Predictive Criteria Research Articles

A review of epilepsy detection and prediction methods based on EEG signal processing and deep learning

Epilepsy is a chronic neurological disorder that poses significant challenges to patients and their families. Effective detection and prediction of epilepsy can facilitate patient recovery, reduce family burden, and streamline healthcare processes. Therefore, it is essential to propose a deep learning method for efficient detection and prediction of epileptic electroencephalography (EEG) signals. This paper reviews several key aspects of epileptic EEG signal processing, focusing on epilepsy detection and prediction. It covers publicly available epileptic EEG datasets, preprocessing techniques, feature extraction methods, and deep learning-based networks used in these tasks. The literature is categorized based on patient independence, distinguishing between patient-independent and non-patient-independent studies. Additionally, the evaluation methods are classified into general classification indicators and specific epilepsy prediction criteria, with findings organized according to the prediction cycles reported in various studies. The review reveals several important insights. Despite the availability of public datasets, they often lack diversity in epilepsy types and are collected under controlled conditions that may not reflect real-world scenarios. As a result, signal preprocessing methods tend to be limited and may not fully represent practical conditions. Feature extraction and network designs frequently emphasize fusion mechanisms, with recent advances in Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) showing promising results, suggesting that new network models warrant further exploration. Studies using patient-independent data generally produce better results than those relying on non-patient-independent data. Metrics based on general classification methods typically perform better than those using specific epilepsy prediction criteria, though future research should focus on the latter for more accurate evaluation. Epilepsy prediction cycles are typically kept under 1 h, with most studies concentrating on intervals of 30 min or less.

Abstract 4119187: Association of CMR derived Global Longitudinal Strain with adverse Outcomes in Arrhythmogenic Cardiomyopathy.

Background: Arrhythmogenic cardiomyopathy (ACM) is associated with high risk of ventricular arrhythmias (VA) and heart failure (HF). Different predictive models and diagnostic criteria include parameters related to myocardial structural abnormalities and right ventricular (RV) ejection fraction (EF). Nevertheless, the value of global longitudinal strain (GLS) ascertained from cardiac magnetic resonance (CMR) for risk-stratification in the context of ACM is unknown. Aim: We aim to study the association of low CMR-derived GLS with adverse outcomes in ACM patients. Methods: This was a retrospective cohort study of ACM patients seen at a single quaternary center. ACM diagnosis followed the modified 2010 ARVC modified task force criteria. Primary outcome was a composite of life-threatening VAs, heart transplant or HF hospitalization during follow up. We excluded patients who suffered events prior to diagnosis, and those with missing or inadequate CMR protocol. Imaging parameters were acquired by CMR including EF, GLS, and late gadolinium enhancement (LGE). Results: A total of 82 patients with suspected ACM were included, with a median age of 40 years, and 43 (52.4%) were females. Most common reason of initial visit was family history of genetic cardiomyopathy in 46 (56%), while 16 (20%) patients visited our clinic due to history of syncope. Out of 60 patients who had genetic testing, 21 (35%) had mutations of DSP gene. Left ventricular involvement (non-ischemic LV-LGE by CMR) was present in 42 (56.8%) out of 74 patients who had CMR with gadolinium. Median LVEF was 62% (56, 66), and RVEF of 53% (48, 59), both by CMR. Median LVGLS was -17.01% (-14.71, -18.43), while median RVGLS was -20.5% (-18.3, -24.4). A total of 15 (18%) patients suffered the composite endpoint during an average follow up period of 8 years. Patients who suffered the composite endpoint had significantly lower mean LVGLS (-15.04% vs -16.70%, p= 0.048), and lower average RVGLS (-18.45% vs -21.33%, p=0.035) compared to patients free from the adverse outcome. Meanwhile there was no significant difference in LVEF (60.13% vs 61.18%, p=0.67), nor in RVEF (49.13% vs 54.01%, p=0.065) between the two groups. Conclusion: Lower GLS was significantly associated with VAs and HF hospitalizations in our patient population. CMR derived GLS can be used as an important prognostic parameter of adverse outcomes in patients with suspected ACM. Especially in those with LV involvement and high genetic predisposition as our cohort.

