Search Results (5,891)

The built environment in the EU accounts for 40% of the total energy consumption and 36% of the total greenhouse gas emissions. To address the inefficiency of existing buildings, renovation could reduce their total energy consumption by 5–6% and lower carbon dioxide emissions by approximately 5%. A retrofit solution for existing buildings involves the use of lightweight prefabricated systems, some of which include integrated HVAC components that are able to enhance their functionality. Indeed, such prefabricated facade elements with integrated HVAC systems can represent a minimally invasive method for reducing the energy consumption of an existing building. To assess the potential of this approach, a full-scale mock-up of a prefabricated timber facade with integrated HVAC system was tested at the Facade System Interactions Lab (FSIL) of Eurac Research, Bolzano. The experimental data were used to develop a calibrated and validated 3D finite element model in COMSOL Multiphysics. The validated model was used to evaluate the facade’s thermal performance under standard heating conditions through a proposed equivalent thermal transmittance indicator (U_eq). Results show that the active facade achieves 0.07 W m⁻² K⁻¹, compared to 0.21 W m⁻² K⁻¹ for the passive facade with identical materials but without active components. Full article

Background: Patients undergoing pancreatoduodenectomy (PD) after preoperative percutaneous transhepatic biliary drainage (PTBD) frequently develop bacterobilia. While bile culture positivity has been variably linked to postoperative infections, the clinical relevance of culture data may be more closely related to perioperative antimicrobial adequacy. We aimed to evaluate whether discordant perioperative antibiotic prophylaxis—defined by mismatch between administered prophylaxis and resistance profiles from preoperative PTBD bile cultures—is independently associated with major postoperative morbidity. Methods: This retrospective cohort study included consecutive patients undergoing PD between January 2020 and October 2025. Major morbidity (primary endpoint) was defined as Clavien–Dindo grade ≥ III. Secondary outcomes included postoperative day 4 inflammatory markers (WBC and CRP), length of stay, and infection-related endpoints. Bile culture findings were categorized by culture status and resistance severity (no growth, low resistance, and high resistance [MDR/XDR/PDR]). Discordant prophylaxis was defined using a predefined coverage-based algorithm incorporating antimicrobial class and susceptibility profiles. Multivariable logistic regression (adjusted for age, dichotomized ASA class, and operative type) and model performance (AUC, DeLong test; Hosmer–Lemeshow calibration) were assessed. Results: A total of 145 patients were analyzed; preoperative bile culture status was no culture (n = 30), culture-negative (n = 59), and culture-positive (n = 56). Bile culture status was not associated with major morbidity (p = 0.406), POD4 inflammatory markers, or length of stay. Resistance severity categories were also not associated with major morbidity (15.3%, 17.4%, and 24.2% across no-growth, low-resistance, and high-resistance groups, respectively; p = 0.77). Discordant prophylaxis occurred in 23 patients (15.9%) and was associated with higher major morbidity compared with concordant coverage (30.4% vs. 18.0%; OR: 1.99, 95% CI: 0.69–5.36; p = 0.25). After adjustment, discordant prophylaxis showed a higher point estimate for major morbidity (adjusted OR: 1.84, 95% CI: 0.63–4.96; p = 0.24), although this did not reach statistical significance. The core clinical model showed poor discrimination (AUC 0.59); adding microbiological variables modestly increased the AUC to 0.63 without significant improvement (DeLong p = 0.46). Model calibration was acceptable (Hosmer–Lemeshow p = 0.88). Conclusions: In this PTBD cohort undergoing PD, bile culture positivity and resistance severity were not independently associated with major postoperative morbidity. Discordant prophylaxis was associated with a numerical increase in major morbidity; however, this finding did not reach statistical significance and should be interpreted cautiously given the limited sample size. These findings support interpreting bile culture data primarily within an antimicrobial stewardship framework to ensure adequate coverage rather than as standalone predictors of severe morbidity and warrant validation in larger prospective cohorts. Full article

