Operator Method Research Articles

Emergence of squeezed coherent states in Kaluza–Klein cosmology

In this work, we consider a propagating scalar field on Kaluza–Klein-type cosmological background. It is shown that this geometrical description of the Universe resembles – from a Hamiltonian standpoint – a damped harmonic oscillator with mass and frequency, both time-dependents. In this scenario, we construct the squeezed coherent states (SCSs) for the quantized scalar field by employing the invariant operator method of Lewis–Riesenfeld (non-Hermitian) in a non-unitary approach. The non-classicality of SCSs has been discussed by examining the quadrature squeezing properties from the uncertainty principle. Moreover, we compute the probability density, which allows us to investigate whether SCSs can be used to seek traces of extra dimensions. We then analyze the effects of the existence of supplementary space on cosmological particle production in SCSs by considering different cosmological eras.

Mourre Theory and Asymptotic Observables in Local Relativistic Quantum Field Theory

We prove the convergence of Araki–Haag detectors in any Haag–Kastler quantum field theory with an upper and lower mass gap. We cover the case of a single Araki–Haag detector on states of bounded energy, which are selected from the absolutely continuous part of the energy-momentum spectrum sufficiently close to the lower boundary of the multi-particle spectrum. These states essentially encompass those states in the multi-particle spectrum lying below the three-particle threshold. In our proof, we draw on insights from proofs of asymptotic completeness in quantum mechanics. Notably, we apply Mourre’s conjugate operator method for the first time within the framework of Haag–Kastler quantum field theory. Furthermore, we discuss applications of our findings for the problem of asymptotic completeness in local relativistic quantum field theory.

Machine learning predicts acute respiratory failure in pancreatitis patients: A retrospective study

PurposeThe purpose of the research is to design an algorithm to predict the occurrence of acute respiratory failure (ARF) in patients with acute pancreatitis (AP). MethodsWe collected data on patients with AP in the Medical Information Mart for Intensive Care IV database. The enrolled observations were randomly divided into a 70 % training cohort and a 30 % validation cohort, and the observations in the training cohort were divided into ARF and non-ARF groups. Feature engineering was conducted using random forest (RF) and least absolute shrinkage and selection operator (LASSO) methods in the training cohort. The model building included logistic regression (LR), decision tree (DT), k-nearest neighbours (KNN), naive bayes (NB) and extreme gradient boosting (XGBoost). Parameters for model evaluation include receiver operating characteristic (ROC) curve, precision-recall curve (PRC), calibration curves, positive predictive value (PPV), negative predictive value (NPV), true positive rate (TPR), true negative rate (TNR), accuracy (ACC) and F1 score. ResultsAmong 4527 patients, 445 patients (9.8 %) experienced ARF. Ca, ALB, GLR, WBC, AG and BUN have been included in the prediction model as features for predicting ARF. The AUC of XGBoost were 0.86 (95 %CI 0.84–0.88) and 0.87 (95 %CI 0.84–0.90) in the training and validation cohorts. In the training cohort, XGBoost demonstrates a true positive rate (TPR) of 0.662, a true negative rate (TNR) of 0.884, a positive predictive value (PPV) of 0.380, a negative predictive value (NPV) of 0.960, an accuracy (ACC) of 0.862, and an F1 score of 0.483. In the validation cohort, XGBoost shows a TPR of 0.620, a TNR of 0.895, a PPV of 0.399, an NPV of 0.955, an ACC of 0.867, and an F1 score of 0.486. ConclusionThe XGBOOST model demonstrates good discriminatory ability, which enables clinicians to ascertain the probability of developing ARF in AP patients.

