Search Results (827)

Background/Objectives: We aim to identify predictors of response to ICIs in patients with advanced solid tumors that exhibiting a TMB ≥ 10 mut/Mb. Methods: Patients treated with ICIs alone at Northwestern University between 1 January 2015 and 31 December 2020 were identified. Progression-free survival (PFS) and overall survival (OS) were calculated using the Kaplan–Meier method, and groups were compared using the log-rank test. Wilcoxon rank sum tests, chi-squared tests, and Fisher’s exact tests were used for univariable analyses evaluating the impact of clinical and genetic variables on response, with significance defined as p < 0.05. Results: A total of 117 patients were classified as ICI-sensitive (n = 88) or non-ICI-sensitive (n = 29). Among evaluable patients (n = 105), the overall response rate was 34% with 14% achieving a complete response. Median PFS and OS were 8.05 months and 26.8 months, respectively. Higher PFS rates were significantly linked to the ICI-sensitive tumor group (p = 0.009), absence of liver metastasis (p = 0.015), and no prior systemic treatment (p = 0.001) in both cohorts. In non-ICI-sensitive patients, a TMB of ≥15 mut/Mb correlated with improved outcomes (p = 0.012). Mutations in the MYC pathway (p = 0.03) and the MLL2 gene (p = 0.014) were associated with poorer responses, while mutations in the TERT gene were linked to better responses (p = 0.031). Conclusions: Patients without liver metastasis, mutations in TERT, and TMB ≥ 15 mut/Mb are associated with superior response, while mutations in the MYC pathway and MLL2 are associated with worse responses. Full article

In this paper, an innovative method for the determination of the dynamic coefficients of tilting pad journal bearings (TPJBs) is described, and some of its characteristics are analyzed. The calculation is based on a parabolic modeling of the dependence of the dynamic coefficients on the excitation frequency, on the estimation of the forces acting on the bearing as a function of the estimated displacements using a linear model and, finally, on the search for the best estimate of the parabola coefficients by minimizing the sum of the squares of the normalized residuals of displacements and forces on the bearings. The normalization is performed by dividing the deviations (between the measured values and those calculated by the model) by an estimate of the standard deviation of the force and displacement measurements. The results for a flooded tilting pad journal bearing, TPJB, are presented and compared with those obtained using traditional methods. The synchronous coefficients are also calculated and compared with those determined by linear interpolation. A preliminary statistical analysis of the sensitivity of the results to the variation in the standard deviation of the forces and displacements is presented. An extension of the model is proposed so that the coefficients of the optimal parabolas can be estimated as a function of the shaft rotation frequency. Full article

Background/Objectives: Olfactory impairment has been proposed as an early marker for Alzheimer’s disease (AD), yet the mechanisms linking sensory decline to genetic and environmental risk factors remain unclear. We aimed to identify early biomarkers and brain network alterations associated with AD risk by multimodal analyses in humanized APOE mice. Methods: We evaluated olfactory behavior, diffusion MRI connectomics, and brain and blood transcriptomics in mice stratified by APOE2, APOE3, and APOE4 genotypes, age, sex, high-fat diet, and immune background (HN). Behavioral assays assessed odor salience, novelty detection, and memory. Elastic Net-regularized multi-set canonical correlation analysis (MCCA) was used to link behavior to brain connectivity. Blood transcriptomics and gene ontology analyses identified peripheral molecular correlates. Results: APOE4 mice exhibited accelerated deficits in odor-guided behavior and memory, especially under high-fat diet, while APOE2 mice were more resilient (ANOVA: APOE x HN, F(2, 1669) = 77.25, p < 0.001, eta squared = 0.08). Age and diet compounded behavioral impairments (diet x age: F(1, 1669) = 16.04, p < 0.001). Long-term memory was particularly reduced in APOE4 mice (APOE x HN, F(2,395) = 5.6, p = 0.004). MCCA identified subnetworks explaining up to 24% of behavioral variance (sum of canonical correlations: 1.27, 95% CI [1.18, 1.85], p < 0.0001), with key connections involving the ventral orbital and somatosensory cortices. Blood eigengene modules correlated with imaging changes (e.g., subiculum diffusivity: r = −0.5, p < 1 × 10⁻³⁰), and enriched synaptic pathways were identified across brain and blood. Conclusions: Olfactory behavior, shaped by genetic and environmental factors, may serve as a sensitive, translatable biomarker of AD risk. Integrative systems-level approaches reveal brain and blood signatures of early sensory–cognitive vulnerability, supporting new avenues for early detection and intervention in AD. Full article