Comparison of machine learning-based breakdown pressure prediction and initiation criteria in hydraulic fracturing testing

Comparison of machine learning-based breakdown pressure prediction and initiation criteria in hydraulic fracturing testing

A novel MissForest-based missing values imputation approach with recursive feature elimination in medical applications

BackgroundMissing values in datasets present significant challenges for data analysis, particularly in the medical field where data accuracy is crucial for patient diagnosis and treatment. Although MissForest (MF) has demonstrated efficacy in imputation research and recursive feature elimination (RFE) has proven effective in feature selection, the potential for enhancing MF through RFE integration remains unexplored.MethodsThis study introduces a novel imputation method, “recursive feature elimination-MissForest” (RFE-MF), designed to enhance imputation quality by reducing the impact of irrelevant features. A comparative analysis is conducted between RFE-MF and four classical imputation methods: mean/mode, k-nearest neighbors (kNN), multiple imputation by chained equations (MICE), and MF. The comparison is carried out across ten medical datasets containing both numerical and mixed data types. Different missing data rates, ranging from 10 to 50%, are evaluated under the missing completely at random (MCAR) mechanism. The performance of each method is assessed using two evaluation metrics: normalized root mean squared error (NRMSE) and predictive fidelity criterion (PFC). Additionally, paired samples t-tests are employed to analyze the statistical significance of differences among the outcomes.ResultsThe findings indicate that RFE-MF demonstrates superior performance across the majority of datasets when compared to four classical imputation methods (mean/mode, kNN, MICE, and MF). Notably, RFE-MF consistently outperforms the original MF, irrespective of variable type (numerical or categorical). Mean/mode imputation exhibits consistent performance across various scenarios. Conversely, the efficacy of kNN imputation fluctuates in relation to varying missing data rates.ConclusionThis study demonstrates that RFE-MF holds promise as an effective imputation method for medical datasets, providing a novel approach to addressing missing data challenges in medical applications.

Ultrasound assessment of embryo cardiac function as a predictive criterion of adverse pregnancy outcome

Purpose to evaluate the value of the embryo/fetus heart rate (HR) in early pregnancy assessed by ultrasound for the formation of a group at high risk of adverse pregnancy outcomes.Materials and methods. A retrospective analysis of ultrasound examinations of 1073 pregnant women at 5+0–10+6 weeks was carried out. The embryo/fetus heart rate was compared with the gestational age calculated by the crown rump length (CRL) and the date of the last menstruation period (LMP). Retrospectively, all examined pregnant women were divided into two groups: group 1—with intrauterine embryo death before 14 weeks of pregnancy (n = 107); group 2—with prolongation of pregnancy until the second trimester (n = 966). When analyzing the data array using a decision tree, the embryo heart rate was assessed at various stages of pregnancy by LMP, CRL, and regardless of the exact period of pregnancy.Results. Analysis of heart rate values at different stages of pregnancy by LMP showed significant differences in heart rate between groups, the cases of later embryo loss characterized by lover heart rate values. There were no significant differences in heart rate between the groups at different stages of pregnancy by CRL (significant differences were obtained only at 8+0–9+6 weeks). Cut-off values of embryo heart rate for prediction of adverse pregnancy outcomes have been proposed for different stages of pregnancy, as well as a universal cut-off for the entire embryonic period.Conclusion. The proposed cut-off values of embryo heart rate for different stages of pregnancy, calculated by LMP, may be used for timely prediction of adverse pregnancy outcomes. When the exact gestational age is unknown, a universal heart rate cut-off value of <116 bpm may be used. All proposed HR cut-off values were characterized by high specificity, but none of them by high sensitivity. A sensitivity of HR cut-off values can be increased by the use in combination with other ultrasound and clinical signs of adverse pregnancy outcome.

Experimental and numerical studies on the propagation paths of gear root cracks

Gear bending fatigue failure has a significant impact on the operational performance of advanced machinery, such as electric vehicles and wind turbines, potentially leading to failures and catastrophic consequences for transmission systems. Several methods are currently used to predict gear crack propagation; however, a comprehensive comparison of their accuracy and efficiency in predicting crack paths is lacking. In this study, the propagation path of root cracks in commonly used automotive gears was investigated using finite element (FE) analysis and experimental testing. Three mixed crack path prediction criteria were integrated to predict the gear root crack propagation using the commercial software ABAQUS by user-defined Python script. The impacts of gear crack parameters, including the gear initial crack length, on gear root crack propagation behaviour were analysed. The simulations were verified by the gear bending fatigue test using a single tooth bending test device. The results indicate that the simulation outcomes align with the experimental tests, demonstrating that all three criteria are effective in predicting gear root crack propagation behaviours.

