Search Results (272,048)

Background: Orthognathic surgery aims to align the jaws with the facial skeleton and correct dental occlusion. This paper introduces the concept of planning the maxillomandibular complex (MMC) as a whole, utilizing a t-forming set of landmarks: the maxillary central incisor, the chin, and the occlusal plane. Methods: The background, hypothesis, and rationale of the new T concept are explained. A case of a 28-year-old male with skeletal class III malocclusion and an open bite was used to illustrate the application of the T concept in step-by-step surgical planning. The planning encompasses four phases: Phase One involves correcting frontal deformity and various asymmetries, Phase Two involves correcting chin anterior–posterior deformity, Phase Three involves correcting anterior–posterior and vertical MMC position, and Phase Four involves correcting MMC rotation. Results: The T concept provided a structured approach to plan MMC as a whole and integrate all structures into harmony. Conclusions: The T concept provides a logical approach to MMC positioning in orthognathic surgery, addressing functional and aesthetic concerns. It acts as a checkpoint to verify MMC position, helping surgeons achieve better results and avoid compensatory procedures. Full article

The line contact behavior of multiscale meshing interfaces necessitates synergistic investigation spanning nano-to centimeter-scale ranges. When nominally smooth gear teeth surfaces come into contact, the mechanical–thermal coupling effect at the meshing interface actually occurs over a collection of microscale asperities (roughness peaks) exhibiting hierarchical distribution characteristics. The emergent deformation phenomena across multiple asperity scales govern the self-organized evolution of interface conformity, thereby regulating both the load transfer efficiency and thermal transport properties within the contact zone. The fractal nature of the roughness topography on actual meshing interfaces calls for the development of a cross-scale theoretical framework that integrates micro-texture optimization with multi-physics coupling contact behavior. Conventional roughness characterization methods based on statistical parameters suffer from inherent limitations: their parameter values are highly dependent on measurement scale, lacking uniqueness under varying sampling intervals and instrument resolutions, and failing to capture the scale-invariant nature of meshing interface topography. A scale-independent parameter system grounded in fractal geometry theory enables essential feature extraction and quantitative characterization of three-dimensional interface morphology. This study establishes a progressive deformation theory for gear line contact interfaces with fractal geometric characteristics, encompassing elastic, elastoplastic transition, and perfectly plastic stages. By systematically investigating the force–thermal coupling mechanisms in textured meshing interfaces under multiscale conditions, the research provides a theoretical foundation and numerical implementation pathways for high-precision multiscale thermo-mechanical analysis of meshing interfaces. Full article

Efficiency in vitro regeneration is a crucial prerequisite for the application of New Nenomics Techniques (NGTs) in grapevine (Vitis vinifera L.) for improving resistance to biotic and abiotic stresses. This is especially true given that their management must be addressed sustainably, considering the impact of climate change. Unfortunately, in vitro plant regeneration and the establishment of embryogenic calluses are two genotype-dependent processes. Up to now, extensive research has been conducted on major international cultivars, whereas studies on the application of in vitro protocols for autochthonous cultivars remain limited. In this study, protocols for the acquisition of embryogenic calluses were applied on the most relevant Sicilian grapevine cultivars: the red-skinned ‘Frappato’, ‘Nerello mascalese’, and ‘Nero d’Avola’, and the white-skinned ‘Grillo’, ‘Carricante’, and ‘Catarratto’. Stamens and pistils were cultured in two different induction media (PIV and MSII) and at three stages (mother cells in the late premeiotic phase, tetrads, and mature pollen) to induce embryogenic calluses. Five thousand explants per cultivar were cultured, forming calluses in four selected cultivars. Plantlets were successfully generated from calluses of ‘Carricante’, ‘Frappato’, and ‘Nero d’Avola’. Moreover, protoplasts were isolated from ‘Frappato’ and ‘Nero d’Avola’. Our results establish a critical foundation for developing successful regeneration protocols for the future application of NGTs in Sicilian grapevine cultivars. Full article

