Variable Model Research Articles

Large vessel occlusion mediated fluid attenuated inversion recovery signal intensity ratio is associated with stroke within 4.5 h.

The primary objective was to investigate the value of the fluid attenuated inversion recovery (FLAIR) signal intensity ratio (SIR) in identifying stroke within 4.5 h. The secondary objective was to ascertain whether large vessel occlusion (LVO) mediated the relationship between the SIR and stroke within 4.5 h. We analyzed 633 acute stroke patients within 24 h of clear symptom onset. The SIR and DWI-FLAIR mismatch were evaluated. First, we determined whether demographic variables, vascular risk factors and LVO were related to stroke within 4.5 h with multivariate logistic regression analyses and stratified regression analysis. Next, we used mediation analysis to determine whether LVO explained the association between SIR and stroke within 4.5 h. Finally, we used receiver operating characteristic (ROC) analysis to assess the value of SIR, independent variable, and multiparameter models in identifying stroke within 4.5 h and compared with DWI-FLAIR mismatch. Hyperlipemia, LVO and SIR were associated with stroke within 4.5 h. Mediation analysis revealed that LVO partially mediated the relationship between SIR and stroke within 4.5 h (p < 0.001). The multiparameter model (hyperlipemia, LVO and SIR) showed significantly improved performance (AUC 0.869) in identifying stroke within 4.5 h over DWI-FLAIR mismatch (0.684), hyperlipemia (0.632), LVO (0.667) and SIR (0.773) models. SIR is associated with stroke within 4.5 h, and LVO partially mediates this relationship. A multiparameter model combining hyperlipemia, LVO and SIR can more accurately identify stroke within 4.5 h than individual parameter models.

Adaptive impedance control method for manipulator based on radial basis function

Purpose In view of the unknown environmental parameters and uncertain interference during gripping by the manipulator, it is difficult to obtain an effective gripping force with the traditional impedance control method. To avoid this dilemma, the purpose of this study is to propose an adaptive control strategy based on an adaptive neural network and a PID search optimization algorithm for unknown environments. Design/methodology/approach The method is based on a variable impedance model, and a new impedance model is established using a radial basis function (RBF) neural network to estimate unknown parameters of the impedance model. The approximation errors of the adaptive neural network and the uncertain disturbance are effectively suppressed by designing the adaptive rate. In the meantime, auxiliary variables are constructed for Lyapunov stability analysis and adaptive controller design, and PSA is used to ensure the stability of the adaptive impedance control system. Based on the Lyapunov stability criterion, the adaptive im-pedance control system is proved to have progressive tracking convergence property. Findings Through comparative simulations and experiments, the superiority of the proposed adaptive control strategy in position and force tracking has been verified. For objects with low flexibility and light-weight (such as a coke, a banana and a nectarine), this control method demonstrates errors of less than 10%. Originality/value This paper uses RBF neural networks to estimate unknown parameters of the impedance model in real-time, enhancing system adaptability. Neural network weights are updated online to suppress errors and disturbances. Auxiliary variables are designed for Lyapunov stability analysis. The PSA algorithm is used to adjust controller parameters in real-time. Additionally, comparative simulations and experi-ments are designed to analyze and validate the performance of controller.

Функциональная модель психологической службы в системе образования: направления деятельности и ее организационно-правовое регулирование

&lt;p&gt;The article presents the results of a monitoring study of the functional model of psychological service in the system of general education and secondary vocational education. The purpose of the monitoring is to study variable approaches to the implementation of psychological and pedagogical support in various regions of the Russian Federation. The study was conducted from March to April 2024 in 8 constituent entities of the Russian Federation, and involved educational psychologists (educational psychologists) from preschool, general education and professional educational organizations. The total sample size was 2288 people. The article discusses the analysis of the results obtained in accordance with the identified functional blocks: organizational and legal regulation of psychological and pedagogical support activities and areas of activity of psychological services in the system of general education and secondary vocational education. The study made it possible to comprehensively present the current state of the psychological service, its development resources and areas of improvement. This study will become the basis for further development of a draft functional model of psychological service in the system of general education and secondary vocational education, as well as variable models of its organization.&lt;/p&gt;

A-321 Rethinking the Role of Race in Prenatal AFP Screening for Open Neural Tube Defects

