Search Results (96,221)

During deep coalbed methane (CBM) drilling, wellbore stability is significantly influenced by the interaction between drilling fluid and coal rock. However, quantitative data on mechanical degradation under long-term high-temperature and high-pressure conditions are lacking. This study subjected coal cores to immersion in field-formula drilling fluid at 60 °C and 10.5 MPa for 0–30 days, followed by uniaxial and triaxial compression tests under confining pressures of 0/5/10/20 MPa. The fracture evolution was tracked using micro-indentation (µ-indentation), nuclear magnetic resonance (NMR), and scanning electron microscopy (SEM), establishing a relationship between water absorption and strength. The results indicate a sharp decline in mechanical parameters within the first 5 days, after which they stabilized. Uniaxial compressive strength decreased from 36.85 MPa to 22.0 MPa (−40%), elastic modulus from 1.93 GPa to 1.07 GPa (−44%), cohesion from 14.5 MPa to 5.9 MPa (−59%), and internal friction angle from 24.9° to 19.8° (−20%). Even under 20 MPa confining pressure after 30 days, the strength loss reached 43%. Water absorption increased from 6.1% to 7.9%, showing a linear negative correlation with strength, with the slope increasing from −171 MPa/% (no confining pressure) to −808 MPa/% (20 MPa confining pressure). The matrix elastic modulus remained stable at 3.5–3.9 GPa, and mineral composition remained unchanged, confirming that the degradation was due to hydraulic wedging and lubrication of fractures rather than matrix damage. These quantitative thresholds provide direct evidence for predicting wellbore stability in deep CBM drilling. Full article

Postimplantation syndrome (PIS) is an early inflammatory response following endovascular stent-graft implantation (EVAR and TEVAR), defined by culture-negative fever and leukocytosis. The patient’s preoperative inflammatory status is thought to play a central role in its development. This study aimed to evaluate whether the systemic inflammatory response index (SIRI) and the eosinophil-to-lymphocyte ratio (ELR) can serve as preoperative predictors of PIS. Clinical data from 300 patients who underwent aortic endograft implantation and laboratory results obtained 24 h before the procedure, and at 24 h, 72 h, and 1 week postoperatively, were prospectively recorded. PIS was defined as culture-negative fever ≥ 37.8 °C accompanied by leukocytosis ≥ 12,000/µL. Inflammation-based indices derived from complete blood count (SIRI and ELR), along with serum C-reactive protein (CRP) and albumin levels, were compared between patients with and without PIS. Logistic regression and receiver operating characteristic (ROC) analyses were performed to identify independent predictors. PIS developed in 55 patients (18.3%). Patients with PIS were younger (70.1 ± 8.6 vs. 72.7 ± 7.3 years; p = 0.042) and had larger aneurysm diameters and greater mural thrombus thickness. Preoperatively, leukocyte count, SIRI, and CRP levels were significantly higher in patients who developed PIS, whereas ELR and albumin levels were lower. Multivariable analysis showed that a larger aneurysm diameter (OR: 1.2; 95% CI: 1.0–1.3; p = 0.003), greater mural thrombus thickness (OR: 1.3; 95% CI: 1.0–1.6; p = 0.012), EVAR procedure (OR: 3.7; 95% CI: 1.2–6.3; p = 0.033), elevated SIRI (OR: 1.9; 95% CI: 1.2–3.1; p = 0.005), and higher CRP (OR: 1.4; 95% CI: 1.1–3.2; p = 0.003) were significantly associated with PIS. In contrast, increasing age, higher ELR, and higher albumin levels were associated with a reduced risk of PIS. Simple biomarkers routinely obtained from standard laboratory tests can contribute meaningfully to the preoperative prediction and postoperative identification of PIS. Their integration into risk stratification models and confirmation against definitive diagnostic criteria will require validation in larger, multicenter studies. Full article