Carbonate reservoirs in the Shunbei area develop pronounced fracture networks after acidized hydraulic fracturing and thus have the potential to be repurposed as underground gas storage (UGS) after hydrocarbon depletion. Characterizing their mechanical behavior is essential for safe UGS operation; however, deep to ultra-deep natural cores are difficult to obtain, and conventional macroscopic tests often cannot provide parameters that meet engineering requirements. To address this issue, nanoindentation combined with QEMSCAN (Quantitative Evaluation of Minerals by Scanning Electron Microscopy) was employed to quantify microscale mineral distributions and the mechanical properties of the major constituents. The investigated rock is calcite-dominated (89.62%), with minor quartz (9.89%) and trace feldspar-group minerals (1.89%). Minerals are randomly embedded, and soft–hard phase boundaries are widely distributed. A finite–discrete element method (FDEM) model was then constructed and calibrated in ABAQUS. The discrepancies in uniaxial compressive strength and elastic modulus relative to laboratory results were 6.51% and 9.91%, respectively, indicating good agreement in both mechanical response and failure mode. Parametric analyses using three additional models with different mineral proportions show that damage preferentially initiates at mineral phase boundaries and stress concentration zones induced by end constraints. Microcracks then propagate and coalesce into a dominant compressive–shear band, and final failure is mainly governed by slip along the shear band with localized tensile cracking. With increasing quartz and feldspar contents, enhanced heterogeneity and a higher density of phase boundaries lead to a higher density of crack nucleation sites and increased crack branching, and the failure pattern transitions from a single shear-band–controlled mode to a more network-like fracture system. Moreover, macroscopic strength is not determined solely by the intrinsic strength of individual minerals; heterogeneity and phase-boundary characteristics strongly govern microcrack behavior, such that higher hard-phase contents may result in a lower peak strength. Full article

Ensuring the crashworthiness of heavy-duty truck cabs is essential for reducing occupant fatalities and improving passive safety in commercial vehicles. Regulatory frameworks such as UNECE Regulation No. 29 (R29) define structural integrity requirements through full-scale destructive impact tests, which are costly and limit iterative design. In this study, an integrated experimental–numerical methodology is presented for the impact assessment of a real Iveco Eurocargo 120E18 truck cab reconstructed using high-resolution 3D scanning. The scanned geometry was used to generate a dimensionally accurate CAD model of the load-bearing cab structure, which was analysed using explicit finite element simulations in ANSYS Academic Mechanical and CFD Teaching package under impact conditions compliant with UNECE R29 and implemented according to the Swedish regulation VVFS 2003:29. In parallel, a full-scale physical pendulum impact test was performed on the same cab using a cylindrical impactor with a diameter of 580 mm, a length of 1800 mm, and a mass of approximately 1000 kg, impacting the upper region of the A-pillar. The experimental setup was instrumented using high-speed optical measurements and an accelerometer to capture impact kinematics and structural response. The numerical predictions showed good agreement with experimental results in terms of acceleration–time histories, absorbed energy evolution, and structural deformation, with differences generally below 6%. Critical regions susceptible to local buckling and plastic collapse were consistently identified in both approaches, while preservation of the driver survival space was confirmed. The results demonstrate that scan-based finite element models, when properly calibrated and validated, can reliably reproduce certification-level impact behaviour. The proposed workflow provides a robust and cost-effective framework for regulatory pre-validation, structural optimisation, and digitalisation of crashworthiness assessment for heavy-duty truck cabs. Full article