Abstract Background The quantification of alpha-fetoprotein (AFP) in maternal serum is commonly used in prenatal screening to estimate the risk of fetal open neural tube defects (ONTDs). This involves comparing a patient’s measured AFP concentration to the median AFP for women of similar gestational age and weight, reported in AFP multiples of the median (MoM). Current guidelines recommend establishing population medians separately grouped by patients’ race, based on historical reports documenting differences in median AFP concentrations across different populations. We aimed to reevaluate this practice of race based ONTD risk stratification given the limited and questionable historical evidence as well as the small observed differences in AFP concentrations in our practice and in a recent report. Methods We collected maternal serum AFP screening data for patients between 12/30/19 and 2/9/24. Informed by the statistical methods used for clinical risk stratification, we developed two log-linear multiple variable regression models for estimating AFP: gestational age (GA), weight, and interaction terms (Model 1) and Model 1 predictors, race, and interaction terms (Model 2). AFP MoMs were calculated by dividing the measured AFP concentration by the model-estimated AFP. Results We analyzed maternal ONTD screening for 7,702 patients with singleton pregnancies. The current clinical process uses race and ethnicity as documented in the electronic health record, which was categorized as Black (N = 2,360), White (N = 3,734), Hispanic (N = 209), Asian (N = 641), and Other (N = 758). The addition of race in Model 2 included statistically significant predictors (p &amp;lt; 0.001) for Black (β = 0.2), Asian (β = 0.05), and Black:weight interaction (β = -5.9 x 10-4). Between Model 1 and Model 2, the mean change in AFP MoMs was -0.05, +0.03, 0.0, -0.02, and +0.01 for Black, White, Hispanic, Asian, and Other patients, respectively. 95% of Black patients had a change &amp;lt; 0.12 in AFP MoM and &amp;gt; 99% of all other patients had a change &amp;lt; 0.1. Using the clinical interpretative threshold, 56 (0.7%) results were considered elevated in both models, 4 (0.05%) elevated in Model 1 only (3 Black, 1 Other), and 4 (0.05%) elevated in Model 2 only (3 White, 1 Hispanic). Chart review of the 8 discordant interpretations showed small changes in MoMs (mean absolute difference = 0.09) and no cases of ONTDs. Conclusions In our health system’s population, maternal race appears to have a small statistically significant association with maternal AFP concentration, even after adjustment for weight and maternal GA. However, preliminary analyses indicate that this difference has a small impact on the clinical interpretation of these studies, with most patients classified similarly when race is not accounted for in the calculation. Further investigation is needed to clarify whether this small impact on estimation of risk warrants the continued inclusion of race in NTD screening, as well as whether other clinical, social, or community factors available for inclusion in this clinical risk stratification model would be better predictors than the poorly defined and discriminatory concept of race.

Dynamic uncertainty evaluation of cylindricity error based on Bayesian deep neural network propagation method

Most of the existing methods for measuring and evaluating product geometric tolerances are for static processes, which use lower dimensions, are more complex to calculate, and have poor traceability and dynamic manageability and control of the measurement process. In this paper, a dynamic evaluation model of cylindricity error uncertainty based on the Bayesian deep neural network propagation method is proposed. Firstly, the input random variable sampling sample containing error uncertainty is generated by proposing the improved Monte Carlo sampling method (IMCS), which has the characteristics of homogeneity and validity; then the principle and construction process of a Bayesian neural network model are introduced to generate the propagation model of multidimensional random error variables; further, the Gaussian mixture model is trained by the Monte Carlo method, and the probability density function of the target response is generated by using the Gaussian mixture model. Through numerical experiments, test analysis of bearing outer rings, and comparison with ISO standard methods, the results show that the model based on the dynamic Bayesian deep neural network propagation method can correctly and effectively assess the uncertainty dynamically, and the whole estimation process is stable.

The JCSS probabilistic model Code, future developments

To assess and verify the reliability of structures, reliability based building codes allow for the application of full-probabilistic methods and semi-probabilistic methods (i.e. the partial factor method). In principle, both methods should be equivalent and lead to (approximately) the same reliability level. Therefore partial factors should be as much as possible determined based on a probabilistic background and calibration exercises. On the other hand, as the probabilistic design method may be considered as more rational and consistent than the partial factor design, there is a tendency to use probabilistic methods directly in the assessment of special of important new structures and also in the assessment of existing structures. In both the calibration exercise and in the full probabilistic assessment of structures, we face the problem that many assumptions have to be made. In particular in regard to the statistical modelling of random variables and in regard to accepted approximative methods of calculation. This often brings the engineer to a challenging position. In the past years the JCSS probabilistic model code (PMC) has served as an often-consulted operational code for this purpose. In the present paper, the JCSS PMC and its future developments are presented and discussed.

