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The interaction between the immune system and the brain, crucial for blood–brain barrier integrity, is a potential factor in migraine development. Although there's evidence of a connection between immune dysregulation and migraine, a clear causal link has been lacking. To bridge this knowledge gap, we performed a two-sample Mendelian randomization (MR) analysis of 731 immune cell phenotypes to determine their causality with migraine, of which parameters included fluorescence, cell abundance, count, and morphology. Sensitivity and pleiotropy checks validated our findings. After applying a false discovery rate correction, our MR study identified 35 of 731 immune phenotypes with a significant causal link to migraine (p < 0.05). Of these, 24 showed a protective effect (inverse variance weighting : p < 0.05, odds ratio <1), and 11 were risk factors (inverse variance weighting : p < 0.05, odds ratio >1). Although limited by population sample size and potential population-specific genetic variations, our study uncovers a significant genetic link between certain immune cell markers and migraine, providing new insights into the disorder's pathophysiology. These discoveries are crucial for developing targeted biomarkers and personalized treatments. The research enhances our understanding of immune cells' role in migraine and may substantially improve patient outcomes and lessen its socio-economic impact. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic came with many setbacks, be it to a country's economy or the global missions of organizations like WHO, UNICEF or GTFCC. One of the setbacks is the rise in cholera cases in developing countries due to the lack of cholera vaccination. This model suggested a solution by introducing another public intervention, such as adding Chlorine to water bodies and vaccination. A novel delay differential model of fractional order was recommended, with two different delays, one representing the latent period of the disease and the other being the delay in adding a disinfectant to the aquatic environment. This model also takes into account the population that will receive a vaccination. This study utilized sensitivity analysis of reproduction number to analytically prove the effectiveness of control measures in preventing the spread of the disease. This analysis provided the mathematical evidence for adding disinfectants in water bodies and inoculating susceptible individuals. The stability of the equilibrium points has been discussed. The existence of stability switching curves is determined. Numerical simulation showed the effect of delay, resulting in fluctuations in some compartments. It also depicted the impact of the order of derivative on the oscillations. [ABSTRACT FROM AUTHOR]

The connection between periodontal disease (PD) and schizophrenia (SCZ) has been reported in observational studies, but it remains unclear. This research aims to examine the bidirectional causal impacts between PD and SCZ. The FinnGen consortium supplied summarized data on PD for 346,731 individuals (87,497 cases and 259,234 controls) of Finnish ancestry, and information on SCZ was acquired from the OpenGWAS repository, encompassing 127,906 individuals (52,017 cases and 75,889 controls) of European ancestry. Next, we conducted Mendelian randomization (MR) analysis to establish a causal inference relationship between PD and SCZ. The inverse variance weighted (IVW) method was utilized as the primary analysis. Additionally, some sensitivity analyses were utilized to verify the stability of the results. The analysis of IVW results indicated no impact of PD on SCZ (IVW OR = 1.10, 95% CI 0.97–1.24, P = 0.14). Nevertheless, the inverse relationship between PD and SCZ was identified through reverse MR analysis (IVW OR = 1.03, 95% CI 1.01–1.05, P = 0.002). The findings from MR-Egger, weighted median, simple mode, and weighted mode approaches aligned with the outcomes of the IVW method. Based on sensitivity analyses, horizontal pleiotropy is unlikely to distort causal estimates. This study presented the initial proof of a genetic causal relationship between SCZ and PD, albeit with a minimal impact. Further exploration is needed to gain a deeper understanding of this relationship. Furthermore, no genetic causal relationship between PD and SCZ was identified. [ABSTRACT FROM AUTHOR]

Hand, foot, and mouth disease (HFMD) is widely spread in China Mainland, seriously threatening the health of infants and young children. We develop a meta‐population model that includes both local and ecdemic populations to study the impacts of fomites and immigration on the transmission of HFMD in Shanghai. The model includes both direct transmission between susceptible and infected individuals (asymptomatic or symptomatic) and indirect transmission via fomites. The forward bifurcation shows that if the basic reproduction number is less than unity, the epidemic will die out; otherwise, the epidemic will spread. The fitting results show that the basic reproduction numbers of the local population and the ecdemic population are 1.4696 and 1.7288, respectively. Sensitivity analysis of the basic reproduction number showed that there was a high correlation between the basic reproduction number and the transmission rate of asymptomatic infected individuals, as well as the parameters related to the fomites (such as indirect transmission rate and virus shedding rate). Hence, asymptomatic infected individuals and fomites have a significant impact on the new HFMD infection. The numerical simulations on the prevention and control strategy show that reducing the transmission rate between susceptible and asymptomatic infected individuals or reducing the fomites can delay the outbreak of the epidemic and weaken the severity of HFMD. Our findings can provide guidance for the eradication of HFMD epidemic in the presence of indirect transmission and other factors. [ABSTRACT FROM AUTHOR]

To quantitatively study the coupled dynamic behavior of vehicles in collision with semirigid guardrails, a coupled numerical model is established based on the finite element method. This model comprehensively integrates the nonlinear dynamic behavior of both the vehicle body and the guardrail structure. We validate the numerical results by comparing them with experimental findings and theoretical empirical formulas, confirming the accuracy of the coupled numerical model. This quantitative study investigates the influence of vehicle speed, column wall thickness, and collision angle on this coupled dynamic behavior to help optimize practical guardrail engineering designs. The results indicate that the sensitivity values of the collision speed, column wall thickness, and collision angle according to the baseline model are 1.0, 1.0, and 0.741, respectively. The parameters with higher sensitivity values are the key factors to consider in designs. Thus, the coupled numerical model can be used to optimize the design of semirigid guardrails in practical engineering. [ABSTRACT FROM AUTHOR]

In this study, we proposed and analyzed a nonlinear deterministic mathematical model of malaria transmission dynamics. In addition to the previous approaches, we incorporated the class of aware people and other control measures. We established the wellposedness of the model, and the asymptotic behavior of the solutions is rigorously studied depending on the basic reproduction number R0. The model system admits two equilibrium points: disease‐free and disease‐persistent equilibrium points. The analytical result of the model system revealed that the disease‐free equilibrium point is both locally as well as globally asymptotically stable whenever R0 < 1 while the disease‐persistence equilibrium point is globally asymptotically stable whenever R0 > 1. Moreover, the forward bifurcation phenomenon of the model system for R0 = 1 was analyzed by using center manifold theory. A sensitivity analysis of the basic reproduction number was performed to identify parameters that will cause to trigger the transmission of malaria disease and should be targeted by control strategies. Then, the model was extended to the optimal control problem, with the use of three time‐dependent controls, namely, preventive measures(treated bednets and indoor residual spraying), continuous awareness campaigns to susceptible individuals, and treatment for infected individuals. By using Pontryan's maximum principle, necessary conditions for the transmission of malaria disease were derived. Numerical simulations are illustrated by using MATLAB ode45 to validate the theoretical results of the model. The numerical findings of the optimal model suggested that integrated control strategies are better than a sole intervention to eliminate malaria disease. [ABSTRACT FROM AUTHOR]

Reliability prediction for complex systems utilizing prognostic methods has attracted increasing attention. Furthermore, achieving accurate reliability predictions for complex systems necessitates considering the interaction between components and the multivariate functional relationship that exists among them. This paper proposes a bi‐level method to evaluate the variability of degradation processes and predictive reliability based on the profile monitoring approach for multicomponent systems. Firstly, a multivariate profile structure is introduced to model the framework of degradation analysis in scenarios where there exists stochastic dependency and a multivariate functional relationship between the degradation processes of components. At the component level, the objective is to evaluate the variability of the degradation process for each component considering the presence of stochastic dependence. For the system level analysis, the proposed approach enables the prediction of degradation variability and system reliability by considering the functional relationships among components, without the need for direct calculation of individual component reliabilities. The performance of the proposed model is evaluated through a numerical study and sensitivity analysis conducted on a multicomponent system with a k‐out‐of‐n structure. The results demonstrate the model's notable flexibility and efficiency. [ABSTRACT FROM AUTHOR]