Can-and should-automaticity be self-reported using a single item? A secondary analysis of 16 datasets.

Understanding the influence of habit on health behaviour, or the formation or disruption of health habits over time, requires reliable and valid measures of automaticity. The most used measure, the Self-Report Behavioural Automaticity Index (SRBAI; derived from the Self-Report Habit Index [SRHI]), comprises four items, which may be impractical in some research contexts. Responding to demand from fellow researchers, this study sought to identify whether and which single items from the SRBAI adequately detect hypothesised effects of automaticity, via secondary analysis of 16 datasets, incorporating 16,838 participants and seven different behaviours. We assessed construct validity through correlations between each item and the full SRBAI (and where possible, the SRHI) and predictive validity by examining correlations with behaviour. All four single-item measures independently met construct and predictive validity criteria. We recognise compelling conceptual and methodological arguments regarding why people should not attempt to assess automaticity via a single, self-report item. However, where circumstances require brief measures, three SRBAI items each offer a credible and practical one-item measure that can substitute for the SRBAI or SRHI. We recommend one item in particular-'Behaviour X is something I do automatically'-because it tended to most closely replicate the effects of the four-item SRBAI.

A review of multiaxial low-cycle fatigue criteria for life prediction of metals

Most of real-world structural components that undergo cyclic loading feature multiaxial fatigue. When the cyclic loading involves also significant plastic deformation, multiaxial low-cycle fatigue takes place. Applications where multiaxial low-cycle fatigue can be observed very often involve metal components. To predict their lives multiple criteria and models have been proposed, but their development has not followed a regular path. Multiple reviews are available in literature. However, many of them are outdated, they often employ different classification methods to categorize available criteria, many focus on specific families of criteria, and others do not include sufficient theoretical background. Moreover, none of the available reviews is based on a systematic literature search method. As a result, approaching the topic can result arduous and chaotic, especially for first timers. This work aims at providing a clear, comprehensive, and definitive review of available criteria for multiaxial low-cycle fatigue. First, the basic theoretical background is explained. Secondly, a systematic approach is described and employed to identify all major currently available criteria. Then, they are classified and commentary about different classification styles that can be found in literature is added. Eventually they are described, together with their latest proposed variations. In this way this review can be employed as a guiding reference, especially for engineers approaching the topic for the first time.

The Epstein-Barr Virus Nuclear Antigen 1 Variant Associated with Nasopharyngeal Carcinoma Defines the Sequence Criteria for Serologic Risk Prediction.

Antibodies to select Epstein-Barr virus proteins can diagnose early-stage nasopharyngeal carcinoma (NPC). We have previously shown that IgA against Epstein-Barr virus nuclear antigen 1 (EBNA1) can predict incident NPC in high- and intermediate-risk cohorts 4 years before diagnosis. Here, we tested EBNA1 variants, with mutants, to define the sequence requirements for an NPC risk assay. Mammalian-expressed constructs were developed to represent EBNA1 variants 487V and 487A, which can differ by ≥15 amino acids in the N- and C-termini. Denatured lysates were evaluated by a refined IgA and IgG immunoblot assay in a case-control study using prediagnostic NPC sera from two independent cohorts in Singapore and Shanghai, the People's Republic of China. At 95% sensitivity, 487V yielded a 94.9% specificity compared with 86.1% for 487A. EBNA1 deleted for the conserved glycine-alanine repeats (GAr) reduced false positives by 22.8%. NPC sera reacted more strongly to the C-terminus than healthy controls, but the C-terminal construct (a.a. 390-641) showed lower specificity (84.8%) than the EBNA1 GAr-deleted construct (92.4%) at 95% sensitivity. Although EBNA1 IgA was present in healthy sera, most epitopes localized to the immunodominant GAr. We conclude that a refined EBNA1 antigen deleted for the GAr, but with residues consistently detected in Southeast Asian NPC tumors, is optimized for risk prediction with an extended sojourn time of 7.5 years. Furthermore, distinct EBNA1 serologic profiles enhanced the utility of the EBNA1 IgA assay for risk stratification. This illustrates the importance of serologically relevant EBNA1 sequences for NPC risk prediction and early detection.