Investigation of essential parameters for the design of offshore wind turbine based on structural reliability

The probabilistic-based design method is gradually gaining attention in the wind industry because it provides more accurate modeling of uncertainty variables than that of traditional methods. Unfortunately, the numerous uncertainty variables involved in structural design are major obstacles to the successful application of this method. Therefore, this study presents a sensitivity analysis (SA) of a benchmark monopile offshore wind turbine (OWT) to screen the top-ranking variables from the viewpoint of reliability. Primarily, a comprehensive reliability SA framework of OWT is proposed, in which a novel measurement of soil uncertainties is conducted using quantitative analysis from the perspective of soil structure interaction (SSI). Subsequently, a reliability SA is conducted to explore the crucial variables influencing the structural safety from the uncertain clusters. The results indicate that Young's modulus, structural geometry, and SSI have significant effects on the structural reliability of excessive vibration failure. The hydrodynamic and aerodynamic load variables exhibit the most prominent influence on excessive deflection failure. Additionally, the SSI uncertainties exhibit a non-negligible effect in affecting the structural reliability, i.e., the lateral bending stiffness shows more sensitivity to the normal operation cases, whereas the impact of joint stiffness is more remarkable in parked scenarios.

Temporal Variations in and Predictive Values of ABG results prior to In-Hospital Cardiac Arrest

In-hospital cardiac arrest (IHCA) has been understudied relative to out-of-hospital cardiac arrest. Further, studies of IHCA have mainly focused on a limited number of pre-arrest patient characteristics (e.g., demographics, number and types of comorbidities). Arterial blood gas (ABG) analysis, one of the most common diagnostic tests for assessing and managing critically or acutely ill hospitalized patients, reflects pathophysiological changes associated with adverse events or complications, including IHCA. Yet the predictive and prognostic values of patterns of pre-arrest ABG parameters for IHCA have not been fully studied. The purpose of this retrospective pilot cohort study was to investigate temporal variations in and predictive values of pre-IHCA ABG values among patients with a history of cardiopulmonary disease. Eligible patients had a history of structural heart disease, heart failure, or pulmonary disease. Patients were excluded if their IHCA was due to trauma, drug overdose, hypothermia, drowning, chronic terminal illness such as cancer or human immunodeficiency virus, or bleeding not caused by hemorrhage in the brain or heart. Also collected were dates, times, and causes of mechanical intubation prior to IHCA and cause of mortality. Co-primary outcomes were initial rhythms of IHCA and return of spontaneous circulation (ROSC). We conducted a pilot study and the ABG results (pH, partial pressure of carbon dioxide [PCO2], partial pressure of oxygen [PaO2], bicarbonate [HCO−3], and lactate) from each of the 3 days prior to IHCA were extracted from the electronic health records of patients (N = 44) who had experienced IHCA at a single medical center. To characterize differences in ABG parameters among study days, coefficients of variation (CVs) were compared using the modified likelihood ratio test (MLRT) using the worst ABG values. Linear regression models were run for the continuous ABG parameters and logistic regression models for the dichotomous ABG variables. Overall model effect and least squares means, SDs, mean differences within and between days (with 95% confidence intervals), p-values and effect sizes are reported for continuous variables. For categorical variables, estimates and standard errors, 95% confidence intervals, Wald X2 variables and p-values are presented. The CVs for pH, PaCO2, and HCO3− differed significant between study days (p <.05). The least squares means with 95% confidence intervals for pH and lactate differed significantly in days (p <.01). Moderate to large effect sizes were obtained for all ABG parameters. Arterial lactate predicted initial rhythm (shockable versus non-shockable) and ROSC, while pH and HCO−3 predicted ROSC. Results demonstrate, for the first time, the presence of significant variability in ABG parameters across 72hr prior to IHCA and the predictive potential of these parameters for initial rhythms of IHCA and ROSC. While validation in a larger sample is necessary, this study confirms the feasibility and potential value of exploring temporal patterns of pre-arrest ABG values from the EHR. Findings of future larger studies on pre-arrest patterns of ABG parameters and other laboratory values may be used to design models that better predict risk for IHCA and guide patient care in the pre and intra-arrest periods.