The mechanical behavior of deep rock masses under high-stress conditions exhibits significant nonlinear characteristics. However, current in situ stress field inversion methods typically rely on linear elastic constitutive models and multiple linear regression analysis. By analyzing the results of triaxial stress–strain tests and confining pressure calibration experiments on rocks, and drawing on the nonlinear concepts from the Duncan-Zhang model, a nonlinear characterization function was developed, represented by mean stress p, bulk modulus K, and shear modulus G. The nonlinear elastic constitutive model was integrated into a numerical simulation framework, and a new in situ stress field inversion fitting method based on nonlinear elastic constitutive modeling was proposed. This method uses initial linear iterations followed by multiple nonlinear iterations until convergence is achieved. Applied to the inversion of the deep in situ stress field at the Xishan Iron Mine, the results demonstrate that compared to traditional linear regression-based methods, the errors in mean stress, deviatoric stress, and the Lode parameter were reduced by 58%, 50%, and 22%, respectively, confirming the effectiveness of this method in in situ stress field inversion in rock mechanics. Full article

This study evaluates the feasibility of using two affordable thermal cameras (UNI-T UTi260M and UTi260T), which are not designed as automotive sensors, for observing pedestrians and warm objects during night-time driving under low-illumination conditions. The experimental setup includes mounting the camera on the vehicle body (e.g., side mirror area/roof), recording road scenes in urban and rural environments, and selecting representative frames for qualitative and quantitative analysis. The study assesses: (i) observable pedestrian detectability in unlit road sections and under oncoming headlight glare, where visible cameras often lose contrast; (ii) the influence of low ambient temperature and strong cold wind on image appearance (including “whitening”/contrast shifts); and (iii) workflow differences, where UTi260M relies on a smartphone application for streaming/recording, while UTi260T supports PC-based image analysis and temperature-profile visualization. In addition, a calibration-based geometric method is proposed for approximate pedestrian distance estimation from single frames using silhouette pixel height and a regression model based on 1$/h_{px}$, valid for a specific mounting configuration and a known subject height. Results indicate that both cameras can highlight warm objects relative to the background and support visual pedestrian identification at low illumination, including in the presence of oncoming headlights, with UTi260M showing more stable behavior in parts of the tests. This work is a feasibility study and does not claim Advanced Driver Assist Systems (ADAS) functionality; it outlines limitations, repeatability considerations, and a minimal set of metrics and procedures for future extension. All quantitative indicators derived from exported frames are explicitly treated as image-level proxy metrics, not as physical sensor characteristics. Full article

To address the complex calibration parameters and low optimization efficiency of dual-fuel engines, this paper innovatively proposes an optimization calibration method based on a simulation model and the Non-Dominated Sorting Whale Optimization Algorithm (NSWOA). Taking the YC6K dual-fuel engine as the research object, a high-precision simulation model was constructed within the GT-Power environment, and its reliability was confirmed through the external characteristic curve (the maximum deviation of torque and specific fuel consumption rate is less than 5%). A total of 260 parameter samples were generated using a Sobol sequence space-filling experimental design, and a performance prediction model was established by combining the Crested Porcupine Optimization algorithm and the Back-Propagation Neural Network (CPO-BP). The experimental results show that the CPO-BP model exhibits excellent predictive capability, with the coefficient of determination (R²) of nitrogen oxides (NO_x) and brake-specific fuel consumption rate (BSFC) reaching 0.98964 and 0.99501 respectively. Based on this, the NSWOA algorithm was introduced to optimize key parameters such as speed, torque, main injection timing, and rail pressure, with the optimization objectives being NO_x emissions and BSFC. The optimization results show that under 100% load conditions, the reduction in BSFC ranges from 1.5% to 4.3%, and NO_x emissions are reduced by 48.6% to 67.1%. The effectiveness of the optimized parameters was also verified through bench tests, providing an efficient solution for complex engineering optimization problems. Full article

