgamating genomic and transcriptomic data in studies of phylogenetic relationships, potentially highlighting a resource-efficient approach for future inquiries into diverse taxa through the integration of varied data sources.

Loneliness is associated with more trust but worse trustworthiness expectations.

Subjective feelings of loneliness emerge due to unsatisfactory social relationships, representing a major risk for mental and physical well-being. Despite its social nature, evidence on how loneliness affects social behaviours and expectations is lacking. Using Bayesian analyses and economic games, we show in three different studies that lonelier individuals trusted their partners to a greater extent despite less favourable trustworthiness expectations, showing a greater discrepancy between their trusting behaviours and their expectations of others' trustworthiness. Such discrepancy was reversed in extravert individuals who also reported to be less lonely. These results provide evidence on two opposing effects of loneliness as a motivator for social connections and promoter of social withdrawal, and demonstrate the moderating role of personality traits. This work contributes to a better understanding of how loneliness impacts social behaviour and social expectations, with important downstream clinical implications for varying health conditions associated with heightened feelings of loneliness.

Systematics of Urostephanus: resurrection of a Mesoamerican taxon of Gonolobinae (Apocynaceae, Asclepiadoideae), with eight new combinations

Background and Aims: Recent studies have focused on resolving the systematics of the tribe Gonolobinae of the family Apocynaceae, as well as the controversial genus Matelea s.l. However, many species within Matelea s.l. have not been evaluated phylogenetically. This is the case of M. gonoloboides, previously recognized as the type species of Urostephanus, and other taxa with similar floral morphology. The aim of this study is to test the monophyly of Urostephanus, including some species with morphology similar to M. gonoloboides. Furthermore, we evaluate the taxonomic position of the Urostephanus clade within the subtribe Gonolobinae and compare morphology among related clades. Methods: To assess phylogenetic relationships within Gonolobinae, we downloaded sequences from four loci for 94 species of the subtribe from GenBank. We extracted DNA and sequenced the trnL-F intergenic spacer and rps16 intron from four species of Dictyanthus and six species of Matelea, which are considered to belong to Urostephanus. We reconstructed the phylogeny with Bayesian inference using the maximum clade credibility tree. Based on the phylogenetic pattern, we performed morphological comparisons between the clades Dictyanthus, Polystemma, and Urostephanus. Key results: Phylogenetic analysis recovered Urostephanus as monophyletic and sister to Dictyanthus. Based on this phylogenetic pattern and floral morphology, we resurrect the genus Urostephanus. We propose eight new combinations for the species included in the phylogeny, as well as for those that share floral morphological similarity. We designate two lectotypes. Finally, we discuss aspects of morphology between Urostephanus and closely related groups. Conclusions: The current circumscription of Matelea s.l. includes species with high morphological diversity. With the phylogenetic and morphological evidence among related taxa, we support the resurrection of Urostephanus, more than 120 years after its description. These results contribute to the resolution of the systematics of Gonolobinae in Mesoamerica.

Bayesian quantile inference and order shrinkage for hysteretic quantile autoregressive models

Hysteretic quantile autoregressive model combines the hysteretic patterns and quantile autoregression, which can capture the dynamic relationship and nonlinear characteristics at different quantiles in time series data. In this paper, the Bayesian quantile inference and order shrinkage are studied for a class of hysteretic quantile autoregressive time series models. By using Markov Chain Monte Carlo (MCMC) techniques, the proposed Bayesian quantile method can handle the sparse hysteretic quantile autoregressive model well. It can accurately determine order of the model and estimate non-zero coefficients. Both simulation studies and a data example show that the proposed methods are feasible, reliable and appropriate for analysing the US Gross National Product data set.

The Crosswise Model for Surveys on Sensitive Topics: A General Framework for Item Selection and Statistical Analysis.

When surveys contain direct questions about sensitive topics, participants may not provide their true answers. Indirect question techniques incentivize truthful answers by concealing participants' responses in various ways. The Crosswise Model aims to do this by pairing a sensitive target item with a non-sensitive baseline item, and only asking participants to indicate whether their responses to the two items are the same or different. Selection of the baseline item is crucial to guarantee participants' perceived and actual privacy and to enable reliable estimates of the sensitive trait. This research makes the following contributions. First, it describes an integrated methodology to select the baseline item, based on conceptual and statistical considerations. The resulting methodology distinguishes four statistical models. Second, it proposes novel Bayesian estimation methods to implement these models. Third, it shows that the new models introduced here improve efficiency over common applications of the Crosswise Model and may relax the required statistical assumptions. These three contributions facilitate applying the methodology in a variety of settings. An empirical application on attitudes toward LGBT issues shows the potential of the Crosswise Model. An interactive app, Python and MATLAB codes support broader adoption of the model.