In order to analyze the stability of the bridge above the goaf, the disturbance influence of goaf residual deformation on the bridge is studied. Firstly, an equivalent numerical simulation method of goaf residual deformation evolution process is studied by quantitative analysis the sensitivity of residual subsidence to the rock parameters using the OAT (one-variable-at-a-time). Then, the collaborative deformation of ground, pile, and bridge floor is studied under the condition of a simply-supported beam bridge above the goaf center. Finally, the mechanism of collaborative deformation of ground, pile, and bridge floor is revealed. The results show that the goaf residual deformation process can be obtained by weakening the elastic modulus of fractured rock in the caving zone. At the final residual deformation stage, the subsidence ratio of ground to pile is about 10, and the subsidence ratio of pile to bridge floor is about 2, while the ground horizontal movement ratio of ground to pile is about 7, and the bridge floor horizontal movement can be ignored. The bridge floor is always in the positive curvature influence zone, and the pile has an inhibitory effect on the curvature deformation of the bridge floor. The compression deformation occurs between the piles locations, while the tensile deformation occurs at the pile location. The evolution of negative frictional resistance derived from goaf residual deformation is the main reason for the change in the collaborative deformation law among the ground, pile and bridge floor. This research can provide scientific support and theoretical basis for the design, construction, and protection of the bridge above the goaf center, and can also provide reference for the stability evaluation of bridge above goaf under other conditions. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusion The incidence of new‐onset atrial fibrillation (NOAF) is increasing in the last decades. NOAF is associated with worse long‐term prognosis. The C2HEST score has been recently proposed to stratify the risk of NOAF. Pooled data on the performance of the C2HEST score are lacking.Systematic review and meta‐analysis of observational studies reporting data on NOAF according to the C2HEST score. We searched PubMed, Web of Science and Google scholar databases without time restrictions until June 2023 according to PRISMA guidelines. Meta‐analysis of the area under the curve (AUC) with 95% confidence interval (95% CI) and a sensitivity analysis according to setting of care and countries were performed.Of 360 studies, 17 were included in the analysis accounting for 11,067,496 subjects/patients with 307,869 NOAF cases. Mean age ranged from 41.3 to 71.2 years. The prevalence of women ranged from 10.6 to 54.75%. The pooled analysis gave an AUC of .70 (95% CI .66–.74). A subgroup analysis on studies from general population/primary care yielded an AUC of 0.69 (95% CI 0.64–0.75). In the subgroup of patients with cardiovascular disease, the AUC was .71 (.69–.79). The C2HEST score performed similarly in Asian (AUC .72, 95% CI .68–.77), and in Western patients (AUC .68, 95% CI .62–.75). The best performance was observed in studies with a mean age <50 years (n = 3,144,704 with 25,538 NOAF, AUC .78, 95% CI .76–.79).The C2HEST score may be used to predict NOAF in primary and secondary prevention patients, and in patients across different countries. Early detection of NOAF may aid prompt initiation of management and follow‐up, potentially leading to a reduction of AF‐related complications. [ABSTRACT FROM AUTHOR]

Background: The gut microbiota (GM) has been implicated in neurological disorders, but the relationship with hydrocephalus, especially the underlying mechanistic pathways, is unclear. Using Mendelian randomization (MR), we aim to discover the mediating role of inflammatory factors in the relationship between GM and hydrocephalus. Methods: After removing confounders, univariable and multivariable MR analyses were performed using summary statistics to assess the causal relationships between GM, inflammatory factors (IL-17A and IL-27), and types of hydrocephalus. Meta-analyses were used to reconcile the differences in MR results between different hydrocephalus sources. Finally, mediator MR analyses were applied to determine the mediating effect of inflammatory factors. Various sensitivity analysis methods were employed to ensure the reliability and stability of the results. Results: After correction for P-values, Firmicutes (phylum) (OR, 0.34; 95%CI, 0.17-0.69; P = 2.71E-03, PFDR = 2.44E-02) significantly reduced the risk of obstructive hydrocephalus. The remaining 18 different taxa of GM had potential causal relationships for different types of hydrocephalus. In addition, Firmicutes (phylum) decreased the risk of obstructive hydrocephalus by increasing levels of IL-17A (mediating effect = 21.01%), while Eubacterium ruminantium group (genus) increased the risk of normal-pressure hydrocephalus by decreasing levels of IL-27 (mediating effect = 7.48%). Conclusion: We reveal the connection between GM, inflammatory factors (IL-17A and IL-27), and hydrocephalus, which lays the foundation for unraveling the mechanism between GM and hydrocephalus. [ABSTRACT FROM AUTHOR]

Objective: Both camrelizumab plus paclitaxel and carboplatin (CTC) and sintilimab plus gemcitabine and cisplatin or carboplatin (SGP) have been approved by the National Medical Products Administration of China (NMPA) for the first-line treatment of local advanced or metastatic sqNSCLC. However, the comparison of the two treatments as first-line treatments in efficacy or pharmacoeconomics has barely been studied. To deeply understand the costs and outcomes of the two treatments, this work directly compared the cost-effectiveness for the first-line treatment of local advanced or metastatic squamous NSCLC in the Chinese mainland. Methods: A network meta-analysis was first performed based on the three clinical trials, namely, CameL-Sq, ORIENT-12, and C-TONG1002, to compare the clinical benefits of the two treatments. The Weibull approximation was applied to further calculate the life expectancy of the two treatments. The partitioned survivalmodel (PSM) was next established, and one-way sensitivity analysis and probabilistic sensitivity analysis were also performed to evaluate the stability of the underlying parameter values and assumptions within the model. Results: CTC treatment gained 0.68 QALYs and cost $14,764. SGP treatment gained 0.54 QALYs and cost $14,584. The CTC arm gained 0.14 additional QALYs and cost $179 more than the SGP arm, and the ICERs was $1,269/QALY, which was lower than one-fold GDP per capita in the Chinese mainland ($12,734 GDP per capita in 2022). In probabilistic sensitivity analysis, when the WTP ranged from $12,734-38,202 (1-3 folds, 2022 GDP per capita in China), the CTC group had higher probabilities than the SGP group for being cost effective, which ranged from 85.65% to 88.38%. Conclusion: From the perspective of the payers, camrelizumab plus chemotherapy was cost-effective compared with sintilimab plus chemotherapy for the first-line treatment of local advanced or metastatic squamous NSCLC in the Chinese mainland. [ABSTRACT FROM AUTHOR]

AbstractIn order to improve the safety of on-board hydrogen storage tanks during collision, a protective shell based on a negative Poisson’s ratio (NPR) core is designed in this paper. After analyzing and comparing the crashworthiness of three typical honeycomb structures, the concave hexagonal negative Poisson’s ratio honeycomb is selected as the energy-absorbing inner core of the hydrogen storage tank protection structure. The sensitivity analysis of the structural parameters of the NPR shell is conducted through orthogonal tests to identify parameters with a significant impact on crash performance. These parameters are then used as experimental variables for subsequent optimization design. Subsequently, a response surface approximation model between the optimization objective and the structural parameters is established based on the response surface method. Finally, the adaptive simulated annealing algorithm (ASA), neighborhood cultivation genetic algorithm (NCGA), and non-dominated sorting genetic algorithm-II (NSGA-II) are used to optimize the structural parameters, and the optimization results are verified by the whole-vehicle crash simulation. The simulation results demonstrate that all three algorithms achieve better optimization results, among which NCGA is more advantageous in improving the overall performance of the protective shell. After NCGA optimization, the specific energy absorption of the protective shell increases by 19.75%, the maximum collision force of the rigid wall decreases by 23.63%, and the maximum stress of the hydrogen storage tank body decreases by 22.77%. These findings indicate that the designed protection shell effectively improves the crashworthiness of the hydrogen storage tank during collisions. [ABSTRACT FROM AUTHOR]

