Prediction Accuracy Research Articles

Steady-State Temperature Prediction for Cluster-Laid Tunnel Cables Based on Self-Modeling in Natural Convection

Accurate temperature prediction of the operating tunnel cable is crucial for its safe and efficient function. To achieve a rapid and accurate prediction of the steady-state temperature of the tunnel cable, the self-modeling pattern in natural convection on the cable surface in the rectangular tunnel is investigated, and the self-modeling method for the convective heat transfer coefficient calculation is proposed. A thermal circuit model for single cables is further established to predict the cable core temperature, and the model is extended to predict the cluster-laid cable core temperature based on the combined method. The results show that when the tunnel size is neglected, the maximum relative deviation of the convective heat transfer coefficient between the self-modeling method and the finite element simulation is only 1.78% in the studied cases, indicating that the natural convection on the cable surface approximately satisfies the self-modeling method. Additionally, applying the self-modeling method to the thermal circuit can accurately predict the temperature of the single cable core. Furthermore, for the three-phase four-circuit cable, the maximum deviation between the temperature prediction results and the finite element results is within 2 K in the studied cases, which verifies the predictive accuracy of the combined method for the cluster-laid tunnel cable.

Utilizing machine learning and molecular dynamics for enhanced drug delivery in nanoparticle systems

Materials data science and machine learning (ML) are pivotal in advancing cancer treatment strategies beyond traditional methods like chemotherapy. Nanotherapeutics, which merge nanotechnology with targeted drug delivery, exemplify this advancement by offering improved precision and reduced side effects in cancer therapy. The development of these nanotherapeutic agents depends critically on understanding nanoparticle (NP) properties and their biological interactions, often analyzed through molecular dynamics (MD) simulations. This study enhances these analyses by integrating ML with MD simulations, significantly improving both prediction accuracy and computational efficiency. We introduce a comprehensive three-stage methodology for predicting the solvent-accessible surface area (SASA) of NPs, which is crucial for their therapeutic efficacy. The process involves training an ML model to forecast the many-body tensor representation (MBTR) for future time steps, applying data augmentation to increase dataset realism, and refining the SASA predictor with both augmented and original data. Results demonstrate that our methodology can predict SASA values 299 time steps ahead with a 40-fold speed improvement and a 25% accuracy increase over existing methods. Importantly, it provides a 300-fold increase in computational speed compared to traditional simulation techniques, offering substantial cost and time savings for nanotherapeutic research and development.

A method for compensating prediction errors to improve the orbit prediction accuracy of Starlink satellites

A method for compensating prediction errors to improve the orbit prediction accuracy of Starlink satellites

A Comparative Study on the Monitoring of Abnormal Power Consumption Behavior by Different Models

This paper investigates and compares the performance of three distinct models for monitoring abnormal electricity consumption behavior: Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and a novel model that incorporates an attention mechanism known as PSO-ATT-LSTM (PAL), which integrates a Particle Swarm Optimization (PSO) algorithm with an attention mechanism into the LSTM framework. The PAL model is specifically designed to enhance forecasting accuracy by optimizing the network parameters through PSO and focusing on significant temporal features via the attention mechanism. This design allows PAL to outperform the other two models in predicting future electricity consumption, particularly in identifying anomalous patterns. The study utilizes a dataset with hourly electricity consumption values and evaluates the models using metrics such as Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). The experimental results demonstrate the superiority of the PAL model in terms of predictive accuracy and its ability to capture abnormal consumption behaviors more effectively than SVR and LSTM.

Utilization finite element and machine learning methods to investigation the axial compressive behavior of elliptical FRP-confined concrete columns