600. DETECTION OF ANASTOMOTIC LEAK THROUGH THE VALUE OF DRAIN FLUID AMYLASE

Abstract Background Anastomotic leak in cervical anastomosis after esophagectomy has consistently been a serious problem. Esophagram has long been utilized to make sure the integrity of anastomosis before starting oral diet. However, the diagnostic accuracy is limited. Drin fluid amylase collected from the cervical anastomotic site is another useful method for its conveniency in specimen collection and the numerical value. But the clinical interpretation varied. The aim of the study was to share the experience in our facility regarding the diagnostic value of esophagram and drain fluid amylase exam. Methods A total of 116 patients with esophageal cancer were reviewed. All patients received open or minimally invasive Mckeown esophagectomy with cervical anastomosis, with or without a neoadjuvant chemoradiotherapy. For the prediction of leak, esophagram and drain fluid amylase were utilized. Esophagram was arranged on the 7th post operative day. And drain fluid amylase was collected twice on the 3rd and the 7th post operative day. We assumed that presence of an elevating trend of the drain fluid amylase level, and the amylase level on the 7th post operative day being more than 80 IU/L, would suggest an anastomotic leak. Results For the entire cohort, the leakage rate was 18.9% (22/116). 67 patients and 48 patients received esophagram exam and drain fluid amylase testing, respectively. The sensitivity and specificity for esophagram were 28.6% and 95%, respectively. The positive and negative predictive values were 40% and 91.9%, respectively. The diagnostic accuracy was 88.1%. The diagnostic value of testing drain fluid amylase by using our criteria yielded a sensitivity and specificity of 75% and 95%, respectively. The positive and negative predictive values were 75% and 95%, respectively. The diagnostic accuracy was 91.7%. Conclusion Comparing with esophagram, testing the drain fluid amylase provided an easier and more accurate way to predict the possible anastomotic leak. The criteria for prediction of leak by using drain fluid amylase were presence of an elevating trend (amylase on POD7 / POD3 >1) and the amylase level on the 7th post operative day being more than 80 IU/L.

Talin-1 variants associated with spontaneous coronary artery dissection (SCAD) highlight how even subtle changes in multi-functional scaffold proteins can manifest in disease.

Variants of talin-1 (TLN1) have recently been linked with spontaneous coronary artery dissection (SCAD) a condition where a tear can form in the wall of a heart artery necessitating immediate medical care. One talin-1 variant, A2013T, has an extensive familial pedigree of SCAD, which led to the screening for, and identification of, further talin-1 variants in SCAD patients. Here we evaluated these variants with commonly used pathogenicity prediction tools and found it challenging to reliably classify SCAD-associated variants, even A2013T where the evidence of a causal role is strong. Using biochemical and cell biological methods, we show that SCAD-associated variants in talin-1, which would typically be classified as non-pathogenic, still cause a measurable impact on protein structure and cell behaviour, including cell movement and wound healing capacity. Together, this indicates that even subtle variants in central mechanosensitive adapter proteins, can give rise to significant health impacts at the individual level, suggesting the need for a possible re-evaluation of the scoring criteria for pathogenicity prediction for talin variants.

Validation of the fully rationalized Tsai-Wu failure criterion for unidirectional laminates under multiaxial stress states through a ring-on-ring test

Validating a failure criterion under complex stress states is important because composites often encounter multiaxial stress in practical applications, but it also helps to expose any deficiencies of the criterion in failure prediction in the presence of stress interactions. This study examines the recently formulated fully rationalized Tsai-Wu criterion for unidirectional laminates under multiaxial stress states created by ring-on-ring loading. Distinct stress distributions under this loading condition are exhibited and quantified by numerical modeling, featuring critical biaxial tension on the bottom surface and triaxial compression on the top surface. The fully rationalized Tsai-Wu criterion and the original Tsai-Wu criterion are used to predict failure, with the results indicating good agreement between the former and experimental data. In comparison with the latter, quadric failure surfaces of the two criteria are constructed to analyze differences in prediction. The findings demonstrate the validity of the rationalization work for the Tsai-Wu criterion and its potential in predicting failure under multiaxial stress states.