Collaboration has an essential role in memory, and how to appropriately use it to affect individual memory positively is a matter of concern. The meta‐analysis generally assessed the effect of collaboration on subsequent individual retrieval, registered on the PROSPERO platform and adhering to the PRISMA guidelines, using the Web of Science, Science Direct, CNKI and WanFang databases with post‐collaborative memory as the main subject, screened studies published up to December 31, 2023, a total of 64 studies with 101 effect sizes, including 13,398 participants from 11 countries. Heterogeneity test, sensitivity analysis, subgroup analysis and meta‐regression analysis were performed on the included studies, while publication bias was assessed. The results found that collaboration improves subsequent individual retrieval memory more than individuals, and collaboration has a moderate facilitating effect on subsequent individual retrieval. Group size, material category, category size, collaboration phase, collaboration approach, task process and test method were among the moderating variables. The study emphasizes the role of collaboration in cognition and demonstrates the post‐collaborative benefits. The conclusions are of value for developing methods to improve individual memory. [ABSTRACT FROM AUTHOR]

Background: This study aimed to explore the causal relationship between age at first birth (AFB) and depression. Methods: Using the univariable Mendelian randomization (UVMR) and multivariable Mendelian randomization (MVMR) methods to examine the potential correlation between age at first birth (AFB) and major depressive disorder and postpartum depression. A public database was used to obtain the genome-wide association studies (GWAS) summary data. We put inverse-variance-weighted (IVW) as the primary method in Mendelian randomization (MR) analysis and used sensitivity analysis to confirm the robustness of our result. Results: We found a significant causal association between AFB and major depressive disorder by using the IVW algorithm (odd ratio [OR] 0.826; 95% confidence interval [CI] 0.793 − 0.861; P = 4.51 × 10− 20). MR-Egger, weighted median, simple mode and weighted mode method concluded the same result (P < 0.05). During the sensitivity analysis, the heterogeneity test (Q-value = 55.061, df = 48, P = 2.81 × 10− 01, I2 = 12.82%) and the leave-one-out plot analysis confirmed the stability of the results. The outcomes of the pleiotropy test (MR-Egger intercept = 8.932 × 10− 3. SE = 6.909 × 10− 3. P = 2.02 × 10− 01) and MR_PRESSO global test (P = 2.03 × 10− 01) indicated there is no pleiotropy. Conclusion: There is solid evidence that a higher age at first birth is associated with a lower risk of major depressive disorder. [ABSTRACT FROM AUTHOR]

Objectives. Construction worker safety behavior (CWSB) may be affected by a confluence of multilevel and interrelated factors. Cultivating and maintaining CWSB is vital for improving construction safety. Current studies focus on organization-level or individual-level CWSB antecedents. However, few studies have examined the influence of psychological factors on CWSB, thereby reducing the joint effects of multilevel factors on CWSB. Methods. To determine effective strategies for strengthening CWSB, this study adopted the Bayesian network technique to explore the interrelationships between CWSB and its antecedent factors. A Bayesian belief network model was developed and trained with data collected from Chinese construction workers, which connected organizational, individual and psychological factors with CWSB. Results. According to the sensitivity analysis, safety knowledge, safety climate and psychological capital are the three most significant influencing factors for CWSB. A combined strategy that enhances safety knowledge, safety climate and communication competence simultaneously is the most effective option for strengthening CWSB. The validation and robustness of the network showed good accuracy for safety behavior judgment. Conclusion. This study proposes an alternative way to improve safety behavior by identifying its interactive causes and illustrates the importance of initiating systematic safety measures, which may help to mitigate the problem of safety plateau. [ABSTRACT FROM AUTHOR]

An optimization design was carried out based on a back propagation (BP) neural network and a genetic algorithm (GA) to improve the stiffness and accuracy of the self-developed MGK6030 five-axis tool grinding machine. First, finite element analysis was carried out on the whole grinding machine based on ANSYS Workbench, and the key parts were found to be the grinding wheel headstock, B axle box body, and column. Sensitivity analysis was carried out after the model parameterization, and ten parameters, which affect the quality, maximum deformation, and first-order mode, were obtained. These parameters were used as input variables. A total of 235 sets of sample data were obtained by using the optimal overall performance of the grinder for the target (large first-order natural frequency, small deformation, and mass). The BP neural network was then used to fit the nonlinear coupling relationship between the input and the output. Thereafter, the optimization function of the GA was used to perform multi-objective optimization in the specified range. Finally, the parameters are verified by software simulation and prototype test. Results showed that the maximum deformation of the optimized machine tool is reduced by 21%, and the first four order natural frequencies are increased by 6.36%, 9%, 6.4%, and 2.84%. The positioning accuracies of the linear axis and rotary axis are increased by 22% and 21%, respectively, which demonstrates the effectiveness of the optimization scheme and provides theoretical and technical support for similar optimization problems. [ABSTRACT FROM AUTHOR]

This paper optimizes a ground replenishment strategy for large-scale LEO satellite constellations, aiming for ensuring high economic efficiency and performance requirements. Firstly, the uncertainties of influential elements are analyzed and satellite lifetime distributions are estimated to determine satellite operating statuses. Secondly, satellite statuses are used for obtaining real-time constellation status and simulating data package transmission paths. Performance degradation function is then derived by selecting time delay as the key performance. To confront with the performance degradation, an optimization model of ground replenishment strategy is established with the objective of minimizing the overall replenishment cost under the time delay and loading capacity constraints. The model is solved by a Dichotomous-domain algorithm. Finally, a case study of a large-scale STARLINK constellation is adopted to validate the effectiveness of the model and sensitivity analyses are conducted to verify its robustness. [ABSTRACT FROM AUTHOR]

Dynamical system models typically involve numerous input parameters whose "effects" and orthogonality need to be quantified through sensitivity analysis, to identify inputs contributing the greatest uncertainty. Whilst prior art has compared total-order estimators' role in recovering "true" effects, assessing their ability to recover robust parameter orthogonality for use in identifiability metrics has not been investigated. In this paper, we perform: (i) an assessment using a different class of numerical models representing the cardiovascular system, (ii) a wider evaluation of sampling methodologies and their interactions with estimators, (iii) an investigation of the consequences of permuting estimators and sampling methodologies on input parameter orthogonality, (iv) a study of sample convergence through resampling, and (v) an assessment of whether positive outcomes are sustained when model input dimensionality increases. Our results indicate that Jansen or Janon estimators display efficient convergence with minimum uncertainty when coupled with Sobol and the lattice rule sampling methods, making them prime choices for calculating parameter orthogonality and influence. This study reveals that global sensitivity analysis is convergence driven. Unconverged indices are subject to error and therefore the true influence or orthogonality of the input parameters are not recovered. This investigation importantly clarifies the interactions of the estimator and the sampling methodology by reducing the associated ambiguities, defining novel practices for modelling in the life sciences. Author summary: In order to gain new insight into a biological system, one often uses mathematical models to predict possible responses from the system. One vital step when using such models is to gain knowledge of the uncertainty associated with the model responses, for any input changes. Utilising two non-linear and stiff cardiovascular models as test cases, we investigate the effects of different choices made when quantifying the uncertainty of mathematical models. Leveraging efficient solving of the mathematical model, we show that in order to truly quantify the effects of inputs on a set of outputs, one must ensure converged estimates of the inputs' influence. Our detailed study provides a robust workflow of good modelling practice for biological systems, thus ensuring a true interpretation of the uncertainty associated with model inputs. [ABSTRACT FROM AUTHOR]

