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In traditional flood frequency analysis, a minimum of 30 observations is required to guarantee the accuracy of design results with an allowable uncertainty, however, there has not been a recommendation for the requirement on the length of data in NFFA (nonstationary flood frequency analysis). Therefore, this study has been carried out with three aims: (i) to evaluate the predictive capabilities of nonstationary (NS) and stationary (ST) models with varying flood record lengths; (ii) to examine the impacts of flood record lengths on the NS and ST design floods and associated uncertainties; and (iii) to recommend the probable requirements of flood record length in NFFA. To achieve these objectives, 20 stations with record length longer than 100 years in Norway were selected and investigated by using both GEV (generalized extreme value)-ST and GEV-NS models with linearly varying location parameter (denoted by GEV-NS0). The results indicate that the fitting quality and predictive capabilities of GEV-NS0 outperform those of GEV-ST models when record length is approximately larger than 60 years for most stations, and the stability of the GEV-ST and GEV-NS0 is improved as record lengths increase. Therefore, a minimum of 60 years of flood observations is recommended for NFFA for the selected basins in Norway.

Avian Influenza (AI) outbreaks are on an increasing trajectory. This disease carries a substantial economic burden, resulting in considerable losses to farmers with profound impacts on economies. As the outbreaks continue in birds and other unusual host species, further virus evolution and spillover to humans’ risk is anticipated to grow and potentially involve into new pandemics. Despite this, the underlying drivers of the outbreaks remain elusive. We develop machine learning models capable of predicting HPAI events in Europe dynamically uncovering the critical determinants of their onset. Temperature, water index, vegetation index, and poultry density play pivotal roles, with their importance coming into play at different times of the year. Temperature, water index, and vegetation index are important in the ecology of pathogen transmission as well as environmental ecological processes while water index determines how birds aggregate at different locations depending on the season of the year. Combining these drivers, the outbreak pattern is predicted with an accuracy of 94% for model two (M2). A true out of sample with the same model yielded 88% accuracy highlighting its predicting capability. These insights lay a robust foundation for elucidating the intricate landscape of AI outbreaks, offering valuable insights for proactive preventive interventions to mitigate spillover.

This study presents a 3D pick-rock contact calculation method for conical picks, aiming to develop a predictive method with high accuracy and lithological tolerance for peak cutting force (PCF). The method is based on the projection profile method and D. L. Sikarskie stress distribution function. By integrating Griffith’s theory with rock damage constitutive model, the energy relationship between the rock fracturing process and crack propagation process is analyzed. Furthermore, in order to accurately correct the PCF, the energy correction function ( C - K f ) is proposed to calculate the damage intensity index ( K e ), which accounts for the relationship between rock brittleness and rock damage elastic–plastic energy. To validate the method, it is compared with full-scale cutting tests and three existing models, and statistical analysis confirms its high lithological tolerance and accuracy, the present model has the highest R 2 of 0.90404, which is at least 12.5% higher relative to the mainstream models. Moreover, incorporating K e into the method further enhances its predictive capability.

Context AlphaFold’s advanced AI technology has transformed protein structure interpretation. By predicting three-dimensional protein structures from amino acid sequences, AlphaFold has solved the complex protein-folding problem, previously challenging for experimental methods due to numerous possible conformations. Since its inception, AlphaFold has introduced several versions, including AlphaFold2, AlphaFold DB, AlphaFold Multimer, Alpha Missense, and AlphaFold3, each further enhancing protein structure prediction. Remarkably, AlphaFold is recognized as the fastest Nobel Prize winner in science history. This technology has extensive applications, potentially transforming treatment and diagnosis in medical sciences by reducing drug design costs and time, while elucidating structural pathways of human body systems. Numerous studies have demonstrated how AlphaFold aids in understanding health conditions by providing critical information about protein mutations, abnormal protein–protein interactions, and changes in protein dynamics. Researchers have also developed new technologies and pipelines using different versions of AlphaFold to amplify its potential. However, addressing existing limitations is crucial to maximizing AlphaFold’s capacity to redefine medical research. This article reviews AlphaFold’s impact on five key aspects of medical sciences: protein mutation, protein–protein interaction, molecular dynamics, drug design, and immunotherapy. Methods This review examines the contributions of various AlphaFold versions AlphaFold2, AlphaFold DB, AlphaFold Multimer, Alpha Missense, and AlphaFold3 to protein structure prediction. The methods include an extensive analysis of computational techniques and software used in interpreting and predicting protein structures, emphasizing advances in AI technology and its applications in medical research. Graphical abstract