Establishment and validation of a clinical risk scoring model to predict fatal risk in SFTS hospitalized patients

BackgroundSevere fever with thrombocytopenia syndrome (SFTS) is an emerging tick-borne infection with a high case fatality rate. Significant gaps remain in studies analyzing the clinical characteristics of fatal cases.MethodsFrom January 2017 to June 2023, 427 SFTS cases were included in this study. A total of 67 variables about their demographic, clinical, and laboratory data were collected. Univariate logistic regression and the least absolute shrinkage and selection operator (LASSO) method was used to screen predictors from the cohort. Multivariate logistic regression was used to identify independent predictors and nomograms were developed. Calibration, decision curves and area under the curve (AUC) were used to assess model performance.ResultsThe multivariate logistic regression analysis screened out the four most significant factors, including age > 70 years (p = 0.001, OR = 2.516, 95% CI 1.452–4.360), elevated serum PT (p < 0.001, OR = 1.383, 95% CI 1.143–1.673), high viral load (p < 0. 001, OR = 1.496, 95% CI 1.290–1.735) and high level of serum urea (> 8.0 μmol/L) (p < 0.001, OR = 4.433, 95% CI 1.888–10.409). The AUC of the nomogram based on these four factors was 0.813 (95% CI, 0.758–0.868). The bootstrap resampling internal validation model performed well, and decision curve analysis indicated a high net benefit.ConclusionsThe nomogram based on age, elevated PT, high serum urea level, and high viral load can be used to help early identification of SFTS patients at risk of fatality.

Radiomics-Based Computed Tomography Urogram Approach for the Prediction of Survival and Recurrence in Upper Urinary Tract Urothelial Carcinoma.

Upper tract urothelial carcinoma (UTUC) is a rare and aggressive malignancy with a poor prognosis. The accurate prediction of survival and recurrence in UTUC is crucial for effective risk stratification and guiding therapeutic decisions. Models combining radiomics and clinicopathological data features derived from computed tomographic urograms (CTUs) can be a way to predict survival and recurrence in UTUC. Thus, preoperative CTUs and clinical data were analyzed from 106 UTUC patients who underwent radical nephroureterectomy. Radiomics features were extracted from segmented tumors, and the Least Absolute Shrinkage and Selection Operator (LASSO) method was used to select the most relevant features. Multivariable Cox models combining radiomics features and clinical factors were developed to predict the survival and recurrence. Harrell's concordance index (C-index) was applied to evaluate the performance and survival distribution analyses were assessed by a Kaplan-Meier analysis. The significant outcome predictors were identified by multivariable Cox models. The combined model achieved a superior predictive accuracy (C-index: 0.73) and higher recurrence prediction (C-index: 0.84). The Kaplan-Meier analysis showed significant differences in the survival (p < 0.0001) and recurrence (p < 0.002) probabilities for the combined datasets. The CTU-based radiomics models effectively predicted survival and recurrence in the UTUC patients, and enhanced the prognostic performance by combining radiomics features with clinical factors.

Integrative radiopathomics model for predicting progression-free survival in patients with nonmetastatic nasopharyngeal carcinoma

PurposeTo construct an integrative radiopathomics model for predicting progression-free survival (PFS) in nonmetastatic nasopharyngeal carcinoma (NPC) patients.Methods357 NPC patients who underwent pretreatment MRI and pathological whole-slide imaging (WSI) were included in this study and randomly divided into two groups: a training set (n = 250) and validation set (n = 107). Radiomic features extracted from MRI were selected using the minimum redundancy maximum relevance and least absolute shrinkage and selection operator methods. The pathomics signature based on WSI was constructed using a deep learning architecture, the Swin Transformer. The radiopathomics model was constructed by incorporating three feature sets: the radiomics signature, pathomics signature, and independent clinical factors. The prognostic efficacy of the model was assessed using the concordance index (C-index). Kaplan-Meier curves for the stratified risk groups were tested by the log-rank test.ResultsThe radiopathomics model exhibited superior predictive performance with C-indexes of 0.791 (95% confidence interval [CI]: 0.724–0.871) in the training set and 0.785 (95% CI: 0.716–0.875) in the validation set compared to any single-modality model (radiomics: 0.619, 95% CI: 0.553–0.706; pathomics: 0.732, 95% CI: 0.662–0.802; clinical model: 0.655, 95% CI: 0.581–0.728) (all, P < 0.05). The radiopathomics model effectively stratified patients into high- and low-risk groups in both the training and validation sets (P < 0.001).ConclusionThe developed radiopathomics model demonstrated its reliability in predicting PFS for NPC patients. It effectively stratified individual patients into distinct risk groups, providing valuable insights for prognostic assessment.