Introduction: Early risk assessment studies usually based on total heavy metal (loid) contents, inevitably leading to an overestimation of the health risks. In addition, inputs are represented as single-point estimates in deterministic models, leading to underestimation or overestimation of the health risks. Methods: To overcome these barriers, a novel probabilistic risk assessment strategy based on the combinational use of bioaccessibility and Monte Carlo simulation was developed to assess heavy metal (loid) associated health risks of earthworms in this study. To obtain a realistic and robust probabilistic risk assessment, heavy metal (loid) exposure duration and frequency were determined using our questionnaire data. Results: As a result, the mean gastrointestinal bioaccessibility was in the order: Cd > As > Cu > Hg. The mean hazard index (HI) values for investigated metal (loid)s were 0.65 and 0.59 for male and female, respectively, demonstrating an acceptable health risk in an average community. However, the 90th percentile of HI values was 1.87 and 1.65 for male and female, respectively. And the total non-cancer risks of heavy metal (loid) exposure exceeded the acceptable threshold for 19.9% and 17.8% of male and female, respectively. In addition, the total cancer risk (TCR) value through co-exposure to As and Cd suggested that the carcinogenic risks may be of concern for average exposure population. Sensitivity analyses revealed that the exposure frequency and bioaccessible As concentration were the dominant contributors to the total risk variance, which provided meaningful implications for environmental management. Conclusion: Altogether, the refined strategy based on bioaccessibility and Monte Carlo simulation is the first of its kind, such effort attempts to scientifically guide the rational clinic use of TCM and the improvement of population-health. [ABSTRACT FROM AUTHOR]

Considering the voltage stability problem brought by large-scale distributed PV access to the distribution network, this paper proposes a generalized load model that considers the fault ride-through capability of distributed PV. Firstly, the detailed model of the distribution network is established, and the detailed model is calibrated based on the measured data, the simulation errors are below 1%. And then establish a generalized load model considering distributed PV high and low voltage traversal ability. The sensitivity analysis results are used to rank the parameters to be identified, and the parameters with higher sensitivity are identified. The parameters are obtained from the detailed model and measured data, and four sets of parameters are identified and simulated under different PV penetration rates and fault conditions. The calculated fitting errors are less than 1%. The results show that the generalized load gray box model of the distribution network with distributed PV high and low voltage ride-through capability can reflect the dynamic characteristics of the distribution network well. [ABSTRACT FROM AUTHOR]

The InSAR technique is known to be a powerful tool for precise monitoring of wide areas in terms of displacements. It is conceivable to also use this technique to monitor landslide areas, but geometrical distortions due to ground morphology and land cover could make InSAR processing ineffective for such applications. Because of the computational burden of InSAR processing, it is important to have preliminary knowledge about the possible suitability of the technique for the inspected area before acquiring and processing the data. This paper aims to perform a preliminary analysis of the InSAR sensitivity for the specific case of landslide monitoring. A new approach is proposed considering aspects specific to landslide displacements, which are basically tangent to the slope direction. Pre-processed coherence maps were used to account for the impact of land cover. The whole analysis can be carried out without acquiring cumbersome SAR datasets and can be used as a preliminary step. The Italian Emilia-Romagna region has been considered as the study area, with landslide areas accounting for more than 12% of its territory. The outcomes show that the inspected area has favourable morphological conditions, mainly thanks to its mild slopes and the limited number of landslides facing north, but the land cover has a strong negative impact on the InSAR sensitivity. Nevertheless, 7.5% of the landslide areas have promising conditions for monitoring using radar interferometry. [ABSTRACT FROM AUTHOR]

There is uncertainty in the results of any mathematical model due to different reasons. It is important to estimate this uncertainty. Sensitivity analysis is commonly used to estimate how the changes in the input parameters affect the solutions of the model. In this paper, we discuss different ways of performing local and global sensitivity analyses and apply them to two models: an epidemic model and a new myocardial infarction model, both based on ordinary differential equations. The first model is a simple model used to explain the ideas, while the second one shows how to apply them to a model with more state variables and parameters. We find that if the parameters are not accurately known, local sensitivity analysis can be misleading and that global sensitivity methods that sample the whole parameter space, varying all the values of the parameters at the same time, are the most reliable. We also show how the sensitivity analysis results can be used to determine the uncertainty in the results of the model. We present numerical simulations. [ABSTRACT FROM AUTHOR]

Industry 4.0 is potentially innovative in the workers' role, which is becoming increasingly involved in smart activities. In this situation, it is necessary to improve highly repetitive uncomfortable working postures to reduce physical risks. This study intends to assess physical risks during VR interaction for manufacturing work. Posture-related physical risk levels were calculated using ergonomic risk assessment tools RULA, REBA, and OWAS. Three task conditions were considered for the experiment in a VR-based car-assembly environment. An analysis of variance was applied to investigate significant differences between task conditions, and it suggested that a higher risk level was obtained while working in the overhead position for RULA and REBA, whereas the squatting position obtained a higher risk level for OWAS. Sensitivity analysis identified that the upper arm and neck were responsible for the highest risk level for RULA, the upper arm, neck, and trunk for REBA, and the back posture parameter for OWAS. [ABSTRACT FROM AUTHOR]

In this manuscript, a novel transformed differential quadrature solution to the coupled thermo‐hydro‐mechanical (THM) problem of layered transversely isotropic (TI) saturated media is proposed, accompanied by a sensitivity analysis of pertinent parameters. Initially, the THM governing equations that encompass the transverse isotropy characteristics of thermal, permeable, and mechanical properties are introduced. Subsequently, utilizing the transformed differential quadrature method (TDQM), the discretized algebraic equations in the transformed domain are derived. Boundary conditions related to stresses, displacements, and temperatures are defined leveraging integral transform techniques and the stress‐strain relationship, while algebraic equations for continuity conditions across adjacent layers are incorporated. The global matrix equation is assembled and solved. After verifying the proposed TDQM solution, a sensitivity analysis on the complex transversely isotropic parameters is conducted to identify the principal factors influencing THM coupling behaviors, thereby simplifying the parameters for discussion in practical engineering applications. [ABSTRACT FROM AUTHOR]

Progress in satellite technology places higher expectations on the design period and reliability of thermal control systems, especially for small satellites with high intensification. Here, a thermal analysis is conducted throughout the satellite development process, with thermal modeling serving as the foundation for thermal analysis in the early and formal design and test verification stages. As a result, designers are required to appreciate, penetrate, and properly apply thermal models for the development of satellite thermal systems. Thus, in this article, we reviewed the research and development of thermal model techniques, as well as the preparation of crucial parameters and means of simplification for accurate and fast solutions. In addition, a summary of the relevant application and state-of-the-art technologies such as uncertainty and sensitivity analysis, intelligent optimization design, and parameter correlation, was dedicated to promoting the comprehensive development and innovation of thermal analysis of satellites. [ABSTRACT FROM AUTHOR]

