Gaussian Model Research Articles

Depressive symptoms, cognitive functions and daily activities: An extended network analysis in monozygotic and dizygotic twins

BackgroundThe network theory posits that depression emerges as the result of individual symptoms triggering each other. Risk factors for depression can impact these between-symptoms interactions through extended networks. The study aimed to model the extended network of depressive symptoms and known depression risk factors – objective cognitive function, intellectual, physical, and social daily activities, and then, compare the observed networks between monozygotic (MZ) and dizygotic (DZ) co-twins. MethodsTwin pairs, 722 MZ and 2200 DZ, aged 40–79, were selected from the Dansh Twin Registry for having complete measures of depressive symptoms (e.g., sadness), cognitive functions (e.g., verbal memory), physical (e.g., brisk walk), intellectual (e.g., reading newspapers) and social activities (e.g., phone calls). Gaussian graphical models were used to estimate and compare the networks first between co-twins and then, between MZ to DZ twin pairs separately. ResultsSpecific intellectual, physical and social activities were central in the extended networks of depressive symptoms and, with the exception of processing speed, more central than cognition. The extended networks' structure was more homogeneous between MZ co-twins relative to DZ co-twins. Cognitive nodes were more central in MZ than DZ co-twins. LimitationsCross-sectional design, participants were middle-aged or older, mostly affective (non-somatic) depressive symptoms. ConclusionsIn depression networks, core connecting elements were intellectual, physical and social activities. The interaction between cognition and daily activities seems critical for triggering depressive symptoms. Thus, clinical interventions aimed at preventing depression and associated cognitive deficits should focus on maintenance and/or engagement in stimulating daily activities.

Computational Fluid Dynamics (CFD) Investigation of NREL Phase VI Wind Turbine Performance Using Various Turbulence Models

This study presents a detailed computational fluid dynamics (CFD) investigation into the aerodynamic performance of the NREL Phase VI wind turbine, focusing on torque and power generation under different turbulence models. The primary objective was to analyse the effect of various turbulence models and their responses in wind turbine torque generation. Furthermore, it also investigates the distance effect on wind velocity deficit. The research utilizes 2D and 3D simulations of the S809 airfoil and the full rotor, examining the predictive capabilities of the k-epsilon, k-omega, and k-omega SST turbulence models. The study incorporates both experimental validation and wake analysis using the Gaussian wake model to assess wind velocity deficits. Simulations were conducted for a wind speed range of (6–10 m/s), with results indicating that the k-epsilon model provided the closest match to experimental data, particularly at higher wind speeds within the targeted range. Even though k-epsilon results had better agreement when validated with experimental data, theoretically k-omega (SST) should perform better as it combines k-epsilon and k-omega advantages in predicting the flow regardless of its farness from the wall. However, in simulations using the k-omega (SST), the separation of flow and the shear stress transients were only visible at wind speeds of 10 m/s or higher. Wake effects, on the other hand, were found to cause significant velocity deficits behind the turbine, following an exponential decay pattern. The findings offer valuable insights into improving wind turbine performance through turbulence model selection and wake impact analysis, providing practical guidelines for future wind energy optimizations.

Understanding the relationship between childhood abuse and affective symptoms in bipolar disorder: New insights from a network analysis

The impact of childhood abuse on the presentation of bipolar disorder could be further elucidated by comparing the networks of affective symptoms among individuals with and with no history of childhood abuse. Data from 476 participants in the Clinical Health Outcomes Initiative in Comparative Effectiveness for Bipolar Disorder study were used to fit several regularised Gaussian Graphical Models. Differences in the presentation of depressive and manic symptoms were uncovered: only among participants with a history of childhood abuse, inadequacy and pessimism were central symptoms in the network of depressive symptoms, while racing thoughts was an important symptom in the network of manic symptoms. Following network theory, focusing treatments at the symptom-level and on central symptoms – like inadequacy, pessimism, and racing thoughts – could be an effective approach for managing affective symptoms among the sizeable proportion of people with bipolar disorder who have experienced childhood abuse. This study contributes a thorough investigation of the networks of affective symptoms among participants with and with no history of childhood abuse, albeit limited by the use of a binary, self-report measure of childhood abuse, thereby emphasising the importance of assessing for childhood abuse and taking needed steps towards identifying novel targets for treating bipolar disorder.

