High Predictive Capability Research Articles

Drilling Rate of Penetration Prediction Based on CBT-LSTM Neural Network

Due to the uncertainty of the subsurface environment and the complexity of parameters, particularly in feature extraction from input data and when seeking to understand bidirectional temporal information, the evaluation and prediction of the rate of penetration (ROP) in real-time drilling operations has remained a long-standing challenge. To address these issues, this study proposes an improved LSTM neural network model for ROP prediction (CBT-LSTM). This model integrates the capability of a two-dimensional convolutional neural network (2D-CNN) for multi-feature extraction, the advantages of bidirectional long short-term memory networks (BiLSTM) for processing bidirectional temporal information, and the dynamic weight adjustment of the time pattern attention mechanism (TPA) for extracting crucial information in BiLSTM, effectively capturing key features in temporal data. Initially, data are denoised using the Savitzky–Golay filter, and five correlation coefficient methods are employed to select input features, with principal component analysis (PCA) used to reduce model complexity. Subsequently, a sliding window approach transforms the time series into a two-dimensional structure to capture dynamic changes, constructing the model input. Finally, the ROP prediction model is established, and search methods are utilized to identify the optimal hyperparameter combinations. Compared with other neural networks, CBT-LSTM demonstrates superior performance metrics, with MAE, MAPE, RMSE, and R2 values of 0.0295, 0.0357, 9.3101%, and 0.9769, respectively, indicating the highest predictive capability. To validate the model’s robustness, noise was introduced into the training data, and results show stable performance. Furthermore, the model’s predictive results for other wells achieved R2 values of 0.95, confirming its strong generalization ability. This method provides a new solution for ROP prediction in real-time drilling operations, assisting drilling engineers in better planning their operations and reducing drilling cycles.

Using deep learning to predict cardiovascular magnetic resonance findings from echocardiography videos

Abstract Background Echocardiography is the most common modality for assessing cardiac structure and function. While cardiac magnetic resonance (CMR) imaging is less accessible, CMR can provide unique tissue characterization including late gadolinium enhancement (LGE), T1 and T2 mapping, and extracellular volume (ECV) which are associated with tissue fibrosis, infiltration, and inflammation. While deep learning has been shown to uncover findings not recognized by clinicians, it is unknown whether CMR-based tissue characteristics can be derived from echocardiography videos using deep learning. Purpose To assess the performance of a deep learning model applied to echocardiography to detect CMR-based parameters. Methods In a retrospective single-center study, adult patients with CMRs and echocardiography studies within 30 days were included. A video-based convolutional neural network was trained on echocardiography videos to predict CMR-derived labels including WMA presence, LGE presence, and abnormal T1, T2 or ECV across echocardiography views. The model performance was evaluated in a held-out test dataset not used for training. Results The study population included 1,453 adult patients (mean age 56±18 years, 42% female) with 2,556 paired echocardiography studies occurring on average 2 days after CMR (interquartile range 2 days prior to 6 days after). The model had high predictive capability for presence of WMA (AUC 0.873 [95%CI 0.816-0.922]), however, the model was unable to reliably detect the presence of LGE (AUC 0.699 [0.613-0.780]), native T1 (AUC 0.614 [0.500-0.715]), T2 0.553 [0.420-0.692], or ECV 0.564 [0.455-0.691]). Conclusions Deep learning applied to echocardiography accurately identified CMR-based WMA, but was unable to predict tissue characteristics, suggesting that signal for these tissue characteristics may not be present within ultrasound videos.Central Figure -Study Pipeline-

Accelerating Optimal Synthesis of Atomically Thin MoS2: A Constrained Bayesian Optimization Guided Brachistochrone Approach