With the increasing number of hydropower plants under construction and proposed in Nepal, the country is anticipated to experience a surplus of hydropower that exceeds its peak load demand. This surplus electricity becomes particularly high during the wet seasons, when hydropower production reaches its maximum capacity. This research focuses on the mathematical modeling of an alkaline electrolyzer, specifically analyzing the stack performance and the electricity flow within the balance of plants required to support the stack operation. The developed model is then used to estimate the production cost of hydrogen by utilizing forecasted surplus electricity up until the year 2030. The output of the study is expected to help in the sustainable utilization of surplus hydropower in the country, thus enhancing the low carbon economic development path. The study shows that there is a significant opportunity for hydrogen production from surplus hydroelectricity, ranging from 91 ktonne/year to maximum, of 414 ktonne/year. The average levelized cost of hydrogen is estimated at 5.65 USD/kg. The cost can be further reduced if policy interventions like tax rebates and tariff rate subsidies are in place. • Hydrogen production system of an alkaline electrolyzer has been modeled. • Estimation of surplus electricity from installed capacity under demand profile. • Calculation of hydrogen costs using surplus electricity scenarios until 2030. • Sensitivity analysis of critical inputs affecting the hydrogen production cost. [ABSTRACT FROM AUTHOR]

This article introduces and formalizes a novel stochastic method that combines inverse simulation with the theory of generalized Polynomial Chaos (gPC) to solve and study inverse problems under uncertainty in energy system design applications. The method is particularly relevant to design tasks where only a deterministic forward model of a physical system is available, in which a target design quantity is an input to the model that cannot be obtained directly, but can be quantified reversely via the outputs of the model. In this scenario, the proposed method offers an analytical and direct approach to invert such system models. The method puts emphasis on user-friendliness, as it enables its users to conduct the inverse simulation under uncertainty directly in the gPC domain by redefining basic algebra operations for computations. Moreover, the method incorporates an optimization-based approach to integrate supplementary constraints on stochastic quantities. This feature enables the solution of inverse problems bounding the statistical moments of stochastic system variables. The authors exemplify the application of the proposed method with proof-of-concept tests in energy system design, specifically performing uncertainty quantification and sensitivity analysis for a Multi-Energy System (MES). The findings demonstrate the high accuracy of the method as well as clear advantages over conventional sampling-based methods when dealing with a small number of stochastic variables in a system or model. However, the case studies also highlight the current limitations of the proposed method such as slow execution speed due to the optimization-based approach and the challenges associated with, for example, the curse of dimensionality in gPC. [ABSTRACT FROM AUTHOR]

Currently, studies on Xinjiang desert water conveyance channels primarily focus on the selection of construction technologies, with the effects of bank slope reinforcement and the impact of internal and external factors on slope stability remaining unclear. This study utilizes finite element method to analyze the safety coefficient, overall displacement, and equivalent plastic zone of aeolian soil bank slopes before and after reinforcement during three distinct phases: the completion phase, the water transfer phase, and the water level plunge phase. Orthogonal tests were used to assess the trend and weighting of the influence of factors on bank slopes inside and outside the channel using the introduction of corrected range and Spearman correlation analysis. The findings indicate that the safety coefficient for the bank slopes of the desert channel both before and after reinforcement exceeds 1.30 during all phases. Post-reinforcement, the overall displacement and equivalent plastic zone are controlled compared to the pre-reinforced state. Both sensitivity analyses yielded that the factors affecting the weighting of bank slopes were, in descending order, aeolian soil cohesion > slope angle > angle of internal friction > slope height > unit-weight > thickness of concrete lining > height of the water level in the channel > rate of water level plunge; where aeolian soil cohesion, slope angle, angle of internal friction and thickness of concrete lining favoured the stability of the bank slopes, while others were unfavourable to the stability of the bank slopes. This study aims to provide a reference for the construction of desert water supply projects. [ABSTRACT FROM AUTHOR]

Background: Research findings indicate a putative indirect or latent association between phenylalanine (Phe) and Parkinson's disease (PD). In this study, we aimed to analyze the causal relationship between Phe and PD by two sample Mendelian randomization (MR) analysis. Methods: In this study, the PD-related dataset and Phe-related dataset were downloaded from Integrative Epidemiology U1nit (IEU) Open Genome-Wide Association Study (GWAS) database. Four algorithms (MR Egger, maximum likelihood, inverse variance weighting (IVW) and unweighted regression) were used to perform MR analysis. The sensitivity analysis (heterogeneity test, horizontal pleiotropy test and Leave-One-Out (LOO) analysis) was used to assess the reliability of MR analyses. Results: In the forward MR analysis, Phe was a safety factor for PD (p-value < 0.05 and odds ratios (OR) < 1). The results of reverse MR analysis showed that there was no causal relationship between PD and Phe (p-value > 0.05). In addition, sensitivity analysis showed that MR analysis was reliable. Conclusion: The results of this study revealed that Phe was a safety factor for PD, meaning that Phe reduced the risk of PD. [ABSTRACT FROM AUTHOR]

Topology optimization has become a popular tool for designing optimal shapes while meeting specific objectives and restrictions. However, the resulting shape from the optimization process may not be easy to manufacture using typical methods like machining and may require interpretation and validation. Additionally, the final shape depends on chosen parameters. In this study, we conduct a sensitivity analysis of the main parameters involved in 3D topology optimization—penalization and filter radius—focusing on the density-based method. We analyze the features and characteristics of the results, concluding that a machinable and low interpretable part is not an attainable result in by-default topology optimization. Therefore, we propose a new method for obtaining more manufacturable and easily interpretable parts. The main goal is to assist designers in choosing appropriate parameters and understanding what to consider when seeking optimized shapes, giving them a new plug-and-play tool for manufacturable designs. We chose the density-based topology optimization method due to its popularity in commercial packages, and the conclusions may directly influence designers' work. Finally, we verify the study results through different cases to ensure the validity of the conclusions. [ABSTRACT FROM AUTHOR]

Power converters are the basic elements of any power electronics system in many areas and applications. Among them, the push–pull converter topology is one of the most widespread due to its high efficiency, versatility, galvanic isolation, reduced number of switching devices and the possibility of implementing high conversion ratios with respect to non-isolated topologies. Optimal design and control requires very accurate models that consider all the non-idealities associated with the actual converter. However, this leads to the use of high-order models, which are impractical for the design of model-based controllers in real-time applications. To obtain a trade-off model that combines the criteria of simplicity and accuracy, it is appropriate to assess whether it is necessary to consider all non-idealities to accurately model the dynamic response of the converter. For this purpose, this paper proposes a methodology based on a sensitivity analysis that allows quantifying the impact of each non-ideality on the converter behaviour response as a function of the converter topology, power and frequency. As a result of the study, practical models that combine the trade-off between precision and simplicity are obtained. The behaviour of the simplified models for each topology was evaluated and validated by simulation against the most complete and accurate non-ideal model found in the literature. The results have been excellent, with an error rate of less than 5% in all cases. [ABSTRACT FROM AUTHOR]

Hydrological modeling plays a vital role in water-resource management and climate-change studies in hyper-arid regions. In the present investigation, surface runoff was estimated by a Soil and Water Assessment Tool (SWAT) model for Wadi Al-Aqul, Saudi Arabia. The Sequential Uncertainty Fitting version 2 (SUFI-2) technique in SWAT-CUP was adopted for the sensitivity analysis, calibration, and validation of the SWAT model's components. The observational runoff data were scarce and only available from 1979 to 1984; such data scarcity is a common problem in hyper-arid regions. The results show good agreement with the observed daily runoff, as indicated by a Pearson Correlation Coefficient (r) of 0.86, a regression (R2) of 0.76, and a Nash–Sutcliffe coefficient (NSE) of 0.61. Error metrics, including the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), were notably low at 0.05 and 0.58, respectively. In the daily validation, the model continued to perform well, with a correlation of 0.76 and regression of 0.58. As a new approach, fitted parameters of daily calibration were incorporated into the monthly simulation, and they demonstrated an even better performance. The correlation coefficient (regression) and Nash–Sutcliffe were found to be extremely high during the calibration period of the monthly simulation, reaching 0.97 (0.95) and 0.73, respectively; meanwhile, they reached 0.99 (0.98) and 0.63 in the validation period, respectively. The sensitivity analysis using the SUFI-2 algorithm highlighted that, in the streamflow estimation, the Curve Number (CN) was found to be the most responsive parameter, followed by Soil Bulk Density (SOL_BD). Notably, the monthly results showed a higher performance than the daily results, indicating the inherent capability of the model in regard to data aggregation and reducing the impact of random fluctuations. These findings highlight the applicability of the SWAT model in predicting runoff and its implication for climate-change studies in hyper-arid regions. [ABSTRACT FROM AUTHOR]