Deciphering prognostic indicators in non-HIV cryptococcal meningitis: Constructing and validating a predictive Nomogram model.

Cryptococcal meningitis (CM) is a well-recognized fungal infection, with substantial mortality in individuals infected with the human immunodeficiency virus (HIV). However, the incidence, risk factors, and outcomes in non-HIV adults remain poorly understood. This study aims to investigate the characteristics and prognostic indicators of CM in non-HIV adult patients, integrating a novel predictive model to guide clinical decision-making. A retrospective cohort of 64 non-HIV adult CM patients, including 51 patients from previous studies and 13 from the First Hospital of Shanxi Medical University, was analyzed. We assessed demographic features, underlying diseases, intracranial pressure, cerebrospinal fluid characteristics, and brain imaging. Using the least absolute shrinkage and selection operator (LASSO) method, and multivariate logistic regression, we identified significant variables and constructed a Nomogram prediction model. The model's calibration, discrimination, and clinical value were evaluated using the Bootstrap method, calibration curve, C index, goodness-of-fit test, receiver operating characteristic (ROC) analysis, and decision curve analysis. Age, brain imaging showing parenchymal involvement, meningeal and ventricular involvement, and previous use of immunosuppressive agents were identified as significant variables. The Nomogram prediction model displayed satisfactory performance with an akaike information criterion (AIC) value of 72.326, C index of 0.723 (0.592-0.854), and area under the curve (AUC) of 0.723, goodness-of-fit test P = 0.995. This study summarizes the clinical and imaging features of adult non-HIV CM and introduces a tailored Nomogram prediction model to aid in patient management. The identification of predictive factors and the development of the nomogram enhance our understanding and capacity to treat this patient population. The insights derived have potential clinical implications, contributing to personalized care and improved patient outcomes.

Fluid Mechanics, and Its Quantum Developments and Applications

Based on the known theories of fluid mechanics, we research the new quantum fluid mechanics by operator method. Further, we discuss its various developments combined the extensive quantum theory and other theories. Finally, we search possible applications in some aspects: in biology many systems are fluids; in astronomy binary stars, the density wave theory and evolutionary direction on spiral galaxies, and sunspot are researched; in geosciences earthquake, formation of basin and hydrocarbon layer are proposed; in social sciences the social hydrodynamics and corresponding waves and tides, the social synergetics, corruption, etc., are discussed.

Prognosis and progression of phagocytic regulatory factor-related gene combinations in clear cell renal cell carcinoma (ccRCC).