Background: Type 2 diabetes mellitus (T2DM) increases sepsis risk due to immune dysfunction and chronic inflammation. Antidiabetic medications, while primarily used for glycemic control, may modulate sepsis susceptibility through immune and inflammatory pathways. This study investigates the association between antidiabetic medication use and sepsis risk in T2DM patients. Methods: A longitudinal cohort study was conducted using clinical registry data from 5009 T2DM patients at the University Hospital, Debrecen, Hungary (2016–2020). Sepsis cases were identified via ICD-10 code A41, and antidiabetic medication use was categorized using ATC codes. Baseline comorbidities and laboratory parameters were extracted. Chi-square and Wilcoxon rank–sum tests assessed associations between sepsis and categorical/numerical variables, respectively. Time-adjusted multivariate logistic regression evaluated predictors of sepsis risk, with odds ratios (ORs) and 95% confidence intervals (CIs) reported. Results: Age, hypertension, ischemic heart disease, nephropathy, elevated blood glucose, C-reactive protein, and creatinine also independently increased sepsis risk. Insulin use was associated with a 2.6-fold increased sepsis risk (OR = 2.6, 95% CI: 2.09–3.34, p < 0.001), while SGLT2 inhibitors (OR = 0.56, 95% CI: 0.34–0.91, p = 0.02) and GLP-1 receptor agonists (OR = 0.39, 95% CI: 0.19–0.79, p = 0.009) were protective. Conclusions: Insulin-treated patients may require closer infection monitoring, while SGLT2 inhibitors and GLP-1 RAs could be prioritized in high-risk individuals. These findings highlight the potential to inform risk stratification and guide personalized antidiabetic therapy to reduce sepsis risk in T2DM. Full article

Background/Objectives: While elevated Lp(a) levels are associated with incident heart failure development, the role of Lp(a) in established heart failure with reduced ejection fraction (HFrEF) versus heart failure with preserved ejection fraction (HFpEF) remains unexplored. Methods: We conducted a retrospective analysis of 387 heart failure patients from our institutional database (January 2018–June 2024). Patients were categorized as HFrEF (n = 201) or HFpEF (n = 186) using ICD-10 codes. Categorical variables were compared between heart failure types using the Chi-square test or Fisher’s Exact test, and continuous variables were compared using the two-sample t-test or Wilcoxon rank-sum test, as appropriate. Logistic regression was utilized to assess heart failure type as a function of Lp(a) levels, adjusting for covariates. Spearman correlation assessed relationships between Lp(a) and pro-BNP levels. Results: Despite significant demographic and clinical differences between HFrEF and HFpEF patients, Lp(a) concentrations showed no significant variation between groups. Median Lp(a) levels were 60.9 nmol/dL (IQR: 21.9–136.7) in HFrEF versus 45.0 nmol/dL (IQR: 20.1–109.9) in HFpEF (p = 0.19). After adjusting for demographic and clinical covariates, Lp(a) showed no association with heart failure subtype (OR: 1.001, 95% CI: 0.99–1.004; p = 0.59). Conclusions: Lp(a) levels do not differ significantly between HFrEF and HFpEF phenotypes, suggesting possible shared pathophysiological mechanisms rather than phenotype-specific biomarker properties. These preliminary findings may support unified screening and treatment strategies for elevated Lp(a) across heart failure, pending confirmation in larger studies. Full article

This paper presents a comparative study of data-driven modeling approaches for vanadium redox flow batteries (VRFBs), utilizing Multiple Linear Regression (MLR) and Random Forest (RF) algorithms. Experimental voltage–capacity datasets from a 1 kW/1 kWh VRFB system were digitized, processed, and used for model training, validation, and testing. The MLR model, built using eight optimized features, achieved a mean error (ME) of 0.0204 V, a residual sum of squares (RSS) of 8.87, and a root mean squared error (RMSE) of 0.1796 V on the test data, demonstrating high predictive performance in stationary operating regions. However, it exhibited limited accuracy during dynamic transitions. Optimized through out-of-bag (OOB) error minimization, the Random Forest model achieved a training RMSE of 0.093 V and a test RMSE of 0.110 V, significantly outperforming MLR in capturing dynamic behavior while maintaining comparable performance in steady-state regions. The accuracy remained high even at lower current densities. Feature importance analysis and partial dependence plots (PDPs) confirmed the dominance of current-related features and SOC dynamics in influencing VRFB terminal voltage. Overall, the Random Forest model offers superior accuracy and robustness, making it highly suitable for real-time VRFB system monitoring, control, and digital twin integration. This study highlights the potential of combining machine learning algorithms with electrochemical domain knowledge to enhance battery system modeling for future energy storage applications. Full article

