Search Results (233)

Background: Zygomatico-maxillary complex (ZMC) fractures are prevalent facial injuries with significant functional and aesthetic implications. Computer-assisted surgery (CAS) offers precise surgical planning and outcome evaluation. The study aimed to evaluate the application of CAS in the analysis of ZMC fracture outcomes and to propose a reproducible workflow for surgical outcome assessment using cephalometric landmarks. Methods: A retrospective cohort study was conducted on 16 patients treated for unilateral ZMC fractures at the Maxillofacial Surgery Unit of Siena University Hospital (2017–2024). Inclusion criteria included ZMC fractures classified as Zingg B or C, treated via open reduction and internal fixation (ORIF). Pre- and post-operative CT scans were processed for two- and three-dimensional analyses. Discrepancies between CAS-optimized reduction and achieved surgical outcomes were quantified using cephalometric landmarks and volumetric assessments. Results: Out of the 16 patients (69% male, mean age 48.1 years), fractures were predominantly on the right side (81%). CAS comparison between the post-operative and the contralateral side revealed significant asymmetries along the X and Y axes, particularly in the fronto-zygomatic suture (FZS), zygo-maxillary point (MP), and zygo-temporal point (ZT). Computer-assisted comparison between the post-operative and the CAS-simulated reductions showed statistical differences along all three orthonormal axes, highlighting the challenges in achieving ideal symmetry despite advanced surgical techniques. CAS-optimized reductions demonstrated measurable improvements compared to traditional methods, underscoring their utility in outcome evaluation. Conclusions: CAS technology enhances the precision of ZMC fracture outcome evaluation, allowing for detailed comparison between surgical outcomes and virtual simulations. Its application underscores the potential for improved surgical planning and execution, especially in complex cases. Future studies should focus on expanding sample size, refining workflows, and integrating artificial intelligence to automate processes for broader clinical applicability. Full article

Quantitatively analyzing the factors influencing the horizontal migration of microplastics (MPs) in water bodies and understanding their movement patterns are crucial for explaining and predicting their transport principles and final destinations. This study used nearly spherical polyethylene (PE), polypropylene (PP), and polystyrene (PS) MPs as experimental subjects. By tracking their motion characteristics through video recording, we established relationships among the Reynolds number (Re), MP density, and floating velocity. The results showed that the Re and MP density jointly affect the horizontal drift of MPs. The horizontal floating velocity of MPs significantly increases with the increase in the Re and shows a power function growth trend. The difference in density of MPs mainly affects their dispersion during the floating process. Moreover, the coefficient of variation (CV) of PP’s horizontal floating velocity increased with the Re, suggesting PP’s motion is more random and discrete than that of PE and PS. Ultimately, we fitted the horizontal floating velocity of MPs to the equation to comprehensively evaluate the relationship between the floating velocity, Re, and density of MPs. This analysis underscores that the Re predominantly influences the MP velocity in water, while the MP density chiefly impacts the discrete nature of their motion. Full article

This paper deals with a “digital twin” (DT) approach for processing, reprocessing, and scrapping (P/R/S) technology running on a modular production system (MPS) assisted by a mobile cyber–physical robotic system (MCPRS). The main hardware architecture consists of four line-shaped workstations (WSs), a wheeled mobile robot (WMR) equipped with a robotic manipulator (RM) and a mobile visual servoing system (MVSS) mounted on the end effector. The system architecture integrates a hierarchical control system where each of the four WSs, in the MPS, is controlled by a Programable Logic Controller (PLC), all connected via Profibus DP to a central PLC. In addition to the connection via Profibus of the four PLCs, related to the WSs, to the main PLC, there are also the connections of other devices to the local networks, LAN Profinet and LAN Ethernet. There are the connections to the Internet, Cloud and Virtual Private Network (VPN) via WAN Ethernet by open platform communication unified architecture (OPC-UA). The overall system follows a DT approach that enables task planning through augmented reality (AR) and uses virtual reality (VR) for visualization through Synchronized Hybrid Petri Net (SHPN) simulation. Timed Petri Nets (TPNs) are used to control the processes within the MPS’s workstations. Continuous Petri Nets (CPNs) handle the movement of the MCPRS. Task planning in AR enables users to interact with the system in real time using AR technology to visualize and plan tasks. SHPN in VR is a combination of TPNs and CPNs used in the virtual representation of the system to synchronize tasks between the MPS and MCPRS. The workpiece (WP) visits stations successively as it is moved along the line for processing. If the processed WP does not pass the quality test, it is taken from the last WS and is transported, by MCPRS, to the first WS where it will be considered for reprocessing or scrapping. Full article