Ambient air pollution and low birth weight in Brazil: A nationwide study of more than 10 million births between 2001 and 2018

Low birth weight (LBW) is a global health concern. While it is commonly associated with maternal health and behavior, exposure to ambient air pollution, can also play a role in contributing to LBW. In Brazil, where diverse environmental conditions and regional disparities exist, assessing the impact of ambient air pollution on LBW becomes particularly pertinent. To our knowledge, there is a gap in the existing literature, as no previous study has specifically investigated the relationship between ambient air pollution and LBW nationwide in Brazil. This study aims to fill this gap by examining the association between ambient air pollution and LBW in each trimester of pregnancy across the Brazilian states. In this work, birth data from January 1, 2001, to December 31, 2018 has been used. We utilized logistic regression models to estimate the odds ratio (OR) for low birth weight (LBW) associated with ambient air pollution (PM2.5, NO2, and O3) during each trimester of pregnancy (1st to 3rd trimester) across all 27 Brazilian states in our nationwide case-control study. We adjusted our model for several variables, including ambient temperature, relative humidity, and socioeconomic status (SES) variables at the individual level. We also conducted effect modification analyses by infant sex, mother's age, and the number of prenatal visits. Our study comprises over 10,213,144 birth records nationwide. Of these, 479,204 (4.92%) infants were included as cases of LBW. Our results indicate positive associations between PM2.5 and LBW, mainly in the Southern region. For example, in the state of Santa Catarina (South region), ORs were 1.003 (95% CI: 1.002, 1.004), 1.003 (95% CI: 1.002, 1.004), and 1.005 (95% CI: 1.003, 1.007) for the 1st, 2nd, and 3rd trimesters of exposure, respectively. NO2 had a robust association with LBW in the Northern and Northeastern states, including the state of Amapá (North region, where the Amazon Forest is located) with ORs of 1.377 (95% CI: 1.010, 1.878), 1.390 (95% CI: 1.020, 1.894), and 1.747 (95% CI: 1.297, 2.352) for the 1st, 2nd, and 3rd trimesters of exposure, respectively. Similarly, O3 had a robust association in the North and Midwest states, as observed in the state of Amapá with ORs of 1.033 (95% CI: 1.012, 1.054), and 1.033 (95% CI: 1.013, 1.053) for the 2nd, and 3rd trimesters, respectively. In the stratified analysis, boys were more vulnerable than girls, and the lower number of prenatal visits was associated with higher OR. Our findings are essential to the development of guidelines to prevent maternal exposure and protection of newborns in Brazil. This study provides valuable insights for region-specific strategies to improve maternal and neonatal health.

Sensitivity of Simulated Arctic Ocean Salinity and Strait Transport to Interannually Variable Hydrologic Model Based Runoff

AbstractAs the Arctic warms at an increased rate compared to the rest of the globe, freshwater runoff has been shown to be increasing into the Arctic Ocean. The effects of this contemporary increase in riverine freshwater into the Arctic Ocean are estimated from ocean model simulations, using two runoff data sets. One runoff data set is based on older climatological data, which has no inter‐annual variability after 2007 and as such does not represent the observed increases in river runoff into the Arctic. The other data set comes from a hydrological model developed for the Arctic drainage basin, which includes contemporary changes in the climate. At the Pan‐Arctic scale this new data set represents an approximately 11% increase in runoff, compared with the older climatological data. Comparing two ocean model runs forced with the different runoff data sets, overall changes in different freshwater markers across the basin were found to be between 5% and 10%, depending on the regions. The strongest increases were seen from the Siberian rivers, which in turn caused the strongest freshening in the Eastern Arctic. As the surface waters of the Arctic Ocean are sensitive to runoff, incorporating hydrological model data can help to better understand current changes and potential future impacts from increased runoff with climate change.