AbstractA machine learning (ML) guided approach is presented for the accelerated optimization of chemical vapor deposition (CVD) synthesis of 2D materials toward the highest quality, starting from low‐quality or unsuccessful synthesis conditions. Using 26 sets of these synthesis conditions as the initial training dataset, our method systematically guides experimental synthesis towards optoelectronic‐grade monolayer MoS2 flakes. A‐exciton linewidth (σA) as narrow as 38 meV could be achieved in 2D MoS2 flakes after only an additional 35 trials (reflecting 15% of the full factorial design dataset for training purposes). In practical terms, this reflects a decrease of the possible experimental time to optimize the parameters from up to one year to about two months. This remarkable efficiency was achieved by formulating a constrained sequencing optimization problem solved via a combination of constraint learning and Bayesian Optimization with the narrowness of σA as the single target metric. By employing graph‐based semi‐supervised learning with data acquired through a multi‐criteria sampling method, the constraint model effectively delineates and refines the feasible design space for monolayer flake production. Additionally, the Gaussian Process regression effectively captures the relationships between synthesis parameters and outcomes, offering high predictive capability along with a measure of prediction uncertainty. This method is scalable to a higher number of synthesis parameters and target metrics and is transferrable to other materials and types of reactors. This study envisions that this method will be fundamental for CVD and similar techniques in the future.

Zinc determination in common beans by pXRF: An easy and versatile calibration strategy applied to biofortification studies

Zinc deficiency is observed in millions of individuals, especially in underdeveloped countries. Therefore, agronomical strategies have been implemented to mitigate this public health issue, mainly by the biofortification of staple food crops, e.g., common beans, one of the most consumed leguminous grains worldwide. Accurate Zn determination by an analytical method is one of the most relevant steps in these initiatives. This study evaluated three calibration methods for Zn determination by portable X-ray Fluorescence Spectrometry (pXRF) in pelletized common bean samples. A Ti/Al primary filter and a 10-second irradiation time were selected as optimized operating conditions. The sample with the highest Zn content was submitted to acid extraction with diluted HCl and HNO3 to obtain synthetic blanks. Increasing amounts of the original sample were mixed with the obtained blanks to prepare the calibration curves standards. An additional calibration model with plant-based CRMs was also evaluated. Zinc mass fractions calculated using the calibration model of HCl-extracted and mixed samples showed statistical agreement with the reference data as demonstrated by the Student t-test at a 95 % confidence level. The linear correlation factor (r) was greater than 0.99, with a detection limit as low as 2.23 mg kg−1. The obtained calibration model also exhibited high prediction capability, as revealed by the low RMSEP (Root Mean Square Error of Prediction), i.e., 2.16 mg kg−1. We concluded that pXRF is an attractive and cost-effective method for direct, quick, and environmentally friendly Zn determination in common beans.

Breast cancer promotes the expression of neurotransmitter receptor related gene groups and image simulation of prognosis model

Breast cancer (BC), a prevalent and severe malignancy, detrimentally affects women globally. Its prognostic implications are profoundly influenced by gene expression patterns. This study retrieved 509 BC-associated oncogenes and 1,012 neurotransmitter receptor-related genes from the GSEA and KEGG databases, intersecting to identify 98 relevant genes. Clinical and transcriptomic expression data related to BC were downloaded from the TCGA, and differential genes were identified based on an FDR value <0.05 & |log2FC| ≥ 0.585. Univariate analysis of these genes revealed that high expression of NSF and low expression of HRAS, KIF17, and RPS6KA1 are closely associated with BC survival prognosis. A prognostic model constructed for these four genes demonstrated significant prognostic relevance for BC-TCGA patients (P < 0.001). Subsequently, an immunofunctional analysis of the BC oncogene-neurotransmitter receptor-related gene cluster revealed the involvement of immune cells such as T cells CD8, T cells CD4 memory resting, and Macrophages M2. Further analysis indicated that immune functions were primarily concentrated in APC_co_inhibition, APC_co_stimulation, CCR, and Check-point, among others. Lastly, a prognostic nomogram model was established, and ROC curve analysis revealed that the nomogram is a vital indicator for assessing BC prognosis, with 1-year, 3-year, and 5-year survival rates of 0.981, 0.897, and 0.802, respectively. This model demonstrates high calibration, clinical utility, and predictive capability, promising to offer an effective preliminary tool for clinical diagnostics.

