Individual Locations Research Articles

HIDRA3: a deep-learning model for multipoint ensemble sea level forecasting in the presence of tide gauge sensor failures

Abstract. Accurate modeling of sea level and storm surge dynamics with several days of temporal horizons is essential for effective coastal flood responses and the protection of coastal communities and economies. The classical approach to this challenge involves computationally intensive ocean models that typically calculate sea levels relative to the geoid, which must then be correlated with local tide gauge observations of sea surface height (SSH). A recently proposed deep-learning model, HIDRA2 (HIgh-performance Deep tidal Residual estimation method using Atmospheric data, version 2), avoids numerical simulations while delivering competitive forecasts. Its forecast accuracy depends on the availability of a sufficiently long history of recorded SSH observations used in training. This makes HIDRA2 less reliable for locations with less abundant SSH training data. Furthermore, since the inference requires immediate past SSH measurements as input, forecasts cannot be made during temporary tide gauge failures. We address the aforementioned issues using a new architecture, HIDRA3, that considers observations from multiple locations, shares the geophysical encoder across the locations, and constructs a joint latent state that is decoded into forecasts at individual locations. The new architecture brings several benefits: (i) it improves training at locations with scarce historical SSH data, (ii) it enables predictions even at locations with sensor failures, and (iii) it reliably estimates prediction uncertainties. HIDRA3 is evaluated by jointly training on 11 tide gauge locations along the Adriatic. Results show that HIDRA3 outperforms HIDRA2 and the Mediterranean basin Nucleus for European Modelling of the Ocean (NEMO) setup of the Copernicus Marine Environment Monitoring Service (CMEMS) by ∼ 15 % and ∼ 13 % mean absolute error (MAE) reductions at high SSH values, creating a solid new state of the art. The forecasting skill does not deteriorate even in the case of simultaneous failure of multiple sensors in the basin or when predicting solely from the tide gauges far outside the Rossby radius of a failed sensor. Furthermore, HIDRA3 shows remarkable performance with substantially smaller amounts of training data compared with HIDRA2, making it appropriate for sea level forecasting in basins with high regional variability in the available tide gauge data.

Metabolic control and its associated factors in type 1 diabetic people: longitudinal trajectory modeling

BackgroundDiabetes is a chronic disease, and hyperglycemia can increase the risk of diabetic complications and the need for more inpatient services. Therefore, the prevention and control of diabetes are important. This study aimed to identify the trajectories of metabolic control and its correlates in people with type 1 diabetes.MethodThis is a longitudinal study with 2020 type 1 diabetic individuals aged 18 to 59 years. The participants’ glycosylated hemoglobin (HbA1c) was measured three times with a six-month interval between each measurement. The data were analyzed using group-based trajectory modeling. Multinomial logistic regression was used to determine the factors related to these groups.ResultsThe results showed four trajectories of safe controlled (46.2%), moderate stable risk (28.7%), moderate increasing risk (12.5%), and high decreasing risk trajectory (12.6%) (entropy = 0.70). The results of multinomial logistic regression showed dyslipidemia could increase the odds of being in the three risk trajectories. Education, physical inactivity, and poor psychological status could also increase the odds of being in the moderate stable and high decreasing trajectories. Moreover, sex, job, and BMI could increase the odds of being in the high decreasing risk group (p < 0.05).ConclusionSince there are different trajectories of metabolic control of diabetes, it is necessary for healthcare providers and health experts to plan behavioral interventions based on the location of individuals in different trajectories and the related significant risk factors. In this way, appropriate prevention, care, and treatment programs can be provided for the people in each group.