Developing signatures based on specific characteristics to predict prognosis has become a research hotspot in oncology. However, the prognostic value of phagocytosis regulators in clear cell renal cell carcinoma (ccRCC) remains unclear. The aim of the present study was to investigate the prognostic significance of phagocytosis regulators in ccRCC by constructing a prognostic model related to phagocytosis regulators, and to use this model to evaluate the prognosis and treatment effects in ccRCC patients. Firstly, kidney renal clear cell carcinoma (KIRC) transcriptome data (RNA-Seq) and clinical data were downloaded from The Cancer Genome Atlas (TCGA) database. Based on literatures PMID 34497417 and PMID 30397336, 167 of the 173 phagocytosis regulator genes collected in the literature were expressed in TCGA-KIRC. The relationship between these regulators and macrophages was revealed through single-sample gene set enrichment analysis (ssGSEA), and their biological and pathway involvements were further analyzed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Univariate Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) method were employed to further select phagocytosis regulators with prognostic potential, leading to the construction of a prognostic regression model. Additionally, univariate and multivariate Cox regression analyses were conducted to confirm the prognostic independence of genes associated with phagocytosis regulators. Finally, the relationship between phagocytosis regulator-related genes and patients' immune microenvironments and immunotherapy responses was explored. We have constructed a prognostic model of a combination of genes associated with phagocytosis regulators using LASSO Cox regression analysis of genes, and our combined model was shown to be an independent prognostic factor. The model had optimal performance in predicting long-term survival. Clinical features were significantly correlated with phagocytosis regulatory gene scores. Tumors with higher levels of grade and stage were more prone to have higher phagocytosis regulatory genes. And our study suggests that phagocytosis regulatory genes do not play an ideal role in predicting the efficacy of immunotherapy in patients. We have constructed a prognostic model using a combination of genes associated with phagocytosis regulators, providing new insights into the prognosis and progression of ccRCC.

Consensus Clustering Analysis Identifies Ferroptosis-Related Patient Clusters and Predictive Signature Construction Based on Ferroptosis-Related Genes in Ischemic Cardiomyopathy.

Ischemic cardiomyopathy (ICM) significantly contributes to global disease burden, while the role of ferroptosis in ICM remains underexplored. We identified differentially expressed ferroptosis-related genes (DEFRGs) by analyzing the GSE57338 dataset and cross-referencing with FerrDb. Consensus clustering was then used to identify ferroptosis-associated clusters within the ICM samples. A ferroptosis-specific predictive signature was developed using the least absolute shrinkage and selection operator (LASSO) method and validated with the GSE5406 dataset. Additionally, quantitative real-time PCR (qRT-PCR) experiments were performed to validate the 11 feature genes in a rat ICM model. We identified 15 DEFRGs in GSE57338, which distinguished two patient clusters with distinct ferroptosis gene expression, pathway enrichment profiles, and metabolic characteristics. All DEFRGs were upregulated in cluster 2. Potential therapeutic targets were also identified for different ICM patient clusters. The 11-gene predictive signature (TXNRD1, STEAP3, STAT3, SCL2A1, PLIN2, NQO1, NNMT, IL33, ENPP2, ARRDC3, ALOX5) showed robust predictive power in both training and validation sets. High-risk patients exhibited increased infiltration of CD8+ T cells, CD4+ naïve T cells, M0/M1 macrophages, and resting mast cells, along with significant enrichment in epithelial mesenchymal transition and interferon responses. Low-risk patients had higher infiltration of regulatory T cells and monocytes. Results of qPCR analysis confirmed the bioinformatic analysis, validating the expression of the 11 feature genes in the rat ICM model. We identified two ferroptosis-related clusters in ICM patients and developed a predictive signature based on ferroptosis-related genes. Our findings highlight the importance of ferroptosis in ICM and offer new insights for its diagnosis and treatment.

Diagnostic value of an interpretable machine learning model based on clinical ultrasound features for follicular thyroid carcinoma.