Exploring soil pollution patterns in Ghana's northeastern mining zone using machine learning models

This study assessed the pollution status and effectiveness of machine learning models in predicting pollution indices in soils from a mining area in Northeastern Ghana. 552 soil samples were analysed with an Energy Dispersive X-ray Fluorescence (ED-XRF) spectrometer for their elemental concentrations. Four pollution indices; Nemerow Integrated Pollution Index (NIPI), degree of contamination (Cdeg), modified degree of contamination (mCdeg) and Pollution Load Index (PLI). Additionally, the Multivariate Adaptive Regression Splines (MARS) machine learning approach were used. The high CV%, skewness, and kurtosis values show a high degree of variability and uneven distribution patterns which denotes dispersed hotspots that can be interpreted as an influence of gold anomalies and illegal mining activities in the area. V (120.86 mg/L), Cr (242.42 mg/L), Co (30.92 mg/L) Ba (337.62 mg/L), and Zn (35.42 mg/L) recorded values higher than the global and regional contaminant thresholds. The NIPI shows that 46.74% and 26.81% of samples are slightly and moderately polluted respectively. The Cdeg analysis supports these findings, with 36.96% and 41.49% of samples classified as having “moderate” to “considerable” contamination, respectively. The PLI indicates progressive soil quality deterioration (43.84%) of samples reflecting substantial environmental disturbance. The pollution indices show the effect of illegal mining on Shaega, Buin and other areas in the eastern boundary of the study. The MARS models developed for the study demonstrated high predictive capabilities with an R2 value of 0.9665 for model 1 (NIPI), and RMSE and MAE values of 0.8227 and 0.4287 respectively. For model 2 (Cdeg), R2 value of 0.9863, RMSE and MAE of 1.0416 and 0.6181, respectively. Model 3 (mCdeg) produced an R2 value of 0.9844, RMSE and MAE of 0.1225 and 0.0670. These findings suggest MARS models can be an integral tool for soil quality analysis in cooperation with pollution indices. The study suggests that remedial and legislative measures be implemented to address the issue of illegal mining in the area.

Prediction and minimization of blasting flyrock distance, using deep neural networks and gravitational search algorithm, JAYA, and multi-verse optimization algorithms

Flyrock represents a significant and fundamental challenge in surface mine blasting, carrying inherent risks to humans and the environment. Consequently, accurate prediction, minimization, and identification of the factors influencing flyrock distance are imperative for effective control and mitigation of its destructive consequences. Machine learning and artificial intelligence methodologies have emerged as viable means to predict and simulate in different scientific fields. This study employs Deep Neural Network in conjunction with three optimization algorithms including the JAYA Algorithm, Multi-Verse Optimization Algorithm, and Gravitational Search Algorithm to predict blasting flyrock distance. The developed model consists of a combination of seven input parameters, encompassing both blasting design parameters and rock geomechanical properties. The output of the Deep Neural Networks model is the flyrock distance. For the training and testing of the model, a dataset comprising of 245 blasting records, collected from Songun copper mine, Iran, was utilized. The DNN model yielded an R2 value of 0.96 and an MSE value of 34.11. These results demonstrate the high accuracy and predictive capability of the model. Furthermore, the application of three optimization algorithms resulted in similar optimized parameter values, which minimized flyrock distances.

Assessing muscle invasion in bladder cancer via virtual biopsy: a study on quantitative parameters and classical radiomics features from dual-energy CT imaging