Extracting Individual Tree Positions in Closed-Canopy Stands Using a Multi-Source Local Maxima Method

The accurate extraction of individual tree positions is key to forest structure quantification, and Unmanned Aerial Vehicle (UAV) visible light data have become the primary data source for extracting individual tree locations. Compared to deep learning methods, classical detection methods require lower computational resources and have stronger interpretability and applicability. However, in closed-canopy forests, challenges such as crown overlap and uneven light distribution hinder extraction accuracy. To address this, the study improves the existing Revised Local Maxima (RLM) method and proposes a Multi-Source Local Maxima (MSLM) method, based on UAV visible light data, which integrates Canopy Height Models (CHMs) and Digital Orthophoto Mosaics (DOMs). Both the MSLM and RLM methods were used to extract individual tree positions from three different types of closed-canopy stands, and the extraction results of the two methods were compared. The results show that the MSLM method outperforms the RLM in terms of Accuracy Rate (85.59%), Overall Accuracy (99.09%), and F1 score (85.21%), with stable performance across different forest stand types. This demonstrates that the MSLM method can effectively overcome the challenges posed by closed-canopy stands, significantly improving extraction precision. These findings provide a cost-effective and efficient approach for forest resource monitoring and offer valuable insights for forest structure optimization and management.

Dynamic reorganization of task-related network interactions in post-stroke aphasia recovery.

Post-stroke aphasia is a network disorder characterized by language impairments and aberrant network activation. While patients with post-stroke aphasia recover over time, the dynamics of the underlying changes in the brain remain elusive. Neuroimaging work demonstrated that language recovery is a heterogeneous process, characterized by varying activation levels in several regions of the left-hemispheric language network and the domain-general bilateral multiple-demand network. Crucially, this activation seems to depend on the time elapsed since stroke and the lesion location. Yet, beyond task-related brain activation, the degree and nature of interactions between regions of the language and the multiple-demand network are not well understood. In this longitudinal functional neuroimaging study, we characterized task-related functional interactions between regions of the language and the multiple-demand network during language processing. We hypothesized that interactions between language regions and between language and multiple-demand regions should change over time and depend on lesion location. We compared changes in effective connectivity in patients with left-hemispheric frontal or temporo-parietal stroke (n=17 per group) and healthy controls (n=17) with Dynamic Causal Modelling. All patients repeatedly underwent an auditory sentence comprehension paradigm during functional neuroimaging in the acute (≤ 1 week), subacute (1-2 weeks), and chronic (> 6 months) phases after stroke. We found overall increased task-related connectivity from regions of the multiple-demand to the language network across patients, resembling the principal pattern of task-related interactions in controls. Early facilitation from multiple-demand to language regions correlated with later language improvement across both groups. Crucially, recruitment of specific connections from regions of the multiple-demand to language network depended on lesion location and changed over time. In the chronic phase, patients with frontal stroke showed facilitatory modulation from the right dorsolateral prefrontal cortex, while patients with temporo-parietal stroke integrated the supplementary motor area/dorsal anterior cingulate cortex. Besides this across-network reorganization, facilitatory connectivity between regions of the language network emerged in all patients in the subacute phase. Additionally, patients with frontal stroke showed facilitatory influences from the right lesion homologue to the remaining undamaged left inferior frontal cortex in the acute phase. Collectively, we provide first evidence that functional interactions of regions within and across the language and the multiple-demand network facilitate aphasia recovery. The identified dynamic reorganization principles over the time course of recovery may inform the future use of personalized treatment protocols with neurostimulation in aphasia rehabilitation. These protocols should be tailored to the individual lesion location and recovery phase.

Spectroscopic aspects of underwater digital holography of plankton

Monitoring the parameters and behavior of plankton makes it possible to assess the state of the aquatic ecosystem and detect the beginning of an environmental disaster at an early stage. In this respect, the most informative method for the in situ plankton study is underwater digital holography. This method allows obtaining information on the size, shape, and location of plankton individuals, as well as performing their classification and biotesting according to their behavioral responses using a submersible holographic camera non-invasively, in real time, and in the automatic mode. The monitoring series of the ecosystem functions can be used to assess the state of the ecosystem. One of them is the time series of the concentration of individuals of various plankton taxa in a certain volume. There are characteristic rhythms in the ecosystem function caused by both plankton biorhythms and changes in habitat parameters, as well as their synchronization, whereas a change in this rhythm may serve as an alarm signal for the ecosystem deprivation. By constructing the analogies based on the spectroscopy of atoms and molecules the paper shows the bioindication capabilities of the Fourier spectra of the plankton ecosystem function, built during monitoring measurements using a submersible digital holographic camera. The spectroscopic study of plankton allows determining the pollution in the plankton habitat at early stages. The in situ experimental data suggest that the order and chaos of plankton biocenosis are reflected in the structure of the spectral lines of the ecosystem functions associated with plankton. Various self-oscillatory processes in the biocenosis that regulate the plankton number and rhythm form the basis for plankton spectroscopy, which may be used for bioindication monitoring.