Nowadays, elliptical sections are among the geometric shapes that are becoming more and more popular in the architecture world. Finite element analysis (FEA) software, such as ABAQUS, is used to simulate elliptical concrete columns confined with fiber reinforced polymer (FRP) jackets based on experimental data published in the literature. The validity of the finite element modeling (FEM) approach was confirmed by comparing it to experimental data obtained from 45 specimens in existing studies, demonstrating a favorable agreement. Additionally, a parametric study was also carried out, which included simulating 40 more specimens, resulting in a total of 85 specimens for analysis. The effect of various test variables such as sectional aspect ratio, the amount of FRP layers, and concrete strength on the elliptical column’s behavior is investigated. The axial compressive strength is predicted using current models from the literature. The outcomes revealed that the higher unconfined concrete strength decreased FRP confinement efficacy. Insufficient confinement with post-peak softening is more likely in FRP-confined high strength concrete columns, especially if the confinement was not stiff enough. Also, by adding more FRP layers, the stress and strain capabilities of elliptical concrete columns confined with FRP composites are improved. Physical experiments require a significant investment of time and resources, but they can produce valuable results. Finite element analysis, on the other hand, depends heavily on the modeler's competence and can minimize the number of experiments required by employing computer simulation, but it also requires high computer settings. In recent years, there has been a major advancement in the usage of machine learning (ML) algorithms in the applications of FRP composites with different concrete components. Taking this into consideration, this investigation is extended to estimate the compressive strength of elliptical FRP-confined columns using four tree-based ML algorithms including Decision Tree, Random Forest, Gradient Boosting and XGBoost. The XGBoost model achieved the highest predictive accuracy with the R2 values of 0.95 for both the compressive strength and axial strain targets.

An Intraoperative Ultrasound Evaluation of Axillary Lymph Nodes: Cassandra Predictive Models in Patients with Breast Cancer—A Multicentric Study

Background and Objectives: Axillary lymph node (ALN) staging is crucial for the management of invasive breast cancer (BC). Although various radiological investigations are available, ultrasound (US) is the preferred tool for evaluating ALNs. Despite its immediacy, widespread use, and good predictive value, US is limited by intra- and inter-operator variability. This study aims to evaluate US and Elastosonography Shear Wave (SW-ES) parameters for ALN staging to develop a predictive model, named the Cassandra score (CS), to improve the interpretation of findings and standardize staging. Materials and Methods: Sixty-three women diagnosed with BC and treated at two Italian hospitals were enrolled in the study. A total of 529 lymph nodes were surgically removed, underwent intraoperative US examination, and were individually sent for a final histological analysis. The study aimed to establish a direct correlation between eight US-SWES features (margins, vascularity, roundness index (RI), loss of hilum fat, cortical thickness, shear-wave elastography hardness (SWEH), peripheral infiltration (PI), and hypoechoic appearance) and the histological outcome (benign vs. malignant). Results: Several statistical models were compared. PI was strongly correlated with malignant ALNs. An ROC analysis for Model A revealed an impressive AUC of 0.978 (S.E. = 0.007, p < 0.001), while in Model B, the cut-offs of SWEH and RI were modified to minimize the risk of false negatives (AUC of 0.973, S.E. = 0.009, p < 0.001). Model C used the same cut-offs as Model B, but excluded SWEH from the formula, to make the Cassandra model usable even if the US machine does not have SW-ES capability (AUC of 0.940, S.E. = 0.015, p < 0.001). A two-tiered model was finally set up, leveraging the strong predictive capabilities of SWEH and RI. In the first tier, only SWES and RI were evaluated: a positive result was predicted if both hardness and roundness were present (SWES > 137 kPa and RI < 1.55), and conversely, a negative result was predicted if both were absent (SWES < 137 kPa and RI > 1.55). In the second tier, if there was a mix of the results (SWES > 137 kPa and RI > 1.55 or SWES < 137 kPa and RI < 1.55), the algorithm in Model B was applied. The model demonstrated an overall prediction accuracy of 90.2% in the training set, 87.5% in the validation set, and 88.9% across the entire dataset. The NPV was notably high at 99.2% in the validation set. This model was named the Cassandra score (CS) and is proposed for the clinical management of BC patients. Conclusion: CS is a simple, non-invasive, fast, and reliable method that showed a PPV of 99.1% in the malignancy prediction of ALNs, potentially being also well suited for young sonographers.