Optimizing ethephon application timing for ‘Meyer’ zoysiagrass seedhead suppression

AbstractZoysiagrass (Zoysia spp. Willd.) is a desirable, low‐input turfgrass species used on golf courses. However, prolific zoysiagrass seedhead production in the spring can increase golf course maintenance costs and reduce aesthetics. Previous research demonstrates that a single autumn ethephon application can suppress zoysiagrass seedhead production the following spring, but the optimum application timing is not well‐defined. The objective of this research was to determine the optimum window for an effective ethephon application for ‘Meyer’ zoysiagrass seedhead suppression. Small‐plot field research was conducted in Indiana, Kansas, Arkansas, and Tennessee. Seedheads were suppressed up to 99% depending on application timing. In Indiana, applications made on September 19 provided 99% seedhead suppression. In Kansas, applications between August 30 and September 18 yielded >64% seedhead suppression. In Arkansas, applications between October 3 and October 17 yielded >52% seedhead suppression. In Tennessee, applications between September 19 and October 23 provided >78% seedhead suppression. Applications made outside these windows resulted in more seedhead production at each respective location. Interestingly, optimum application timing was approximately 2 weeks later in Arkansas and Tennessee compared to Indiana and Kansas. Using growing degree days, a nonlinear Gaussian model was fit to predict the optimum ethephon application timing. In addition to data from this research, the proposed model accurately predicted observed zoysiagrass seedhead suppression in previously published research. This research better characterizes the optimum autumn application timing for Meyer zoysiagrass seedhead suppression across the transition zone.

Trivariate Joint Modeling for Family Data with Longitudinal Counts, Recurrent Events and a Terminal Event with Application to Lynch Syndrome.

Trivariate joint modeling for longitudinal count data, recurrent events, and a terminal event for family data has increased interest in medical studies. For example, families with Lynch syndrome (LS) are at high risk of developing colorectal cancer (CRC), where the number of polyps and the frequency of colonoscopy screening visits are highly associated with the risk of CRC among individuals and families. To assess how screening visits influence polyp detection, which in turn influences time to CRC, we propose a clustered trivariate joint model. The proposed model facilitates longitudinal count data that are zero-inflated and over-dispersed and invokes individual-specific and family-specific random effects to account for dependence among individuals and families. We formulate our proposed model as a latent Gaussian model to use the Bayesian estimation approach with the integrated nested Laplace approximation algorithm and evaluate its performance using simulation studies. Our trivariate joint model is applied to a series of 18 families from Newfoundland, with the occurrence of CRC taken as the terminal event, the colonoscopy screening visits as recurrent events, and the number of polyps detected at each visit as zero-inflated count data with overdispersion. We showed that our trivariate model fits better than alternative bivariate models and that the cluster effects should not be ignored when analyzing family data. Finally, the proposed model enables us to quantify heterogeneity across families and individuals in polyp detection and CRC risk, thus helping to identify individuals and families who would benefit from more intensive screening visits.

RNAfcg: RNA Flexibility Prediction Based on Topological Centrality and Global Features.

The dynamics of RNAs are related intimately to their functions. Molecular flexibility, as a starting point for understanding their dynamics, has been utilized to predict many characteristics associated with their functions. Since the experimental measurement methods are time-consuming and labor-intensive, it is urgently needed to develop reliable theoretical methods to predict RNA flexibility. In this work, we develop an effective machine learning method, RNAfcg, to predict RNA flexibility, where the Random Forest (RF) is trained by features including the topological centralities, flexibility-rigidity index, and global characteristics first introduced by us, as well as some traditional sequence and structural features. The analyses show that the three types of features introduced first have significant contributions to RNA flexibility prediction, among which the topological type contributes the most, which indicates the importance of structural topology in determining RNA flexibility. The performance comparison indicates that RNAfcg outperforms the state-of-the-art machine learning methods and the commonly used Gaussian Network Model (GNM) models, achieving a much higher Pearson correlation coefficient (PCC) of 0.6619 on the test data set. This work is helpful for understanding RNA dynamics and can be used to predict RNA function information. The source code is available at https://github.com/ChunhuaLab/RNAfcg/.