Background/Objectives: Mild autonomous cortisol secretion (MACS) is increasingly recognized in patients with adrenal incidentalomas. While associated with cardiometabolic risk, management strategies remain controversial, particularly regarding functional outcomes. This retrospective comparative cohort study evaluated the impact of adrenalectomy versus conservative management on muscle strength in MACS patients. Materials and Methods: Forty patients with MACS (1-mg dexamethasone suppression test > 1.8 µg/dL) were enrolled: 15 underwent adrenalectomy and 25 received conservative management. Hand grip strength was measured using calibrated dynamometry, and gait speed was assessed with the 1-m walk test at baseline and 6 months. Results: Baseline characteristics are summarized descriptively for the surgical and conservative cohorts. At 6 months, the surgery group showed significant improvements in right hand grip strength (+1.19 ± 0.64 kg, p < 0.001) and left hand grip strength (+1.15 ± 0.49 kg, p < 0.001), representing approximately 5% improvement. In contrast, the conservative group exhibited significant decreases in strength over the same period (right: −0.40 ± 0.25 kg; left: −0.28 ± 0.28 kg, both p < 0.001). The post-surgical 1-mg dexamethasone suppression test decreased from 4.05 ± 1.44 to 1.01 ± 0.34 µg/dL (p < 0.001). Conclusions: Adrenalectomy results in significant improvement in objective muscle strength in MACS patients, with improvement observed in parallel to biochemical resolution of cortisol excess. In contrast, conservative management was associated with progressive decline in grip strength over 6 months. Hand grip dynamometry provides valuable functional outcome data that may guide surgical decision-making in MACS management. Full article

Additive manufacturing, particularly 3D printing, is increasingly shaping the production of polymer-based components, enabling complex geometries and tailored functional performance. Yet, predicting their mechanical behavior remains challenging due to material anisotropy and sensitivity to processing conditions. This work presents an exploratory study designed to provide the experimental basis for the development and calibration of predictive models of mechanical properties in 3D-printed components. Standard ISO 527-2 Type 1A specimens were fabricated using thermoplastic PLA (polylactic acid) with systematic variations in layer orientation, infill overlap, and printing velocity. Mechanical characterization was carried out through uniaxial tensile testing to determine tensile strength and stiffness of the material specimens, while scanning electron microscopy (SEM) provided complementary insights into interlayer bonding, filament alignment, porosity, and fracture morphology. Results showed that material type and processing strategies strongly influenced mechanical response, with SEM highlighting microstructural features that govern interlayer adhesion and failure mechanisms. These findings contribute to a deeper understanding of process–structure–property relationships in additive manufacturing and establish the groundwork for predictive model development. Ongoing efforts will integrate these experimental insights into numerical simulations employing homogenized material models, thereby enhancing design optimization and reliability of 3D-printed structural components. Full article

A hybrid metaheuristic methodology that combines the Red-billed Blue Magpie Optimization (RBMO) algorithm with the Aquila Optimizer (AO) is introduced in this work as the RBMOAO method. The novel algorithm addresses a critical shortcoming of the standard AO: its exploration-to-exploitation ratio across different optimization stages is inefficient, yielding premature convergence and low diversity within the population. This is achieved by using RBMO’s Group-Based Directional Perturbation (GDP) and its dynamic convergence factor (CF) as part of the methodology. The early stages of the optimization process are characterized by a grouping methodology to maintain population diversity through coordinated exploration across subgroups of varying sizes using GDP. Later iterations are characterized by a CF-guided updating process that increases the resolution of the search for the best areas, thereby improving convergence precision without sacrificing solution quality. Empirical testing of the proposed methodology using the CEC 2015 and CEC 2020 test sets demonstrated RBMOAO’s superior performance compared to other metaheuristics, outperforming other optimizers in 73.33% of CEC 2015 functions and 80% of CEC 2020 functions, with statistical significance in the increased precision and robustness of solutions across all problem types. Additionally, the RBMOAO methodology demonstrated outstanding performance in constrained engineering design problems. In addition to optimization, an RBMOAO-optimized ensemble architecture was implemented to predict cybersecurity intrusion threats, achieving an accuracy of 89.6%. Through the dynamic calibration of the base learner weights via metaheuristic search, the RBMOAO ensemble achieved the top ranking. These results illustrate the wide range of applications of the RBMOAO methodology and provide support for its deployment in the context of high-stakes predictive analytics. Full article