Vehicle-Following Control Based on Continuous Synthesis Variable Time Headway Model

Vehicle-Following Control Based on Continuous Synthesis Variable Time Headway Model

A vine-copulas based multi-sensor fusion structural damage monitoring method and its application in dam engineering

As a civil engineer that is susceptible to the mechanism of slow and continuous deterioration, the dam must detect damage according to the static data of the arranged sensors, which is an important issue in structural health monitoring. Due to the characteristics of huge solid structures, damage with a slight degree and small range usually does not cause significant changes in the monitoring data of a single sensor. Therefore, it is difficult to effectively detect local damage by traditional monitoring methods that establish operating warning values for a single sensor. Currently, there have been some approaches to fuse sensor information that consider correlations between multiple sensors as well as the overall operational behavior of the dam. However, their objectives are focused on improving the accuracy of response variable prediction models without addressing the local damage identification aspect. In this study, we apply the Vine-Copula model for the first time in a multi-sensor information fusion dam damage detection method based on static monitoring data. In this way, it improves the sensitivity of local damage identification for huge solid structures. A prediction model is fitted with the measuring data of each sensor to obtain the residual series. Then the joint probability distribution of the multidimensional residuals is acquired by constructing the Vine-Copula model. Finally, the joint cumulative distribution function value is used as an alarm limit to determine whether the structure has an abnormal damage condition. The effectiveness and accuracy of the proposed method were demonstrated in both a numerical arch dam simulating local damage and an actual rockfill dam engineering with cracks at the crest.

Joint estimation of SOH and RUL for lithium batteries based on variable frequency and model integration

The safe and stable operation of lithium-ion batteries in electric vehicles is crucial. Aiming at the problems of a large workload of online estimation and prediction and inability to weigh the whole life cycle of the battery in the joint estimation of SOH (State of Health) and RUL (Remaining Useful Life) for traditional lithium-ion batteries, this paper proposes an optimization scheme for the joint analysis of SOH-RUL. First, the article extracts suitable health features. It utilizes an integrated Extreme Learning Machine (ELM) to estimate the SOH of the battery and inputs the estimates into a Regression Vector Machine (RVM) model for RUL prediction. To better cope with the phenomenon of "sudden capacity change" in batteries, this paper divides the entire life cycle into the early and late stages. A lower estimation frequency and model integration are employed in the early stage.In contrast, the estimation frequency and model integration are increased later to balance speed and safety in total life cycle estimation. Finally, validation was performed on the NASA and CALCE datasets, comparing the effect of constant estimated frequency and model integration with that of varying estimated frequency and model integration at different aging stages. The RMSE of SOH estimation error for both datasets is within 2% during the early and late stages, and the SOH estimation error for the entire lifecycle is within 2%. The absolute RUL prediction error for the NASA dataset is below five times; for the CALCE dataset, it is below 20 times in most cases.

The effect of one-room CT guided brachytherapy on procedure time and cost in the treatment of cervical cancer

IntroductionBrachytherapy is associated with improved overall survival in cervical cancer patients, but the utilization seems hindered by high costs and relatively low reimbursement, particularly in the US. A one-room brachytherapy suite with CT (ORBT) could optimize the treatment workflow. By eliminating transport and waiting times, limiting applicator movement, and providing real-time applicator placement feedback, treatment time and costs could potentially be reduced. This study assesses the potential value of implementing ORBT in cervical cancer treatment. MethodsA variable cost model was developed to compare current (multi-room) brachytherapy workflows (MBRT) to ORBT, taking into account staff utilization, staff, equipment and consumables costs and room expenses. Two current care scenarios were simulated; applicator placement performed in the operating room (S1), and applicator placement performed in a brachytherapy suite (S2). For both scenarios literature reported fraction times of MBRT were compared to a range of ORBT times. Sensitivity analyses were performed to determine the influence of input parameters. ResultsIn scenario one, the results showed yearly savings of $45,572 up to $339,439 (USD), assuming a 5% and 20% reduction in fraction duration, respectively, in ORBT compared to MRBT. In scenario two, ORBT does not result in costs savings at 5% to 15% improvement. Therefore, only when ORBT results in a >20% improvement of fraction time, cost will be saved. DiscussionThe results indicate that reducing procedure time (using ORBT) can lead to cost savings, depending on the current workflow. Savings appear to depend mostly on applicator placement location, number of patients per year, and involved personnel.