Background: Pediatric heart failure (PHF) remains a major contributor to morbidity and mortality, yet standardized diagnostic and prognostic frameworks–particularly those leveraging left ventricular ejection fraction (LVEF)–are not well-established. This study evaluates clinical profiles, therapeutic interventions, and mortality outcomes across LVEF thresholds while identifying an optimal cutoff to refine risk stratification in PHF. Methods: This multicenter retrospective cohort study analyzed 1449 PHF patients (aged 1–18 years) across 30 tertiary centers (2013–2022). LVEF stratification employed conventional thresholds (50%, 55%) and an ROC-optimized cutoff (53%, derived via Youden index maximization). The primary outcome was in-hospital all-cause mortality. Multivariable logistic regression models, adjusted for clinical covariates, evaluated mortality predictors. The discriminative performance of LVEF thresholds was compared using area under the curve (AUC) analysis. Results: Distinct clinical profiles, etiologies, and treatments were observed across LVEF strata (50% vs. 55%; p < 0.05). A data-driven optimized LVEF threshold of 53% was identified for mortality prediction, demonstrating superior diagnostic accuracy with enhanced sensitivity and specificity across age groups. Multivariate analysis revealed LVEF ≥ 55% as protective (OR = 0.81, 95% CI: 0.68–0.96, p = 0.003), while ≥50% was non-significant (OR = 0.91, 95% CI: 0.74–1.12, p = 0.06). Elevated BNP (OR = 2.78, p < 0.001) and NT-proBNP (OR = 2.34, p < 0.001) strongly correlated with mortality risk. Age and sex showed no significant association with outcomes. Conclusion: In conclusion, an LVEF of 53% emerged as the optimal pediatric threshold for mortality prediction, outperforming conventional cutoffs of 50% and 55%. The integration of LVEF with biomarkers (BNP/NT-proBNP) provides a robust prognostic framework, underscoring the necessity for pediatric-specific LVEF criteria and multidimensional risk assessment in PHF management. Full article

EsBax (bcl-2 Associated X protein), a member of the bcl-2 family involved in the mitochondrial apoptosis pathway, plays a crucial role in immune response and defense in invertebrates. In this study, we successfully cloned the full-length cDNA of EsBax from the Chinese mitten crab (Eriocheir sinensis) and investigated its immune-related functions. The EsBax cDNA is 3374 bp in length, including a 1563 bp open reading frame (ORF) encoding 521 amino acids, a 142 bp 5′ untranslated region (UTR), and a 1699 bp 3′ UTR. The predicted EsBax protein has a molecular weight of 58.0786 kD, a theoretical isoelectric point of 7.28, and contains three conserved BH domains (BH1-BH3), and a transmembrane domain (TM). Amino acid sequence analysis revealed the highest sequence identity (99.42%) with E. sinensis. For the expression analysis, three biological replicates were performed for each tissue and treatment group. Real-time quantitative PCR showed that EsBax mRNA was ubiquitously expressed in all examined tissues, with the highest expression in the hepatopancreas, followed by hemocytes, intestine, gill, and the lowest in muscle. Upon stimulation with lipopolysaccharide (LPS), Aeromonas hydrophila (AH), or cycloheximide (CHX), EsBax expression increased and peaked at 24 h (LPS and CHX) or 48 h (A. hydrophila), then decreased. These results suggest that EsBax expression is dynamically responsive to exogenous stimulants (LPS, A. hydrophila, and CHX) in E. sinensis, implying a potential role of EsBax in the molecular events associated with pathogen-induced apoptosis in this species. Full article

Seasonal fires in northern Thailand are a persistent environmental and public health concern, yet existing fire probability mapping approaches in Thailand rely heavily on subjective multi-criteria analysis (MCA) methods and temporally static data aggregation methods. To address these limitations, we present a flexible, replicable, and operationally viable seasonal fire probability mapping methodology using a Random Forest (RF) machine learning model in the Google Earth Engine (GEE) platform. We trained the model on historical fire occurrence and fire predictor layers from 2016–2023 and applied it to 2024 conditions to generate a probabilistic fire prediction. Our novel approach improves upon existing operational methods and scientific literature in several ways. It uses a more representative sample design which is agnostic to the burn history of fire presences and absences, pairs fire and fire predictor data from each year to account for interannual variation in conditions, empirically refines the most influential fire predictors from a comprehensive set of predictors, and provides a reproducible and accessible framework using GEE. Predictor variables include both socioeconomic and environmental drivers of fire, such as topography, fuels, potential fire behavior, forest type, vegetation characteristics, climate, water availability, crop type, recent burn history, and human influence and accessibility. The model achieves an Area Under the Curve (AUC) of 0.841 when applied to 2016–2023 data and 0.848 when applied to 2024 data, indicating strong discriminatory power despite the additional spatial and temporal variability introduced by our sample design. The highest fire probabilities emerge in forested and agricultural areas at mid elevations and near human settlements and roads, which aligns well with the known anthropogenic drivers of fire in Thailand. Distinct areas of model uncertainty are also apparent in cropland and forests which are only burned intermittently, highlighting the importance of accounting for localized burning cycles. Variable importance analysis using the Gini Impurity Index identifies both natural and anthropogenic predictors as key and nearly equally important predictors of fire, including certain forest and crop types, vegetation characteristics, topography, climate, human influence and accessibility, water availability, and recent burn history. Our findings demonstrate the heavy influence of data preprocessing and model design choices on model results. The model outputs are provided as interpretable probability maps and the methods can be adapted to future years or augmented with local datasets. Our methodology presents a scalable advancement in wildfire probability mapping with machine learning and open-source tools, particularly for data-constrained landscapes. It will support Thailand’s fire managers in proactive fire response and planning and also inform broader regional fire risk assessment efforts. Full article