Economic diplomacy: Analyzing theories and models for informed policy

This paper examines the intricate relationship between economic theories, trade policies, and diplomatic engagements in the context of international relations. Utilizing theoretical frameworks such as the theory of comparative advantage and the Heckscher-Ohlin model, we analyze the foundational principles guiding global trade and policy-making. We explore the role of probabilistic models, including Bayesian statistics and regression analysis, in forecasting economic trends and their implications for international relations. Furthermore, we investigate the impact of trade policies, including tariffs, non-tariff barriers, and global trade agreements, on economic dynamics and diplomatic interactions. Through case studies of economic sanctions, we assess the effectiveness of sanctions as a diplomatic tool and propose policy recommendations for their optimal implementation. Our analysis highlights the complexity of economic diplomacy and underscores the importance of quantitative analysis in informing policy decisions and fostering international cooperation.

Measurement error models with zero inflation and multiple sources of zeros, with applications to hard zeros.

We consider measurement error models for two variables observed repeatedly and subject to measurement error. One variable is continuous, while the other variable is a mixture of continuous and zero measurements. This second variable has two sources of zeros. The first source is episodic zeros, wherein some of the measurements for an individual may be zero and others positive. The second source is hard zeros, i.e., some individuals will always report zero. An example is the consumption of alcohol from alcoholic beverages: some individuals consume alcoholic beverages episodically, while others never consume alcoholic beverages. However, with a small number of repeat measurements from individuals, it is not possible to determine those who are episodic zeros and those who are hard zeros. We develop a new measurement error model for this problem, and use Bayesian methods to fit it. Simulations and data analyses are used to illustrate our methods. Extensions to parametric models and survival analysis are discussed briefly.

Quantitative Stylistic Analysis of Middle Chinese Texts Based on the Dissimilarity of Evolutive Core Word Usage

Stylistic analysis enables open-ended and exploratory observation of languages. To fill the gap in the quantitative analysis of the stylistic systems of Middle Chinese, we construct lexical features based on the evolutive core word usage and scheme a Bayesian method for feature parameters estimation. The lexical features are from the Swadesh list, each of which has different word forms along with the language evolution during the Middle Ages. We thus count the varied word of those entries along with the language evolution as the linguistic features. With the Bayesian formulation, the feature parameters are estimated to construct a high-dimensional random feature vector in order to obtain the pair-wise dissimilarity matrix of all the texts based on different distance measures. Finally, we perform the spectral embedding and clustering to visualize, categorize and analyze the linguistic styles of Middle Chinese texts. The quantitative result agrees with the existing qualitative conclusions and furthermore, betters our understanding of the linguistic styles of Middle Chinese from both the inter-category and intra-category aspects. It also helps unveil the special styles induced by the indirect language contact.

First report of Diaporthe eres causing leaf spot on Rosa roxburghii Tratt in Guizhou Province, China.