Follicular thyroid carcinoma (FTC) and follicular thyroid adenoma (FTA) present diagnostic challenges due to overlapping clinical and ultrasound features. Improving the diagnosis of FTC can enhance patient prognosis and effectiveness in clinical management. This study seeks to develop a predictive model for FTC based on ultrasound features using machine learning (ML) algorithms and assess its diagnostic effectiveness. Patients diagnosed with FTA or FTC based on surgical pathology between January 2009 and February 2023 at Zhejiang Provincial Cancer Hospital and Zhejiang Provincial People's Hospital were retrospectively included. A total of 562 patients from Zhejiang Provincial Cancer Hospital comprised the training set, and 218 patients from Zhejiang Provincial People's Hospital constituted the validation set. Subsequently, clinical parameters and ultrasound characteristics of the patients were collected. The diagnostic parameters were analyzed using the least absolute shrinkage and selection operator and multivariate logistic regression screening methods. Next, a comparative analysis was performed using seven ML models. The area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, specificity, positive predicted value (PPV), negative predicted value (NPV), precision, recall, and comprehensive evaluation index (F-score) were calculated to compare the diagnostic efficacy among the seven models and determine the optimal model. Further, the optimal model was validated, and the SHapley Additive ExPlanations (SHAP) approach was applied to explain the significance of the model variables. Finally, an individualized risk assessment was conducted. Age, echogenicity, thyroglobulin antibody (TGAb), echotexture, composition, triiodothyronine (T3), thyroglobulin (TG), margin, thyroid-stimulating hormone (TSH), calcification, and halo thickness >2 mm were influential factors for diagnosing FTC. The XGBoost model was identified as the optimal model after a comprehensive evaluation. The AUC of this model in the validation set was 0.969 [95% confidence interval (CI), 0.946-0.992], while its precision sensitivity, specificity, and accuracy were 0.791, 0.930, 0.913 and 0.917, respectively. XGBoost model based on ultrasound features was constructed and interpreted using the SHAP method, providing evidence for the diagnosis of FTC and guidance for the personalized treatment of patients.

Risk assessment tool for anemia of chronic disease in systemic lupus erythematosus: a prediction model.

This study aims to develop a predictive model for estimating the likelihood of anemia of chronic disease (ACD) in patients with systemic lupus erythematosus (SLE) and to elucidate the relationship between various factors and ACD METHODS: Individuals diagnosed with SLE for at least one year were enrolled and categorized into two groups: those with ACD and those without anemia symptoms. Patients were randomly assigned to training and test sets at an 8:2 ratio. The least absolute shrinkage and selection operator (LASSO) method was used to select predictors, followed by logistic regression for modeling. Model performance was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) for both training and test sets. The study included a total of 216 patients, with 172 in the training set and 44 in the test set. LASSO identified 6 variables for constructing the predictive model, resulting in an area under the curve (AUC) of 0.833 (95% CI, 0.773-0.892) in the training set and 0.861 (95% CI, 0.750-0.972) in the test set. Calibration curves indicated consistency between expected and observed probabilities. DCA indicated that the model yielded a net benefit with threshold probabilities ranging from 20% to 90% in the training set and from 10% to 80% in the test set. This study presents a predictive model for assessing the risk of ACD in SLE patients. The model effectively captures the underlying mechanism of ACD in SLE and empowers clinicians to make well-informed treatment adjustments. Key Points • Development of a New Predictive Model: This study introduces a new predictive model to evaluate the likelihood of anemia of chronic disease (ACD) in patients with systemic lupus erythematosus (SLE). The model utilizes routine laboratory parameters to identify high-risk individuals, addressing a significant gap in current clinical practice. • Reflection of Potential Mechanisms for ACD Development: By incorporating the factors needed to construct the predictive model, this study also sheds light on the potential mechanisms of ACD development in SLE patients.

Development and Validation of a Clinical Prediction Model for Diagnosing Mycoplasma Infections in Gynecological Patients.

In adult females, mycoplasma infection is common and challenging to diagnose. This study aimed to use retrospective laboratory data to construct a nomogram for predicting the mycoplasma infection of individuals with probable urogenital tract mycoplasma infection. A total of 2,859 patients with suspected urogenital tract mycoplasma infection were retrospectively enrolled in this study. Demographics and routine examinations of leucorrhea were used to develop a nomogram for predicting mycoplasma infection. The least absolute shrinkage and selection operator (LASSO) method was applied to filter variables and select predictors, and multivariable logistic regression was used to construct a nomogram. The discriminatory ability of the model was determined by calculating the area under the curve (AUC). The performance and clinical utility of the nomogram were generated by using Harrell's concordance index, calibration curve, and decision curve analysis (DCA). By using the LASSO regression method, seven variables (age, white blood cell, epithetical cell, cleanliness, candidiasis vaginalis, sialidases, and leukocyte esterase) were chosen, and a nomogram was constructed using these variables. The prediction nomogram (0.676, 95% CI: 0.611 - 0.744) demonstrated a satisfactory performance. The prediction model's AUC was 0.679 (95% CI: 0.660 - 0.691). Furthermore, the DCA showed a good clinical net benefit based on the mycoplasma infection nomogram. A nomogram was created in this study, which included seven demographic and clinical characteristics of female patients. The nomogram could be of great value for the diagnosis of mycoplasma infection.