Optimization of Covariance Matrices of Kalman Filter with Unknown Input Using Modified Directional Bat Algorithm

The proper selection of the model error covariance matrix and the measurement noise covariance matrix of Kalman filter is an optimization problem. Some scholars have studied this, but there is relatively little research on the selection of the two covariance matrices for Kalman filters with an unknown input. Recently, the authors proposed a modified directed bat algorithm (MDBA) which introduces the best historical location of individuals and the elimination strategy to effectively prevent falling into local optimal solution. So, two methods are proposed in this paper to optimize the model error covariance matrix and measurement noise covariance matrix of Kalman filter with unknown inputs (KF-UI) and extended Kalman filter with unknown inputs (EKF-UI) by MDBA, respectively. The objective functions are constructed using the measurement vectors and the corresponding estimated values, and MDBA is adopted to optimize the two covariance matrices of KF-UI and EKF-UI. To validate the effectiveness of proposed methods, two simple structure examples and a benchmark example are adopted. The influence of structural parameter uncertainties on KF-UI is also considered. The result shows that the MDBA-optimized KF-UI has a strong convergence and can take into account the effect of parameter uncertainties. Then, the effectiveness of the proposed MDBA-optimized EKF-UI method is validated by comparing it with EKF-UI with empirically selected covariance values through trial-and-error. The identification results showed that the proposed methods achieved better identification accuracy and enhanced convergence compared to KF-UI and EKF-UI with empirical covariance values.

Digital marketing in mountain tourism: Challenges of the new time and development priorities

The tourism industry is increasingly playing a special role in the economy of countries, regions, cities and individual locations. It not only becomes a source of replenishment of territorial budgets directly, but also contributes to the development of small and medium-sized businesses, contributes to the creation of new jobs, influences the technological and digital transformation of territories. Modern conditions impose their own restrictions on the development of the industry and, at the same time, open up new opportunities for domestic tourism in Russia. The purpose of the study was to identify structural changes and trends in the development of the tourism industry in connection with demographic, socio-economic, epidemiological and foreign policy transformations. The author of the article updated the role of territorial marketing and presented proposals for its transformation taking into account the challenges of the new time. The main research methods are content analysis, evaluation of statistical information, spatio-temporal analysis. Based on an in-depth study of trends, new marketing tasks for territorial authorities and tourism market participants in the creation and promotion of destinations are substantiated. The territorial marketing tools proposed by the author, aimed at attracting tourists and developing the tourism industry as part of territorial economies as a whole, take into account modern trends in the development of commercial marketing and are adapted to the conditions and capabilities of individual destinations. The issues of transformation of territorial labor and service markets in the context of the development of the tourism industry are covered. The key conclusion is the need to revise the conceptual approaches of locations to the issues of building territorial marketing and implementing marketing strategies. The author proposes measures, the implementation of which will make the socio-economic development of territories more sustainable and effective in a rapidly changing world.

Digital marketing in mountain tourism: Challenges of the new time and development priorities