This paper investigates the thermal-pseudo mechanical (TPM) model’s residual stress prediction capability for its utility in developing friction stir processing (FSP). Specifically, two FSP tests under different processing conditions were conducted, and the corresponding simulations were carried out to verify if the TPM model can predict residual stresses for various tool radii and workpiece materials. The model successfully predicted residual stresses with an error less than 4% for one of the tests but failed to work for the other test. Further simulations under different FSP conditions proved that the TPM model works for cast aluminum alloys and wrought aluminum alloys. In addition, the large FSP tool used was found to be the reason for the model’s failure on one of the tests. This indicates that there is a range of tool radii for which the TPM model is applicable. As a solution, this paper suggests modifications to the TPM model based on calibration to the FSP test temperatures. The resulting residual stress prediction is accurate and differs from the experimentally characterized stress values by only 6.5 MPa. The calibrated TPM model requires FSP to be carried out when using a tool with a different radius. Following that, the effect on residual stresses due to changes in the other process parameters, such as the tool traverse & rotation speeds and the clamping conditions, can be predicted.

Background Transmembrane 9 superfamily member 1 (TM9SF1) is involved in inflammation. Since both inflammatory and autoimmune diseases are linked to immune cells regulation, this study investigated the association between TM9SF1 expression and autoimmune disease activity. As B cell differentiation and autoantibody production exacerbate autoimmune disease, the signaling pathways involved in these processes were explored. Methods Tm9sf1 −/− mouse rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) models were used to verify the relationship between gene expression and disease severity. Peripheral blood mononuclear cells (PBMCs) from 156 RA and 145 SLE patients were used to explore the relationship between TM9SF1 expression and disease activity. The effectiveness of TM9SF1 as a predictor of disease activity was assessed using multiple logistic regression and receiver operating characteristic (ROC) curves. The signaling pathways regulated by TM9SF1 in B cell maturation and antibody production were conducted by plasma cell induction experiment in vitro. Results The Tm9sf1 −/− RA and SLE model mice produced fewer autoantibodies and showed reduced disease severity relative to wild-type (WT) mice. TM9SF1 levels in PBMCs of patients were higher than those in healthy controls, and were reduced in patients with low disease activity relative to those with active RA and SLE. Furthermore, TM9SF1 levels were positively linked with autoantibody titers and pro-inflammatory cytokine levels in both diseases. ROC analyses indicated TM9SF1 outperformed several important clinical indicators in predicting disease activity (area under the curve (AUC) were 0.858 and 0.876 for RA and SLE, respectively). In vitro experiments demonstrated that Tm9sf1 knockout blocked differentiation of B cells into antibody-producing plasma cells by activating mTOR and inhibiting autophagy, and mTOR inhibitors such as rapamycin could reverse this effect. Conclusions The primary finding was the identification of the molecular mechanism underlying autophagy regulation in B cells, in which Tm 9sf1 knockout was found to modulate mTOR-dependent autophagy to block B cell differentiation into antibody-secreting plasma cells. It was also found that TM9SF1 expression level in PBMCs was an accurate indicator of disease activity in patients with RA and SLE, suggesting its clinical potential for monitoring disease activity in these patients.

The global crisis caused by the COVID-19 pandemic has taught us the importance of reflecting on the essential resources and capabilities that enable companies to react to disruptions. In this regard, studies have shown that social sustainability is a crucial resource for the operational performance of supply chains in emerging contexts. Although the literature has responded to the call for research on the social dimension of sustainability in emerging economies, most research has focused on emerging Asia, leaving a void in Latin America. Two socially focused frameworks are used to address the ontological challenge of defining sustainable human well-being around the firm. Amartya Sen’s capabilities approach and the theoretical extension of the Social-Resource-Based View (SRBV) are appropriate to address social sustainability under two essential aspects: (1) the firm as a generator of social performance and (2) social sustainability as a generator of firm performance. This paper aims to analyze the predictive capacity of Social Sustainability Orientation on social performance and supply chain operational performance in the context of emerging Latin America, with representative cases from Mexico, Colombia, and Chile. The methodology was empirical–statistical and based on a structured questionnaire applied to 217 purchasing managers of large multisector companies (Mx n = 64, Co n = 100, and Cl n = 53). Hypotheses were tested using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show the intrasample and extrasample predictive ability of Social Sustainability Orientation on social and Supply Chain Performance. It is concluded that socially sustainable culture and investment orientation is a valuable resource that provides the capability for Latin supply chain welfare and operational performance. A call is made to procurement and public policy managers to disseminate and care for the social aspects of sustainability as a resource that enhances business competitiveness and social justice in the Latin American region.