Integrating StEP-COMPAC definition and enhanced recovery after surgery status in a machine-learning-based model for postoperative pulmonary complications in laparoscopic hepatectomy

BackgroundPostoperative pulmonary complications (PPCs) contribute to high mortality rates and impose significant financial burdens. In this study, a machine learning-based prediction model was developed to identify patients at high risk of developing PPCs following laparoscopic hepatectomy. MethodsData were collected from 1022 adult patients who underwent laparoscopic hepatectomy at two centres between January 2015 and February 2021. The dataset was divided into a development set and a temporal external validation set based on the year of surgery. A total of 42 factors were extracted for pre-modelling, including the implementation status of Enhanced Recovery after Surgery (ERAS). Feature selection was performed using the least absolute shrinkage and selection operator (LASSO) method. Model performance was assessed using the area under the receiver operating characteristic curve (AUC). The model with the best performance was externally validated using temporal data. ResultsThe incidence of PPCs was 8.7%. Lambda.1se was selected as the optimal lambda for LASSO feature selection. For implementation of ERAS, serum gamma-glutamyl transferase levels, malignant tumour presence, total bilirubin levels, and age-adjusted Charleston Comorbidities Index were the selected factors. Seven models were developed. Among them, logistic regression demonstrated the best performance, with an AUC of 0.745 in the internal validation set and 0.680 in the temporal external validation set. ConclusionsBased on the most recent definition, a machine learning model was employed to predict the risk of PPCs following laparoscopic hepatectomy. Logistic regression was identified as the best-performing model. ERAS implementation was associated with a reduction in the number of PPCs.

A Multi-Machine Learning Consensus Model Based on Clinical Features Reveals That Interleukin-10 Derived from Monocytes Leads to a Poor Prognosis in Patients with Coronavirus Disease-2019.

Despite ongoing interventions, SARS-CoV-2 continues to cause significant global morbidity and mortality. Early diagnosis and intervention are crucial for effective clinical management. However, prognostic features based on transcriptional data have shown limited effectiveness, highlighting the need for more precise biomarkers to improve COVID-19 treatment outcomes. We retrospectively analyzed 149 clinical features from 189 COVID-19 patients, identifying prognostic features via univariate Cox regression. The cohort was split into training and validation sets, and 77 prognostic models were developed using seven machine learning algorithms. Among these, the least absolute shrinkage and selection operator (Lasso) method was employed to refine the selection of prognostic variables by ten-fold cross-validation strategy, which were then integrated with random survival forests (RSF) to build a robust COVID-19-related prognostic model (CRM). Model accuracy was evaluated across training, validation, and entire cohorts. The diagnostic relevance of interleukin-10 (IL-10) was confirmed in bulk transcriptional data and validated at the single-cell level, where we also examined changes in cellular communication between mononuclear cells with differing IL-10 expression and other immune cells. Univariate Cox regression identified 43 prognostic features. Among the 77 machine learning models, the combination of Lasso and RSF produced the most robust CRM. This model consistently performed well across training, validation, and entire cohorts. IL-10 emerged as a key prognostic feature within the CRM, validated by single-cell transcriptional data. Transcriptome analysis confirmed the stable diagnostic value of IL-10, with mononuclear cells identified as the primary IL-10 source. Moreover, differential IL-10 expression in these cells was linked to altered cellular communication in the COVID-19 immune microenvironment. The CRM provides accurate prognostic predictions for COVID-19 patients. Additionally, the study underscores the importance of early IL-10 level testing upon hospital admission, which could inform therapeutic strategies.