PR-FCNN: a data-driven hybrid approach for predicting PM2.5 concentration

The atmosphere’s fine articulate Matter (PM2.5) poses various health-related risks. Even though multiple efforts have been made to lower the emissions of these substances, the mortality rate is continuously increasing, requiring immediate inclination of the scientific community towards the design and development of advanced predictive models. Conventional statistical approaches have become dormant due to their limitations in capturing the innate relationships between the pollutants, particularly for predicting PM2.5 concentrations. In contrast, machine and deep learning techniques have shown great potential for forecasting air quality, providing more accuracy than its predecessor techniques. The present study investigates the utilization of hybrid approaches by integrating machine learning models with deep learning models to improve the prediction capabilities of PM2.5 concentration. It uses datasets from the World Air Quality Index (WAQI) and the State of Global Air (SOGA) to analyze the performance of the models on both the daily and annual data, respectively. This ensures the model’s effectiveness on a diversified dataset. The present study implements Random Forest (RF), Polynomial Regression (PR), XGBoost, and Extra Tree Regressor (ETR) coupled with Fully Connected Neural Network (FCNN), Long Short-Term Memory (LSTM), and Bi-directional LSTM (Bi-LSTM) for obtaining optimized results. Finally, after a thorough investigation, the hybrid PR model coupled with FCNN (PR-FCNN) is found to be the best model with improved R-squared (R2) values, portraying its potential for predicting PM2.5 concentration accurately. Based on the experimentation, the preset study recommends implementing hybrid approaches, offering better predictive accuracy in forecasting air pollutants, especially PM2.5.

Deep learning-based ground motion inversion through recursive structural acceleration response using DRA-LSTM Net

Known ground motion time history is crucial for the precise numerical analysis of structural response and evaluation of its safety. This study introduces the Deep Recursive Attentive Long Short-Term Memory Network (DRA-LSTM Net), a framework designed for the inversion of ground motion (GM) from recorded structural response (SR) during an earthquake. By integrating an attention mechanism in network along with a strategic recursive approach for dataset management, the DRA-LSTM Net achieves high precision in GM inversion. The method incorporates a novel pre-processed matrix format to capture unique recursive behaviour patterns in GM data, enhancing the model’s training, validation efficiency, and prediction accuracy on unseen test datasets. Case studies on single-degree-of-freedom (SDOF) and multi-degree-of-freedom (MDOF) structures highlight the method’s potential for real-time and high-precision GM inversion. Additionally, transfer learning was implemented to fine-tune the pre-trained model with real-world sensor data and structural responses from different floors, demonstrating the model’s adaptability and ability to retain high prediction accuracy across diverse datasets. This approach is particularly useful for regions with limited seismic instrumentation but equipped with structural response sensors.

The utilization of cytology for intraoperative diagnosis of primary central nervous system lymphoma

To investigate the diagnostic value of intraoperative cytology and rapid immunocytochemistry in primary central nervous system lymphoma. 254 cases of lymphoma and 82 cases of non-lymphoma were collected from 2010 to 2023. Frozen section(FS) was using alone in 44 cases during 2010–2014, FS and intraoperative cytology(IC) were using in 251 cases during 2015 to 2022. Rapid immunocytochemical(RICC, CD20, GFAP) were using with FS + IC in 41 cases during 2021 to 2023. Method One: According to the results of archives, statistic the diagnostic accuracy of lymphoma during three time periods. Method Two: All cases were randomly renumbered, 4 neuropathologists compared the accuracy of independent histology and that of combining cytology. The archives showed the diagnostic accuracy of FS in PCNSL was 77.27%, FS + IC was 86.06%, FS + IC + RICC was 92.68%. The retrospective study demonstrated the diagnostic accuracy of FS was 79.76%, FS + IC was 87.33% and FS + IC + RICC was 92.68%. The positive predictive value, negative predictive value, sensitivity, specificity and accuracy of CD20 were 100%, 76.92%, 90.32%, 100% and 92.68%, respectively. The results of the paired χ2 test was no statistically significant difference (0.05 < P < 0.1) between FS + IC + RICC and immunohistochemical (IHC) diagnosis of paraffin sections. The integration of IC + RICC + FS diagnosis can significantly enhance the intraoperative diagnostic accuracy of PCNSL and rectify potential errors that may occurred when relying solely on FS diagnosis.