ObjectiveTo evaluate the prediction value of Dual-energy CT (DECT)-based quantitative parameters and radiomics model in preoperatively predicting muscle invasion in bladder cancer (BCa).Materials and methodsA retrospective study was performed on 126 patients with BCa who underwent DECT urography (DECTU) in our hospital. Patients were randomly divided into training and test cohorts with a ratio of 7:3. Quantitative parameters derived from DECTU were identified through univariate and multivariate logistic regression analysis to construct a DECT model. Radiomics features were extracted from the 40, 70, 100 keV and iodine-based material-decomposition (IMD) images in the venous phase to construct radiomics models from individual and combined images using a support vector machine classifier, and the optimal performing model was chosen as the final radiomics model. Subsequently, a fusion model combining the DECT parameters and the radiomics model was established. The diagnostic performances of all three models were evaluated through receiver operating characteristic (ROC) curves and the clinical usefulness was estimated using decision curve analysis (DCA).ResultsThe normalized iodine concentration (NIC) in DECT was an independent factor in diagnosing muscle invasion of BCa. The optimal multi-image radiomics model had predictive performance with an area-under-the-curve (AUC) of 0.867 in the test cohort, better than the AUC = 0.704 with NIC. The fusion model showed an increased level of performance, although the difference in AUC (0.893) was not statistically significant. Additionally, it demonstrated superior performance in DCA. For lesions smaller than 3 cm, the fusion model showed a high predictive capability, achieving an AUC value of 0.911. There was a slight improvement in model performance, although the difference was not statistically significant. This improvement was observed when comparing the AUC values of the DECT and radiomics models, which were 0.726 and 0.884, respectively.ConclusionThe proposed fusion model combing NIC and the optimal multi-image radiomics model in DECT showed good diagnostic capability in predicting muscle invasiveness of BCa.

DEVELOPMENT OF MATHEMATICAL MODELS FOR EVALUATING DEMAND PARAMETER ELASTICITY IN RETAIL NETWORKS UNDER CONSUMER-DRIVEN LOGISTICS

The retail market is characterised by its dynamic nature, requiring sophisticated models for accurately assessing demand elasticity, especially within consumer-driven logistics. This study aims to develop and validate comprehensive mathematical models to evaluate demand parameters within retail networks. The study focuses on the main factors that significantly influence demand, including the number of participants within a retail network, the average number of end consumers, and the ratio of the freight flow cost in a particular retail network compared to the average price in other networks. The authors conducted extensive data collection across various retail networks, capturing essential parameters such as demand volume, the number of participants, consumer numbers, and pricing strategies. The analysis led to the development of regression models that provide valuable insights into demand dynamics. The results indicate that increasing network participants and the average number of end consumers positively correlates with higher demand volumes. On the other hand, a higher ratio of freight flow cost within a retail network negatively impacts demand, highlighting consumers’ sensitivity to price changes. This inverse relationship between cost and demand underlines the importance of pricing strategies in influencing consumer behaviour. The statistical validation of the developed models demonstrated their reliability, with high correlation coefficients and low approximation errors, confirming their high predictive capabilities. These models are not only theoretically sound but also offer substantial practical applications. Retail networks can leverage these models to optimise their marketing strategies, plan their product assortments more effectively, and manage inventory more precisely. By integrating multiple factors influencing demand, this study provides a more nuanced understanding of consumer behaviour, enabling retail networks to make well-informed, data-driven decisions. The unique contribution of this research lies in its holistic approach to demand modelling, where multiple variables are considered in conjunction rather than in isolation. This integration allows for a deeper comprehension of how different elements interact to influence consumer demand. Moreover, the models developed in this study are versatile and can be adapted to various retail settings, offering a valuable tool for academic researchers and industry practitioners. Future research could extend these models by incorporating additional variables such as seasonality, shifts in consumer preferences, and the impact of technological advancements on retail logistics. Doing so makes it possible to continuously refine the models to maintain relevance and accuracy in an ever-changing market landscape. This ongoing evolution of demand modelling is crucial for retail networks aiming to stay competitive and responsive to consumer needs in a highly dynamic environment. The findings from this study underscore the importance of a data-driven approach in retail logistics, where precise modelling and analysis can lead to significant improvements in operational efficiency and market responsiveness. Keywords: demand elasticity, retail network, logistics, mathematical modelling, demand parameters, consumer behaviour.