Physics-informed neural networks coupled with flamelet/progress variable model for solving combustion physics considering detailed reaction mechanism

In recent years, physics-informed neural networks (PINNs) have shown potential as a method for solving combustion physics. However, current efforts using PINNs for the direct predictions of multi-dimensional flames only use global reaction mechanisms. Considering detailed chemistry is crucial for understanding detailed combustion physics, and how to accurately and efficiently consider detailed mechanisms under the framework of PINNs has not been explored yet and is still an open question. To this end, this paper proposes a PINN/flamelet/progress variable (FPV) approach to accurately and efficiently solve combustion physics, considering detailed chemistry. Specifically, the combustion thermophysical properties are tabulated using several control variables, with the FPV model considering detailed chemistry. Then, PINNs are used to solve the governing equations of continuity, momentum, and control variables with the thermophysical properties extracted from the FPV library. The performance of the proposed PINN/FPV approach is assessed for diffusion flames in a two-dimensional laminar mixing layer by comparing it with the computational fluid dynamics (CFD) results. It has been found that the PINN/FPV model can accurately reproduce the flow and combustion fields, regardless of the presence or absence of observation points. The quantitative statistics demonstrated that the mean relative error was less than 10%, and R2 values were all higher than 0.94. The applicability and stability of this model were further verified on other unseen cases with variable parameters. This study provides an efficient and accurate method to consider detailed reaction mechanisms in solving combustion physics using PINNs.

A novel variable selection algorithm based on neural network for near-infrared spectral modeling

BackgroundPartial least squares (PLS) is a widely used technique for modeling spectral data. Researchers have developed numerous PLS-based variable selection algorithms to enhance model predictive ability and interpretability. In recent years, as neural network technology has advanced, these algorithms have been increasingly applied to spectral data modeling. However, current research on neural network modeling tends to prioritize network structure over variable selection. ResultsOur study introduces a neural network-based variable selection algorithm called VSNN (Variable Selection based on Neural Network). By iteratively eliminating unimportant variables using an exponentially decreasing function (EDF), the algorithm achieves the selection of variables in spectral data. VSNN can easily integrate different types of neural networks. In this study, we analyzed the impact of neural network types, activation functions, and variable importance vectors on algorithm performance. We tested the algorithm on four datasets: corn moisture, corn oil, tablets, and meat. The results indicate that VSNN significantly enhances the predictive ability of the model compared to partial least squares (PLS), neural networks (NN), and Joint Mutual Information Maximisation (JMIM). Specifically, non-linear activation functions markedly improve performance on non-linear meat datasets. Compared to PLS, the Root Mean Square Error of Prediction (RMSEP) values for the four datasets—corn moisture, corn oil, tablets, and meat—decreased from 0.0409, 0.0728, 3.97, and 3.2 to 0.002, 0.0236, 3.12, and 0.36, respectively, after applying the VSNN variable selection algorithm. SignificanceVSNN can serve as a versatile framework to enhance variable selection, modeling, and predictive performance by adapting neural network types and variable importance evaluation indicators. As machine learning technology advances, the strength of VSNN is poised to increase. This study highlights the potential of VSNN as an effective algorithmic framework for variable selection in spectroscopy applications.

Short-Term Photovoltaic Power Probabilistic Forecasting Based on Temporal Decomposition and Vine Copula

With the large-scale development of solar power generation, highly uncertain photovoltaic (PV) power output has an increasing impact on distribution networks. PV power generation has complex correlations with various weather factors, while the time series embodies multiple temporal characteristics. To more accurately quantify the uncertainty of PV power generation, this paper proposes a short-term PV power probabilistic forecasting method based on the combination of decomposition prediction and multidimensional variable dependency modeling. First, a seasonal and trend decomposition using a Loess (STL)-based PV time series feature decomposition model is constructed to obtain periodic, trend, and residual components representing different characteristics. For different components, this paper develops a periodic component prediction model based on TimeMixer for multi-scale temporal feature mixing, a long short-term memory (LSTM)-based trend component extraction and prediction model, and a multidimensional PV residual probability density prediction model optimized by Vine Copula optimized with Q-Learning. These components’ results form a short-term PV probabilistic forecasting method that considers both temporal features and multidimensional variable correlations. Experimentation with data from the Desert Knowledge Australia Solar Center (DKASC) demonstrates that the proposed method reduced root mean square error (RMSE) and mean absolute percentage error (MAPE) by at least 14.8% and 22%, respectively, compared to recent benchmark models. In probability interval prediction, while improving accuracy by 4% at a 95% confidence interval, the interval width decreased by 19%. The results show that the proposed approach has stronger adaptability and higher accuracy, which can provide more valuable references for power grid planning and decision support.