The tourism industry is increasingly playing a special role in the economy of countries, regions, cities and individual locations. It not only becomes a source of replenishment of territorial budgets directly, but also contributes to the development of small and medium-sized businesses, contributes to the creation of new jobs, influences the technological and digital transformation of territories. Modern conditions impose their own restrictions on the development of the industry and, at the same time, open up new opportunities for domestic tourism in Russia. The purpose of the study was to identify structural changes and trends in the development of the tourism industry in connection with demographic, socio-economic, epidemiological and foreign policy transformations. The author of the article updated the role of territorial marketing and presented proposals for its transformation taking into account the challenges of the new time. The main research methods are content analysis, evaluation of statistical information, spatio-temporal analysis. Based on an in-depth study of trends, new marketing tasks for territorial authorities and tourism market participants in the creation and promotion of destinations are substantiated. The territorial marketing tools proposed by the author, aimed at attracting tourists and developing the tourism industry as part of the territorial economy as a whole, take into account modern trends in the development of commercial marketing and are adapted to the conditions and capabilities of individual destinations. The issues of transformation of territorial labor and service markets in the context of the development of the tourism industry are covered. The key conclusion is the need to revise the conceptual approaches of locations to the issues of building territorial marketing and implementing marketing strategies. The author proposes measures, the implementation of which will make the socio-economic development of territories more sustainable and effective in a rapidly changing world.

Improving dynamic vessel analysis: A new pipeline for localized retinal vessel response evaluation

Aims/Purpose: Dynamic Vessel Analysis (DVA) is a non‐invasive imaging modality that evaluates the reactivity of retinal arteriolar and venular vessels in response to flicker light stimulation. This study aims to introduce a novel data‐processing pipeline that involves noise reduction techniques localized to different locations along the vessel wall. Additionally, it employs standardized parameter extraction, allowing for a more precise assessment of retinal vessel responses across these locations.Methods: Previous studies used robust principal component analysis (RPCA) on averaged time series data for arteries and veins, combining one averaged time series per patient into arterial and venous matrices. Our method retains individual location time series for each arteriole and venule, preserving local responses. We combine these individual time series into arteriolar and venular matrices and apply RPCA, resulting in sparse matrices containing noise and low‐rank matrices capturing common features. The low‐rank matrices, containing the noise‐reduced information, are then used for further analysis. This approach maintains the granularity of local vascular responses.Results: We identified and integrated key parameters from existing literature to delineate vasodilation and vasoconstriction profiles. By applying analysis of covariance (ANCOVA) for age, sex, baseline vessel dilation, and mean arterial pressure, we identified significantly lower Baseline‐Corrected Flicker Response of the proximal retinal venules in normal tension glaucoma patients (2.46%, n = 23) compared to healthy controls (4.23%, n = 21, p = 0.049).Conclusions: Our novel pipeline adds to all previously published methods and effectively identified altered neurovascular responses in glaucoma patients after the necessary adjustments. This standardized and automated approach is a promising catalysator for DVA research, offering a more localized and precise understanding of retinal vascular dynamics.

Assessment of radon level and the associated radiological risk from soil samples of quarry area at Hakim Gara, Ethiopia.

Excavation of terrestrial surface of the Earth could enhance the chance of exposure to radon while gases in the underground get access to escape. This study was aimed to assess the level of radon concentration from soil samples of quarrying sites at Hakim Gara in Ethiopia using CR-39 detectors in sealed container technique. The results of the measured radon concentration level were ranging from 164.3 to 494.0Bq/m3 with average value of 295.7 ± 87.09Bq.m-3. The activity concentration value of radium ranging from 0.63 to 2.74Bq.kg-1 with average value of 1.59 ± 0.55Bq.kg-1. The radon surface exhalation rate ranging between 0.95 to 4.1 Bqm-2h-1 with mean value of 2.38 ± 0.83 Bqm-2h-1. The mass exhalation rate ranging between 0.01 and 0.11 Bqkg-1h-1 with average 0.06 ± 0.03 Bqkg-1h-1. The AED and ELCR values for the study area were calculated and the mean value of the quarry area were determined on the basis of altitude variation and foundto be different for individual and group sample locations. Compared to values recommended by WHO for outdoor radon exposure, the measured levels of radon concentration were observedto be higher in the study area. But residential areas under construction in the area need further studies since the measured and calculated values were high for indoors in some soil samples.

Ultra-Narrowband Green-Emitting Transparent Composite Ceramics for Laser-Driven Display.