Machine learning-based q-RASAR predictions of the bioconcentration factor of organic molecules estimated following the organisation for economic co-operation and development guideline 305

In this study, we utilized an innovative quantitative read-across (RA) structure-activity relationship (q-RASAR) approach to predict the bioconcentration factor (BCF) values of a diverse range of organic compounds, based on a dataset of 575 compounds tested using Organisation for Economic Co-operation and Development Test Guideline 305 for bioaccumulation in fish. Initially, we constructed the q-RASAR model using the partial least squares regression method, yielding promising statistical results for the training set (R2 =0.71, Q2LOO=0.68, mean absolute error [MAE]training=0.54). The model was further validated using the test set (Q2F1=0.77, Q2F2=0.75, MAEtest=0.51). Subsequently, we explored the q-RASAR method using other regression-based supervised machine-learning algorithms, demonstrating favourable results for the training and test sets. All models exhibited R2 and Q2F1 values exceeding 0.7, Q2LOO values greater than 0.6, and low MAE values, indicating high model quality and predictive capability for new, unidentified chemical substances. These findings represent the significance of the RASAR method in enhancing predictivity for new unknown chemicals due to the incorporation of similarity functions in the RASAR descriptors, independent of a specific algorithm.

Risk factors and prognostic modeling in bridging extracorporeal membrane oxygenation patients before lung transplantation.

The increased use of preoperative extracorporeal membrane oxygenation (ECMO) as a life support system before lung transplantation demands a better understanding of the associated prognostic factors. This study aims to discern the critical factors influencing the survival outcomes of ECMO patients and design a prognostic model tailored to this patient group. We retrospectively gathered and analyzed baseline and clinical data of patients who underwent preoperative bridging ECMO before lung transplantation from the United Network for Organ Sharing (UNOS) database. Univariate and multivariate Cox regression analyses were conducted and a prognostic model was generated to identify the independent factors influencing survival outcomes in these patients. The predictive model was cross-validated using the k-fold method where k=5. Our study included 1,202 patients. Both single and multiple analyses showed that age over 51 years, high body mass index (BMI), a history of dialysis before transplantation, donor hypertension, prolonged cold ischemia time, and high serum total bilirubin are adverse prognostic factors for the survival of ECMO-bridged lung transplant patients. Using the multivariate analysis, we created a prognosis model and a nomogram to predict 1-year post-transplant survival, with a receiver operating characteristic (ROC) curve area of 0.760 in internal validation. The 1-year survival rate calibration curve supported the nomogram's accuracy. This study involved the development of a survival prognosis model for patients undergoing lung transplantation with preoperative ECMO bridging, which was validated through extensive data analysis. The prognosis model exhibited high accuracy and predictive capability, effectively predicting the survival outcomes of patients both pre- and post-surgery.

The Role of Triglyceride/HDL Ratio, Triglyceride-Glucose Index, and Pan-Immune-Inflammation Value in the Differential Diagnosis of Acute Coronary Syndrome and Predicting Mortality.

Objectives: We aimed to evaluate the predictive importance of various clinical and laboratory parameters in the differential diagnosis of Acute Coronary Syndrome (ACS). Understanding these predictors is critical for improving diagnostic accuracy, guiding therapeutic decisions, and ultimately enhancing patient outcomes. Methods: The study included a total of 427 patients diagnosed with ACS, comprising 142 with unstable angina, 142 with non-ST elevation myocardial infarction (NSTEMI), and 143 with ST elevation myocardial infarction (STEMI). The data were collected from medical records of patients treated at a tertiary care hospital between January 2020 and December 2024. In addition to other biochemical parameters, triglyceride/HDL ratio (THR), triglyceride-glucose index (TGI), and Pan-Immune-Inflammation Value (PIV) were calculated and compared. Results: THR, TGI, PIV, and mortality rate were statistically higher in the STEMI group (p = 0.034, p = 0.031, p = 0.022, p = 0.045, respectively). The risk factors were found to be significantly associated with STEMI in the multiple logistic regression analysis and included age, total cholesterol, triglycerides, diabetes mellitus, smoking, cTnI, LVEF, THR, TGI, and PIV. High THR increases the risk of STEMI (AUC = 0.67, 95% CI: 0.62-0.72, p = 0.020). High THR increases the risk of mortality in ACS patients (AUC = 0.70, 95% CI: 0.65-0.75, p = 0.004). THRs above 3.5 are associated with higher risk. Sensitivity is 75% and specificity is 60%. High TGI increases the risk of mortality in ACS patients (AUC = 0.73, 95% CI: 0.68-0.78, p = 0.007). TGIs above 8.5 are associated with higher risk. Sensitivity is 78% and specificity is 63%. High PIVs increase the risk of mortality in ACS patients (AUC = 0.75, 95% CI: 0.70-0.80, p = 0.009). PIVs above 370 are associated with higher risk. Sensitivity is 80% and specificity is 65%. The combination of TGI, THR, PIV, and cTnI has the highest predictive capability over individual parameters for STEMI and mortality. Conclusions: We found that age, total cholesterol, triglycerides, cTnI, THR, TGI, and PIV increase, low LVEF, presence of diabetes mellitus, and smoking have predictive values for STEMI and mortality in patients with ACS. Unlike the studies in the literature, this is the first study in which cTnI, THR, TGI, and PIV values were evaluated together in ACS and mortality prediction.