An extensive thermochemical study of γ-undecanolactone and δ-undecanolactone has been developed using two complementary calorimetric techniques. The combustion energy of each compound was determined by static-bomb combustion calorimetry, and the corresponding enthalpy of vaporization was determined by high-temperature Calvet microcalorimetry, in which both properties of each compound are reported at T = 298.15 K. The standard molar enthalpy of formation in the gas phase of each lactone was derived by the combination of the experimental results. Additionally, high-level computational calculations were carried out, using composite ab initio G4 and G4(MP2) methods, as well as DFT M06-2X/6-311++G(d,p) approach, to estimate the corresponding enthalpy of formation in the gas phase. The experimental and computational results are in good agreement. The G4 and G4(MP2) methods show the best accordance with experimentally determined gas phase enthalpies of formation. The experimental results are discussed in terms of structural contributions to the energetic properties of the lactones studied, as well as to other alkylated γ- and δ-lactones, and empirical correlations are suggested for the estimation of the standard molar enthalpies of formation, at T = 298.15 K, for other alkylated γ- and δ-lactones, both in the liquid and gaseous phases, as well as for the respective enthalpies of vaporization. Finally, the thermochemistry of individual steps of lactone ring opening and successive decarboxylation mechanism, including the identification of transition states, was studied using the M06-2X/6-311++G(d,p) approach. Full article

A nuclear electromagnetic pulse (NEMP) is the fourth effect of a nuclear explosion, characterized by a strong electromagnetic field that can instantly damage electronic devices. To investigate the spatial field value distribution characteristics of the source region of low-altitude NEMPs, this study employed a finite-difference time-domain (FDTD) method based on a rotating ellipsoidal hyperbolic coordinate system. Due to intense field variations near the explosion center, non-uniform grids were employed for both spatial and temporal steps, and an OpenMP parallel algorithm was utilized to enhance computational efficiency. Analysis focused on the following two scenarios: varying angles at a constant distance and varying distances at a constant angle, considering both transverse magnetic (TM) and transverse electric (TE) waves. The results indicate that the spatial field value distribution characteristics differ between the two wave types. For TM waves, the electric and magnetic fields share the same polarity, but their waveform polarities are opposite above and below the explosion center. A TE wave is exactly the opposite. Compared with a TM wave, a TE wave has stronger peak electromagnetic fields but narrower pulse widths and lower overall energy. This research provides significant support for the development of nuclear explosion detection technology and offers theoretical foundations for the protection of surrounding environmental facilities. Full article

Background: Prostate cancer (PCa) is the most frequent neoplasia in the male population. According to the International Society of Urological Pathology (ISUP), PCa can be divided into two major groups, based on their prognosis and treatment options. Multiparametric magnetic resonance imaging (mpMRI) holds a central role in PCa assessment; however, it does not have a one-to-one correspondence with the histopathological grading of tumors. Recently, artificial intelligence (AI)-based algorithms and textural analysis, a subdivision of radiomics, have shown potential in bridging this gap. Objectives: We aimed to develop a machine-learning algorithm that predicts the ISUP grade of manually contoured prostate nodules on T2-weighted images and classifies them into clinically significant and indolent ones. Materials and Methods: We included 55 patients with 76 lesions. All patients were examined on the same 1.5 Tesla mpMRI scanner. Each nodule was manually segmented using the open-source 3D Slicer platform, and textural features were extracted using the PyRadiomics (version 3.0.1) library. The software was based on machine-learning classifiers. The accuracy was calculated based on precision, recall, and F1 scores. Results: The median age of the study group was 64 years (IQR 61–68), and the mean PSA value was 11.14 ng/mL. A total of 85.52% of the nodules were graded PI-RADS 4 or higher. Overall, the algorithm classified indolent and clinically significant PCas with an accuracy of 87.2%. Further, when trained to differentiate each ISUP group, the accuracy was 80.3%. Conclusions: We developed an AI-based decision-support system that accurately differentiates between the two PCa prognostic groups using only T2 MRI acquisitions by employing radiomics with a robust machine-learning architecture. Full article