In response to escalating concerns about climate change, there is a growing imperative to prioritize the decarbonization of the telecom sector and effectively reduce its carbon emissions. This study presents a thorough techno-economic optimization framework for implementing renewable-dominated hybrid standalone systems for the base transceiver station (BTS) encapsulation telecom sector in Pakistan. It is noted that from the results obtained from 42 BTS sites overall, 21 BTS sites had a feasible combination of a photovoltaic battery system, having a diesel generator as a backup source with an average LCOE of 0.1246 USD/kWh to 0.2325 USD/kWh. Thus, seven BTS sites had an optimal combination of biomass, with photovoltaic and battery storage systems and with a varied LCOE of 0.1175 USD/kWh to 0.1318 USD/kWh. Moreover, due to the high flow of hydro water in the north region, five BTS sites presented an ideal configuration of a hydro system coupled with a photovoltaic, wind, and battery storage system, with a varied LCOE of 0.04547 USD/kWh to 0.07419 USD/kWh. Wind energy systems are dominant in the southern region; therefore, five BTS sites presented an ideal combination of a wind energy system coupled with a photovoltaic battery storage system, having DGs as backup sources for sustainability and with a varied LCOE of 0.1096 USD/kWh to 0.1294 USD/kWh. In addition, 02 BTSs had an optimal combination of photovoltaic systems coupled with hydro and wind systems, with diesel generators having a varied LCOE of 0.07618 USD/kWh to 0.04575 USD/kWh. The remaining 02 BTS sites had a feasible combination of wind–hydro-battery and diesel generator–photovoltaic–hydro-battery systems, with an LCOE of 0.7035 USD/kWh and 0.1073 USD/kWh, respectively. Finally, an environmental analysis based on carbon emissions, as well as sensitivity analyses based on different uncertainties, i.e., wind speed, solar irradiance, inflation rate, discount rate, and load demand, was performed to evaluate the behavior of the proposed systems. The optimization of these systems and comparative study findings indicate that the hybrid BTS system is the best option, better than conventional diesel-operated BTS systems in terms of cost-effectiveness, environmental friendliness, and sustainability. [ABSTRACT FROM AUTHOR]

The Pareto frontier, a concept rooted in economics and multi-objective optimization, represents the interplay between two objectives. In the context of power systems, it is often the case that different objectives have to be considered at the same time, such as the minimization of the operational cost and the minimization of greenhouse gas emissions. However, whether both objectives are achievable or not largely depends on the specific technoeconomic characteristics of the generation units involved. In this context, the current paper presents the Pareto frontier for different combinations of technoeconomic characteristics of generation units, and different types of functions for the operational cost and CO2 emissions, as well as various technologies, including Combined Heat and Power, heat-only and thermal power stations. The analysis reveals a range of shapes for the resulting Pareto frontier and underlines the critical patterns and dependencies within the energy system's operational framework, highlighting the complex interplay between environmental impact and economic feasibility. [ABSTRACT FROM AUTHOR]

The sediment transport, involving the movement of the bedload and suspended sediment in the basins, is a critical environmental concern that worsens water scarcity and leads to degradation of land and its ecosystems. Machine learning (ML) algorithms have emerged as powerful tools for predicting sediment yield. However, their use by decision-makers can be attributed to concerns regarding their consistency with the involved physical processes. In light of this issue, this study aims to develop a physics-informed ML approach for predicting sediment yield. To achieve this objective, Gaussian, Center, Regular, and Direct Copulas were employed to generate virtual combinations of physical of the sub-basins and hydrological datasets. These datasets were then utilized to train deep neural network (DNN), conventional neural network (CNN), Extra Tree, and XGBoost (XGB) models. The performance of these models was compared with the modified universal soil loss equation (MUSLE), which serves as a process-based model. The results demonstrated that the ML models outperformed the MUSLE model, exhibiting improvements in Nash–Sutcliffe efficiency (NSE) of approximately 10%, 18%, 32%, and 41% for the DNN, CNN, Extra Tree, and XGB models, respectively. Furthermore, through Sobol sensitivity and Shapley additive explanation–based interpretability analyses, it was revealed that the Extra Tree model displayed greater consistency with the physical processes underlying sediment transport as modeled by MUSLE. The proposed framework provides new insights into enhancing the accuracy and applicability of ML models in forecasting sediment yield while maintaining consistency with natural processes. Consequently, it can prove valuable in simulating process-related strategies aimed at mitigating sediment transport at watershed scales, such as the implementation of best management practices. [ABSTRACT FROM AUTHOR]

A laboratory-scale mesophilic submerged anaerobic hybrid membrane bioreactor (An-HMBR) was operated for 270 days for the treatment of high-strength synthetic wastewater at different hydraulic retention times (HRTs) (3 days, 2 days, 1 day, and 0.5 days). Chemical oxygen demand (COD) removal efficiency of 92% was obtained with methane yield rate of 0.18 LCH4/g CODremoval at 1-day HRT. The results of lab scale reactor at 1-day HRT were utilized for upscaling and cost analysis. Cost analysis revealed that the total capital cost comprised tank system (48%), membrane cost (32%), screen and PUF sponge (5% each), PLCs (4%), liquid pumps (3%), and others (2%). The operational cost comprised chemical cost (46%), pumping energy (42%), and sludge disposal (12%). The results revealed that the tank and heating costs accounted for the largest fraction of the total life cycle cost for full-scale An-HMBR. The heating cost can be compensated by gas recovery. Sensitivity analysis revealed that the interest rates, influent flow, and membrane flux were the most crucial parameters which affected the total cost of An-HMBR. [ABSTRACT FROM AUTHOR]

In this article, a modular model is proposed for the reliability modeling of phased mission systems (PMSs) with complicated system behaviors. By the modular method, the system is divided into several levels: system, subsystem, and components. At the component level, the shock effect, including self‐degradation, additional wear and damage caused by shocks, is considered and the component reliability is evaluated. Then, the reliability modeling method of the K/N system consisting of multiple K/N subsystems is proposed, by a mathematics reasoning method. The correctness of the proposed method is also verified by a Monte Carlo (MC) simulation procedure. At last, the modular method is applied at the system level, and the reliability of a practical engineering case, the attitude and orbit control system (AOCS), is evaluated for illustration. Meanwhile, the parameter sensitivity analysis is also carried out for implementation. [ABSTRACT FROM AUTHOR]

In recent years, some interested data can be recorded only if the values fall within an interval range, and the responses are often subject to censoring. Attempting to perform effective statistical analysis with censored, especially heavy-tailed and asymmetric data, can be difficult. In this paper, we develop a novel linear regression model based on the proposed skewed generalized t distribution for censored data. The likelihood-based inference and diagnostic analysis are established using the Expectation/Conditional Maximization Either algorithm in conjunction with smoothing approximate functions. We derive relevant measures to perform global influence for this novel model and develop local influence analysis based on the conditional expectation of the complete-data log-likelihood function. Some useful perturbation schemes are discussed. We illustrate the finite sample performance and the robustness of the proposed method by simulation studies. The proposed model is compared with other procedures based on a real dataset, and a sensitivity analysis is also conducted. [ABSTRACT FROM AUTHOR]