Background: Radiation-induced cystic brain necrosis (RCN) can progress rapidly to life-threatening cerebral herniation. This study aimed to develop a predictive model integrating radiomic features and clinical variables to assess the risk of cerebral herniation in RCN patients. Methods: A total of 130 patients diagnosed with RCN following radiotherapy for nasopharyngeal carcinoma were retrospectively enrolled and randomly assigned to training (n = 91) and testing (n = 39) cohorts in a 7:3 ratio. Radiomic features were extracted from baseline T2-weighted magnetic resonance imaging (MRI), and a radiomic signature was constructed using least absolute shrinkage and selection operator regression. A multivariate Cox regression model was then developed by incorporating the radiomic signature and clinical variables to predict cerebral herniation. The model’s discriminative ability, calibration, and clinical utility were evaluated. Results: The radiomic signature based on five selected radiomic features demonstrated good predictive performance. The radiomic model, which integrated the radiomic signature and ratios of perilesional enhancement, exhibited favorable performance in both the training cohort (C-index: 0.841) and testing cohort (C-index: 0.867). The model successfully stratified patients into high- and low-risk groups. The calibration curves showed good agreement and the decision curve confirmed the clinical utility of the model. Conclusions: The MRI-based radiomic model, which integrates radiomic features and clinical variables, demonstrates robust performance in predicting cerebral herniation in RCN patients, offering a practical and user-friendly tool to support clinical decision-making. Full article

This paper describes a new methodology implemented in the ESTE decision support system for evaluating the source term resulting from a nuclear weapon detonation. The methodology is based on a model of a stabilized radioactive mushroom cloud, parameterized as the source term for a Lagrangian particle dispersion model. It includes radionuclide composition, spatial distribution of aerosol and gaseous particles, and particle size distribution. This method is designed for rapid assessment of radiological impacts primarily at medium- and long-range distances, for example, in neighboring countries. The parametrization has been calibrated and adjusted using data from historical nuclear tests, and its performance is evaluated in terms of impacted area, range, and spatial overlap of fallout regions. A comparison is presented between ESTE calculations and field measurements obtained after the British nuclear tests conducted in the 1950s at the Maralinga Range (Australia), using historical ERA5 meteorological reanalyses from ECMWF. Full article

This study presents a machine learning framework for the reconstruction of fatigue life and fracture toughness in natural fiber-reinforced composites, evaluating the predictive accuracy of six regression algorithms—Random Forest, Gradient Boosting, Support Vector Machine, Neural Network, Ridge Regression, and Lasso Regression—using a controlled synthetic dataset of 600 samples generated from established Basquin fatigue and Rule of Mixtures fracture equations, incorporating stochastic noise calibrated to experimental scatter (CV = 15–50%), with log-normal noise standard deviation of 0.20 for fatigue life and Gaussian noise standard deviation of 0.15 for fracture toughness. The dataset encompasses eight natural fiber types (flax, jute, sisal, hemp, bamboo, coconut, banana, and pineapple) and five matrix systems (epoxy, polyester, PLA, vinyl ester, and polyurethane). Models were evaluated using a 70-15-15 train–validation–test split with 5-fold cross-validation and exhaustive grid search hyperparameter optimisation. Gradient Boosting achieved R² = 0.93 for fatigue life and Stacking Ensemble achieved R² = 0.87 for fracture toughness, representing 97% and 89% of their respective noise-ceiling values (theoretical maximum R² of 0.96 and 0.98 given the programmed noise levels). The ML models perform supervised function approximation—learning to reconstruct the programmed generation equations rather than discovering novel physical composite behaviour—and function as automated surrogates for the governing equations. Feature importance analysis identified engineered composite indicators, stress amplitude, and fiber length as the most influential parameters. The framework provides a reproducible ML evaluation pipeline as a methodological template for future experimental composite studies. Full article