Laser-driven projection displays face a critical challenge in developing laser-excitable and high-performance narrowband green emitters. Herein, new Al2O3-LaMgAl11O19: Mn2+ (Al2O3-LMA: Mn2+) transparent composite ceramics are reported via high-temperature vacuum sintering, which produces a high-color-purity (95.4%) green emission with full width at half maximum of 24nm and superior thermal and moisture and laser irradiation stability. These are attributed to low electron-phonon couple, weak crystal-field effect, an individual lattice location of Mn2+ activators in high structural rigid host, and the incorporation of a high-thermal-conductivity Al2O3 secondary phase. As a result, the composite ceramics are demonstrated as an attractive color converter with a high external quantum efficiency (38%) and absorption coefficient (53%), which ensures a luminous flux of 2012lm @40.0W, a luminous efficacy of 67.7lmW-1, and a green light conversion efficiency of 20.3% upon blue laser irradiation. This enables to construct a brand-new laser-driven prototype display with a record color gamut beyond Rec.2020 standard (100.8%), outperforming commercial YAG: Ce3+ and β-SiAlON: Eu2+. This exploration in ultra-narrowband green luminescent materials is poised to accelerate the development of "ideal displays" for laser-driven projection display technology.

Multipopulation Whale Optimization-Based Feature Selection Algorithm and Its Application in Human Fall Detection Using Inertial Measurement Unit Sensors.

Feature selection (FS) is a key process in many pattern-recognition tasks, which reduces dimensionality by eliminating redundant or irrelevant features. However, for complex high-dimensional issues, traditional FS methods cannot find the ideal feature combination. To overcome this disadvantage, this paper presents a multispiral whale optimization algorithm (MSWOA) for feature selection. First, an Adaptive Multipopulation merging Strategy (AMS) is presented, which uses exponential variation and individual location information to divide the population, thus avoiding the premature aggregation of subpopulations and increasing candidate feature subsets. Second, a Double Spiral updating Strategy (DSS) is devised to break out of search stagnations by discovering new individual positions continuously. Last, to facilitate the convergence speed, a Baleen neighborhood Exploitation Strategy (BES) which mimics the behavior of whale tentacles is proposed. The presented algorithm is thoroughly compared with six state-of-the-art meta-heuristic methods and six promising WOA-based algorithms on 20 UCI datasets. Experimental results indicate that the proposed method is superior to other well-known competitors in most cases. In addition, the proposed method is utilized to perform feature selection in human fall-detection tasks, and extensive real experimental results further illustrate the superior ability of the proposed method in addressing practical problems.

Density, Climate, and Stochasticity Shape Four Centuries of Population Dynamics for Two Long-Lived Tree Species.

The dynamics of colonizing populations may be strongly influenced by both extrinsic (e.g., climate and competition) and intrinsic (e.g., density) forces as well as demographic and environmental stochasticity. Understanding the impacts of these effects is crucial for predicting range expansions, trailing edge dynamics, and the viability of rare species, but the general importance of each of these forces remains unclear. Here, we assemble establishment time and spatial locations of most individuals that have reached maturity in six isolated, establishing populations of two pine species. These data allow us to quantify the relative importance of multiple factors in controlling growth of these populations. We found that climate, density, site, and demographic stochasticity were of varying importance both within and across species, but that no driver appeared to dominate dynamics across all populations and time periods. Indeed, exclusion of any one of these effects greatly reduced predictive power of our population growth models. Given the similarity in the abiotic characteristics of these sites, the varying importance of these classes of effects was surprising but speaks to the need to consider multiple effects when predicting the dynamics of small and colonizing populations.

An evaluation of two-way coupled Euler–Lagrange methodology through direct comparison with particle-resolved simulations

The two-way coupled Euler–Lagrange (EL) methodology is an efficient computational tool for investigating multiphase flows, enabling simulations with tens of millions of particles without Reynolds number limitations. This method resolves the fluid motion on scales larger than a filter length scale, which typically exceeds the particle size and the inter-particle spacing. However, EL simulations require closure models to account for unresolved scales. This work compares particle-resolved (PR) and EL simulations to assess the accuracy of EL solutions. We examine how well EL simulations capture the statistical distribution of particle and fluid quantities in multiphase flows by comparing them with PR solutions. The focus is on modeling the force on particles and understanding the influence of the filter scale on EL simulation accuracy. The results show that, due to variations in feedback forces at individual particle locations, the forces computed in the EL method do not consistently correlate with those obtained from the PR simulations.