Monitoring large-gradient surface displacement caused by underground mining remains a significant challenge for conventional Synthetic Aperture Radar (SAR)-based techniques. This study introduces optical flow methods to monitor large-gradient displacement in mining areas and conducts a comprehensive comparison with Small Baseline Subset Interferometric SAR (SBAS-InSAR) and Pixel Offset Tracking (POT) methods. Using 12 high-resolution TerraSAR-X (TSX) SAR images over the Daliuta mining area in Yulin, China, we evaluate the performance of each method in terms of sensitivity to displacement gradients, computational efficiency, and monitoring accuracy. Results indicate that SBAS-InSAR is only capable of detecting displacement at the decimeter level in the Dalinta mining area and is unable to monitor rapid, large-gradient displacement exceeding the meter scale. While POT can detect meter-scale displacements, it suffers from low efficiency and low precision. In contrast, the proposed optical flow method (OFM) achieves sub-pixel accuracy with root mean square errors of 0.17 m (compared to 0.26 m for POT) when validated against Global Navigation Satellite System (GNSS) data while improving computational efficiency by nearly 30 times compared to POT. Furthermore, based on the optical flow results, mining parameters and three-dimensional (3D) displacement fields were successfully inverted, revealing maximum vertical subsidence exceeding 4.4 m and horizontal displacement over 1.5 m. These findings demonstrate that the OFM is a reliable and efficient tool for large-gradient displacement monitoring in mining areas, offering valuable support for hazard assessment and mining management. Full article

The automated inspection of civil infrastructure with Unmanned Aerial Vehicles (UAVs) is hampered by the challenge of accurately segmenting multi-damage in high-resolution imagery. While foundational models like the Segment Anything Model (SAM) offer data-efficient segmentation, their effectiveness is constrained by prompting strategies, especially for geometrically complex defects. This paper presents a comprehensive comparative analysis of deep learning strategies to identify an optimal deep learning pipeline for segmenting cracks, efflorescences, and exposed rebars. It systematically evaluates three distinct end-to-end segmentation frameworks: the native output of a YOLO11 model; the Segment Anything Model (SAM), prompted by bounding boxes; and SAM, guided by a point-prompting mechanism derived from the detector’s probability map. Based on these findings, a final, optimized hybrid pipeline is proposed: for linear cracks, the native segmentation output of the SAHI-trained YOLO model is used, while for efflorescence and exposed rebar, the model’s bounding boxes are used to prompt SAM for a refined segmentation. This class-specific strategy yielded a final mean Average Precision (mAP50) of 0.593, with class-specific Intersection over Union (IoU) scores of 0.495 (cracks), 0.331 (efflorescence), and 0.205 (exposed rebar). The results establish that the future of automated inspection lies in intelligent frameworks that leverage the respective strengths of specialized detectors and powerful foundation models in a context-aware manner. Full article

COVID-19 is a public health issue with a significant mortality rate and potential long-lasting disabling symptoms responsible for the long-COVID syndrome. Mitochondrial dysfunction is a key mechanism but whether peripheral blood mononuclear cell (PBMC) mitochondrial respiration changes might be associated with mortality and/or occurrence and severity of long-COVID syndrome remains to be investigated. We determined mitochondrial respiratory chain oxygen consumption in twenty COVID-19 patients hospitalized in the intensive care unit and analyzed their remaining symptoms at the third year after hospital release. PBMC mitochondrial respiration was decreased in COVID-19 patients compared to the control group (14.13 ± 2.35 vs. 6.21 ± 0.88 pmol/s/10⁶ cell, p = 0.0006 for the OXPHOS state by CII). Considering COVID severity, such a decrease was greater in long-COVID patients and in patients who deceased (4.91 ± 0.75, p = 0.008 and 4.94 ± 1.11 pmol/s/10⁶ cell, p = 0.04, respectively). PBMC markers of inflammation also increased with the severity of COVID (1.0 ± 0.08 vs. 14.45 ± 2.07, p = 0.02 for ISG15 in patients who died) and ISG15 negatively correlated with PBMC mitochondrial respiration (r = −0.67, p = 0.02 for CII). In conclusion, this study shows that the greater the impairment in PBMC mitochondrial respiration in patients hospitalized in the intensive care unit for COVID-19, the greater the mortality rate and the more severe the long-COVID symptoms—three years after hospital discharge. Further, PBMC markers of inflammation also increased with the severity of COVID and ISG15 negatively correlated with PBMC mitochondrial respiration. These results support that PBMC mitochondrial respiration might be a biomarker of COVID severity and further studies investigating whether modulation of PBMC mitochondrial respiration might improve COVID-19 patients’ prognosis. Full article