Simultaneous identification of propeller fluctuating forces and unknown parameters based on a Bayesian inversion approach

Accurate quantification of propeller excitation is essential for effective vibro-acoustic analysis and mitigation in marine vessels. This study introduces a Bayesian statistical framework that indirectly reconstruct uncertain forces and system parameters by fusing available prior information, forward modeling and measured vibration responses through Bayesian theorem. The Markov Chain Monte Carlo (MCMC) algorithm, enhanced with Polynomial Chaos Kriging (PC-Kriging) surrogates, is employed to facilitate efficient estimation of likelihood functions and robust uncertainty propagation, providing posterior probabilistic estimates and credible intervals for the identified forces. Additionally, the framework incorporates model class selection based on model evidence to determine the most plausible empirical spectrum model for the propeller broadband thrust spectrum. Specifically, this study proposes and evaluates two candidate empirical spectrum models, the Ornstein–Uhlenbeck (OU)-Gaussian and OU-Weibull models, which effectively capture the frequency–amplitude characteristics of unsteady thrust. Numerical results demonstrate substantial parameter uncertainty reduction, accurate thrust spectrum reconstruction, and effective quantification of statistical properties. Moreover, the OU-Gaussian model is revealed to be more plausible for characterizing the propeller thrust spectrum and response prediction. This framework may offer a practical solution for propeller excitation identification, enabling rapid vibro-acoustic performance assessments of marine vessels.

Unpacking interplays between competitiveness, cooperativeness, and social comparison orientation: A network psychometric approach and replication.

Emerging evidence highlights the role of social comparison in competition and cooperation, yet the dynamics among competitiveness, cooperativeness, and social comparison orientation (SCO) remain underexplored. In the present study, we sought to unravel these complex interplays employing graphical Gaussian models (GGMs) and characterize the network structures utilizing cross-sectional data derived from 1073 Chinese participants. A pre-registered independent sample (n = 1348) served to examine the replicability and robustness of our initial findings. Moreover, we explored the Bayesian networks (i.e., directed acyclic graphs) to delve into their probabilistic directed dependencies. Our analysis identified an interpersonal co-opetition network where SCO bridges competitiveness and cooperativeness, emphasizing the key influence of the desire to surpass others. We also found a significant gender effect on the levels of these orientations but not on the network patterns. Additionally, the replication sample offered validation for the resultant network structure and its key nodes. In sum, these findings provide a new insight into the symbiotic relationship between competitiveness and cooperativeness within the framework of social comparison, offering significant theoretical and practical implications.

The Common Structure of the Major Psychoses: More Similarities Than Differences in the Network Structures of Schizophrenia, Schizoaffective Disorder, and Psychotic Bipolar Disorder.

There has been a century-long debate about whether the major psychoses (eg, bipolar disorder, schizophrenia, and schizoaffective disorder) are one disorder with various manifestations or different disease entities. Traditional approaches using dimensional models have not provided decisive findings. Here, we address this question by examining the network constellation of affective and psychotic syndromes. Comparable symptom data of 1882 patients with psychotic bipolar disorder, schizoaffective disorders, and schizophrenia were extracted from three datasets: B-SNIP 1, B-SNIP2, and PARDIP. Twenty-six items from the Positive and Negative Syndrome Scale, YMRS, and the Montgomery-Asberg Depression Rating Scale were selected for the analysis using a principled approach to eliminate overlapping/redundant items. Gaussian graphical models were estimated and assessed for stability, and their communities were identified using bootstrapped exploratory graph analysis. The structures and global densities of the networks were compared with network comparison tests. The network structures were highly similar (r >. 80) across diagnostic groups. For all diagnoses, manic symptoms were more connected with positive symptoms while depressive symptoms were more linked with negative symptoms. The depressive and negative symptoms were the strongest indicators of depressive and psychotic communities. Theoretically interesting variability in network edge weights between symptoms was found relating to thought disorder and pessimistic thinking. The same broad structure of psychopathology underlies the symptom expressions of bipolar disorder, schizoaffective disorder, and schizophrenia. Future studies should build on the present finding by comparing specific inter-relations between symptoms in the different diagnostic groups using methods capable of detecting causality.

Improved Discrimination of Mass Spectral Isomers Using the High-Dimensional Consensus Mass Spectral Similarity Algorithm.