Conductivity Prediction Method of Solid Electrolyte Materials Based on Pearson Coefficient Method and Ensemble Learning

In order to screen solid electrolytic materials with high ionic conductivity more quickly and accurately, a conductivity prediction method for solid electrolyte materials based on logistic regression model and random forest regression model is constructed. The method first selects 20 characteristic descriptors related to ionic conductivity from material library according to four dimensions: structural stability, metal stability, electronic conductivity and oxidative decomposition stability. The enumeration method and feature selection method are used to combine different features. Then, the dimension reduction of the feature combinations is carried out by Pearson coefficient method, so as to screen the optimal feature combinations. Finally, according to the optimal feature combination and data subset, the constructed random forest ensemble learning model is utilized to predict the ionic conductivity of other solid-state electrolyte materials in the material library, in order to find the solid-state electrolyte materials that can meet the high conductivity. The results indicate that as the number of features in the combination increases, the prediction performance of the model shows a trend of first increasing and then decreasing. When the feature descriptor in the combination is 7, the maximum AUC (Accuracy) value is reached, and the optimal feature in this case contains 7 optimal feature subsets corresponding to feature descriptors, namely the standard deviation of the average adjacency number of Li atoms, the standard deviation of Li-X ion bonds, the average electronegativity of the straight path, the average width of the straight path, the average atomic volume, the average Li-Li bond, and the filling fraction of the sublattice. On the basis of the optimal feature screening, the average prediction accuracy of the logistic regression model is 87.13%, which has a high accuracy. Using the random forest regression model for prediction, the average absolute error and root mean square error obtained are only 0.237 and 0.134, and compared with classical classification methods such as KNN, SVM and Adaboost, they are smaller, which can better predict the ionic conductivity of solid electrolyte materials. This proves that the constructed method can quickly and accurately produce high ionic conductivity materials in solid electrolyte materials, which is worthy of further research and promotion.

Machine Learning Framework for Conotoxin Class and Molecular Target Prediction

Conotoxins are small and highly potent neurotoxic peptides derived from the venom of marine cone snails which have captured the interest of the scientific community due to their pharmacological potential. These toxins display significant sequence and structure diversity, which results in a wide range of specificities for several different ion channels and receptors. Despite the recognized importance of these compounds, our ability to determine their binding targets and toxicities remains a significant challenge. Predicting the target receptors of conotoxins, based solely on their amino acid sequence, remains a challenge due to the intricate relationships between structure, function, target specificity, and the significant conformational heterogeneity observed in conotoxins with the same primary sequence. We have previously demonstrated that the inclusion of post-translational modifications, collisional cross sections values, and other structural features, when added to the standard primary sequence features, improves the prediction accuracy of conotoxins against non-toxic and other toxic peptides across varied datasets and several different commonly used machine learning classifiers. Here, we present the effects of these features on conotoxin class and molecular target predictions, in particular, predicting conotoxins that bind to nicotinic acetylcholine receptors (nAChRs). We also demonstrate the use of the Synthetic Minority Oversampling Technique (SMOTE)-Tomek in balancing the datasets while simultaneously making the different classes more distinct by reducing the number of ambiguous samples which nearly overlap between the classes. In predicting the alpha, mu, and omega conotoxin classes, the SMOTE-Tomek PCA PLR model, using the combination of the SS and P feature sets establishes the best performance with an overall accuracy (OA) of 95.95%, with an average accuracy (AA) of 93.04%, and an f1 score of 0.959. Using this model, we obtained sensitivities of 98.98%, 89.66%, and 90.48% when predicting alpha, mu, and omega conotoxin classes, respectively. Similarly, in predicting conotoxins that bind to nAChRs, the SMOTE-Tomek PCA SVM model, which used the collisional cross sections (CCSs) and the P feature sets, demonstrated the highest performance with 91.3% OA, 91.32% AA, and an f1 score of 0.9131. The sensitivity when predicting conotoxins that bind to nAChRs is 91.46% with a 91.18% sensitivity when predicting conotoxins that do not bind to nAChRs.