Free pectinase is commonly employed as a biocatalyst in wine clarification; however, its removal, recovery, and reuse are not feasible. To address these limitations, this study focuses on the immobilization of a commercial pectinolytic preparation (Pec) onto highly porous polymer microparticles (MPs). Seven microparticulate polyamide (PA) supports, namely PA4, PA6, PA12 (with and without magnetic properties), and the copolymeric PA612 MP, were synthesized through activated anionic ring-opening polymerization of various lactams. Pectinase was non-covalently immobilized on these supports by adsorption, forming Pec@PA conjugates. Comparative activity and kinetic studies revealed that the Pec@PA12 conjugate exhibited more than twice the catalytic efficiency of the free enzyme, followed by Pec@PA6-Fe and Pec@PA4-Fe. All Pec@PA complexes were tested in the clarification of industrial rosé must, demonstrating similar or better performance compared to the free enzyme. Some immobilized biocatalysts supported up to seven consecutive reuse cycles, maintaining up to 50% of their initial activity and achieving complete clarification within 3–30 h across three consecutive cycles of application. These findings highlight the potential for industrial applications of noncovalently immobilized pectinase on various polyamide microparticles, with possibilities for customization of the conjugates’ properties. Full article

Aquaponics is an innovative agricultural method combining aquaculture and hydroponics. However, this balance can lead to the gradual depletion of essential micronutrients, particularly iron. Over time, decreasing iron levels can negatively impact plant health and productivity, making the monitoring and management of iron in aquaponic systems vital. This study investigates the use of Fe-Alg-CaCO₃ microparticles (MPs) as foliar fertilizer on lettuce plants in an aquaponic system. The research investigated Lactuca sativa L. cv. Foglia di Quercia Verde plants as the experimental cultivar. Three iron concentrations (10, 50, and 250 ppm) were tested, with 15 plants per treatment group, plus a control group receiving only sterile double-distilled water. The Fe-Alg-CaCO₃ MPs and ultrapure water were applied directly to the leaves using a specialized nebulizer. Foliar nebulization was chosen for its precision and minimal resource use, aligning with the sustainability goals of aquaponic cultivation. The research evaluated rosette diameter, root length, fresh weight, soluble solids concentration, levels of photosynthetic pigments, and phenolic and flavonoid content. The 250 ppm treatment produced the most notable enhancements in both biomass yield and quality, highlighting the potential of precision fertilizers to boost sustainability and efficiency in aquaponic systems. In fact, the most significant increases involved biomass production, particularly in the edible portions, along with photosynthetic pigment levels. Additionally, the analysis of secondary metabolite content, such as phenols and flavonoids, revealed no reduction compared to the control group, meaning that the proposed fertilizer did not negatively impact the biosynthetic pathways of these bioactive compounds. This study opens new possibilities in aquaponics cultivation, highlighting the potential of precision fertilizers to enhance sustainability and productivity in soilless agriculture. Full article

Digital non-metric cameras with high-resolution capabilities are being used in various domains such as digital heritage, artifact documentation, art conservation, and engineering applications. In this study, a novel approach consisting of the application of the combined use of close-range photogrammetry (CRP) and mapping techniques is used to capture the depth of a mural digitally, serving as a database for the preservation and archiving of historic murals. The open hall next to the main sanctuary of the Virupaksha temple in Hampi, Karnataka, India, which is a UNESCO World Heritage site, depicts cultural events on a mural-covered ceiling. A mirrorless Sony Alpha 7 III camera with a full-frame 24 MP CMOS sensor mounted with a 50 mm lens and 24 mm lens has been used to acquire digital photographs with an image size of 6000 × 6000 pixels. The suggested framework incorporates five main steps: data acquisition, color correction, image mosaicking, orthorectification, and image filtering. The results show a high level of accuracy and precision attained during the image capture and processing steps. A comparative study was performed in which the 24 mm lens orthoimage resulted in an image size of 9131 × 14,910 and a pixel size of 1.05 mm, whereas the 50 mm lens produced a 14,283 × 21,676 image size and a pixel size of 0.596 mm of the mural on the ceiling. This degree of high spatial resolution is essential for maintaining the fine details of the artwork in the digital documentation as well as its historical context, subtleties, and painting techniques. The study’s findings demonstrate the effectiveness of using digital sensors with the close-range photogrammetry (CRP) technique as a useful method for recording and preserving historical ceiling murals. Full article