Drought stress severely affects the physio-biochemical processes and yield of nutritious crops like vegetable soybean (Glycine max L. Merrill), threatening global food security and emphasising the need for effective strategies to improve drought tolerance. This study, conducted under controlled conditions in a greenhouse, investigates the effects of three selenium application methods (seed priming, foliar spray, and soil application) on photosynthesis efficiency, relative water content (RWC), hydrogen peroxide (H₂O₂), antioxidative responses, and yield traits of two vegetable soybean cultivars, UVE14 (drought-tolerant) and UVE17 (drought-susceptible), under drought stress. Among the three Se application methods, soil application was the most effective in improving drought tolerance and yield performance in both cultivars. In UVE17 (drought-susceptible), soil application significantly increased the number of seeds per plant (SPP) and the number of pods per plant (PPP), while in UVE14 (drought-tolerant) SPP increased. Selenium foliar spray and seed priming treatments did not increase yield in drought-stressed UVE14, suggesting that they are unlikely to further enhance tolerance in drought-tolerant cultivars. For UVE17 under drought conditions, selenium soil application improved key physio-biochemical indicators of drought tolerance, including photosynthesis efficiency (total performance of photosystems I and II, total chlorophyll content, and stomatal conductance), water retention (RWC), and carotenoid content. These improved physio-biochemical responses directly impacted yield outcomes. Notably, RWC and total chlorophyll content at the pod-filling stage in drought-stressed UVE17 were positively correlated with an increase in PPP under selenium soil application. Selenium soil application stands out as the most effective method for enhancing drought tolerance in vegetable soybean, presenting a promising and practical solution for enhanced crop production under climate change. Full article

This paper introduces TRIDENT-DE, a novel ensemble-based variant of Differential Evolution (DE) designed to tackle complex continuous global optimization problems. The algorithm leverages three complementary trial vector generation strategies best/1/bin, current-to-best/1/bin, and pbest/1/bin executed within a self-adaptive framework that employs jDE parameter control. To prevent stagnation and premature convergence, TRIDENT-DE incorporates adaptive micro-restart mechanisms, which periodically reinitialize a fraction of the population around the elite solution using Gaussian perturbations, thereby sustaining exploration even in rugged landscapes. Additionally, the algorithm integrates a greedy line-refinement operator that accelerates convergence by projecting candidate solutions along promising base-to-trial directions. These mechanisms are coordinated within a mini-batch update scheme, enabling aggressive iteration cycles while preserving diversity in the population. Experimental results across a diverse set of benchmark problems, including molecular potential energy surfaces and engineering design tasks, show that TRIDENT-DE consistently outperforms or matches state-of-the-art optimizers in terms of both best-found and mean performance. The findings highlight the potential of multi-operator, restart-aware DE frameworks as a powerful approach to advancing the state of the art in global optimization. Full article

Efficient acquisition of rock mass parameters is a critical step for conducting numerical simulations in tunneling and ensuring the safety of subsequent construction. This paper proposes an intelligent back-analysis method for key rock mass parameters (Young’s modulus, Poisson’s ratio, cohesion, and friction angle) based on excavation-induced deformation data, using a deformation database that incorporates multi-feature values from tunnel excavation. This study employs five machine learning algorithms with single-feature inputs and three deep neural networks (DNNs) with multi-feature inputs, with a particular focus on convolutional neural network (CNN) due to their superior performance in terms of both accuracy and efficiency. The results demonstrate that the CNN model incorporating excavation features achieves excellent performance in parameter back-analysis, with an R² of 0.99 and 97.8% of predictions having errors within 5%. Compared with machine learning models using single-feature inputs, the CNN-based approach improves predictive performance by an average of 13.9%. Furthermore, compared with other DNNs, the CNN consistently outperforms across various evaluation metrics. This study also investigates the CNN’s capability to predict rock mass parameters using deformation data from early-stage excavation. After ten excavation steps, 96.9% of test samples had prediction errors within 5%. Finally, the proposed method was validated using field-monitored deformation data from a real highway tunnel project, confirming the method’s effectiveness and practical applicability. Full article