This study quantitatively assesses the risk of water shortage (WSR) in the Baiyangdian area due to the Inter-Basin Water Transfer (IBWT) project, focusing on the impact of water transfer on regional water security. The actual evapotranspiration (ETa) is calculated, and the correlation simulation using Archimedes’ Copula function is implemented in Python 3.7.1, with optimization using the sum of squares of deviations (OLS) and the AIC criterion. The joint distribution model between ETa and three water supply scenarios is constructed. Key findings include (1) ETa increased by 27.3% after water transfer, far exceeding the slight increase in water supply before the transfer; (2) various Archimedean Copulas effectively capture the dependence and joint probability distribution between water supply and ETa; (3) water shortage risk increased after water transfer, with rainfall and upstream water unable to alleviate the problem in Baiyangdian; and (4) cross-basin water transfer reduced risk, with a reduction of 8.90% in the total probability of three key water resource scheduling combinations. This study establishes a Copula-based framework for water shortage risk assessment, providing a scientific basis for water allocation strategies in ecologically sensitive areas affected by human activities. Full article

As a core device in low-voltage distribution networks, the distribution zone transformer (DZT) is influenced by short circuits, overloads, and unbalanced loads, which cause thermal aging, mechanical stress, and eventually deformation of the winding, resulting in parameter deviations from nameplate values and impairing system operation. However, existing identification methods typically require synchronized high- and low-voltage data and are limited to symmetric three-phase conditions, which limits their application in practical distribution systems. To address these challenges, this paper proposes a parameter identification method for DZTs under three-phase unbalanced conditions. Firstly, based on the transformer’s T-equivalent circuit considering the load, the power flow equations are derived without involving the synchronization issue of high-voltage and low-voltage side data, and the sum of the impedances on both sides is treated as an independent parameter. Then, a novel power flow equation under three-phase unbalanced conditions is established, and an adaptive recursive least squares (ARLS) solution method is constructed using the measurement data sequence provided by the smart meter of the intelligent transformer terminal unit (TTU) to achieve online identification of the transformer winding parameters. The effectiveness and robustness of the method are verified through practical case studies. Full article

Background/Objectives: A common approach to investigating visual form processing is through studying responses to visual stimuli that comprise illusory contours. Such stimuli induce contours where none exist physically and thus reveal the constructive nature of visual perception and the conditions that engender it. The present work used IC stimuli to study the development of visual form detection and extraction in infants and adults. Methods: Infant and adult participants viewed square stimulus forms with either real or illusory contours, while their looking behavior was measured with an eye tracker. Fixations of the stimuli were coded by region, distinguishing between the contours of the forms and within the forms themselves. Fixations were summed by region, and fixations on forms were interpreted to index the detection of coherent, whole forms. Fixations on contours (real and illusory) were interpreted to index the extraction of form edges. Results: Total form fixations differed by age. For real contours, fixations by infants exceeded those by adults; when contours were illusory, adult fixations were greater than those of infants. Contour fixations were similar between ages. Infants and adults both looked more at contours when illusory than when real. Conclusions: Together, the results provide new conclusions about change and continuity in the visual analysis of form and contour. The results suggest that the visual detection and binding of simple form structure appears to develop between infancy and adulthood. However, the exploration of contours that support the extraction of form contours from backgrounds appears to change little between infancy and adulthood. Full article