Additive manufacturing using cement has evolved rapidly in recent decades, revolutionizing the construction industry. This technology automates building structures through computer-aided design, offering benefits such as reduced material waste, optimized material distribution, and the ability to use composite materials. This paper aims to examine the potential of using copper-slag-based geopolymers in 3D printing. Geopolymers have gained popularity as an alternative and more energy-efficient material to traditional building materials, while copper slag allows for reducing and managing mining industry waste. Moreover, samples formed in molds based on the same material were produced to evaluate the method of manufacturing on the mechanical properties of geopolymers. This paper presents an evaluation of the mechanical properties including the compressive, flexural, and shear strength of the layered material. It reveals promising results, with strength development mainly observed within the first 14 days. The results show that the compressive strength after 28 days of curing is 46.4 MP and 42.1 MPa for formed and printed samples, respectively. Furthermore, the average bending strength value ranges between 7.4 MPa and 7.8 MPa, regardless of the bending direction and forming method. The obtained results show that printed geopolymers demonstrate adequate layer bonding, confirming the profitability of the 3D printing technology. This research confirms that 3D printing technology enables the use of geopolymer binder materials based on copper slag, which opens the door to sustainable alternatives in construction practices. Full article

Efficiently simulating solid mechanics is vital across various engineering applications. As constitutive models grow more complex and simulations scale up in size, harnessing the capabilities of modern computer architectures has become essential for achieving timely results. This paper presents advancements in running parallel simulations of solid mechanics on multi-core CPUs and GPUs using a single-code implementation. This portability is made possible by the C++ matrix and array (MATAR) library, which interfaces with the C++ Kokkos library, enabling the selection of fine-grained parallelism backends (e.g., CUDA, HIP, OpenMP, pthreads, etc.) at compile time. MATAR simplifies the transition from Fortran to C++ and Kokkos, making it easier to modernize legacy solid mechanics codes. We applied this approach to modernize a suite of constitutive models and to demonstrate substantial performance improvements across different computer architectures. This paper includes comparative performance studies using multi-core CPUs along with AMD and NVIDIA GPUs. Results are presented using a hypoelastic–plastic model, a crystal plasticity model, and the viscoplastic self-consistent generalized material model (VPSC-GMM). The results underscore the potential of using the MATAR library and modern computer architectures to accelerate solid mechanics simulations. Full article

Seasonal vertical ground movement (SVGM), which refers to the periodic vertical displacement of the Earth’s surface, has significant implications for infrastructure stability, agricultural productivity, and environmental sustainability. Understanding how SVGM correlates with climatic conditions—such as temperatures and drought—is essential in managing risks posed by land subsidence or uplift, particularly in regions prone to extreme weather events and climate variability. The correlation of periodic SVGM with climatic data from Earth observation was investigated in this work. The European Ground Motion Service (EGMS) vertical ground movement measurements, provided from 2018 to 2022, were compared with temperature and precipitation data from MODIS and CHIRP datasets, respectively. Measurement points (MP) from the EGMS over Italy provided a value for ground vertical movement approximately every 6 days. The precipitation and temperature datasets were processed to provide drought code (DC) maps calculated ad hoc for this study at a 1 km spatial resolution and daily temporal resolution. Seasonal patterns were analyzed to assess correlations with Spearman’s rank correlation coefficient ($\rho$) between this measure and the DCs from the Copernicus Emergency Management Service ($D{C}_{CEMS}$), from MODIS + CHIRP ($D{C}_{1\mathrm{km}}$) and from the temperature. The results over the considered area (Italy) showed that 0.46% of all MPs (32,826 MPs out of 7,193,676 MPs) had a $\rho$ greater than 0.7; 12,142 of these had a positive correlation, and 20,684 had a negative correlation. $D{C}_{1\mathrm{km}}$ was the climatic factor that provided the highest number of correlated MPs, roughly giving +59% more correlated MPs than $D{C}_{CEMS}$ and +300% than the temperature data. If a $\rho$ greater than 0.8 was considered, the number of MPs dropped by a factor of 10: from 12,142 to 1275 for positive correlations and from 20,684 to 2594 for negative correlations between the $D{C}_{1\mathrm{km}}$ values and SVGM measurements. Correlations that lagged in time resulted in most of the correlated MPs being within a window of ±6 days (a single satellite overpass time). Because the DC and temperature are strongly co-linear, further analysis to assess which was superior in explaining the seasonality of the MPs was carried out, resulting in $D{C}_{1\mathrm{km}}$ significantly explaining more variance in the SVGM than the temperature for the inversely correlated points rather than the directly correlated points. The spatial distribution of the correlated MPs showed that they were unevenly distributed in clusters across the Italian territory. This work will lead to further investigation both at a local scale and at a pan-European scale. An interactive WebGIS application that is open to the public is available for data consultation. This article is a revised and expanded version of a paper entitled “Detection and correlation analysis of seasonal vertical ground movement measured from SAR and drought condition” which was accepted and presented at the ISPRS Mid-Term Symposium, Belem, Brasil, 8–12 November 2024. Data are shared in a public repository for the replication of the method. Full article