Optimization of testing protocols to screen for COVID-19: a multi-objective model.

In this paper we develop a new multi-objective simulated annealing (MOSA) algorithm to generate optimal testing protocols for infectious diseases, using the COVID-19 pandemic as our context. A SEIR (susceptible-exposed-infected-recovered) epidemiological model is embedded as the computational platform for our MOSA algorithm to optimize testing protocols for screening across three joint objectives: minimum cost of test materials, minimum total infections over the testing horizon, and minimum number of false negatives over the horizon. We demonstrate the application of this optimization tool to recommend screening protocols for K-12 school districts in the U.S.State of North Carolina. Our approach is scalable by population coverage and can be employed at the level of individual school districts or regional collections of districts, individual schools or collections of schools across a district, business sites, or nursing homes, among other congregate settings where individuals may be screened prior to gaining entry to the site. The algorithm can be solved two ways, generating either independent optimal protocols across individual testing locations, or a common protocol covering all locations in the collection of testing sites. Our findings can be used to inform policy decisions to guide the development of effective testing strategies for controlling the spread of COVID-19 or other pandemic diseases in a wide range of congregate settings across various geographic regions.

ЗАБВЕНИЕ КАК ФОРМА СТРАДАНИЯ АРТЕФАКТОВ. Статья II

The text is a continuation of the article published in the previous issue of the journal. In the first part, the problem of abandoned and forgotten objects was posed, its individual facets were revealed (in particular, culturally conditioned features of the perception of abandonment and the psychomental foundations of the «experience» of this phenomenon), and groups of artifacts whose oblivion is most acute were identified. Objects of religious worship and human burials were examined through the prism of the problems of abandonment. This article provides an overview of such groups of artifacts as deserted manors; abandoned cities and individual urban locations and structures; monuments consigned to oblivion (including monumental works of art). Various theoretical aspects of the studied sphere are touched upon, which finds expression in many cultures and has great social significance: attention is paid to the relationship of a person with the «world of things»; the sacred and everyday levels of perception of the phenomenon of abandonment, as well as various parameters and markers of oblivion are considered; the deep connections of the phenomenon under consideration with the problems of historical and cultural memory are noted. It is indicated that culturology has a special heuristic significance in the study of the problem of abandonment of artifacts, the potential of which allows us to reach a high level of generalization of the topic under study.

Neurometabolic Profile in Obese Patients: A Cerebral Multi-Voxel Magnetic Resonance Spectroscopy Study.

Background and Objectives: Obesity-related chronic inflammation may lead to neuroinflammation and neurodegeneration. This study aimed to evaluate the neurometabolic profile of obese patients using cerebral multivoxel magnetic resonance spectroscopy (mvMRS) and assess correlations between brain metabolites and obesity markers, including body mass index (BMI), waist circumference, waist-hip ratio, body fat percentage, and indicators of metabolic syndrome (e.g., triglycerides, HDL cholesterol, fasting blood glucose, insulin, and insulin resistance index (HOMA-IR)). Materials and Methods: This prospective study involved 100 participants, stratified into two groups: 50 obese individuals (BMI ≥ 30 kg/m2) and 50 controls (18.5 ≤ BMI < 25 kg/m2). Anthropometric measurements, body fat percentage, and biochemical markers were evaluated. All subjects underwent long- and short-echo mvMRS analysis of the frontal and parietal supracallosal subcortical and deep white matter, as well as the cingulate gyrus, analyzing NAA/Cr, Cho/Cr, and mI/Cr ratios, along with absolute concentrations of NAA and Cho. Results: Obese participants exhibited significantly decreased NAA/Cr and Cho/Cr ratios in the deep white matter of the right cerebral hemisphere (p < 0.001), while absolute concentrations of NAA and Cho did not differ significantly between groups (p > 0.05). NAA levels showed negative correlations with more reliable obesity parameters (waist circumference and waist-to-hip ratio) but not with BMI, particularly in the deep frontal white matter and dorsal anterior cingulate gyrus of the left cerebral hemisphere. Notably, insulin demonstrated a significant negative impact on NAA (ρ = -0.409 and ρ = -0.410; p < 0.01) and Cho levels (ρ = -0.403 and ρ = -0.392; p < 0.01) at these locations in obese individuals. Conclusions: Central obesity and hyperinsulinemia negatively affect specific brain regions associated with cognitive and emotional processing, while BMI is not a reliable parameter for assessing brain metabolism.