Background: Advanced non-small cell lung cancer (NSCLC) has limited curative options and poor survival. Racotumomab, an anti-idiotype monoclonal antibody vaccine targeting tumor gangliosides, has shown efficacy in clinical trials. This study evaluated its real-world effectiveness as maintenance therapy following first-line chemotherapy. Materials and Methods: A multi-center observational study was conducted on 162 patients with advanced NSCLC who received racotumomab from 2012 to 2024. Effectiveness was evaluated in the intention-to-treat (ITT) cohort. Overall survival (OS) was estimated, with subgroup analyses conducted according to clinical and demographic factors. Results: The median OS was 14.9 months (95% CI: 11.7–18.1), and the 5-year survival rate reached 20%. Patients diagnosed with stage III disease, those with better Eastern Cooperative Oncology Group (ECOG) performance status, and individuals younger than 65 years experienced significantly longer survival. Racotumomab demonstrated a favorable hazard ratio compared to historical controls (HR 0.44 vs. supportive care; HR 0.55 vs. docetaxel). Conclusions: In the era of immune checkpoint inhibitors, these real-world results indicate a promising role for racotumomab in the maintenance setting for advanced NSCLC. These findings provide a strong rationale for further investigation of racotumomab in the context of modern immunotherapy, particularly in combination trials with other immunomodulatory antibodies, along with the validation of clinical and biologic predictive biomarkers. Full article

In order to investigate the basalt fiber influences on drying shrinkage of coal gangue ceramsite concrete, specimens with varying fiber dosages and matrix strength were prepared. The drying shrinkage (DS) was compared. To elucidate the characteristics of the DS, the internal humidity (IH) and electrical resistivity (ER) were also tested. The properties of the variation in the DS, IH, and ER were verified. The correlation between the values of the DS, IH, and ES was systematically analyzed, and a prediction model of DS considering the influence of fiber dosage and coal gangue ceramsite was proposed. The results showed that the incorporation of basalt fiber can significantly reduce the DS, and the value of the DS decreased with the increment of fiber dosage. The value of the DS also decreased with the enhancement of the matrix strength. An inverse relationship existed between the variation in the IH and DS, whereas the variation in the ER demonstrated a direct proportionality with the variation in the DS. The prediction model for the basalt fiber-reinforced coal gangue ceramsite concrete was obtained by modifying the AFREM model. The values predicted by the improved AFREM model demonstrated excellent consistency with the test data. Full article

Metamaterial lattice structures based on a Triply Periodic Minimal Surface (TPMS) structure have attracted much attention due to their excellent mechanical properties and energy absorption capabilities. In this study, a novel TPMS lattice metamaterial structure (IWP-X) is designed to enhance the axial mechanical properties by fusing an X-shaped plate with an IWP surface structure. A selective laser melting (SLM) machine was utilized to print the designed lattice structures with Ti6Al4V powder. The thickness of the plate and the density of the IWP are varied to explore the responsivity of the mechanical and energy absorption properties with the volume ratio of IWP-X. The finite element simulation analysis is used to effectively predict the stress distribution and fracture site of each structure in the compression test. The results show that the IWP-X structure obtains the ultimate compressive strength of 122.06% improvement, and the energy absorption of 282.03% improvement. The specific energy absorption (SEA) reaches its maximum value in the plate-to-IWP volume ratio of 0.7 to 0.8. Full article