Remarks on the separation of Navier–Stokes flows

Recently, strong evidence has accumulated that some solutions to the Navier–Stokes equations in physically meaningful classes are not unique. The primary purpose of this paper is to establish necessary properties for the error of hypothetical non-unique Navier–Stokes flows under conditions motivated by the scaling of the equations. Our first set of results show that some scales are necessarily active—comparable in norm to the full error—as solutions separate. ‘Scale’ is interpreted in several ways, namely via algebraic bounds, the Fourier transform and discrete volume elements. These results include a new type of uniqueness criteria which is stated in terms of the error. The second result is a conditional predictability criteria for the separation of small perturbations. An implication is that the error necessarily activates at larger scales as flows de-correlate. The last result says that the error of the hypothetical non-unique Leray–Hopf solutions of Jia and Šverák locally grows in a self-similar fashion. Consequently, within the Leray–Hopf class, energy can hypothetically de-correlate at a rate which is faster than linear. This contrasts numerical work on predictability which identifies a linear rate. Our results suggest that this discrepancy may be explained by the fact that non-uniqueness might arise from perturbation around a singular flow.

Coproporphyrin-I as a Selective OATP1B Biomarker Can Be Used to Delineate the Mechanisms of Complex Drug-Drug Interactions: Cedirogant Case Study.

Cedirogant is an inverse agonist of retinoic acid-related orphan receptor gamma thymus developed for the treatment of chronic plaque psoriasis. Cedirogant induces cytochrome P450 (CYP) 3A4 while inhibiting P-glycoprotein (P-gp), breast cancer resistance protein (BCRP), organic anion transporting polypeptide (OATP) 1B1, and OATP1B3 in vitro. Static drug-drug interactions (DDIs) predictions suggested possible clinical induction of CYP3A4, and inhibition of P-gp, BCRP, and OATP1B1, leading to challenges in interpreting DDI studies between cedirogant and substrates of CYP3A, P-gp, BCRP, and OATP1B1/3. Here the effects of cedirogant on the pharmacokinetics of two statin drugs were investigated in healthy participants. Coproporphyrin-I (CP-I), a selective endogenous OATP1B biomarker, was used to assess the impact of cedirogant on OATP1B. Cedirogant (375 mg once daily) increased rosuvastatin maximum plasma concentration (Cmax) and area under the plasma concentration curve (AUCtau) by 141% and 55%, respectively when co-administered, whereas atorvastatin Cmax increased by 40% with no effect on its AUCtau compared with administration of rosuvastatin/atorvastatin alone. Cedirogant did not increase CP-I exposures, indicating no clinical OATP1B inhibition. The increased rosuvastatin exposure and minimal change in atorvastatin exposure with co-administration of cedirogant is attributed to BCRP inhibition and interplay between P-gp/BCRP inhibition and CYP3A induction, respectively. Correlation analysis with data from two investigational drugs (glecaprevir and flubentylosin) demonstrated that OATP1B1 R-value of > 1.5 and [Cmax,u]/[OATP1B1 IC50] of > 0.1 are associated with > 1.25-fold increase in CP-I Cmax ratio. This demonstrates the utility of CP-I in disentangling mechanisms underlying a complex DDI involving multiple transporters and enzymes and proposes refined criteria for static OATP1B inhibition predictions.

