Search Results (1,851)

Background and Objectives: Meniscal pathologies are common abnormalities of the knee joint and a frequent cause of knee pain. Prompt and accurate diagnosis is essential to ensure appropriate treatment. Ultrasonography is increasingly used due to its accessibility, cost- and time-efficiency, and capacity for dynamic assessment. This study aimed to evaluate the usefulness of ultrasonography in identifying specific types of meniscal tears and to assess their frequency of occurrence. Materials and Methods: A retrospective study was conducted to assess the frequency and sonographic appearance of various meniscal pathologies. The study population included all patients who underwent ultrasonographic examination of the knee in our clinic over one year for various indications (n = 430). Archived ultrasound images were retrospectively reviewed and analyzed. Results: Meniscal pathologies were identified in 134 patients. The findings included 95 cases of degenerative lesions (70.9%), 18 meniscal cyst-related pathologies (13.4%), 8 complex tears (6.0%), 5 flap tears (3.7%), 3 vertical pericapsular tears (2.2%), 3 partial thickness tears (2.2%), and 2 bucket-handle-type tears (1.5%). Each lesion type was characterized and illustrated through representative ultrasound images. Conclusions: Ultrasound imaging of meniscal pathology offers a valuable diagnostic option. By characterizing and visually documenting different meniscal lesions, this study highlights the practical potential of ultrasonography in routine clinical settings. These findings may enhance diagnostic accuracy and guide more targeted management strategies. Moreover, the results contribute to the expanding body of research on musculoskeletal ultrasonography and may encourage broader adoption of ultrasound in orthopedic diagnostics. Full article

The use of aerial robots for inspection and maintenance in industrial settings demands high maneuverability, precise control, and reliable measurements. This study explores the development of a fully customized unmanned aerial manipulator (UAM), composed of a tilting drone and an articulated robotic arm, designed to perform non-destructive in-contact inspections of iron structures. The system is intended to operate in complex and potentially hazardous environments, where autonomous execution is supported by shared-control strategies that include human supervision. A parallel force–impedance control framework is implemented to enable smooth and repeatable contact between a sensor for ultrasonic testing (UT) and the inspected surface. During interaction, the arm applies a controlled push to create a vacuum seal, allowing accurate thickness measurements. The control strategy is validated through repeated trials in both indoor and outdoor scenarios, demonstrating consistency and robustness. The paper also addresses the mechanical and control integration of the complex robotic system, highlighting the challenges and solutions in achieving a responsive and reliable aerial platform. The combination of semi-autonomous control and human-in-the-loop operation significantly improves the effectiveness of inspection tasks in hard-to-reach environments, enhancing both human safety and task performance. Full article

The central and deep reservoirs of the Wushi Sag in the Beibu Gulf Basin, China, are characterized by structurally complex settings, strong heterogeneity, multiple controlling factors for physical properties of reservoirs, rapid lateral variations in reservoir thickness and petrophysical properties, and limited seismic resolution. To address these challenges, this study integrates the INPEFA inflection point technique and Morlet wavelet transform to delineate system tracts and construct a High-Frequency Stratigraphic Framework (HFSF). Sedimentary facies are identified through the integration of core descriptions and seismic data, enabling the mapping of facies distributions. The vertical constraints provided by the stratigraphic framework, combined with the lateral control from facies distribution, which, based on identification with logging data and geological data, support the construction of a geologically consistent low-frequency initial model. Subsequently, geostatistical seismic inversion is performed to derive acoustic impedance and lithological distributions within the central and deep reservoirs. Compared with the traditional methods, the accuracy of the inversion results of this method is 8% higher resolution than that of the conventional methods, with improved vertical resolution to 3 m, and enhances the lateral continuity matched with the sedimentary facies structure. This integrated workflow provides a robust basis for predicting the spatial distribution of sandstone reservoirs in the Wushi Sag’s deeper stratigraphic intervals. Full article