The purpose of this study is to design a predictive model for a daily quality assurance (QA) system that remains unaffected by specific patterns in correlated time series data. All data were sampled from the measured output factor at specific times over a 5-year period during the daily QA process for a 6 MV photon beam of the Varian linear accelerator (LINAC) system. Before constructing predictive structures, an autocorrelation function (ACF) analysis was conducted to verify the correlation of the given time series data. This study determined the optimal configuration for the autoregressive integrated moving average (ARIMA) and nonlinear autoregressive (NAR) neural network models for prediction. Additionally, it utilized correlated time series data to evaluate its impact on the predictive capability. We then compared the actual QA values to those predicted by the selected ARIMA and NAR models for the sampled daily output. Our findings suggest that while the ARIMA model offers a quick and relatively easy approach without requiring complex computational methods, the NAR model outperforms ARIMA, especially in the context of correlated time series data, demonstrating its real clinical utility as a prediction model. This result reveals that correlations are frequently observed in daily QA data. We concluded that these correlations can substantially influence the accuracy of machine behavior predicted based on historical observations. Consequently, analyzing specific patterns and correlated data is imperative for designing predictive structures.

Surface heat source (SHS) is a crucial factor affecting local weather systems. Particularly SHS on the Tibetan Plateau (TP) significantly influences East Asian atmospheric circulation and global climate. Accurate prediction of summer SHS on the TP is of urgent demand for economic development and local climate change. To evaluate the performance of SHS on the TP, the observed SHS data from the eleven sites on the TP verified against CRA40-land (CRA) is evidenced significantly better than ERA5-land (ERA5), another widely used reanalysis. The predictive capability of the CMA Climate Prediction System Model (CMA-CPS) for SHS on the TP was assessed using multiple scoring methods, including the anomaly correlation coefficient and temporal correlation coefficient, among others. Furthermore, relative variability and trend analysis were conducted. Finally, based on these assessments, the causes of the biases were preliminarily discussed. The CMA-CPS demonstrates a reasonable ability to predict the spatial distribution patterns of SHS, sensible heat (SH), and latent heat (LH) on the TP in summer. Specifically, the prediction results of SHS and LH exhibit an “east-high and west-low” distribution, while the distribution of the predicted SH is opposite. Nevertheless, the predicted values are generally lower than CRA, particularly in interannual variations and trends. Among the predictions, LH exhibits the highest temporal correlation coefficients, consistently above 0.6, followed by SHS, while SH predictions are less accurate. The spatial distribution and skill scores indicate that LH on the TP contributes more significantly to SHS than SH in summer. Furthermore, discrepancies in the predictions of surface temperature gradients, ground wind speed, and humidity on the TP may partly explain the biases in SHS and their components.

Uncertainty quantification (UQ) is critical for modeling complex dynamic systems, ensuring robustness and interpretability. This study extends Physics-Guided Bayesian Neural Networks (PG-BNNs) to enhance model robustness by integrating physical laws into Bayesian frameworks. Unlike Artificial Neural Networks (ANNs), which provide deterministic predictions, and Bayesian Neural Networks (BNNs), which handle uncertainty probabilistically but struggle with generalization under sparse and noisy data, PG-BNNs incorporate the laws of physics, such as governing equations and boundary conditions, to enforce physical consistency. This physics-guided approach improves generalization across different noise levels while reducing data dependency. The effectiveness of PG-BNNs is validated through a one-degree-of-freedom vibration system with multiple noise levels, serving as a representative case study to compare the performance of Monte Carlo (MC) dropout ANNs, BNNs, and PG-BNNs across interpolation and extrapolation domains. Model accuracy is assessed using Mean Squared Error (MSE), Mean Absolute Percentage Error (MAE), and Coefficient of Variation of Root Mean Square Error (CVRMSE), while UQ is evaluated through 95% Credible Intervals (CIs), Mean Prediction Interval Width (MPIW), the Quality of Confidence Intervals (QCI), and Coverage Width-based Criterion (CWC). Results demonstrate that PG-BNNs can achieve high accuracy and good adherence to physical laws simultaneously, compared to MC dropout ANNs and BNNs, which confirms the potential of PG-BNNs in engineering applications related to dynamic systems.