Machine learning assisted designing of Y-series small molecule acceptors: Library generation and property prediction

The designing of new small molecule acceptors (SMAs) for organic solar cells has been a prominent area of research for many decades. It is challenging to find unique materials due to expensive experimentation. Machine learning emerges as a promising tool for rapid and cost-effective prediction of properties. In the current study, electron affinity is predicted using machine learning models. Molecular descriptors are calculated for model training. Four machine learning models are used. A reasonably high predictive capability is obtained for random forest model (r-squared values of 0.92 and 0.82 for training and test set, respectively). Furthermore, the chemical database of small molecule acceptors is generated. Generated virtual space is anticipated by exploiting the t-distributed stochastic neighbor embedding (t-SNE) method. Structure Activity Landscape Index (SALI) analysis is conducted to examine how properties change with structural variations. A minimal change in electron affinity resulted from structural modifications. Additionally, clustering analysis is performed on selected small molecule acceptors for structural grouping of SMAs. Five SMAs with lowest synthetic accessibility (SA) score are selected for density functional theory (DFT) calculations. The introduced multiple dimensional framework has potential screened the materials in short-time and efficient way.

Contextual AI models for single-cell protein biology

Understanding protein function and developing molecular therapies require deciphering the cell types in which proteins act as well as the interactions between proteins. However, modeling protein interactions across biological contexts remains challenging for existing algorithms. Here we introduce PINNACLE, a geometric deep learning approach that generates context-aware protein representations. Leveraging a multiorgan single-cell atlas, PINNACLE learns on contextualized protein interaction networks to produce 394,760 protein representations from 156 cell type contexts across 24 tissues. PINNACLE’s embedding space reflects cellular and tissue organization, enabling zero-shot retrieval of the tissue hierarchy. Pretrained protein representations can be adapted for downstream tasks: enhancing 3D structure-based representations for resolving immuno-oncological protein interactions, and investigating drugs’ effects across cell types. PINNACLE outperforms state-of-the-art models in nominating therapeutic targets for rheumatoid arthritis and inflammatory bowel diseases and pinpoints cell type contexts with higher predictive capability than context-free models. PINNACLE’s ability to adjust its outputs on the basis of the context in which it operates paves the way for large-scale context-specific predictions in biology.

Contextual AI models for single-cell protein biology.

Understanding protein function and developing molecular therapies require deciphering the cell types in which proteins act as well as the interactions between proteins. However, modeling protein interactions across biological contexts remains challenging for existing algorithms. Here, we introduce Pinnacle, a geometric deep learning approach that generates context-aware protein representations. Leveraging a multi-organ single-cell atlas, Pinnacle learns on contextualized protein interaction networks to produce 394,760 protein representations from 156 cell type contexts across 24 tissues. Pinnacle's embedding space reflects cellular and tissue organization, enabling zero-shot retrieval of the tissue hierarchy. Pretrained protein representations can be adapted for downstream tasks: enhancing 3D structure-based representations for resolving immuno-oncological protein interactions, and investigating drugs' effects across cell types. Pinnacle outperforms state-of-the-art models in nominating therapeutic targets for rheumatoid arthritis and inflammatory bowel diseases, and pinpoints cell type contexts with higher predictive capability than context-free models. Pinnacle's ability to adjust its outputs based on the context in which it operates paves way for large-scale context-specific predictions in biology.