Straw mulching is an important strategy for regulating soil moisture, nutrient availability, and thermal conditions in agricultural systems. However, the mechanisms by which the mulching period, thickness, and planting density interact to influence yield formation in wheat–soybean rotation systems remain insufficiently understood. In this study, we systematically examined the combined effects of straw mulching at the seedling and jointing stages of winter wheat, as well as varying mulching thicknesses and soybean planting densities, on soil properties and crop yields through field experiments. The experimental design included straw mulching treatments during the seedling stage (T₁) and the jointing stage (T₂) of winter wheat, with soybean planting densities classified as low (D₁, 1.8 × 10⁵ plants·ha⁻¹) and high (D₂, 3.6 × 10⁵ plants·ha⁻¹). Mulching thicknesses were set at low (S₁, 2830.19 kg·ha⁻¹), medium (S₂, 8490.57 kg·ha⁻¹), and high (S₃, 14,150.95 kg·ha⁻¹), in addition to a no-mulch control (CK) for each treatment. The results demonstrated that (1) straw mulching significantly increased soil water content in the order S₃ > S₂ > S₁ > CK and exerted a temperature-buffering effect. This resulted in increases in soil organic carbon, available phosphorus, and available potassium by 1.88−71.95%, 1.36−165.8%, and 1.92−36.34%, respectively, while decreasing available nitrogen content by 1.42−17.98%. (2) The T₁ treatments increased wheat yields by 1.22% compared to the control, while the T₂ treatments resulted in a 23.83% yield increase. Soybean yields increased by 23.99% under D₁ and by 36.22% under D₂ treatments. (3) Structural equation modeling indicated that straw mulching influenced yields by modifying interactions among soil organic carbon, available nitrogen, available phosphorus, available potassium, bulk density, soil temperature, and soil water content. Wheat yields were primarily regulated by the synergistic effects of soil temperature, water content, and available potassium, whereas soybean yields were determined by the dynamic balance between organic carbon and available potassium. This study provides empirical evidence to inform the optimization of straw return practices in wheat–soybean rotation systems. Full article

Objectives: The objective of this study was to evaluate the agreement and clinical interchangeability of intraocular pressure (IOP) measurements obtained with the mechanical Haag-Streit AT900 Goldmann applanation tonometer (mGAT) and the digital Huvitz HT5000 applanation tonometer (dGAT). Methods: This retrospective comparative study included 53 eyes of 28 patients undergoing routine ophthalmologic evaluation. Each eye underwent IOP measurement using both mGAT and dGAT in a randomized sequence. Central corneal thickness (CCT) was also recorded. Pearson’s correlation coefficient was used to determine correlation between paired IOP measurements. Bland–Altman plots were graphed for the analysis of differences for IOP between the instruments. Results: A total of 53 eyes of 28 patients (15 males) were included in the study. The mean age of the patients was 62.6 years. The mean mGAT and dGAT measurements were 16.3 ± 6.6 mmHg (range 9–50) and 16.4 ± 6.2 mmHg (range 8.8–45.9), respectively (p = 0.53). A strong, significant positive correlation was found for paired IOP measurements by the two instruments (r = 0.98; p < 0.0001). Bland–Altman analysis revealed 95% limits of agreement from −2.5 to +2.3 mmHg, with a small but statistically significant proportional bias favoring mGAT at higher IOP levels. Additionally, 91% of paired measurements were within ±2 mmHg. CCT-related differences were statistically and clinically insignificant. Conclusions: IOP measurements obtained with mGAT and dGAT were highly correlated and clinically interchangeable for the range tested. The Huvitz HT5000 may serve as a reliable alternative to the classic Goldmann tonometer in routine clinical settings. Full article