An improved three-dimensional extension of Hoek–Brown criterion for rocks

The Hoek–Brown (H–B) criterion has found widespread application in numerous rock engineering projects. However, its efficacy is compromised by an underestimation of rock strength due to its neglect of the influence of the intermediate principal stress (σ2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sigma_{2}$$\\end{document}). Experimental evidence underscores the significant impact of σ2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sigma_{2}$$\\end{document}. Consequently, there exists an imperative to formulate a three-dimensional (3D) criterion. In this study, a new deviatoric function with two additional parameters (k\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$k$$\\end{document} and A\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$A$$\\end{document}) is developed firstly, which ensure compliance with the prerequisites of smoothness and convexity. In addition, the parameters k\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$k$$\\end{document} and A\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$A$$\\end{document} are bonded with the weakening effect of the Lode angle (θσ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ heta_{\\sigma }$$\\end{document}) and the strengthening effect of the mean stress (σm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sigma_{m}$$\\end{document}), respectively. Then a new 3D strength criterion for rocks is proposed by combining this new deviatoric function and the triaxial compression meridian function of the original H–B criterion. Four distinct sets of test data encompassing various rock types are employed to validate the proposed criterion. The results demonstrate that the proposed criterion adeptly captures the strength characteristics of the four rock types, providing a good depiction of failure surfaces within the 3D principal stress space. Comparative analyses involve the utilization of several existing 3D H–B criteria for strength predictions. The proposed criterion exhibits superior fitting performance for all the selected rocks.

Modeling and Monitoring CO2 Emissions in G20 Countries: A Comparative Analysis of Multiple Statistical Models

The emission of carbon dioxide (CO2) is considered one of the main factors responsible for one of the greatest challenges faced by the world today: climate change. On the other hand, with the increase in energy demand due to the increase in population and industrialization, the emission of CO2 has increased rapidly in the past few decades. However, the world’s leaders, including the United Nations, are now taking serious action on how to minimize the emission of CO2 into the atmosphere. Towards this end, accurate modeling and monitoring of historical CO2 can help in the development of rational policies. This study aims to analyze the carbon emitted by the Group Twenty (G20) countries for the period 1971–2021. The datasets include CO2 emissions, nonrenewable energy (NREN), renewable energy (REN), Gross Domestic Product (GDP), and Urbanization (URB). Various regression-based models, including multiple linear regression models, quantile regression models, and panel data models with different variants, were used to quantify the influence of independent variables on the response variable. In this study, CO2 is a response variable, and the other variables are covariates. The ultimate objective was to choose the best model among the competing models. It is noted that the USA, Canada, and Australia produced the highest amount of CO2 consistently for the entire duration; however, in the last decade (2011–2021) it has decreased to 12.63–17.95 metric tons per capita as compared to the duration of 1971–1980 (14.33–22.16 metric tons per capita). In contrast, CO2 emissions have increased in Saudi Arabia and China recently. For modeling purposes, the duration of the data has been divided into two independent, equal parts: 1971–1995 and 1996–2021. The panel fixed effect model (PFEM) and panel mixed effect model (PMEM) outperformed the other competing models using model selection and model prediction criteria. Different models provide different insights into the relationship between CO2 emissions and independent variables. In the later duration, all models show that REN has negative impacts on CO2 emissions, except the quantile regression model with tau = 0.25. In contrast, NREN has strong positive impacts on CO2 emissions. URB has significantly negative impacts on CO2 emissions globally. The findings of this study hold the potential to provide valuable information to policymakers on carbon emissions and monitoring globally. In addition, results can help in addressing some of the sustainable development goals of the United Nation Development Programme.

Artificial Neural Network Models for Solution Concentration Measurement during Cooling Crystallization of Ceritinib

The development of a quantitative in-line UV spectroscopic method for monitoring of solute concentration during the crystallization process of the active pharmaceutical ingredient (API), ceritinib is described. The method is based on artificial neural networks (ANN). A seeded cooling crystallization process of ceritinib from tetrahydrofuran was studied as a model system. The model was constructed from collected ATR-UV spectra and temperature records within the metastable zone. The collected spectra were preprocessed with the first derivative using the Savitzky-Golay filter. ANN models with different architectures were created and the optimal architecture was chosen based on the root mean square error of prediction (RMSEP) criterion. In addition, ANN models were compared with the models obtained by the linear partial least squares regression (PLSR). Due to the nonlinear relationship in the data set, ANN models predict the solution concentration with higher accuracy compared to linear models. The developed models were successfully used in real-time solution concentration measurement during ceritinib crystallization along with a supersaturation control module developed in-house.