Diagnostic performance of plasma pTau217, pTau181, Aβ1-42 and Aβ1-40 in the LUMIPULSE automated platform for the detection of Alzheimer disease

BackgroundRecently developed blood markers for Alzheimer's disease (AD) detection have high accuracy but usually require ultra-sensitive analytic tools not commonly available in clinical laboratories, and their performance in clinical practice is unknown.MethodsWe analyzed plasma samples from 290 consecutive participants that underwent lumbar puncture in routine clinical practice in a specialized memory clinic (66 cognitively unimpaired, 130 participants with mild cognitive impairment, and 94 with dementia). Participants were classified as amyloid positive (A +) or negative (A-) according to CSF Aβ1–42/Aβ1–40 ratio. Plasma pTau217, pTau181, Aβ1–42 and Aβ1–40 were measured in the fully-automated LUMIPULSE platform. We used linear regression to compare plasma biomarkers concentrations between A + and A- groups, evaluated Spearman’s correlation between plasma and CSF and performed ROC analyses to assess their diagnostic accuracy to detect brain amyloidosis as determined by CSF Aβ1–42/Aβ1–40 ratio. We analyzed the concordance of pTau217 with CSF amyloidosis.ResultsPlasma pTau217 and pTau181 concentration were higher in A + than A- while the plasma Aβ1–42/Aβ1–40 ratio was lower in A + compared to A-. pTau181 and the Aβ1–42/Aβ1–40 ratio showed moderate correlation between plasma and CSF (Rho = 0.66 and 0.69, respectively). The areas under the ROC curve to discriminate A + from A- participants were 0.94 (95% CI 0.92–0.97) for pTau217, and 0.88 (95% CI 0.84–0.92) for both pTau181 and Aβ1–42/Aβ1–40. Chronic kidney disease (CKD) was related to increased plasma biomarker concentrations, but ratios were less affected. Plasma pTau217 had the highest fold change (× 3.2) and showed high predictive capability in discriminating A + from A-, having 4–7% misclassification rate. The global accuracy of plasma pTau217 using a two-threshold approach was robust in symptomatic groups, exceeding 90%.ConclusionThe evaluation of blood biomarkers on an automated platform exhibited high diagnostic accuracy for AD pathophysiology, and pTau217 showed excellent diagnostic accuracy to identify participants with AD in a consecutive sample representing the routine clinical practice in a specialized memory unit.

Parameter Sensitivity Analysis for Machining Operation of Autofrettaged Cylinder Using Taguchi Method

This study investigates the impact of material parameters such as yield strength (Sy), Young’s modulus (E), and tangent modulus (T) on the safety factor (SF) of autofrettaged cylinders under 400 MPa working pressure, considering the three scenarios: no machining, internal machining, and external machining. Finite element (FE) simulations were conducted based on the Taguchi experimental design and converted into signal-to-noise (S/N) ratios to determine the optimal settings. ANOVA was utilized to evaluate the significance and percentage contributions of each factor. The analysis indicated that Sy is the most influential parameter on SF, contributing approximately 98.20% across all scenarios, including no machining, internal machining, and external machining. The contributions of E and T were minimal, but T had a slightly greater effect than E. The analytical validation of the FE model showed good agreement, with a maximum deviation of 4.37% for no machining, 4.75% for internal machining, and 5.20% for external machining. Regression analysis further confirmed the high prediction capability of the model, validated using AISI 4340 steel. The study concludes that internal machining results in higher residual stress loss compared to external machining. Overall, the analytical method tends to provide lower SF values than the numerical method, highlighting its conservative nature.