This study employs a high-dimensional consensus mass spectral (HDCMS) similarity scoring technique to discriminate isomers collected using an electron ionization mass spectrometer. The HDCMS method was previously introduced and applied to the discrimination of mass spectra of constitutional isomers, methamphetamine and phentermine, collected with direct analysis real-time mass spectrometry (DART-MS). The method formulates the problem of discriminating mass spectra in a mathematical Hilbert space and is hence called "high dimensional." It requires replicate mass spectra to build a Gaussian model and evaluate the inner products between these functions. The resulting measurement variability is used as a signature by which to discriminate spectra. In this work, HDCMS is tested on electron impact ionization (EI) mass spectra of 7 terpene and terpene-related (C10H16 and C10H14) isomers with experimental retention indices that differ by less than 30 and with traditional cosine similarity scores greater than 0.9, on a scale of 0 to 1, when compared with at least one other compound in the test set. Using identical instrument parameters, 15 replicate gas chromatography-electron ionization-mass spectrometry (GC-EI-MS) spectra of each isomer were collected and separated into distinct library and query sets. The HDCMS algorithm discriminated each isomer, indicating the method's potential. Because the method requires replicate measurements, observations from a simple heuristic study of the number of replicates required to discriminate these isomers is presented. The paper concludes with a discussion of compound discrimination using HDCMS and the benefits and drawbacks of applying the method to EI-MS data.

Spatial Variability in Soil Water-Physical Properties in Southern Subtropical Forests of China

Quantification of soil water-physical properties and their spatial variation is important to better predict soil structure and functioning, as well as associated spatial patterns in the vegetation. The provision of site-specific soil data further facilitates the implementation of enhanced land use and management practices. Using geostatistical methods, this study quantified the spatial distribution of soil bulk density (SBD), soil moisture (SM), capillary water-holding capacity (CWHC), capillary porosity (CP), non-capillary porosity (NCP), and total porosity (TP) in southern subtropical forests located at the Tropical Forest Research Center in Pingxiang City, China. A topographic map (scale = 1:10,000) was used to create a grid of l km squares across the study area. At the intersections of the grid squares, the described soil water-physical properties were measured. By calculating the coefficient of variation for each soil water-physical property, all measured soil water-physical properties were found to show moderate spatial heterogeneity. Exponential, gaussian, spherical, and linear models were used to fit the semivariograms of the measured soil water-physical properties. Across all soil water-physical properties, the range A0 variable (i.e., the separation distance between the semivariance and the sill value) measured between 3419 m and 14,156 m. The nugget-to-sill ratio ranged from 9 to 41%, indicating variations in the level of spatial autocorrelation among the soil water-physical properties. Many of the soil water-physical properties were strongly correlated (as assessed using Pearson correlation coefficients). Spatial distribution maps of the soil water-physical properties created via ordinary kriging (OK) showed that most water-physical properties had clumped (aggregated) distributions. SBD showed the opposite spatial pattern to SM and CWHC. Meanwhile, CP and TP showed similar distributions.

Differences in Musculoskeletal Injury and Disability Rates in U.S. Navy Sailors Aboard Aircraft Carrier and Amphibious Assault Ships.

Musculoskeletal injuries (MSKI) are the most common clinical condition in the military that affect medical readiness. Evaluation of MSKI burden and the effects of these injuries on readiness in large deck Navy ships is warranted. A retrospective cohort study assessing population-level MSKI rates, short-term disability (restricted duty), and long-term disability episode counts of all Sailors assigned to U.S. Navy Aircraft Carriers (CVNs) and Amphibious Assault Ships (LHA/LHD) from November 2016 to February 2023 was extracted from the Musculoskeletal Naval Epidemiological Surveillance Tool. A negative binomial regression and general additive (Gaussian) models evaluate the association of ship platform, deployment status, days underway, and sex with MSKI rates and the proportion of cases that resulted in short-term disability, returned-to-duty following restricted duty, or progressed to long-term disability. Sailors attached to CVNs contributed a mean 17,893.8 ± 23,280.6 person-months, with those attached to LHA/LHDs contributing an average 5,981.8 ± 8,432.7 person-months. Aboard CVNs, MSKI occurred at a rate of 0.30 ± 0.16/1,000 person-months while deployed and 0.64 ± 0.31/1,000 person-months in homeport. Aboard LHA/LHDs, Sailors incurred MSKI at a rate of 0.59 ± 0.58/1,000 person-months while on deployment and 1.24 ± 0.68/1,000 person-months in homeport. Among Sailors aboard CVNs, short-term disability occurred in 7.95 ± 7.75% of MSKI cases while deployed and 5.13 ± 5.26% while in homeport. Aboard LHA/LHDs, 8.57 ± 13.42% of MSKI cases were placed on short-term disability while deployed and 4.95 ± 5.27% while in homeport. In the multivariable assessment of short-term disability, being deployed underway was a significant factor (B = 3.62 P = .03, variance explained = 3.86%). Sailors that were female and served aboard LHA/LHDs returned to full duty at a significantly greater frequency compared to their male counterparts and Sailors serving aboard CVNs. None of the independent variables evaluated were associated with long-term disability. The findings in the current study demonstrate the substantial burden of MSKI aboard large deck ships, both in homeport and while deployed. Inclusion of a physical therapist aboard LHA/LHDs, like the CVN, may help to prevent and mitigate the effects of MSKI through early access to specialized care and integral injury prevention and performance optimization methods.