Specialized research on large trees in Northeast China is rare. To strengthen the understanding of local large trees, a survey of 4055 tree individuals from 75 plots in southeastern Jilin Province was conducted. The individual number and species composition of large trees in the community, as well as large individual standards in diameter at breast height (DBH) and population structures of typical tree species, were analyzed. By setting a DBH ≥ 50 cm as the threshold, 155 individuals across all the recorded trees were determined as large trees in the community, and 32.9% (51/155) of them were national second-class protected plant species in China. By setting the top 5% in DBH of a certain tree species as the threshold of large individuals of that tree species, the large individual criteria of six typical tree species were determined. The proportion of basal area of large trees to all trees was 30.4%, and the mean proportion of basal area of large individuals across the six typical tree species was 23.9% (±4.0%). As for the population characteristics, Abies nephrolepis and Picea jezoensis had large population sizes but relatively thin individuals, Tilia amurensis and Pinus koraiensis had small population sizes but relatively thick individuals, while Betula costata and Larix olgensis had medium population sizes and medium-sized individuals. Full article

Accurate identification of active-layer compositions in organic photovoltaic (OPV) devices often relies on invasive techniques such as electrical measurements or material extraction, which risk damaging the device. In this study, we propose a non-invasive classification approach based on simulated full-device absorption spectra. To account for fabrication-related variability, the active-layer thickness varied by over ±15% around the optimal value, creating a realistic and diverse training dataset. A multilayer perceptron (MLP) neural network was applied with various activation functions, optimization algorithms, and data split ratios. The optimized model achieved classification accuracies exceeding 99% on both training and testing sets, with minimal sensitivity to random initialization or data partitioning. These results demonstrate the potential of applying deep learning to spectral data for reliable, non-destructive OPV composition classification, paving the way for integration into automated manufacturing diagnostics and quality control workflows. Full article

We inspected a sector of the Apennines (central–southern Italy) in geographic and structural continuity with the Quaternary-active extensional belt but where clear geomorphic and seismological signatures of normal faulting are unexpectedly missing. The evidence of active tectonics in this area, between Abruzzo and Molise, does not align with geodetic deformation data and the seismotectonic setting of the central Apennines. To investigate the apparent disconnection between active deformation and the absence of surface faulting in a sector where high lithologic erodibility and landslide susceptibility may hide its structural evidence, we combined multi-scale and multi-source data analyses encompassing morphometric analysis and remote sensing techniques. We utilised high-resolution topographic data to analyse the topographic pattern and investigate potential imbalances between tectonics and erosion. Additionally, we employed aerial-photo interpretation to examine the spatial distribution of morphological features and slope instabilities which are often linked to active faulting. To discern potential biases arising from non-tectonic (slope-related) signals, we analysed InSAR data in key sectors across the study area, including carbonate ridges and foredeep-derived Molise Units for comparison. The topographic analysis highlighted topographic disequilibrium conditions across the study area, and aerial-image interpretation revealed morphologic features offset by structural lineaments. The interferometric analysis confirmed a significant role of gravitational movements in denudating some fault planes while highlighting a clustered spatial pattern of hillslope instabilities. In this context, these instabilities can be considered a proxy for the control exerted by tectonic structures. All findings converge on the identification of an ~20 km long corridor, the Castel di Sangro–Rionero Sannitico alignment (CaS-RS), which exhibits varied evidence of deformation attributable to active normal faulting. The latter manifests through subtle and diffuse deformation controlled by a thick tectonic nappe made up of poorly cohesive lithologies. Overall, our findings suggest that the CaS-RS bridges the structural gap between the Mt Porrara–Mt Pizzalto–Mt Rotella and North Matese fault systems, potentially accounting for some of the deformation recorded in the sector. Our approach contributes to bridging the information gap in this complex sector of the Apennines, offering original insights for future investigations and seismic hazard assessment in the region. Full article

Deep saline aquifers in the eastern part of Drava Basin were screened for potential storage sites. The input dataset included three seismic volumes, a rather extensive set of old seismic sections and 71 wells. Out of all identified potential storage objects, only two sites were found to be situated in the favorable geological settings, meaning that the inspected wells drilled through structural traps had a seal at least 20 m thick which was intersected by only a few faults with rather limited displacement. Many more closed structures in the area were tested by exploration wells, but in all other wells, various problems were encountered, including inadequate reservoir properties, inadequate seal or inadequate depth of the identified trap. Analysis was highly affected by the insufficient quality and spatial distribution of the seismic input data, as well as in places with insufficient quality of input well datasets. An initial characterization of identified storage sites was performed, and their attributes were compared, with potential storage object B recognized as the one that should be further developed. However, given the depth and increased geothermal gradient of the potential storage object B, it is possible that it will be developed as a geothermal reservoir, and this brings forward the problem of concurrent subsurface use. Full article