Rosa roxburghii Tratt is a plant from the Rosaceae family whose fruits are rich in vitamins, dietary fiber, flavonoids, phenolic acids, and other active components (Jiang, et al. 2024). In July 2023, about R. roxburghii 500 plants were investigated in a field of 6000 m2 in Guiding County (107°14'E, 26°45'N), Guizhou province, China, and the results showed a leaf spot incidence of s 20 to 30%. . The affected leaves had irregular, black lesions with a clear blackish brown boundary and faint black conidiomata in a brown center. Fifteen symptomatic leaves were collected from 10 plants washed with sterile distilled water, and 5 × 5 mm pieces of the infected tissues were cut. After surface sterilization for30 s with 75% ethanol, 2 min with 3% NaOCl, three washes in sterilized distilled water, the leaf pieces were dried and placed on potato dextrose agar (PDA) and incubated at 25℃ for 5 days. Three isolates (H3-Y-1-1, H3-Y-1-2, H3-Y-1-3) with identical morphology were obtained, and the isolate H3-Y-1-1was selected for further study. The colonies on PDA exhibited irregular growth patterns, with white felty aerial mycelium on the upper surface, and white mycelium on the lower surface. Conidiomata were irregularly distributed over the agar surface. The isolate H3-Y-1-1 produced darkly pigmented pycnidia on PDA after 30 days and oozed milky mucilaginous drops. The fungus produced two types of conidia, α and β. Regular α conidia were 4.74 - 5.96 × 1.52 - 2.24 μm (n = 50), hyaline, elongated, biguttulate and non-septate. Beta conidia were 20.13 - 25.74 × 0.86 - 1.29 μm (n = 50), aseptate, hyaline, smooth, spindle shaped, slightly curved to bent. The morphological features were consistent with the description of Diaporthe eres (Pereira, et al. 2022). The pathogen was confirmed to be D. eres by amplification and sequencing of the internal transcribed spacer region (ITS), the partial β-tubulin (TUB), the partial translation elongation factor 1-alpha (TEF) genes using primers ITS1/ITS4, Bt-2a/Bt-2b, EF1-728F/EF1-986R, respectively. Sequences from PCR amplification were deposited in GenBank with accession numbers PP411998 (ITS), PP502153 (TUB), PP502156 (TEF). BLAST searches of the sequences revealed (96%) (500/523nt), 97% (479/494 nt) and 99% (334/338 nt) homology with those of D. eres CBS 138594 from GenBank (OM698848, OM752196 and OM752197), respectively. Phylogenetic analysis using maximum-likelihood and Bayesian methods placed the isolate H3-Y-1-1 in a well-supported cluster with D. eres CBS 101742. The pathogen was thus identified as D. eres based on the morphological characterization and molecular analyses (Feng, et al. 2013; Tao, et al. 2020). To assess its pathogenicity, healthy R. roxburghii potted plants were inoculated with H3-Y-1-1 spore suspensions. Symptomatic leaves mirroring field symptoms were observed after XX days of incubations at XX°C, while control plants exhibited no symptoms. Diaporthe eres was consistently reisolated from the infected leaves showing brown irregular or round lesions at the initial stage of the disease, expanding and become more irregular over time ultimately causing leaf curling and plant death. To our knowledge, this is the first report of leaf spot on R. roxburghii caused by D. eres in China. The disease may become a serious threat to fruit of R. roxburghii production in China. Therefore, detection of this pathogen is very important to ensure timely disease management.

Bayesian dynamic modelling for probabilistic prediction of pavement condition

Significant funds have been allocated to maintain road networks each year in developed countries. Performance prediction is crucial for pavement management systems to adjust working plans and budget allocation. As a Bayesian nonparametric method, Gaussian process regression (GPR) is powerful in predicting nonlinear time series and quantifying uncertainty. However, it remains computationally intensive and fails to adapt to the time-varying characteristics. To address such issues, a dynamic GPR model is proposed for probabilistic prediction of the International Roughness Index (IRI) for flexible pavements. A moving window strategy is developed to substantially shrink the size of training data, which effectively alleviates computational cost and thus leads to a dynamic GPR. A genetic algorithm is then adopted to determine the optimal window size by considering the trade-off between computational efficiency and accuracy. A dataset acquired from Long-Term Pavement Performance (LTPP) is used to demonstrate the feasibility of the dynamic GPR. Its performance is compared to traditional GPR as well as dynamic and static Bayesian linear regression (BLR) models. The comparison results indicate that the proposed dynamic GPR can increase the accuracy by 0.86, 1.52, and 2.27 times for dynamic BLR, static GPR, and static BLR, respectively. It exhibits the best results in terms of accuracy and uncertainty metrics due to its nonlinear modelling and time-varying ability.