Background: Heart disease continues to be one of the foremost causes of mortality worldwide, emphasizing the urgent need for reliable and early diagnostic tools. Accurate prediction methods can support timely interventions and improve patient outcomes. Methods: This study presents the development and comparative evaluation of multiple machine learning models for heart disease prediction using a structured clinical dataset. Algorithms such as Logistic Regression, Random Forest, Support Vector Machine (SVM), XGBoost, and Deep Neural Networks were implemented. Additionally, a hybrid ensemble model combining XGBoost and SVM was proposed. Models were evaluated using key performance metrics including accuracy, precision, recall, and F1-score. Results: Among all models, the proposed hybrid model demonstrated the best performance, achieving an accuracy of 89.3%, a precision of 0.90, recall of 0.91, and an F1-score of 0.905, and outperforming all individual classifiers. These results highlight the benefits of combining complementary algorithms for improved generalization and diagnostic reliability. Conclusions: The findings underscore the effectiveness of ensemble and deep learning techniques in addressing key challenges such as data imbalance, feature selection, and model interpretability. The proposed hybrid model shows significant potential as a clinical decision-support tool, contributing to enhanced diagnostic accuracy and supporting medical professionals in real-world settings. Full article

This in vitro study aimed to quantitatively evaluate the accuracy of reproducing attachments for clear aligner therapy (CAT) using a standardized in-house fabrication protocol and to analyze discrepancies across maxillary tooth types. A custom attachment was designed on a symmetrical master model, and 30 experimental models were fabricated by three-dimensional (3D) printing, template construction, and bonding. Following scanning and superimposition, dimensional, angular, and positional deviations were quantified and statistically analyzed (p < 0.05). Results showed minor mean discrepancies but a consistent pattern of under-reproduction, most evident in the mesial and distal wall angles, as well as in the gingival bevel angle and attachment height. A significant trend was observed in the occlusal bevel, demonstrating marked extrusion in the anterior region that decreased posteriorly. Positional errors were minimal mesiodistally but substantial in the lingual and occlusal directions, with magnitudes varying by tooth type. In conclusion, this study identified consistent, predictable inaccuracies in a simulated in-house attachment reproduction protocol. These findings indicate that similar deviations may occur clinically, potentially affecting the predictability of CAT. Full article

The integration of renewable energy sources (RES) into energy systems is becoming increasingly widespread around the world, driven by various factors, the most relevant of which is the high environmental friendliness of these types of energy resources and the possibility of creating stable generation systems that are independent of the economic and geopolitical situation. The large-scale involvement of green energy leads to the creation of distributed energy networks that combine several different methods of generation, each with its own characteristics. As a result, the issues of data collection and processing necessary for optimizing the operation of such energy systems are becoming increasingly relevant. The first stage of renewable energy integration involves building models to assess theoretical potential, allowing the feasibility of using a particular type of resource in specific geographical conditions to be determined. The second stage of assessment involves determining the technical potential, which allows the actual energy values that can be obtained by the consumer to be determined. The paper discusses a method for assessing the technical potential of solar energy using the example of a private consumer’s energy system. For this purpose, a generator circuit with load models was implemented in the SimInTech dynamic simulation environment, accepting various sets of parameters as input, which were obtained using an intelligent information search procedure and intelligent forecasting methods. This approach makes it possible to forecast the amount of incoming solar insolation in the short term, whose values are then fed into the simulation model, allowing the forecast values of the technical potential of solar energy for the energy system configuration under consideration to be determined. The implementation of such a hybrid assessment system allows not only the technical potential of RES to be determined based on historical datasets but also provides the opportunity to obtain forecast values for energy production volumes. This allows for flexible configuration of the parameters of the elements used, which makes it possible to scale the solution to the specific configuration of the energy system in use. The proposed solution can be used as one of the elements of distributed energy systems with RES, where the concept of demand distribution and management plays an important role. Its implementation is impossible without predictive models. Full article

A collectible rotor hybrid aircraft (CRHA) represents a novel type of vertical takeoff and landing (VTOL) unmanned aircraft configuration, combining the typical rotor and transmission systems of helicopters with the wing and propulsion systems of fixed-wing aircraft. Its weight estimation and parameter design during the conceptual design stage cannot directly use existing rotorcraft or fixed-wing methods. This paper presents a rapid key design parameter sizing and maximum takeoff weight (MTOW) estimation approach tailored to CRHA, explicitly scoped to the 5–8-metric-ton (t) MTOW class. Component weight models are first formulated as explicit functions of key design parameters—including rotor disk loading, power loading, and wing loading. Segment-specific fuel weight fractions for VTOL and transition flight are then updated from power calculations, yielding a complete mission fuel model for this weight class. A hybrid optimization framework that minimizes MTOW is constructed by treating the key design parameters as design variables and combining a genetic algorithm (GA) with sequential quadratic programming (SQP). The empty-weight model, fuel-weight model, and optimization framework are validated against compound-helicopter, tilt-rotor, and twin-turboprop benchmarks, and parameter sensitivities are evaluated locally and globally. Results show prediction errors of roughly 10% for empty weight, fuel weight, and MTOW. Sensitivity analysis indicates that at the baseline design point, wing loading exerts the greatest influence on MTOW, followed by power loading and disk loading. Full article