Exploring the association between knee osteoarthritis outpatient visits and Asian dust storms: a time-series analysis

Osteoarthritis (OA) is one of the most prevalent musculoskeletal diseases in Taiwan, posing a significant public health challenge. In recent years, outdoor air pollution has become an increasingly critical global health issue. Asian Dust Storms (ADS) are known to exacerbate various health conditions due to elevated levels of particulate matter and other pollutants. However, the relationship between ADS and knee OA remains insufficiently explored. This study investigates the association between ADS occurrences and knee OA outpatient visits from January 2006 to December 2012, aiming to understand the potential health impacts of dust storms on OA patients. Using data from the National Health Insurance Research Database (NHIRD), the Taiwan Environmental Protection Agency (TEPA), and the Taiwan Central Weather Bureau, we conducted a time-series analysis employing the autoregressive moving average with exogenous variables (ARMAX) model. This approach accounted for daily outpatient visits related to knee OA, ADS events, and various environmental and meteorological factors. The results revealed a significant increase in knee OA outpatient visits on days immediately following ADS events, with peaks observed one to two days after the event. This increase was most pronounced among females, individuals aged 61 and above, and residents in the western regions. The study demonstrates an association between ADS and increased knee OA outpatient visits, highlighting the need for public health strategies to mitigate the health impacts of dust storms.

Investigating the shared genetic links between hypothyroidism and psychiatric disorders: a large-scale genomewide cross-trait analysis

BackgroundAssociations between thyroid diseases and psychiatric disorders have been mainly described before. However, the genetic mechanism behind hypothyroidism and psychiatric disorders remains unexplained. MethodsWe examined the genetic architecture of hypothyroidism and 8 psychiatric disorders. Firstly, the global and local genetic relationship between the paired traits was explored. Secondly, cross-trait analysis was performed to investigate the genomic loci and genes between psychiatric disorders and hypothyroidism. Thirdly, the significant expression of these genes and the causal relationships were investigated. Lastly, enrichment analysis was conducted on these genes to explore their biological mechanisms. ResultsWe observed significant positive genetic correlations between psychiatric disorders and hypothyroidism. The cross-trait meta-analysis identified 62 shared genetic loci between hypothyroidism and psychiatric disorders. The colocalization analysis additionally revealed 15 potential pleiotropic loci with a posterior probabilities.H4 (PP·H4) value >0.7. We also found 2308 genes shared between both traits, which were highly enriched in biological pathways such as immune cell differentiation and autoimmune diseases, as well as in tissue structures like the frontal cortex and cerebral cortex. Especially, many pleiotropic genes were significantly expressed for multiple pairwise traits, such as BCL11B, RERE, and SUOX. Lastly, the Latent causal variable model (LCV) analysis did not find any causal components in the genetic structure between them. LimitationsThe limitations of this study include that the conclusions were drawn from a European population. ConclusionsThese findings not only deepens our understanding of their biological mechanisms but also has significant implications for the intervention and treatment of these diseases.

The profitability implications of supplier concentration during economic recession and restoration: the moderating role of supply localization

Economic recessions and restoration change the dynamics of demand and supply in the supply chain. How to match supplier relationships with the dynamic changes to sharp profitability performance is pivotal for firms’ supply chain management during economic waves. Based on the Resource Dependence Theory, changes in demand-side and supply-side resources alter the power-balanced relationship between a firm and its concentrated supplier, affecting the implications of supplier concentration on a firm’s profitability performance in the economic recessions and restoration stages. Furthermore, we consider the moderating role of suppliers’ spatial location, exploring whether a higher proportion of intra-provincial suppliers (i.e., supply localization) helps improve the profitability implications of supplier concentration. Based on the sample of 3319 listed companies in China from 2020 to 2021, we find that: (1) in the economic recession stage, supplier concentration is positively correlated with firm profitability, and this positive relationship is amplified by a higher proportion of intra-provincial supplier transactions; (2) in the economic restoration stage, supplier concentration is negatively correlated with firm profitability, and the higher proportion of intra-provincial supplier transactions aggravates this negative relationship. The 2SLS method is applied to verify the main effects, and we used alternative variable measurements and different model settings to verify the robustness of the findings. The results provide a deeper reference for firms to consider appropriate supplier relationships in response to economic waves.