Privileged transmigrant motherhood and its practices of distinction

ABSTRACT Transnational motherhood and Latin-American migrants in the U.S. have been explored in the literature primarily in the context of labor migration and marginalized identities. Existing research delves into how mothers negotiate the challenges and opportunities posed by migration in their parenting journeys. However, scholars advocate to look closer at how the social location of individuals create specific sets of experiences. Using the lenses of transnationalism and translocational positionalities, I look at the motherhood identities of 32 mothers in the U.S. who belong to higher socioeconomic levels in Colombia. I find that through their motherhood practices and discourses, participants perform class distinctions to distance themselves from negative stereotypes associated with migrants from Latin America and white U.S. American culture. Addressing the nuances of motherhood, class, and race within the context of migration is a step toward understanding how identities and ideologies transcend transnational boundaries and the role they play in simultaneously fostering and hindering social inequalities.

Fatigue reliability analysis of floating offshore wind turbines under the random environmental conditions based on surrogate model

Fatigue reliability analysis is essential for ensuring the safe operation of floating offshore wind turbines (FOWTs) under random wind and wave loads. Traditionally, fatigue assessments are computationally expensive due to the need for numerous numerical simulations. To reduce computational costs, a fatigue reliability analysis method is proposed in the present study by implementing the surrogate model, C-vine copula, and Monte Carlo simulation. The multivariate distribution of environmental conditions is modeled using the C-vine copula and marginal mixed distribution models, while short-term fatigue damages are estimated by the surrogate model. Finally, Monte Carlo simulation is employed to assess the fatigue reliability. The proposed method is applied to evaluate fatigue reliability at three critical locations on a FOWT. Results show that both the back propagation neural network (BPNN) and the Kriging model can accurately predict short-term fatigue damage at various locations. However, the BPNN-based surrogate model is recommended for its lower computationally cost. Furthermore, the proposed method not only assesses the probability of fatigue failure at individual locations but also evaluates system-level fatigue reliability by accounting for correlation between fatigue damage at different locations.

Method of estimating sea-surface paleotemperatures through biotic proxies: A case study in Upper Paleozoic paleoclimatic, paleogeographic and paleotectonic reconstructions of Siberia.

This study introduces a novel approach for quantitatively assessing sea-surface paleotemperatures examined in the Upper Paleozoic of Siberia, utilizing the obtained in the region data as a case study of the use of this method. The method relies on evaluating the taxonomic composition and ecological proxies of biota. It utilizes a comprehensive dataset encompassing the geographic distribution and ecology of various biotic groups in Siberia and adjacent regions, leveraging the newly developed by the authors large PaleoSib database and partially the Paleobiology Database (paleobiology.org) The taxonomy has been used according to the database of Global Biodiversity Information Facility (gbif.org). Fossils collected from individual locations often exhibit a wide spectrum of paleotemperatures. To address this variability, we developed an algorithm for calculating average biotic paleotemperatures in each locality/time slice. Our computations of the available data have unveiled a coherent pattern of paleoclimate dynamics, particularly Sea Surface Temperature, across Siberian basins and surrounding areas during the Late Paleozoic era. These findings significantly contribute to a refined comprehension of paleoclimate and paleotectonic dynamics in the region during that specific time. To enhance paleotemperature analyses, we have integrated lithological indices with biotic ones, fortifying the overall methodology and furnishing a more robust framework for interpreting paleoclimate data. The method could be a helpful tool in regional and interregional studies, regardless of the utilized rock's age and fossil groups.