A pressing task is to develop a mathematical model and calculation method that most accurately describes the radiant component of heat exchange between an animal and its environment. This will help determine the optimal design parameters and temperature conditions for infrared (IR) heaters in livestock premises. The mathematical models considered describe the animal's heat exchange with the environment during IR heating. However, they do not take into account the hidden surface temperature of the premises’ enclosing structures and their emissivity factor, or the relationship between animal thermal comfort and the IR heater surface temperature. The proposed radiant heat exchange mathematical model is applicable to diffusely absorbing and radiating isothermic surface system typical of pigsties. It takes into account the emissivity factors of all of the enclosing structures’ surfaces and determines the effective (apparent) premises temperature value t_ef, corresponding to the thermal comfort conditions. The IR heater surface temperature’s dependence on the emissivity of the pigsty’s enclosing structures (walls, ceiling, and floor) is given, calculated using three methods. As the emissivity of the premises’ enclosing structures decreases, the difference between the results obtained via methods 1, 2, and 3 increases significantly and reaches 50…60% at ε = 0.8. The IR heater radiating surface temperature range is defined in order to create suitable thermal conditions on premises designed for keeping 1- to 4-week-old newborn piglets depending on the enclosing structure temperature and emissivity, taking into account hidden heat exchange surfaces. Full article

Wood-based materials in the form of wood veneer composites (WVCs) possess a high lightweight construction potential for load-bearing applications in mechanical engineering due to their high strength properties combined with low density. However, in order to substitute energy-intensive metallic construction materials (such as steel or aluminum), additional structural space is required to compensate for the comparatively low stiffness by means of the area moment of inertia. Under bending loads, an increase in cross-sectional height at a constant span length leads to elevated shear stresses. Owing to the low shear strength and stiffness of wood-based materials, the influence of shear stresses must be considered in both the design of wooden components and in material testing. Current standards for determining the bending properties of wood-based materials only describe methods for assessing pure bending behavior, without accounting for shear effects. The present contribution introduces a method for determining both bending and shear properties of WVC using the three-point bending test. This approach allows for the derivation of bending and shear modulus values through an analytical model based on Timoshenko beam theory by testing various span-to-height ratios. These modulus values represent material constants and enable the numerical design of wooden components for arbitrary geometric parameters. Full article

Background/Objectives: This proof-of-concept study evaluated whether optical coherence tomography angiography (OCTA) can non-invasively capture micro-vascular alterations in non-melanoma skin cancer (NMSC) lesions during and after superficial orthovoltage radiotherapy (RT) using radiomics and vascular features analysis. Methods: Eight patients (13 NMSC lesions) received 36–50 Gy in 6–20 fractions. High-resolution swept-source OCTA volumes (1.1 × 10 × 10 mm³) were acquired from each lesion at three time points: pre-RT, immediately post-RT, and three months post-RT. Additionally, healthy skin baseline was scanned. After artifact suppression and region-of-interest cropping, (i) first-order and texture radiomics and (ii) skeleton-based vascular features were extracted. Selected features after LASSO (least absolute shrinkage and selection operator) were explored with principal-component analysis. An XGBoost model was trained to classify time points with 100 bootstrap out-of-bag validations. Kruskal–Wallis tests with Benjamini–Hochberg correction assessed longitudinal changes in the 20 most influential features. Results: Sixty-one OCTA volumes were analyzable. LASSO retained 47 of 103 features. The first two principal components explained 63% of the variance, revealing a visible drift of lesions from pre- to three-month post-RT clusters. XGBoost achieved a macro-averaged AUC of 0.68 ± 0.07. Six features (3 texture, 2 first order, 1 vascular) changed significantly across time points (adjusted p < 0.05), indicating dose-dependent reductions in signal heterogeneity and micro-vascular complexity as early as treatment completion, which deepened by three months. Conclusions: OCTA-derived radiomic and vascular signatures tracked RT-induced micro-vascular remodeling in NMSC. The approach is entirely non-invasive, label-free, and feasible at the point of care. As an exploratory proof-of-concept, this study helps to refine scanning and analysis protocols and generates knowledge to support future integration of OCTA into adaptive skin-cancer radiotherapy workflows. Full article