The sparse matrix–vector product (SpMV) is a fundamental computational kernel utilized in a diverse range of scientific and engineering applications. It is commonly used to solve linear and partial differential equations. The parallel computation of the SpMV product is a challenging task. Existing solutions often employ a fixed number of threads assignment to rows based on empirical formulas, leading to sub-optimal configurations and significant performance losses. Elegante, our proposed machine learning-powered tool, utilizes a data-driven approach to identify the optimal thread configuration for SpMV computations within a shared memory architecture. It accomplishes this by predicting the best thread configuration based on the unique sparsity pattern of each sparse matrix. Our approach involves training and testing using various base and ensemble machine learning algorithms such as decision tree, random forest, gradient boosting, logistic regression, and support vector machine. We rigorously experimented with a dataset of nearly 1000+ real-world matrices. These matrices originated from 46 distinct application domains, spanning fields like robotics, power networks, 2D/3D meshing, and computational fluid dynamics. Our proposed methodology achieved 62% of the highest achievable performance and is 7.33 times faster, demonstrating a significant disparity from the default OpenMP configuration policy and traditional practice methods of manually or randomly selecting the number of threads. This work is the first attempt where the structure of the matrix is used to predict the optimal thread configuration for the optimization of parallel SpMV computation in a shared memory environment. Full article

Numerical tesseroid and radial-type approaches are presented and compared in terms of their efficiency for deriving the regional geoid height, vertical gravity, and gradiometric anomalies from sea floor topography grids. The vertical gradient function is particularly suitable for representing shorter wavelengths of gravity, typically less than 10 km. These two modeling methods were applied to the Great Meteor guyot in the Atlantic Ocean using its bathymetry. To optimize the computation of high-resolution gravity anomalies, the Armadillo, GSL, and OpenMP libraries were used to provide an environment for fast vector implementation, numerical integration for tesseroid calculation, and parallelization for loop iterations, resulting in a computation speed increase. The tesseroid and radial methods remain equivalent up to a resolution of about 1 min, with the radial method being faster when dealing with a large number of model points for the geoid. Aside from optimization enabling high-resolution gravity simulations, these fast modeling data can be used as the main operators in gravimetric inversion or to reduce the terrain effects in gravity observations, revealing gravity and sedimentary layers. Full article

This paper presents software advances to easily exploit computer architectures consisting of a multi-core CPU and CPU+GPU to accelerate diverse types of high-performance computing (HPC) applications using a single code implementation. The paper describes and demonstrates the performance of the open-source C++ matrix and array (MATAR) library that uniquely offers: (1) a straightforward syntax for programming productivity, (2) usable data structures for data-oriented programming (DOP) for performance, and (3) a simple interface to the open-source C++ Kokkos library for portability and memory management across CPUs and GPUs. The portability across architectures with a single code implementation is achieved by automatically switching between diverse fine-grained parallelism backends (e.g., CUDA, HIP, OpenMP, pthreads, etc.) at compile time. The MATAR library solves many longstanding challenges associated with easily writing software that can run in parallel on any computer architecture. This work benefits projects seeking to write new C++ codes while also addressing the challenges of quickly making existing Fortran codes performant and portable over modern computer architectures with minimal syntactical changes from Fortran to C++. We demonstrate the feasibility of readily writing new C++ codes and modernizing existing codes with MATAR to be performant, parallel, and portable across diverse computer architectures. Full article