Magneto-plasmonic structures have been a subject of tremendous attention of researchers in recent decades as they provide unique approaches regarding the efficient control of optical, magneto-optical, and nonlinear-optical effects. Among others, magneto-plasmonic crystals (MPCs) have become one of the most studied structures, known for their high-quality tunable resonant optical properties. Here, we present the results of experimental and numerical studies on the functional magneto-optical (MO) response of planar 1D plasmonic crystals composed of Co/Au stripes of submicron period on the surface of a 3 μm thick rare-earth garnet layer. The experimental and numerical studies confirm that the wavelength–angular spectra of such structures contain a set of tunable resonant features in their optical and magneto-optical response, associated with the excitation of (i) surface plasmon polaritons at the Co/Au grating–garnet interface, as well as (ii) waveguide (WG) modes propagating in the garnet slab. A comparison of the MO effects in the transversal and longitudinal magnetization of the plasmonic structures is presented. We show that the most efficient Fano-type MPC magneto-optical response is realized for the WG modes of the first order for the longitudinal magnetization of the structure. Further perspectives regarding the optimization of this type of plasmonic crystal are discussed. Full article

The ability to predict capacity retention is critical for ensuring the long-term reliability of supercapacitors in energy storage systems. This study presents a comprehensive machine learning framework that integrates both electrical cycling data and experimentally derived material and structural features to forecast the degradation behavior of commercial supercapacitors. A total of seven supercapacitor samples were tested under various current and voltage conditions, resulting in over 70,000 charge–discharge cycles across three case studies. In addition to electrical measurements, detailed physical and material characterizations were performed, including electrode dimension analysis, Scanning Electron Microscopy (SEM), Energy Dispersive X-ray Spectroscopy (EDS), and Thermogravimetric Analysis (TGA). Three machine learning models, Linear Regression (LR), Random Forest (RF), and Multi-Layer Perceptron (MLP), were trained using both cycler-only and combined cycler + material features. Results show that incorporating material features consistently improved prediction accuracy across all models. The MLP model exhibited the highest performance, achieving an R² of 0.976 on the training set and 0.941 on unseen data. Feature importance analysis confirmed that material descriptors such as porosity, thermal stability, and electrode thickness significantly contributed to model performance. This study demonstrates that combining electrical and material data offers a more holistic and physically informed approach to supercapacitor health prediction. The framework developed here provides a practical foundation for accurate and robust lifetime forecasting of commercial energy storage devices, highlighting the critical role of material-level insights in enhancing model generalization and reliability. Full article

During centralizer hydroforming, internal pressure and axial feed critically influence the forming outcome. Insufficient feed causes excessive thinning and cracking, while excessive feed causes thickening and wrinkling. Achieving uniform wall thickness necessitates careful design of the pressure and feed curves. Using max/min wall thickness as objectives and key control points on these curves as variables, the study integrated Non-dominated Sorting Genetic Algorithm (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), Neighborhood Cultivation Genetic Algorithm (NCGA), and Archive-based Micro Genetic Algorithm (AMGA) with LS-DYNA to automatically optimize loading paths. The results demonstrate the following: ① NSGA-II, NCGA, and AMGA successfully generated optimized paths; ② NSGA-II and AMGA produced larger sets of higher-quality Pareto solutions; ③ AMGA required more iterations for satisfactory Pareto sets; ④ MOPSO exhibited a tendency towards premature convergence, yielding inferior results; ⑤ Multi-objective optimization efficiently generated diverse Pareto solutions, expanding the design space for process design. Full article