The safety associated with the bearing capacity of pile foundations is intrinsically linked to the overall safety, stability, and economic viability of structural systems. In response to the need for rapid and precise predictions of pile bearing capacity, this study introduces a kernel extreme learning machine (KELM) prediction model optimized through a multi-strategy improved beetle optimization algorithm (IDBO), referred to as the IDBO-KELM model. The model utilizes the pile length, pile diameter, average effective vertical stress, and undrained shear strength as input variables, with the bearing capacity serving as the output variable. Initially, experimental data on pile bearing capacity was gathered from the existing literature and subsequently normalized to facilitate effective integration into the model training process. A detailed introduction of the multi-strategy improved beetle optimization algorithm (IDBO) is provided, with its superior performance validated through 23 benchmark functions. Furthermore, the Wilcoxon rank sum test was employed to statistically assess the experimental outcomes, confirming the IDBO algorithm’s superiority over other prevalent metaheuristic algorithms. The IDBO algorithm was then utilized to optimize the hyperparameters of the KELM model for predicting pile bearing capacity. In conclusion, the statistical metrics for the IDBO-KELM model demonstrated a root mean square error (RMSE) of 4.7875, a coefficient of determination (R²) of 0.9313, and a mean absolute percentage error (MAPE) of 10.71%. In comparison, the baseline KELM model exhibited an RMSE of 6.7357, an R² of 0.8639, and an MAPE of 18.47%. This represents an improvement exceeding 35%. These findings suggest that the IDBO-KELM model surpasses the KELM model across all evaluation metrics, thereby confirming its superior accuracy in predicting pile bearing capacity. Full article

The sorption process is most commonly described by Langmuir isotherms, which can be calculated from either a non-linear form or various linear forms. Despite the fact that the non-linear model is now preferred, articles using linear models continue to be submitted to journals. On the basis of 68 isotherms, it was found that the linear Hanes–Woolf model (the most commonly used) gives the most similar q_m and K_L values to the non-linear model. The largest differences were obtained by determining the isotherm from the non-linear and linear forms of the Lineweaver–Burk model (this is the model often used by researchers). The evaluation of isotherms should not be performed solely on the basis of the coefficient of determination R², which was intended for linear equations. Statistical measures such as the mean relative error, sum of squares of errors, chi-square statistic, sum of absolute errors, hybrid fractional error function, mean squared error were analysed. On the basis of the coefficient of determination, the Hanes–Woolf linear model was found to best describe the actual results, and on the basis of the other statistical measures, the isotherm determined from the non-linear form was found to be the best fit for the study. Full article

This research targets prediction of power consumption change (PCC) in the branch of electrical distribution grid between a sum meter and consumer meter in response to consumer load change. The problem is relevant for power preservation law-based event-driven methods aiming for detection of anomalies like meter errors, electricity thefts, etc. The PCC in the branch is due to the change of technical (wiring) losses as well as change of power consumption of loads connected to the same distribution branch. Using synthesized dataset set a data-driven model is built to predict PCC in the branch. Model performance is assessed using root mean squared error (RMSE), mean absolute, and mean relative error, together with their standard deviations. The preliminary experimental verification using a test bed confirmed the potential of the method. The accuracy of the PCC in the branch prediction is influenced by the systematic error of the meters. Therefore, the error of the consumer meter and the PCC in the branch cannot be evaluated separately. It was observed that the absolute error of the estimate of power measurement gain error was observed to be within ±0.3% and the relative error of PCC in the branch prediction was within ±10%. Full article

Various nature-based solutions (NbS)—such as constructed wetlands, drainage ditches, and vegetated buffer strips—have recently demonstrated strong potential for mitigating pollutant transport in open channels and river systems. Numerical modeling is a widely adopted and effective approach for assessing the performance of these interventions. This study presents the first development of a two-dimensional (2D) meshless advection–diffusion model based on an Eulerian smoothed particle hydrodynamics (SPH) framework, specifically designed to simulate passive pollutant transport in open channel flows. The proposed model marks a pioneering application of the ESPH technique to environmental pollutant transport problems. It couples the 2D depth-averaged shallow water equations with an advection–diffusion equation to represent both fluid motion and pollutant concentration dynamics. A uniform particle arrangement ensures that each fluid particle interacts symmetrically with eight neighboring particles for flux computation. To represent the pollutant transport process, the dispersion coefficient is defined as the sum of molecular and turbulent diffusion components. The turbulent diffusion coefficient is calculated using a prescribed turbulent Schmidt number and the eddy viscosity obtained from a Smagorinsky-type mixing-length turbulence model. Three analytical case studies, including one-dimensional transcritical open channel flow, 2D isotropic and anisotropic diffusion in still water, and advection–diffusion in a 2D uniform flow, are employed to verify the model’s accuracy and convergence. The model demonstrates first-order convergence, with relative root mean square errors (RRMSEs) of approximately 0.2% for water depth and velocity, and 0.1–0.5% for concentration. Additionally, the model is applied to a laboratory experiment involving 2D pollutant dispersion in a 90° junction channel. The simulated results show good agreement with measured velocity and concentration distributions. These findings indicate that the developed model is a reliable and effective tool for evaluating the performance of NbS in mitigating pollutant transport in open channels and river systems. Full article