Enhanced machine learning models for predicting one-year mortality in individuals suffering from type A aortic dissection

ObjectiveThe study objective was to develop and validate an interpretable machine learning model to predict 1-year mortality in patients with type A aortic dissection, improving risk classification and aiding clinical decision-making. MethodsWe enrolled 289 patients with type A aortic dissection, dividing them into a training cohort (202 patients) and a validation cohort (87 patients). The Least Absolute Shrinkage and Selection Operator method with 10-fold cross-validation identified 8 key factors related to 1-year mortality. The Treebag model's performance was assessed using accuracy, F1-Score, Brier score, area under the curve, and area under the precision-recall curve with calibration and clinical utility evaluated through decision curves. Shapley Additive Explanations analysis determined the most influential predictors. ResultsThe Treebag model outperformed others, achieving a Brier score of 0.128 and an area under the curve of 0.91. Key risk factors included older age and elevated white blood cell count, whereas higher systolic blood pressure, lymphocyte, carbon dioxide combining power, eosinophil, β-receptor blocker use, and surgical intervention were protective. A web-based application, TAAD One-Year Prognostic Risk Assessment Web, was developed for clinical use (available at https://taad-1year-mortality-predictor.streamlit.app/). This platform allows for the prediction of 1-year mortality in patients with type A aortic dissection based on the identified predictive factors, facilitating clinical decision-making and patient management. ConclusionsThe Treebag machine learning model effectively predicts 1-year mortality in patients with type A aortic dissection, stratifying risk profiles. Key factors for enhancing survival include surgical intervention, β-blocker administration, and management of systolic blood pressure, lymphocyte, carbon dioxide combining power, eosinophil, and white blood cell levels, offering a valuable tool for improving patient outcomes.

Dynamic risk assessment of Uncertain Random System considering operator's simple emergency-stop action in short time window

Human-machine systems can be considered as typical Uncertain Random Systems (URS). In human-machine systems, operator's simple emergency-stop actions act as a vital soft barrier. Conventional Human Reliability Analyses face challenges in evaluating operator's simple emergency-stop action due to their static characteristics, incompatibility with short time windows, and problems posed by small samples. This paper provides a novel solution to risk assessment of human-machine systems involving operator's simple emergency-stop mechanism. Specifically, an evaluation method of operator's simple emergency-stop action is established, where human cognition is decomposed according to its temporal and logical dimensions, key indicators of human sub-cognition are evaluated, and uncertainty theory is used to integrate these indicators. The output of proposed method is uncertainty of operator's behavior (UOB), which can offer more information than traditional Human Error Probability (HEP) and can be readily converted into uncertainty of human error. Further, a precise simulation method for dynamic risk assessment of Uncertain Random Systems is developed, in which the integration of the UOB is achieved. A case study demonstrates the effectiveness of the proposed methods, and its results show that the proposed methods can clearly reflect the differences in system risk under short time window conditions that have minor distinctions.

Learning integral operators via neural integral equations

Nonlinear operators with long-distance spatiotemporal dependencies are fundamental in modelling complex systems across sciences; yet, learning these non-local operators remains challenging in machine learning. Integral equations, which model such non-local systems, have wide-ranging applications in physics, chemistry, biology and engineering. We introduce the neural integral equation, a method for learning unknown integral operators from data using an integral equation solver. To improve scalability and model capacity, we also present the attentional neural integral equation, which replaces the integral with self-attention. Both models are grounded in the theory of second-kind integral equations, where the indeterminate appears both inside and outside the integral operator. We provide a theoretical analysis showing how self-attention can approximate integral operators under mild regularity assumptions, further deepening previously reported connections between transformers and integration, as well as deriving corresponding approximation results for integral operators. Through numerical benchmarks on synthetic and real-world data, including Lotka–Volterra, Navier–Stokes and Burgers’ equations, as well as brain dynamics and integral equations, we showcase the models’ capabilities and their ability to derive interpretable dynamics embeddings. Our experiments demonstrate that attentional neural integral equations outperform existing methods, especially for longer time intervals and higher-dimensional problems. Our work addresses a critical gap in machine learning for non-local operators and offers a powerful tool for studying unknown complex systems with long-range dependencies.