We present ReactionMechanismSimulator.jl (RMS), a modern differentiable software for the simulation and analysis of chemical kinetic mechanisms, including multiphase systems. RMS has already been applied to problems in combustion, pyrolysis, polymers, pharmaceuticals, catalysis, and electrocatalysis. RMS is written in Julia, making it easy to develop and allowing it to take advantage of Julia's extensive numerical computing ecosystem. In addition to its extensive library of optimized analytic Jacobians, RMS can generate and use Jacobians computed using automatic differentiation and symbolically generated analytic Jacobians. RMS is demonstrated to be faster than Cantera and Chemkin in several benchmarks. RMS also implements an extensive set of features for analyzing chemical mechanisms, including a library of easy‐to‐call plotting functions, molecular structure resolved flux diagram generation, crash analysis, traditional sensitivity analysis, transitory sensitivity analysis, and an automatic mechanism analysis toolkit. RMS implements efficient adjoint and parallel forward sensitivity analyses. We also demonstrate the ease of adding new features to RMS. [ABSTRACT FROM AUTHOR]

Potentially acting as a source or a sink for plastic pollution to the open ocean, nearshore waters remain a challenging context for predicting the transport and deposition of plastic debris. In this study, we present an advanced modelling approach based on the SWASH wave model and the TrackMPD (v3.0) particle transport model to investigate the transport dynamics of floating and sinking microplastics in wave-dominated environments. This approach introduces novel features such as coupling with advanced turbulence models, simulating resuspension and bedload processes, implementing advanced settling and rising velocity formulations, and enabling parallel computation. The wave laboratory experiments conducted by Forsberg et al. (2020) were simulated to validate the model's ability to reproduce the transport of diverse microplastics (varying in density, shape, and size) along a comprehensive beach profile, capturing the whole water column. Our results underscore the robustness of the proposed model, showing good agreement with experimental data. High-density microplastics moved onshore near the bed accumulating in proximity to the wave-breaking zone, while the distribution of low-density particles varied along the coastal profile depending on the particle properties. The study also sheds light on the primary mechanisms driving microplastic transport, such as Stokes drift, wave asymmetry and settling/rising velocities. Sensitivity analyses on calibration parameters further confirm the robustness of the model results and the influence of these factors on transport patterns. This research establishes the SWASH-TrackMPD approach as a valuable tool, opening avenues for future studies to contextualize laboratory findings within the complexities of real-world nearshore environments and further refine our comprehension of microplastic dynamics across different beaches and wave-climate conditions. [ABSTRACT FROM AUTHOR]

The model divides the population into five sub-populations of humans and three vectors. Boundedness, non-negativity, and continuous dependence of solutions on initial data prove their well-posedness. An explicit expression of the basic reproduction number and stability analysis of equilibria offer insights into disease mitigation and persistence. The Disease-Free Equilibrium is globally stable when the basic reproduction number is below 1, while the Endemic Equilibrium is globally stable when it is greater than 1. Sensitivity analysis identifies key mitigation parameters, and bifurcation analysis reveals the bifurcation parameter. Optimal control strategies are identified through the maximum principle, and numerical simulations exemplify the analytical results. [ABSTRACT FROM AUTHOR]

The modeling of engineering systems considering fractional approach configures a tendency as due to the generalization of traditional models that use integer order and to the number of applications in different science fields that can benefit from this approach. The present contribution aims to analyze the physical parameters of a near-field acoustic levitation system, as described by the Reynolds equation, and to evaluate the influence of fractional orders on the obtained profiles. For this purpose, both time and space fractional derivatives found in the fractional differential model are approximated using Caputo and Grünwald–Letnikov formulas, respectively. The discretized nonlinear algebraic system is solved considering the Newton method. The proposed methodology was able to solve the time–space fractional near-field acoustic levitation model for both integer and fractional orders. The performed sensitivity analyses demonstrated the influence of the model parameters and the fractional orders on the pressure fields and levitation force. Physically, for a squeeze film levitation system, the increase of the squeeze number is necessary for levitation purposes. In addition, the maximum achievable levitation capacity of a squeeze film levitation system is determined by the excitation amplitude. As expected, the levitation domain is reduced as the mass increases and the reduction rate of the integer model is higher than the one of the fractional order. In this case, for mass bigger than 1.629 Kg, the levitation domain is larger for the fractional order model. It is worth mentioning that the proposed methodology can be used to solve other dynamic systems modeled by time–space fractional differential equations. [ABSTRACT FROM AUTHOR]

Introduction: Social media has become a ubiquitous part of everyday life; however, evidence suggests patterns of social media use can affect sleep health in children and adolescents. This study aimed to examine the associations of intense and problematic social media use (SMU) with sleep‐onset difficulties in adolescence. Methods: We analysed data from 212,613 adolescents aged 11‐15 years (51.1% girls) from 40 European and North American countries that participated in the 2017/2018 Health Behaviour in School‐aged Children survey. Intense SMU assessed how often respondents had online contact through social media, and problematic SMU was assessed by symptoms of addiction to social media. Sleep‐onset difficulties were assessed using a self‐reported item. Multilevel mixed‐effects logistic regression was used to obtain the estimates. Results: Sleep‐onset difficulties were more common among girls than boys (27.1% vs 20.8%). Intense SMU was significantly associated with sleep‐onset difficulties in boys in 17 countries and in girls in 25 countries, while problematic SMU was significantly associated in most of the participating countries. Overall, exposure to problematic SMU alone was highly associated with sleep‐onset difficulties both in girls (OR 2.20, 2.04–2.38) and boys (OR 1.88, 1.73–2.04), while the association estimates for intense SMU were smaller and comparable across gender (Girls: OR 1.27, 1.23–1.31; Boys: OR 1.22, 1.18–1.27). Sensitivity analyses supported the above findings. Conclusions: Intense and/or problematic SMU were associated with sleep‐onset difficulties across gender with associations being higher for problematic compared to intense SMU. Prospective research with objective measures is needed to understand the causal mechanisms underlying these relationships. [ABSTRACT FROM AUTHOR]

In this paper, we put forth an analysis of sensitivity which aims to discern individual from merely statistical evidence. We argue that sensitivity is not to be understood as a factive concept, but as a purely epistemic one. Our resulting analysis of epistemic sensitivity gives rise to an account of legal proof on which a defendant is only found liable based on epistemically sensitive evidence. [ABSTRACT FROM AUTHOR]

This paper presents the results of a severe accident parametric sensitivity study performed on a model of a generic integral pressurized water reactor (iPWR). The analyzed sequence is a loss of coolant accident (LOCA)-type scenario, postulating that safety systems are not available. In this work, a MELCOR 2.2 input deck of a generic iPWR was developed based on publicly available data. The iPWR used in this work is a generic iPWR with a thermal power of about 160 MWth, characterized by a compact steam generator and a submerged containment configuration. A hypothetical scenario considered in this work was an unmitigated small break LOCA leading to a severe accident with partial core degradation, due to the postulated assumptions. In the presented paper, 16 sensitivity cases were calculated and analyzed, focusing mainly on heat transfer, decay heat, and core degradation parameters. The selected parameters and their combination caused partial core degradation and showed significant differences in investigated variables such as core degradation and hydrogen generation, as well as examined CPU time consumption. This a preparatory work performed in the framework of the Horizon Euratom SASPAM-SA project, which aims to investigate the applicability and transfer of the operating large light-water reactor knowledge and know-how to the iPWR, taking into account European licensing analysis needs for the severe accident and emergency planning zone. [ABSTRACT FROM AUTHOR]