THE IMPACT OF A CLOSING PROTOCOL (CP) IMPLEMENTATION ON WOUND MORBIDITY IN ABDOMINAL WALL RECONSTRUCTION (AWR) WITH MESH

Abstract Background Wound complications (WC) are associated with increased cost, recurrence, and mesh infection. Operative CPs have not been evaluated in AWR. Our aim was to evaluate the effect of a CP on wound infection (WI) and WC. Method A prospective, institutional database was used to compare AWR with and without CP. Descriptive statistics and a Bayesian Structured time series analysis (BSTA) were performed to evaluate rates of WI and WC. Data is reported as means unless stated. The CP involves antibiotic wound irrigation, glove and instrument exchange, re-draping the field, surgeon-only handling the mesh prior to mesh implantation. The introduction of on-going quality improvement efforts were accounted for in the model. Results A total of 2541 patients were examined with an age of 57.9 ± 12.6 years, 56.7% female, BMI 32.9 ± 9.8 kg/m2, 24.2% diabetic, and 41.2% former-smokers. Defect size was 203.1 ± 205.8 cm2 and 83.1% were CDC Class 1 or 2 wounds. Follow-up was 31.5 ± 41.4 months. Overall WI and WC rate decreased after CP (14.5% vs. 2.6% and 29.3% vs. 10.3%, P < 0.001). Specifically, the rate of wound cellulitis (9.7% vs. 2.7%, P < 0.001), wound infection (13.8 vs. 1.8%, P < 0.001), and mesh infection (2.1% vs. 0.6%, P < 0.004) were reduced after CP. For WI, BSTA revealed a relative effect of 83% (±2%, 95%CI:-87%,-78%; P = 0.0002) reduction compared to counterfactual. For WC, the BSTA revealed a relative effect reduction compared to counterfactual of 67% (±3%, 95%CI:-60%, -72%; P = 0.0002). Conclusion Introduction of a CP prior to mesh implantation in open AWR led to a significant reduction in overall wound infections and wound complications. The use of a CP should be strongly considered.

Youth not engaged in education, employment, or training: a discrete choice experiment of service preferences in Canada

BackgroundPrior research has showed the importance of providing integrated support services to prevent and reduce youth not in education, employment, or training (NEET) related challenges. There is limited evidence on NEET youth’s perspectives and preferences for employment, education, and training services. The objective of this study was to identify employment, education and training service preferences of NEET youth. We acknowledge the deficit-based lens associated with the term NEET and use ‘upcoming youth’ to refer to this population group.MethodsCanadian youth (14–29 years) who reported Upcoming status or at-risk of Upcoming status were recruited to the study. We used a discrete choice experiment (DCE) survey, which included ten attributes with three levels each indicating service characteristics. Sawtooth software was used to design and administer the DCE. Participants also provided demographic information and completed the Global Appraisal of Individual Needs–Short Screener. We analyzed the data using hierarchical Bayesian methods to determine service attribute importance and latent class analyses to identify groups of participants with similar service preferences.ResultsA total of n=503 youth participated in the study. 51% of participants were 24–29 years of age; 18.7% identified as having Upcoming status; 41.1% were from rural areas; and 36.0% of youth stated that they met basic needs with a little left. Participants strongly preferred services that promoted life skills, mentorship, basic income, and securing a work or educational placement. Three latent classes were identified and included: (i) job and educational services (38.9%), or services that include career counseling and securing a work or educational placement; (ii) mental health and wellness services (34.9%), or services that offer support for mental health and wellness in the workplace and free mental health and substance use services; and (iii) holistic skills building services (26.1%), or services that endorsed skills for school and job success, and life skills.ConclusionsThis study identified employment, education, and training service preferences among Upcoming youth. The findings indicate a need to create a service model that supports holistic skills building, mental health and wellness, and long-term school and job opportunities.

Probabilistic Prediction of Floating Offshore Wind Turbine Platform Motions via Uncertainty Quantification and Information Integration

The accurate prediction of short-term platform motions in a real environment is crucial for the safe design, operation, and maintenance of floating offshore wind turbines (FOWTs). Numerical simulations of motions are typically associated with high uncertainties due to abstracted theoretical models, empirical parameters, initial environment parameters, etc. Therefore, it is necessary to integrate other sources of information associated with less uncertainty, e.g., monitoring data, for accurate predictions. In this paper, we propose a probabilistic prediction based on the Bayesian approach that logically integrates motion monitoring data with simulated motion predictions of FOWTs, considering uncertainties in the environment model, structural properties, motion prediction method, monitoring data, etc. The approach consists of constructing a prior probability density function (PDF) of a random variable (which characterizes the largest value of the initial motion response) via numerical simulations and a likelihood function based on platform motion monitoring data and deriving a posterior PDF of the random variable by Bayesian updating. Then, posterior distributions of short-term extreme motion responses are derived using the posterior PDF of the random variable, representing lower uncertainty and improved accuracy. A Metropolis–Hastings algorithm is adopted to obtain PDFs of complex probability distributions. The effectiveness of the approach is demonstrated on a real FOWT platform in Scotland. The proposed probabilistic prediction approach results in posterior distributions of short-term extreme platform motions associated with less uncertainty and higher accuracy, which is attributed to integrating prior knowledge with monitoring data.