Background and Objectives: Cardiac injury caused by cancer therapy can be detected early using high-sensitivity cardiac troponins (hs-cTns), and this is crucial for preventing irreversible consequences. Clinically relevant issues regarding hs-cTns in oncologic settings—such as reliable cut-off values, the optimal assessment timeframe, factors influencing their levels, and their prognostic ability in relation to functional echocardiographic parameters—require further investigation. In this study, we aimed to examine the determinants of hs-cTnT variations during cancer therapy and the relationship between the biomarker and functional conventional echocardiographic parameters. Materials and Methods: We prospectively evaluated adult patients scheduled for chemotherapy for either breast or gastrointestinal cancers, excluding those with pulmonary and cardiac disorders. We enrolled 40 patients who underwent a minimum of one cycle of potentially cardiotoxic regimens containing at least one of the following agents: anthracyclines, cyclophosphamide, taxanes, 5-fluorouracil, platinum compounds, trastuzumab, or bevacizumab. We observed two-dimensional and tissue Doppler echocardiographic parameters and hs-cTnT levels for a median of 360 days (IQR 162, 478) following the start of chemotherapy. Results: The generalised estimating equation (GEE) analysis revealed significant elevations in hs-cTnT levels at three months (β = 1.2; p = 0.005) and six months (β = 2.3; p = 0.02) from baseline, influenced by anthracycline treatment (p = 0.009), renal function (p = 0.003), and increased cardiotoxicity risk (high: p = 0.013; medium: p < 0.001). Elevated hs-cTnT levels independently predicted the deterioration of the LV longitudinal myocardial function, measured by the systolic tissue velocities, according to the GEE analysis. The receiver operating characteristic curve-derived hs-cTnT thresholds—of 8.23 ng/L and 8.08 ng/L—had a high negative predictive value for identifying Average and Lateral LVS′ decreases, respectively. Conclusions: Our research supports the use of baseline and continuing hs-cTnT testing in cancer patients, showing the dependence of the biomarker on renal function, cardiovascular toxicity risk level, and anthracycline treatment. The hs-cTnT cut-off value of approximately 8 ng/L may suggest a low probability of longitudinal myocardial function impairment and this observation needs further validation in larger cohorts. Full article

This paper presents a proposed LSTM-based vertical-gradient prediction combined with three-dimensional kriging that enables reconstruction of greenhouse 3D temperature fields under sparse-sensor deployments while capturing temporal dynamics and spatial correlations. In northern China, winter solar greenhouses rely on standardized structures and passive climate-control strategies, which often lead to non-uniform thermal conditions that complicate precise regulation. To address this challenge, 24 sensors were deployed, and their time-series data were used to train a long short-term memory (LSTM) model for vertical temperature-gradient prediction. The predicted values at multiple heights were fused with in situ observations, and three-dimensional ordinary kriging (3D-OK) was applied to reconstruct the spatiotemporal temperature field. Compared with conventional 2D monitoring and computationally intensive CFD, the proposed approach balances accuracy, efficiency, and deployability. LSTM–Kriging validation showed Trend + Residual Kriging had the lowest RMSE (0.45558 °C) and bias (−0.03148 °C) (p < 0.01), outperforming Trend-only RMSE (3.59 °C) and Kriging-only RMSE (0.48 °C); the 3D model effectively distinguished sunny and rainy dynamics. This cost-effective framework balances accuracy, efficiency, and deployability, overcoming limitations of 2D monitoring and CFD. It provides critical support for adaptive greenhouse climate regulation and digital-twin development, directly advancing precision management and yield stability in CEA. Full article

Objectives: To establish normative data for anterior segment parameters in healthy pediatric and adult populations using swept-source optical coherence tomography (SS-OCT), and to evaluate the influence of age and sex on these parameters. Methods: This retrospective study included the right eyes of 390 healthy participants. Subjects were divided into three age groups: Group 1 (6–17 years, n = 97), Group 2 (18–45 years, n = 144), and Group 3 (46–77 years, n = 149). All patients were categorized according to their biological sex as female and male. Exclusion criteria were corneal pathology, prior intraocular/refractive surgery, recent contact lens use, severe dry eye, ectatic disorders, low-quality imaging, and refractive error of ±2.0 D or greater. Measurements of anterior and posterior keratometry, total corneal power (TCP), central corneal thickness (CCT), thinnest corneal thickness (TCT), pupil diameter (PD), lens thickness (LT), and white-to-white distance (WTW) were obtained using the Anterion^® SS-OCT system. Data were analyzed using SPSS software. Results: Group 1 demonstrated the highest PD and CCT values, whereas LT was lowest. In adults, LT increased with age and was significantly higher in males older than 45 years. Keratometric analysis revealed greater anterior and total steep astigmatism in the pediatric group, independent of sex. Adult females had significantly higher anterior and posterior keratometry values compared with males. In the pediatric cohort, females exhibited greater CCT, while WTW varied with age. PD decreased with age, whereas LT increased. Conclusions: Anterior segment parameters measured with SS-OCT show significant variations across different age groups and between sexes. Normative data, particularly for pediatric and adult populations, may serve as valuable reference values in keratorefractive surgical planning and corneal pathology assessment. Future studies with larger cohorts, especially in pediatric populations, are warranted. Full article