To address the decline in measurement accuracy of magnetometers due to process errors and environmental interference, as well as the insufficient robustness of traditional calibration algorithms under strong interference conditions, this paper proposes an ellipsoid fitting algorithm based on Dynamic Adaptive Elite Particle Swarm Optimization (DAEPSO). The proposed algorithm integrates three enhancement mechanisms: dynamic stratified elite guidance, adaptive inertia weight adjustment, and inferior particle relearning via Lévy flight, aiming to improve convergence speed, solution accuracy, and noise resistance. First, a magnetometer calibration model is established. Second, the DAEPSO algorithm is employed to fit the ellipsoid parameters. Finally, error calibration is performed based on the optimized ellipsoid parameters. Our simulation experiments demonstrate that compared with the traditional Least Squares Method (LSM) the proposed method reduces the standard deviation of the total magnetic field intensity by 54.73%, effectively improving calibration precision in the presence of outliers. Furthermore, when compared to PSO, TSLPSO, MPSO, and AWPSO, the sum of the absolute distances from the simulation data to the fitted ellipsoidal surface decreases by 53.60%, 41.96%, 53.01%, and 27.40%, respectively. The results from 60 independent experiments show that DAEPSO achieves lower median errors and smaller interquartile ranges than comparative algorithms. In summary, the DAEPSO-based ellipsoid fitting algorithm exhibits high fitting accuracy and strong robustness in environments with intense interference noise, providing reliable theoretical support for practical engineering applications. Full article

The primary objective of this paper is to quantify a realistic pathway for Canada to reach net-zero emissions by 2050. This study analyzed greenhouse gas (GHG) emissions from the 10 provinces and 3 territories of Canada based on the emissions from their economic sectors. A time series analysis was performed to understand the trajectory of the emissions profile from 1990 to 2023. Using the 2023 emissions as the baseline, a linear reduction, based on the GHG proportions from each jurisdiction, was performed and projected to 2050 (except for Prince Edward Island (PEI), where net zero was targeted for 2040). Moreover, a machine learning technique (k-means unsupervised algorithm) was used to group all the jurisdictions into homogeneous regions for national strategic climate policy initiatives. The within-cluster sum of squares identified the following clusters: Cluster 1: Manitoba (MB), New Brunswick, Nova Scotia, and Newfoundland and Labrador; Cluster 2: Alberta (AB); Cluster 3: Quebec (QC) and Saskatchewan; Cluster 4: Ontario (ON); and Cluster 5: PEI, Northwest Territories, Nunavut, and Northwest Territories. Considering the maximum GHG reductions needed per cluster (Clusters 1–5), the results show that 0.309 Mt CO₂ eq/year, 5.447 Mt CO₂ eq/year, 1.293 Mt CO₂ eq/year, 2.217 Mt CO₂ eq/year, and 0.04 Mt CO₂ eq/year must be targeted from MB (transportation), AB (stationary combustion), QC (transportation), ON (stationary combustion) and PEI (transportation), respectively. The concept of climate stabilization wedges, which provides a practical framework for addressing the monumental challenge of mitigating climate change, was introduced to each derived region to cut GHG emissions in Canada through tangible, measurable actions that is specific to each sector/cluster. The clustering-based method breaks climate mitigation problems down into manageable pieces by grouping the jurisdictions into efficient regions that can be managed effectively by fostering collaboration across jurisdictions and economic sectors. Actionable and strategic recommendations were made within each province to reach the goal of net-zero. The implications of this study for policy and climate action include the fact that actionable strategies and tailored policies are applied to each cluster’s emission profile and economic sector, ensuring equitable and effective climate mitigation strategies in Canada. Full article