Computational fluid dynamics simulation of ammonia leakage scenarios during ship-to-ship bunkering

Ammonia is emerging as a potential marine fuel, yet its toxicity and risk of accidental leaks pose significant safety challenges compared to other alternatives. This study focuses on the critical ship-to-ship bunkering process using Computational Fluid Dynamics (CFD) simulations to analyse diverse ammonia leakage scenarios considering various leak orientations relative to the wind and the ships. The numerical setup was initially validated against controlled experiments, demonstrating satisfactory agreement. Results indicate that atmospheric ammonia dispersion is significantly influenced by obstacles like the superstructure and hull in the near field, with diminishing effect in the far field. Conventional Gaussian plume models are inadequate within the first few hundred metres from the release source due to the complex near-field flow patterns and increased turbulence generated by the wakes of the hull and superstructure. These findings underscore the necessity for improved ship-scale dispersion models to accurately evaluate the impact of obstacles on ammonia dispersion. The consequence analysis reveals that upward leaks create larger risk zones with potential for irreversible health effects, while horizontal leaks may expose personnel to life-threatening concentrations. Vertical leaks in crosswind configurations generate lethal zones covering tens of metres on deck. These results highlight the importance of considering the exact location of the transfer hose relative to the ship and all potential wind orientations in risk assessments. Given the extensive range of potential dispersion patterns and the inadequacy of conventional Gaussian dispersion models in this context, we strongly recommend vessel-specific CFD simulations before certifying ammonia bunkering operations as safe. This approach is crucial for accurate risk assessment at the ship scale, where complex geometries and multi-directional flows invalidate the assumptions of traditional point-source, uni-directional dispersion models.

Rethinking livestock encroachment at a protected area boundary

The presence of livestock inside protected areas, or "livestock encroachment," is a global conservation concern because livestock is broadly thought to negatively affect wildlife. The Maasai Mara National Reserve (MMNR), Kenya, exemplifies this tension as livestock is believed to have resulted in the declining wildlife populations, contributing to the strict and sometimes violent exclusion measures targeting Maasai pastoralists. However, research embedded in the real-world setting that draws insights from the social-ecological contexts is lacking. In this study, we conducted 19 mo of ecological monitoring covering 60 sites in MMNR and found that cattle presence inside the reserve did not significantly impact most co-occurring wild herbivores at the current intensity. Using the Hierarchical Modeling of Species Communities and Gaussian copula graphic models, we showed that cattle had no direct associations-neither negative nor positive-with nearly all wild herbivores despite frequently sharing the same space. Moreover, we did not detect resource degradation correlated with cattle presence near the MMNR boundary. Given the colonial legacy and land use history of Mara, entering MMNR becomes the only viable option for many herders. These results corroborate the emerging perspective that the ecological impacts of extensively herded livestock on wildlife might be more nuanced than previously thought. To effectively balance the needs of people, livestock, and wildlife, the current rigid livestock exclusion measures need to be reassessed to holistically consider herbivore ecology, local land use history, and modern politics of protected area management.

Clay resource estimation using a geostatistical approach in Tam Binh district, Vinh Long province, Viet Nam

ABSTRACT Clay is an essential resource for improving the brick and ceramic industries. However, the demand for clay needs to grow. Estimating industrial mineral reserves requires an optimal and unbiased statistical tool. Geographical information systems and geostatistics are two promising tools that could be used to predict clay resources. The study objective was to apply these two tools to compare the performance of the methods for spatial clay resource prediction, considering clay thickness and spatial distribution. The demonstration site is in Tam Binh district, Vinh Long Province, Viet Nam. The results show that the Gaussian variation model at a 600 × 400 m distance is good for sampling and extrapolating clay thickness. This is based on 240 geolocated samples with different densities. Projected clay reserves match measurements, and the spatial statistical method is more accessible and valuable than the traditional method. The interpolated method’s total selected soil borehole number was optimized for clay resource estimation, and geostatistics can help manage and plan clay resources and save money. Future research should examine various spatial interpolation techniques and approaches in management, land use, and topography to improve the accuracy of calculations.