Floating LiDAR systems (FLSs) may replace conventional offshore met masts, and they have been developed well in Europe. However, before using them in Japan, we must determine whether they demonstrate the same performance under the unique East-Asian meteorological and oceanographic conditions. Therefore, herein, we investigate the performance of FLSs by focusing on the differences among models. Four independent wind datasets from three FLSs were simultaneously verified against a reference met mast and vertical LiDAR at a Japanese site. The data availability was confirmed to vary from 62.7 to 98.0% over the period at 63 m. This was strongly affected by the system availability of the buoy and LiDAR, suggesting that buoy system robustness is key to better campaigns with higher data availability. The 10 min averaged wind speed and direction largely satisfied the Carbon Trust's key performance indicators, with a low sensitivity to wave conditions depending on the buoy shape. The standard deviation of the wind speed and turbulence intensity had poorer accuracy than that of the 10 min averaged statistics because of the wave-induced buoy motion, especially for small buoys. In short, this paper provides an overview of a measurement by FLS in Japan. Also, the unique verification with multiple units suggests the need for a low-motion buoy or motion compensation to improve the measurement accuracy of the turbulence component. [ABSTRACT FROM AUTHOR]

The quality of asphalt pavement (HMA) is often assessed by its dielectric permittivity, e.g., using ground-penetrating radar technology. The mostly employed relationships were developed for dry HMA pavement as follows: Complex Refractive Index Model (CRIM), Bottcher, Rayleigh, and Al-Qadi–Lahouar–Leng models (so-called ALL model), while the relationships for the estimation of wet HMA properties are empiric and not consistent with those for dry HMA mixtures. The article presents the results of the development and analysis of novel relationships between the values of dielectric permittivity and the mechanical characteristics of wet asphalt. It is shown that the new relationships are the general consistent cases of the expressions previously developed for dry HMA. The new expressions for the Rayleigh and ALL models are shown providing close estimates for the value of moisture content in the range of porosity and degree of water saturation: 0–0.12 and 0–1, respectively, and in the wide range of aggregate specific gravity (2.5–2.9), the binder content (0.04–0.08), the dielectric permittivity of aggregate (6–11), and the maximum specific gravity (2.5–2.9). Since the ALL model is an implicit function, in which the dielectric permittivity variable cannot be extracted, the direct problem (to calculate the dielectric constant value from density/specific gravity and porosity value) can be solved numerically while the inverse problem can be solved algebraically. The newly developed expression for the Rayleigh model (for wet asphalt) can be used for the estimation of dielectric permittivity based on expected values of porosity and density, and vice versa, and hence it is simpler for using. [ABSTRACT FROM AUTHOR]

Travel time reliability is known to be a critical issue in the contexts of both travellers' choices and decisions and freight transportation. The temporal variability of travel time is known as reliability and is affected by numerous factors. Traffic volume, incidents and inclement weather are among the most profound factors, and their effects have been the subject of many studies. The work reported in this article is unique due to the simultaneous implementation of a genetic algorithm (GA) with multiple machine learning (ML) methods. A GA can eliminate overfitting, which is a common problem in ML models. The numerical results showed that the performance of the K-nearest neighbours method was significantly enhanced when a GA was imposed on it. In terms of the stability ratio, a 12% decrease was observed; the mean squared errors for the training set and the testing set decreased, but the reductions were not significant. To further illustrate the advantages of GA implementation, the numbers of predictions with a mean absolute percentage error greater than 0.05 were compared and a notable reduction was found. Sensitivity analysis was carried out to determine how the planning time index responds to fluctuations of independent variables. [ABSTRACT FROM AUTHOR]

Machine learning (ML) methods are widely used in particulate matter prediction modelling, especially through use of air quality sensor data. Despite their advantages, these methods' black-box nature obscures the understanding of how a prediction has been made. Major issues with these types of models include the data quality and computational intensity. In this study, we employed feature selection methods using recursive feature elimination and global sensitivity analysis for a random-forest (RF)-based land-use regression model developed for the city of Berlin, Germany. Land-use-based predictors, including local climate zones, leaf area index, daily traffic volume, population density, building types, building heights, and street types were used to create a baseline RF model. Five additional models, three using recursive feature elimination method and two using a Sobol-based global sensitivity analysis (GSA), were implemented, and their performance was compared against that of the baseline RF model. The predictors that had a large effect on the prediction as determined using both the methods are discussed. Through feature elimination, the number of predictors were reduced from 220 in the baseline model to eight in the parsimonious models without sacrificing model performance. The model metrics were compared, which showed that the parsimonious_GSA-based model performs better than does the baseline model and reduces the mean absolute error (MAE) from 8.69 µg/m3 to 3.6 µg/m3 and the root mean squared error (RMSE) from 9.86 µg/m3 to 4.23 µg/m3 when applying the trained model to reference station data. The better performance of the GSA_parsimonious model is made possible by the curtailment of the uncertainties propagated through the model via the reduction of multicollinear and redundant predictors. The parsimonious model validated against reference stations was able to predict the PM2.5 concentrations with an MAE of less than 5 µg/m3 for 10 out of 12 locations. The GSA_parsimonious performed best in all model metrics and improved the R2 from 3% in the baseline model to 17%. However, the predictions exhibited a degree of uncertainty, making it unreliable for regional scale modelling. The GSA_parsimonious model can nevertheless be adapted to local scales to highlight the land-use parameters that are indicative of PM2.5 concentrations in Berlin. Overall, population density, leaf area index, and traffic volume are the major predictors of PM2.5, while building type and local climate zones are the less significant predictors. Feature selection based on sensitivity analysis has a large impact on the model performance. Optimising models through sensitivity analysis can enhance the interpretability of the model dynamics and potentially reduce computational costs and time when modelling is performed for larger areas. [ABSTRACT FROM AUTHOR]

Visual ranging technology holds great promise in various fields such as unmanned driving and robot navigation. However, complex dynamic environments pose significant challenges to its accuracy and robustness. Existing monocular visual ranging methods are susceptible to scale uncertainty, while binocular visual ranging is sensitive to changes in lighting and texture. To overcome the limitations of single visual ranging, this paper proposes a fusion method for monocular and binocular visual ranging based on an adaptive Unscented Kalman Filter (AUKF). The proposed method first utilizes a monocular camera to estimate the initial distance based on the pixel size, and then employs the triangulation principle with a binocular camera to obtain accurate depth. Building upon this foundation, a probabilistic fusion framework is constructed to dynamically fuse monocular and binocular ranging using the AUKF. The AUKF employs nonlinear recursive filtering to estimate the optimal distance and its uncertainty, and introduces an adaptive noise-adjustment mechanism to dynamically update the observation noise based on fusion residuals, thus suppressing outlier interference. Additionally, an adaptive fusion strategy based on depth hypothesis propagation is designed to autonomously adjust the noise prior of the AUKF by combining current environmental features and historical measurement information, further enhancing the algorithm's adaptability to complex scenes. To validate the effectiveness of the proposed method, comprehensive evaluations were conducted on large-scale public datasets such as KITTI and complex scene data collected in real-world scenarios. The quantitative results demonstrate that the fusion method significantly improves the overall accuracy and stability of visual ranging, reducing the average relative error within an 8 m range by 43.1% and 40.9% compared to monocular and binocular ranging, respectively. Compared to traditional methods, the proposed method significantly enhances ranging accuracy and exhibits stronger robustness against factors such as lighting changes and dynamic targets. The sensitivity analysis further confirmed the effectiveness of the AUKF framework and adaptive noise strategy. In summary, the proposed fusion method effectively combines the advantages of monocular and binocular vision, significantly expanding the application range of visual ranging technology in intelligent driving, robotics, and other fields while ensuring accuracy, robustness, and real-time performance. [ABSTRACT FROM AUTHOR]