Currently, the determination of the molten zone thickness in HDPE pipes during butt fusion welding primarily depends on experimental and numerical methods, leading to high costs and reduced efficiency. In this study, a mathematical (MM) model based on Neumann’s solution for the melting of a semi-infinite region was developed to efficiently predict the average molten zone (AMZ) thickness of HDPE pipes under varying heating temperatures and heating times while incorporating the effects of heat convection. Additionally, a two-dimensional CFD model was constructed using finite element analysis (FEA) to validate the MM model. Welding pressure was not considered in this study. The effects of heating temperature, heating time, and heat convection on the AMZ thickness in HDPE pipes were systematically analyzed. The heating temperature at the heated end of HDPE ranged from 190 °C to 350 °C in 20 °C increments, with a temperature of 28 °C as the ambient and initial setting, and the heating time was set to 180 s for both the MM and CFD models. The results demonstrate a strong correlation between the AMZ thickness predictions from the MM and CFD models. The relative error between the MM and CFD models ranges from 0.280% to 10,830% with heat convection and from −2.398% to 8.992% without heat convection. Additionally, for the MM model, the relative error between cases with and without heat convection ranges from 0.243% to 0.433%, whereas for the CFD model, it varies between 1.751% and 3.189%. These findings confirm the reliability of the MM model developed in this study and indicate that thermal convection has a minimal impact on AMZ thickness prediction for large-diameter, thick-walled HDPE pipes. Full article

With the rapid advancement of urbanization in China, the demand for community-based elderly care facilities (CECFs) has been increasing. One pressing challenge is the question of how to provide CECFs that not only meet the health needs of the elderly but also make efficient use of limited urban land resources. This study addresses this issue by adopting an integrated multi-method research framework that combines multi-objective optimization (MOO) algorithms, Spearman rank correlation analysis, ensemble learning methods (Random Forest combined with SHapley Additive exPlanations (SHAP), where SHAP enhances the interpretability of ensemble models), and Self-Organizing Map (SOM) neural networks. This framework is employed to identify optimal building configurations and to examine how different architectural parameters influence key daylight performance indicators—Useful Daylight Illuminance (UDI) and Daylight Factor (DF). Results indicate that when UDI and DF meet the comfort thresholds for elderly users, the minimum building area can be controlled to as little as 351 m² and can achieve a balance between natural lighting and spatial efficiency. This ensures sufficient indoor daylight while mitigating excessive glare that could impair elderly vision. Significant correlations are observed between spatial form and daylight performance, with factors such as window-to-wall ratio (WWR) and wall thickness (WT) playing crucial roles. Specifically, wall thickness affects indoor daylight distribution by altering window depth and shading. Moreover, the ensemble learning models combined with SHAP analysis uncover nonlinear relationships between various architectural parameters and daylight performance. In addition, a decision support method based on SOM is proposed to replace the subjective decision-making process commonly found in traditional optimization frameworks. This method enables the visualization of a large Pareto solution set in a two-dimensional space, facilitating more informed and rational design decisions. Finally, the findings are translated into a set of practical design strategies for application in real-world projects. Full article

Injection molding is a fundamental process for transforming plastics into various industrial components. Among the critical aspects studied in this process, volumetric contraction and warpage of plastic parts are of particular importance. Achieving precise control over warpage is crucial for ensuring the production of high-quality components. This research explores optimizing injection process parameters to minimize volumetric contraction and warpage in rectangular polypropylene (PP) parts. The study employs experimental analysis, MoldFlow simulation, and Artificial Neural Network (ANN) modeling. MoldFlow simulation software provides valuable data on warpage, serving as input for the ANN model. Based on the Backpropagation Neural Network algorithm, the optimized ANN model accurately predicts warpage by considering factors such as part thickness, flow path distance, and flow path tangent. The study highlights the importance of accurately setting injection parameters to achieve optimal warpage results. The BPNN-based approach offers a faster and more efficient alternative to computer-aided engineering (CAE) processes for studying warpage. Full article