Elastic optical networks (EONs) must allocate resources dynamically to accommodate heterogeneous, high-bandwidth demands. However, the continuous setup and teardown of connections with different bit rates can fragment the spectrum and lead to blocking. The blocking predictors enable proactive defragmentation and resource reallocation within network controllers. In this paper, we propose two novel deep learning models (based on CNN–BiLSTM and CNN–LSTM) to predict blocking in EONs by combining spatial feature extraction from spectrum snapshots using 2D convolutional layers with temporal sequence modeling. This hybrid spatio-temporal design learns how local fragmentation patterns evolve over time, allowing it to detect impending blocking scenarios more accurately than conventional methods. We evaluate our model on the simulated NSFNET topology and compare it against multiple baselines, namely 1D CNN, 2D CNN, k-nearest neighbors (KNN), and support vector machines (SVMs). The results show that the proposed CNN–BiLSTM/LSTM models consistently achieve higher performance. The CNN–BiLSTM model achieved the highest accuracy in blocking prediction, while the CNN–LSTM model shows slightly lower accuracy; however, it has much lower complexity and a faster learning time. Full article

The primary aim of this study is to predict the Marshall stability and flow values of hot-mix asphalt samples prepared according to the Marshall design method using regression-based machine learning algorithms. To overcome the limited number of experimental observations, synthetic data generation was applied using the Conditional Tabular Generative Adversarial Network (CTGAN), while the structural consistency of the generated data was validated through Principal Component Analysis (PCA). Two datasets containing 17 physical and mechanical input variables were analyzed, and multiple regression models were compared, including Extra Trees, Random Forest, Gradient Boosting, AdaBoost, and K-Nearest Neighbors. Among these, the Extra Trees Regressor consistently achieved the best results with near-perfect accuracy in flow predictions (MAE ≈ 4.06 × 10⁻¹⁵, RMSE ≈ 4.97 × 10⁻¹⁵, Accuracy ≈ 99.99%) and high performance in stability predictions (MAE = 109.52, RMSE = 150.67, accuracy = 90.45%). Furthermore, model interpretability was ensured by applying SHapley Additive Explanations (SHAP), which revealed that parameters such as softening point, VMA, penetration, and void ratios were the most influential features. These findings demonstrate that regression-based ensemble models, combined with synthetic data augmentation and explainable AI methods, can serve as reliable and interpretable tools in asphalt mixture design. Full article

Additive manufacturing has promising potential for the development of 3D-printed protective structures such as stab-resistant body armor. However, no research to date has examined the impact of 3D printing parameters on the protective performance of such 3D-printed structures manufactured using fused filament fabrication technology. This study, therefore, investigates the effects of five key printing parameters: layer thickness, print speed, print temperature, infill density (Id), and layer width, on the mechanical and protective performance of 3D-printed polycarbonate (PC) armor. A Taguchi L₂₇ matrix was employed to systematically analyze these parameters, with toughness, stab penetration depth, and armor panel weight as the primary responses. ANOVA results, along with the Taguchi approach, demonstrated that Id was the most influential factor across all print parameters. This is because a higher Id led to denser structures, reduced voids and porosities, and enhanced energy absorption, significantly increasing toughness while reducing penetration depth. Morphological analysis supported the statistical findings regarding the role of Id on the performance of such structures. With optimized printing parameters, no penetration to the armor panels was recorded, outperforming the UK body armor standard of a maximum permitted knife penetration depth of 8 mm. Moreover, an artificial neural network (ANN) utilizing the 5-14-12-3 topology was created to predict the toughness, stab penetration depth, and armor panel weight of 3D-printed armors. The ANN model demonstrated better prediction performance for stab penetration depth compared to the Taguchi method, confirming the successful application of such an approach. These findings provide a critical foundation for the development of high-performance 3D-printed protective structures. Full article