Pro‑coagulant Considerations in Patients with Arterial Hypertension and Multifocal Atherosclerosis

The aim of the work to determine the features of hemostasis and blood rheology in patients with arterial hypertension (HTN) and multifocal atherosclerosis (MFA).Materials and methods. The study enrolled male and female patients aged from 40 to 70 years, including 59 patients with controlled stage I–II hypertension and multifocal vascular lesions (Group 1) and 42 patients with controlled stage III hypertension, who have suffered an ischemic stroke and have multifocal vascular lesions (Group 2). A test using the Thrombodynamics analyser system T-2 (GemaKor, LLC, Moscow, Russia) was performed to detect the impairment of hemostasis.Results. Patients with HTN and MFA have an impaired coagulation hemostasis system (an increase in the initial and stationary clot growth rate and the size of the clot, as well as the presence of spontaneous clots), which leads to a high residual risk of cerebral complications, despite BP lowering to target figures and control of lipid profile.Discussion. Elevated blood pressure in patients with MFA leads to endothelial dysfunction, which is accompanied by activation of coagulation and fibrinolysis pathways. These changes lead to a violation of microcirculatory hemostasis in the brain, the development and progression of cerebral ischemia, and subsequently the occurrence of stroke, both the first and repeated.Conclusion. An important result of this study is the first registered change in the coagulation link of hemostasis in patients with HTN and MFA aged from 40 to 70 years, which can be considered an additional criterion for stroke prediction.

Extension of K-nearest neighbors and introduction of an applicable prediction criterion for a novel Monte Carlo simulation-based method in structural reliability

Efficiency is an important issue in structural reliability analysis. In this paper, a novel method is proposed to increase the efficiency of Monte Carlo Simulation by means of a machine learning algorithm. Using K-nearest neighbors method, the safe or failure region of many generated samples can be predicted without evaluating the limit state function. The proposed method represents three novel parts as its main core. In the first part of the proposed method, the classical K-nearest neighbors algorithm has been extended to a new fuzzy level such that the proposed method could predict even if all K neighbors are not in the same region. In previous similar methods, there is a prerequisite that K neighbors must be all in the failure region or all in the safe region. This extension to the fuzzy level is carried out by assigning a specific failure weight to each neighbor. In the second part of the proposed method, an innovative diagnostic criterion has been defined to check whether the region of a sample could be predicted or the sample has to be evaluated in the limit state function. The criterion describes the relative position of the newly generated sample with respect to the neighbors. This newly defined criterion is an effective alternative for the laborious action of drawing the convex hull around K neighbors, which is done as a time-consuming part in previous similar methods. Promotion of the accuracy or efficiency in the proposed method could be set using the adjusting parameter of the represented criterion. Since the implementation of the algorithm for each individual sample needs much time, in the third part, the proposed method introduces a technique to perform function evaluations or predictions in clusters. This part effectively reduces the computational burden, i.e. the number of function evaluations and run time. The performance of the proposed method, applying three novel and newly represented parts, has been verified using a number of mathematical and structural examples.

New Insight into the Effects of Different Types of Midfoot and Hindfoot Deformities on the Functional Outcomes of Rheumatoid Arthritis Patients

In advanced stages of rheumatoid arthritis (RA), the pattern of joint involvement leads to varied types of foot deformities such as forefoot, midfoot, and hindfoot deformities. We aim to evaluate midfoot and hindfoot deformities and their effect on functional outcomes in RA patients. In this prospective cross-sectional study clinical and radiological evaluations of 100 adult RA patients were studied for midfoot and hindfoot deformities. The RA disease activity, functional disability level, specific foot and ankle assessments, gait speed, and balance tests were assessed. Radiological examinations of the weight-bearing feet in a standing position were done. We found that the most frequent deformity type was the planovalgus foot (24%). There were statistically significant differences between the types of foot deformities and disease duration, disease activity, falls, Tinetti and Berg balance Test, and health assessment questionnaire-disability index. The multivariate regression analysis of independent correlates of falling detected that patients with equinovarus deformity were 2.5 times more liable for falling with 63.5% predictive power. According to the predictive criteria of deformity type for falling, the equinocavovarus type had accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 91%, 84.5%, 98.5%, 99%, and 84%, respectively. We found in RA patients, midfoot and hindfoot deformities cause a significant reduction of functional ability and quality of life. The early detection of foot deformities requires a good awareness of the clinical and radiographic diagnosis of different types of deformities thus reducing the possible functional disabilities via early management by combined orthotic, physiotherapy, and surgical treatments.