Longitudinal analysis of ARDS variability and biomarker predictive power in burn patients

Acute Respiratory Distress Syndrome (ARDS) is a severe complication in burn patients, characterized by rapid lung inflammation and hypoxemia. Managing ARDS is challenging due to its high mortality rates and the complex interplay of various pathophysiological factors. This study aims to investigate the heterogeneity of ARDS in burn patients admitted to the Intensive Care Unit (ICU) and evaluate the predictive efficacy of biomarkers in comparison to the PaO2/FiO2 (PF) ratio. A retrospective cohort study was conducted at the Burn Intensive Care Unit of Hangang Sacred Heart Hospital in Seoul, Korea, from July 2010 to December 2022, involving 2,318 patients. Longitudinal k-means clustering was employed to identify patient subgroups based on PF ratio trajectories. Biomarkers, including albumin, white blood cell (WBC) count, and lactate, were assessed for their predictive accuracy using multivariable logistic regression, area under the curve (AUC) analysis, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Four distinct patient clusters were identified. Cluster A exhibited the highest mortality rate at 54.9%, while Cluster D showed the lowest at 7.9%. Albumin demonstrated significantly higher predictive accuracy in Cluster A (AUC: 0.905, NRI: 0.421) and Cluster C (AUC: 0.915, NRI: 0.845), outperforming the PF ratio in both groups. However, in Clusters B and D, biomarkers did not significantly improve upon the predictive power of the PF ratio. Across all clusters, the integration of biomarkers with the PF ratio led to modest improvements but did not consistently outperform the PF ratio as a standalone predictor. This study reveals substantial heterogeneity in ARDS progression among burn patients, with varying mortality rates and biomarker efficacy across clusters. While biomarkers such as albumin showed potential in specific subgroups, their overall contribution to predictive accuracy was limited. Further multicenter, prospective studies are required to validate these findings and develop more refined predictive models. Personalized treatment strategies, based on biomarker profiles and traditional clinical metrics, could enhance ARDS management in burn patients.

Enhancing runoff predictions in data-sparse regions through hybrid deep learning and hydrologic modeling

Amidst growing concerns over climate-induced extreme weather events, precise flood forecasting becomes imperative, especially in regions like the Chaersen Basin where data scarcity compounds the challenge. Traditional hydrologic models, while reliable, often fall short in areas with insufficient observational data. This study introduces a hybrid modeling approach that combines the deep learning capabilities of the Informer model with the robust hydrological simulation by the WRF-Hydro model to enhance runoff predictions in such data-sparse regions. Trained initially on the diverse and extensive CAMELS dataset in the United States, the Informer model successfully applied its learned insights to predict runoff in the Chaersen Basin, leveraging transfer learning to bridge data gaps. Concurrently, the WRF-Hydro model, when integrated with The Global Forecast System (GFS) data, provided a basis for comparison and further refinement of flood prediction accuracy. The integration of these models resulted in a significant improvement in prediction precision. The synergy between the Informer’s advanced pattern recognition and the physical modeling strength of the WRF-Hydro significantly enhanced the prediction accuracy. The final predictions for the years 2015 and 2016 demonstrated notable increases in the Nash–Sutcliffe Efficiency (NSE) and the Index of Agreement (IOA) metrics, confirming the effectiveness of the hybrid model in capturing complex hydrological dynamics during runoff predictions. Specifically, in 2015, the NSE improved from 0.5 with WRF-Hydro and 0.63 with the Informer model to 0.66 using the hybrid model, while in 2016, the NSE increased from 0.42 to 0.76. Similarly, the IOA in 2015 rose from 0.83 with WRF-Hydro and 0.84 with the Informer model to 0.87 using the hybrid approach, and in 2016, it increased from 0.78 to 0.92. Further investigation into the respective contributions of the WRF-Hydro and the Informer models revealed that the hybrid model achieved the optimal performance when the contribution of the Informer model was maintained between 60%-80%.