Assessing the predictive ability of information globalization under global value chains‐environmental sustainability nexus in the BRICS economies: A nonparametric causality approach

AbstractThe expansion of cross‐border information and production resources is facilitated by information globalization through the transfer of fresh ideas, products, technologies, and business models. This encourages information globalization's potential to achieve environmental and other technological advancements in the meantime and helps to make greener production possible through value‐added trade. Prior research, however, largely ignored this aspect of globalization in global value chains' studies. In order to anticipate carbon emissions (CO2) in BRICS economies, this novel study aims to assess the significance of participation in global value chains (GVCs) and information globalization (ING). The innovative research uses nonparametric causality‐in‐quantiles techniques on quarterly data from 1995Q1 to 2018Q4 to quantify for causality‐in‐variance and causality‐in‐mean because there might not be any causation at the first stage but there might be higher‐order interdependencies. The results show that GVC and ING had high predictive capability for carbon emissions, indicating asymmetry regarding environmental sustainability. Additionally, GVC and information globalization asserted that there is a significant interaction effect when it comes to forecasting pollution levels in chosen nations. The provision of financial and R&amp;D assistance for energy efficiency and green production, as well as the use of mass and social media to raise awareness among the firms participating in global value chains, may assist in achieving SDG 13 and Cope26's goal of reducing pollution by 2030. The finding contributes crucial insights for policymakers and enhances the discourse on sustainable hones inside GVCs. The study proposes prioritizing information transparency, worldwide measures, and motivations for eco‐friendly advances to improve environmental sustainability in GVCs. Policymakers are encouraged to foster public–private associations for cohesive global endeavors in diminishing CO2 emanations inside GVCs.

Development and validation of a nomogram predictive model for cognitive impairment in cerebral small vessel disease: a comprehensive retrospective analysis.

Cerebral small vessel disease (CSVD) is a common neurodegenerative condition in the elderly, closely associated with cognitive impairment. Early identification of individuals with CSVD who are at a higher risk of developing cognitive impairment is crucial for timely intervention and improving patient outcomes. The aim of this study is to construct a predictive model utilizing LASSO regression and binary logistic regression, with the objective of precisely forecasting the risk of cognitive impairment in patients with CSVD. The study utilized LASSO regression for feature selection and logistic regression for model construction in a cohort of CSVD patients. The model's validity was assessed through calibration curves and decision curve analysis (DCA). A nomogram was developed to predict cognitive impairment, incorporating hypertension, CSVD burden, apolipoprotein A1 (ApoA1) levels, and age. The model exhibited high accuracy with AUC values of 0.866 and 0.852 for the training and validation sets, respectively. Calibration curves confirmed the model's reliability, and DCA highlighted its clinical utility. The model's sensitivity and specificity were 75.3 and 79.7% for the training set, and 76.9 and 74.0% for the validation set. This study successfully demonstrates the application of machine learning in developing a reliable predictive model for cognitive impairment in CSVD. The model's high accuracy and robust predictive capability provide a crucial tool for the early detection and intervention of cognitive impairment in patients with CSVD, potentially improving outcomes for this specific condition.

Prediction of COD in industrial wastewater treatment plant using an artificial neural network

In this investigation, the modeling of the Aksaray industrial wastewater treatment plant was performed using artificial neural networks with various architectures in the MATLAB software. The dataset utilized in this study was collected from the Aksaray wastewater treatment plant over a 9-month period through daily records. The treatment efficiency of the plants was assessed based on the output values of chemical oxygen demand (COD) output. Principal component analysis (PCA) was applied to furnish input for the Feedforward Backpropagation Artificial Neural Networks (FFBANN). The model’s performance was evaluated using the Mean Squared Error (MSE), the Mean Absolute Error (MAE) and correlation coefficient (R2) parameters. The optimal architecture for the neural network model was determined through several trial and error iterations. According to the modeling results, the ANN exhibited a high predictive capability for plant performance, with an R2 reaching up to 0.9997 when comparing the observed and predicted output variables.