Iron Trace Elements Concentration in PM10 and Alzheimer's Disease in Lima, Peru: Ecological Study.

Alzheimer's disease (AD) has been linked to air pollution, especially particulate matter (PM). PM comprises various elements, including iron-rich particles that may reach the brain through inhalation. Lima, Peru is one of the most polluted cities in Latin America, with a high rate of AD. The study aims to evaluate the association between iron (Fe) trace elements in PM10 and AD cases in Lima, Peru. This retrospective ecological study used monthly Fe concentration data from the Peruvian Ministry of Health. AD cases (ICD-10-G30) and dementia in AD cases (DAD, ICD-10-F00) were obtained from the Peruvian CDC. Fe trace element data were available for six districts in Lima for the years 2017-2019 and 2022. Cases were standardized based on ≥60-year-old populations of each district. Hierarchical mixed-effects models of Gaussian and negative binomial families were constructed to evaluate both outcomes jointly (AD + DAD) and separately (AD, and DAD). A sensitivity analysis was conducted by excluding data from Lima's downtown district. In the complete model, log-Fe concentration was associated with a higher rate of AD + DAD and DAD, and with a higher IRR for the three outcomes. After controlling for other metals, a higher DAD rate was observed (β-coeff = 6.76, 95%CI 0.07; 13.46, p = 0.048), and a higher IRR for AD + DAD (1.55, 95%CI 1.09; 2.20, p = 0.014) and DAD (1.83, 95%CI 1.21; 2.78, p = 0.004). The association was not significant in the sensitivity analysis. In conclusion, exposure to Fe through PM10 inhalation may be associated with the presence of AD in Lima.

Predictive Modeling of Sleep Slow Oscillation Emergence on the electrode manifold: Toward Personalized Closed-Loop Brain Stimulation.

Sleep slow oscillations (SOs), characteristic of NREM sleep, are causally tied to cognitive outcomes and the health-promoting homeostatic functions of sleep. Due to these known benefits, brain stimulation techniques aiming to enhance SOs are being developed, with great potential to contribute to clinical interventions, as they hold promise for improving sleep functions in populations with identified SO deficits (e.g., mild cognitive impairment). SO-targeting closed-loop stimulation protocols currently strive to identify SO occurrences in real time, a computationally intensive step that can lead to reduced precision (compared to post-hoc detection). These approaches are also often limited to focusing on only one electrode location, thus inherently precluding targeting of SOs that is informed by the overall organization of SOs in space-time. Prediction of SO emergence across the electrode manifold would establish an alternative to online detection, thus greatly advancing the development of personalized and flexible brain stimulation paradigms. This study presents a computational model that predicts SO occurrences at multiple locations across a night of sleep. In combination with our previous study on optimizing brain stimulation protocols using the spatiotemporal properties of SOs, this model contributes to increasing the accuracy of SO targeting in brain stimulation applications. SOs were detected in a dataset of nighttime sleep of 22 subjects (9 females), acquired with polysomnography including 64 EEG channels. Modeling of SO occurrence was achieved for SOs in stage N3, or in a combination of stages N2 and N3 (N2&N3). We study SO emergence at progressively more refined time scales. First, the cumulative SO occurrences in successive sleep cycles were successfully fit with exponentials. Secondly, the SO timing in each individual was modeled with a renewal point process. Using an inverse Gaussian model, we estimated the probability density function of SO timing and its parameters μ (mean) and λ (shape, representing skewness) in successive cycles. We observed a declining trend in the SO count across sleep cycles, which we modeled using a power law relationship. The decay rate per cycle was 1.473 for N3 and 1.139 for N2&N3, with variances of the decay rates across participants being 1 and 0.53, respectively. This pattern mirrors the declining trend of slow wave activity (SWA) across sleep cycles, likely due to the inherent relationship between SWA and SO. Additionally, the SO timing model for N3 showed an increasing trend in the model parameters (μ, λ) across cycles. The increase rate per cycle followed a power law relationship with a rate of 0.83 and an exponential relationship with a rate of 4.59, respectively. The variances of the increase rates were 0.02 for μ and 0.44 for λ across participants. This study establishes a predictive model for SO occurrence during NREM sleep, providing insights into its organization in successive cycles and at different EEG channels, which is relevant to development of personalized stimulation paradigms. These findings imply that personalized model parameters can be estimated by incorporating SO information in the first sleep cycle, and hence SO timing can be predicted before its occurrence with a probability distribution, enabling more precise targeting of SOs.