Fine analysis and size optimization of spherical hinge in swivel construction

Contact stress and friction torque are the two primary mechanical responses of a spherical hinge. In general engineering practice, the calculation methods for contact stress and friction in spherical hinges often involve simplifying the spherical hinge into a planar hinge. To accurately analyze the stress state of the spherical hinge, it is essential to establish a detailed model. In this study, 17 groups of spherical hinge models were developed. The support radius, curvature radius, and thickness of the spherical hinge are considered influencing factors, while the maximum contact stress, friction torque, and amount of steel used are treated as responses. A three-factor, three-level, and three-response design is implemented. Using the response surface method, optimization design is conducted, resulting in two proposed optimization schemes. The maximum contact stress is reduced by 40.10% and 10.91%, respectively, while the friction torque is decreased by 0.82% and 26.50%, respectively. The maximum error between the predicted and calculated values is 7.04%. This indicates that the model presented in this study possesses sufficient predictive accuracy and can effectively guide the selection of spherical hinge dimensions.

Construction of a nomogram risk prediction model for depressive symptoms in elderly breast cancer patients

The primary objective of this study was to identify the factors associated with the development of depressive symptoms in elderly breast cancer (BC) patients and to construct a nomogram model for predicting these symptoms. We recruited 409 patients undergoing BC treatment in the breast departments of two tertiary-level hospitals in Jiangsu Province from November 2023 to April 2024 as our study cohort. Participants were categorized into depressed and non-depressed groups based on their clinical outcomes. Univariate and multivariate logistic regression analyses were employed to identify independent risk factors for depression among BC patients. Multivariate analysis revealed that monthly income, pain score, family support score, and physical activity score significantly influenced the onset of depression in older BC patients (P < 0.05).The risk prediction model, constructed using these identified factors, demonstrated excellent discriminatory power, as evidenced by an area under the ROC curve (AUC) of 0.824. The maximum Youden index was 0.627, with a sensitivity of 90.60%, specificity of 72.10%, and a diagnostic threshold value of 1.501. The results of the Hosmer-Lemeshow goodness-of-fit test (χ² = 3.181, P = 0.923) indicated that the model fit the data well. The calibration curve for the model closely followed the ideal curve, suggesting a strong fit and high predictive accuracy. Our nomogram model exhibited superior predictive performance, enabling healthcare professionals to identify high-risk patients early and implement preventative measures to mitigate the development of depressive symptoms. This study is a cross-sectional study that lacks longitudinal data and has a small sample size. Future research could involve larger samples, multicenter studies, and prospective designs to build better clinical predictive models.

Validation of the adult asthma epidemiological score: a secondary analysis of the EPI-ASTHMA population-based study.

The A2 score is an eight-question patient-reported outcome measure that has been validated for ruling in (score ≥4) and ruling out (score 0-1) asthma. However, this screening tool has been validated in a cohort similar to the derivation cohort used. This study aims to validate the predictive accuracy of the A2 score in a primary care population against general practitioner (GP) clinical assessment and to determine whether the proposed cut-offs are the most appropriate. This accuracy study is a secondary analysis of the EPI-ASTHMA population-based study. Primary care centres in Portugal. Random adult participants answered the A2 score by phone interview. Those with an A2 score ≥1 (plus 5% with an A2 score of 0) were invited to a diagnostic visit carried out by a GP to confirm or not a diagnosis of asthma. Diagnostic accuracy was assessed using receiver operating characteristic (ROC) curves. A total of 1283 participants (median 54 (p25-p75 43-66) years; 60% women) were analysed. The A2 score showed high discriminatory power in identifying asthma, with an area under the ROC curve of 82.9% (95% CI 80.4% to 85.4%). The proposed cut-off ≥4 was the most appropriate to rule in asthma (specificity 83.1%, positive predictive value 62.4%, accuracy 78%). Similarly, the proposed cut-off<2 was the most suitable for excluding asthma (sensitivity 92.7%, negative predictive value 93.7%, accuracy 60.5%). The A2 score is a useful tool to identify patients with asthma in a primary care population. NCT0516961.