Background and Objectives: Circulating microRNAs may provide minimally invasive biomarkers for laryngeal squamous cell carcinoma (LSCC), but clinically interpretable data for miR-99a-5p and miR-1246 remain limited. We compared circulating levels of these two miRNAs between LSCC patients and controls and explored stage-associated differences within the cancer cohort. Methods: This single-center case–control study was conducted in Timișoara, Romania. Circulating miRNAs were quantified by RT-qPCR. Expression was summarized as ΔCt [Ct(target) − Ct(miR-16)] and as the relative expression (2^−ΔΔCt) using the control group as a calibrator. Group comparisons used Mann–Whitney U tests, associations used Spearman correlation, and the diagnostic performance was assessed by ROC analysis and multivariable logistic regression. Results: Fourteen controls were compared with cancer patients with available miRNA measurements (miR-99a-5p, n = 53; miR-1246, n = 49). miR-99a-5p showed significantly higher ΔCt values in cancer patients than in the controls (5.308 [IQR 4.139–6.864] vs. 3.184 [2.142–3.708], p < 0.001), corresponding to a lower relative expression (fold-change 0.200 [0.068–0.449], p < 0.001). miR-1246 did not differ significantly between cancer and controls (p = 0.09). Within the cancer cohort, advanced-stage disease showed a lower relative miR-1246 expression than early-stage disease (ΔCt 5.820 [4.502–6.972] vs. 4.233 [3.109–5.372], p = 0.01; fold-change 0.363 vs. 1.091, p = 0.01), while miR-99a-5p showed a non-significant difference in the same direction (p = 0.052). miR-99a-5p discriminated cancer patients from the controls with an AUC of 0.842 (95% CI 0.744–0.931), sensitivity of 77.4%, and specificity of 92.9% at ΔCt = 4.018. In multivariable analysis, ΔCt(miR-99a-5p) remained independently associated with cancer status (OR 1.89, 95% CI 1.19–3.00; p = 0.007). Conclusions: Circulating miR-99a-5p showed the strongest diagnostic signal in LSCC, whereas miR-1246 appeared more informative for stage-associated biological stratification. Full article

Compaction quality governs asphalt pavement durability, but conventional density checks are intermittent. Reliable compaction control of asphalt mixtures requires real-time information on internal responses rather than relying solely on endpoint density measurements. In this study, an embedded smart-particle framework is developed for in situ monitoring and index-based evaluation of vibratory compaction quality, integrating multi-source sensing, feature extraction, and compaction degree mapping. The smart particle integrates inertial/orientation sensing together with thermal–mechanical measurements, and its high-temperature survivability and calibratability are verified through thermal exposure and calibration tests. During laboratory vibratory compaction of representative asphalt mixtures, raw signals are converted into stable attitude responses via attitude estimation and filtering; posture-dominant descriptors are then extracted and used to establish a data-driven mapping from internal responses to compaction degree using regression models. Results show that the device remains stable under typical hot-mix asphalt conditions, with calibration exhibiting high linearity (temperature channel R² > 0.990; force channel R² > 0.980 in the relevant range). Filtering markedly enhances inertial-signal usability under strong vibration and improves the interpretability of attitude-response evolution during compaction. The evolution of attitude features is consistent with the “rapid-to-slow densification” process, yielding correlations of |r| ≈ 0.35–0.47 with compaction degree evolution. Nonlinear regressors outperform linear baselines, and the better-performing nonlinear models achieve strong predictive performance across all six specimens, with R² values reaching 0.740–0.960 and RMSE reaching 0.016–0.043. Moreover, machine-learning-based feature-importance analysis reveals distinct mixture-type-dependent characteristics, indicating that AC and SMA transmit compaction-state information through partly different dominant response features. These findings demonstrate the feasibility of embedded smart particles for online compaction-quality evaluation and provide a basis for real-time feedback in intelligent compaction. Full article