Comparing Large-Eddy Simulation and Gaussian Plume Model to Sensor Measurements of an Urban Smoke Plume

The fast prediction of the extent and impact of accidental air pollution releases is important to enable a quick and informed response, especially in cities. Despite this importance, only a small number of case studies are available studying the dispersion of air pollutants from fires in a short distance (O(1 km)) in urban areas. While monitoring pollution levels in Southampton, UK, using low-cost sensors, a fire broke out from an outbuilding containing roughly 3000 reels of highly flammable cine nitrate film and movie equipment, which resulted in high values of PM2.5 being measured by the sensors approximately 1500 m downstream of the fire site. This provided a unique opportunity to evaluate urban air pollution dispersion models using observed data for PM2.5 and the meteorological conditions. Two numerical approaches were used to simulate the plume from the transient fire: a high-fidelity computational fluid dynamics model with large-eddy simulation (LES) embedded in the open-source package OpenFOAM, and a lower-fidelity Gaussian plume model implemented in a commercial software package: the Atmospheric Dispersion Modeling System (ADMS). Both numerical models were able to quantitatively reproduce consistent spatial and temporal profiles of the PM2.5 concentration at approximately 1500 m downstream of the fire site. Considering the unavoidable large uncertainties, a comparison between the sensor measurements and the numerical predictions was carried out, leading to an approximate estimation of the emission rate, temperature, and the start and duration of the fire. The estimation of the fire start time was consistent with the local authority report. The LES data showed that the fire lasted for at least 80 min at an emission rate of 50 g/s of PM2.5. The emission was significantly greater than a ‘normal’ house fire reported in the literature, suggesting the crucial importance of the emission estimation and monitoring of PM2.5 concentration in such incidents. Finally, we discuss the advantages and limitations of the two numerical approaches, aiming to suggest the selection of fast-response numerical models at various compromised levels of accuracy, efficiency and cost.

Non-smooth Bayesian optimization in tuning scientific applications

Tuning algorithmic parameters to optimize the performance of large, complicated computational codes is an important problem involving finding the optima and identifying regimes defined by non-smooth boundaries in black-box functions. Within the Bayesian optimization framework, the Gaussian process surrogate model produces smooth mean functions, but functions in the tuning problem are often non-smooth, which is exacerbated by the fact that we usually have limited sequential samples from the black-box function. Motivated by these issues encountered in tuning, we propose a novel Gaussian process model called a clustered Gaussian process (cGP), where the components are dynamically updated by clustering. In our studies, the performance of cGP can be better than stationary GPs in nearly 90% of the experiments and better than non-stationary GPs in nearly 70% of the repeated experiments while requiring less computational cost. cGP provides a novel approach for dynamic GP, computes more efficiently than recursive partitioning, and discovers non-smoothness regimes. We provide extensive experiments including high-performance computing (HPC) and industrial simulation functions to show the effectiveness of our methods.

Development and validation of a three-dimensional wind-turbine wake model based on high-order Gaussian function

Under natural conditions, the wake velocity distribution approximates top-hat shape in the near wake center, and a Gaussian shape in the far wake. To improve the prediction accuracy of the wake distribution, this paper proposes a three-dimensional high-order Gaussian linear entrainment wake model by modifying the wake velocity distribution with high-order Gaussian function. Field tests and wind tunnel tests are then conducted. The obtained results show that the proposed model can accurately describe the radial and vertical spatial distributions of the wake flow. Under different wind speeds, the maximum and minimum radial relative errors are respectively 7.29% and 1.4%, while the maximum and minimum vertical relative errors are respectively 7.64% and 3.87%. This demonstrates that the proposed model can accurately predict the velocity distribution in the whole wake region. The proposed model is simple, has low cost, and exhibits high accuracy. Thus, it can be used in wind farm layout optimization and wake control.