Machine learning and FPGA implementation for predicting luminance decay and temperature distribution in micro-LED displays

AbstractA machine learning-based method predicts the luminance decay of micro light emitting diode (micro-LED) displays, utilizing temperature distribution and degradation experiments alongside implementation on field-programmable gate array (FPGA). Micro-LEDs, used in outdoor and indoor billboards, experience degradation due to harsh environmental conditions. To model temperature distribution in indoor advertising displays using minimal data, a temperature model is constructed based on sensor from the panel and thermal images captured by a camera, in relation to the display pattern. The input data is processed using an FPGA, which transmits the sensed temperatures and display patterns to the micro-LED panel. Multilayer Perceptron (MLP), a type of neural network, predicts the temperature distribution over the panel surface, achieving an error of less than 1.1 °C. Separate degradation models forecast luminance decay, factoring in enclosure temperature, input current, and usage time, with distinct models for red, green, and blue LEDs. Exponential curve-fitting and interpolation, following TM-21 standards, ensure long-term accuracy. The luminance decay predictions have an average error below 1.05% (approximately 9 nits). The FPGA implementation minimizes resource consumption while maintaining prediction accuracy, making it suitable for real-time applications. The degradation model accurately predicts performance over tens or even hundreds of thousands of hours, aligning with the exponential decay trends defined by TM-21.

Land Subsidence Predictions Based on a Multi-Component Temporal Convolutional Gated Recurrent Unit Model in Kunming City

Land subsidence (LS) is a geological hazard driven by both natural conditions and human activities. Traditional LS time-series prediction models often struggle to accurately capture nonlinear data characteristics, leading to suboptimal predictions. To address this issue, this paper introduces a multi-component temporal convolutional gate recurrent unit (MC-TCGRU) model, which integrates a fully adaptive noise-ensemble empirical-mode decomposition algorithm with a deep neural network to account for the complexity of time-series data. The model was validated using typical InSAR subsidence data from Kunming, analyzing the impact of each component on the prediction performance. A comparative analysis with the TCGRU model and models based on seasonal-trend decomposition using LOESS (STL) and empirical-mode decomposition (EMD) revealed that the MC-TCGRU model significantly enhanced the prediction accuracy by reducing the complexity of the original data. The model achieved R² values of 0.90, 0.93, 0.51, 0.93, and 0.96 across five points, outperforming the compared models.

A Method to Determine the Optimal Period for Field-Scale Yield Prediction Using Sentinel-2 Vegetation Indices

This study proposes a method for determining the optimal period for crop yield prediction using Sentinel-2 Vegetation Index (VI) measurements. The method operates at the single-field scale to minimize the influence of external factors, such as soil type, topography, microclimate variations, and agricultural practices, which can significantly affect yield predictions. By analyzing historical VI data, the method identifies the best time window for yield prediction for specific crops and fields. It allows adjustments for different space–time intervals, crop types, cloud probability thresholds, and variable time composites. As a practical example, this method is applied to a wheat field in the Po River Valley, Italy, using NDVI data to illustrate how the approach can be implemented. Although applied in this specific context, the method is exportable and can be adapted to various agricultural settings. A key feature of the approach is its ability to classify variable-length periods, leveraging historical Sentinel-2 VI compositions to identify the optimal window for yield prediction. If applied in regions with frequent cloud cover, the method can also identify the most effective cloud probability threshold for improving prediction accuracy. This approach provides a tool for enhancing yield forecasting over fragmented agricultural landscapes.

Development and validation of a nomogram for predicting overall survival of head and neck adenoid cystic carcinoma

This study aimed to develop and validate a nomogram using clinical variables to guide personalized treatment strategies for adenoid cystic carcinoma of the head and neck (ACCHN). Data from 1069 patients with ACCHN diagnosed between 2004 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were used to construct the nomogram. External validation was performed using an independent cohort of 70 patients from Fujian Cancer Hospital. Multivariate Cox regression analysis was conducted using IBM SPSS version 26.0 and R Software version 4.2.3. The concordance index (C-index) and receiver operating characteristic (ROC) curves were used to assess the predictive accuracy of the nomogram. Age, tumor site, surgery, N stage, M stage, and TNM stage were identified as independent prognostic factors through univariate and multivariate Cox analyses. The nomogram demonstrated superior predictive performance compared to the TNM staging system, effectively stratifying patients into high-risk and low-risk groups. This nomogram offers a valuable tool for predicting overall survival in patients with ACCHN and tailoring individualized treatment approaches.