Building a predictive model for depression risk in fracture patients: insights from cross-sectional NHANES 2005–2020 data and an external hospital-based dataset

BackgroundDepression represents a frequent mental health challenge in individuals with fractures, negatively impacting their recuperation and overall well-being. The purpose of this research was to formulate and corroborate a prognostic framework for pinpointing depression risk among fracture sufferers by utilizing data from the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2020 and a separate hospital-based group.MethodsWe analyzed records from 1,748 individuals with fractures documented in the NHANES database spanning 2005 to 2020, of which 362 were diagnosed with depression, as indicated by a Patient Health Questionnaire-9 (PHQ-9) score of 10 or higher. An additional validation group comprised 360 fracture patients sourced from a medical center. Considered variables for prediction encompassed demographic details, lifestyle habits, past medical conditions, and laboratory results. The method of least absolute shrinkage and selection operator (LASSO) regression facilitated the narrowing down of variables, while multivariate logistic regression was employed to pinpoint significant predictors. To assist in prediction, a nomogram was designed and subsequently validated.ResultsFive independent predictors were identified: drinking, insomnia, poverty-to-income ratio, education level, and white blood cell count. The nomogram showed good discrimination in the NHANES cohorts (training area under the curve (AUC) 0.734, validation AUC 0.740) and hospital-based external validation (AUC 0.711). Calibration curves and decision analysis supported its predictive accuracy and clinical value.ConclusionThe constructed nomogram offers a precise and clinically relevant instrument for forecasting depression risk in patients with fractures, facilitating the early detection of individuals at high risk and enabling prompt intervention.

A Predictive Model of the Progression to Alzheimer's Disease in Patients with Mild Cognitive Impairment Based on the MRI Enlarged Perivascular Spaces.

Mild cognitive impairment (MCI) is a heterogeneous condition that can precede various forms of dementia, including Alzheimer's disease (AD). Identifying MCI subjects who are at high risk of progressing to AD is of major clinical relevance. Enlarged perivascular spaces (EPVS) on MRI are linked to cognitive decline, but their predictive value for MCI to AD progression is unclear. This study aims to assess the predictive value of EPVS for MCI to AD progression and develop a predictive model combining EPVS grading with clinical and laboratory data to estimate conversion risk. We analyzed 358 patients with MCI from the ADNI database, consisting of 177 MCI-AD converters and 181 non-converters. The data collected included demographic information, imaging data (including perivascular spaces grade), clinical assessments, and laboratory test results. Variable selection was conducted using the Least Absolute Shrinkage and Selection Operator (LASSO) method, followed by logistic regression to develop predictive model. In the univariate logistic regression analysis, both moderate (OR = 5.54, 95% CI [3.04-10.18]) and severe (OR = 25.04, 95% CI [10.07-62.23]) enlargements of the centrum semiovale perivascular space (CSO-PVS) were found to be strong predictors of disease progression. LASSO analyses yielded 12 variables, refined to six in the final model: APOE4 genotype, ADAS11 score, CSO-PVS grade, and volumes of entorhinal, fusiform, and midtemporal regions, with an AUC of 0.956 in the training and 0.912 in the validation cohort. Our predictive model, emphasizing EPVS assessment, provides clinicians with a practical tool for early detection and management of AD risk in MCI patients.