Search Results (82,267)

A geodesic or geodetic line on a sphere is called the orthodrome. Research has shown that the orthodrome can be defined in a large number of ways. This article provides an overview of various definitions of the orthodrome. We recall the definitions of the orthodrome according to the greats of geodesy, such as Bessel and Helmert. We derive the equation of the orthodrome in the geographic coordinate system and in the Cartesian spatial coordinate system. A geodesic on a surface is a curve for which the geodetic curvature is zero at every point. Equivalent expressions of this statement are that at every point of this curve, the principal normal vector is collinear with the normal to the surface, i.e., it is a curve whose binormal at every point is perpendicular to the normal to the surface, and that it is a curve whose osculation plane contains the normal to the surface at every point. In this case, the well-known Clairaut equation of the geodesic in geodesy appears naturally. It is found that this equation can be written in several different forms. Although differential equations for geodesics can be found in the literature, they are solved in this article, first, by taking the sphere as a special case of any surface, and then as a special case of a surface of rotation. At the end of this article, we apply calculus of variations to determine the equation of the orthodrome on the sphere, first in the Bessel way, and then by applying the Euler–Lagrange equation. Overall, this paper elaborates a dozen different approaches to orthodrome definitions. Full article

Twin-screw compressors (TSCs) are widely used in various industries. Their performance is influenced by several parameters, such as rotor profiles, clearance gaps, operating speed, and thermal effects. Traditionally, optimizing these parameters relied on experimental methods, which are costly and time-consuming. However, advancements in computational tools, such as Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA), have revolutionized compressor analysis. This study presents a CFD analysis of a specific model of a TSC in a 5 male/6 female lobe configuration using the SolidWorks Flow Simulation environment—an approach not traditionally applied to such positive displacement machines. The results visually present internal flow trajectories, fluid velocities, pressure distributions, temperature gradients, and leakage behaviors with high spatial and temporal resolution. Additionally, torque fluctuations and isosurface visualizations revealed insights into mechanical loads and flow behavior. The proposed method allows for relatively easy adaptation to different TSC configurations and can also be a useful tool for engineering and educational purposes. Full article

Polarimetric Synthetic Aperture Radar (PolSAR) image classification is one of the most important applications in remote sensing. The impressive superpixel generation approaches can improve the efficiency of the subsequent classification task and restrain the influence of the speckle noise to an extent. Most of the classical PolSAR superpixel generation approaches use the features extracted manually and even only consider the pseudocolor images. They do not make full use of polarimetric information and do not necessarily lead to good enough superpixels. The deep learning methods can extract effective deep features but they are difficult to combine with superpixel generation to achieve true end-to-end training. Addressing the above issues, this study proposes an end-to-end fully convolutional superpixel generation network for PolSAR images. It integrates the extraction of polarization information features and the generation of PolSAR superpixels into one step. PolSAR superpixels can be generated based on deep polarization feature extraction and need no traditional clustering process. Both the performance and efficiency of generations of PolSAR superpixels can be enhanced effectively. The experimental results on various PolSAR datasets show that the proposed method can achieve impressive superpixel segmentation by fitting the real boundaries of different types of ground objects effectively and efficiently. It can achieve excellent classification performance by connecting a very simple classification network, which is helpful to improve the efficiency of the subsequent PolSAR image classification tasks. Full article

Molecularly imprinted polymers (MIPs) have emerged as robust, cost-effective analogues of bioreceptors, offering high selectivity and stability. When applied in sensors, one key step is the integration of MIPs with the transducer, which critically affects sensor performance. Demanding challenges come when such integration involves nanoscaling processes, meaning that the transducer is nanostructured or the MIP itself is nanosized on a bulk transducer. In both cases, the integration results in the development of nanosensors, with advantages arising from the nanoscale, such as a high MIP surface-to-volume ratio, with surface-located, easily accessible binding sites, fast binding kinetics, and, thus, a rapid sensor response. Major advantages come also from nanostructured transducers, with nanoscale geometry enabling highly sensitive signal generation processes, not allowed on their bulk counterparts. In this review, we discuss advances in imprinting technologies, focusing on techniques that, enabling the nanoscale control of MIP synthesis, are conveniently applied to directly integrate MIPs with nanosensors in a one-step process. Two main approaches are reviewed, consisting in MIP nanostructuring on bulk transducers and in the direct growth of MIPs on nanotransducers, highlighting how different strategies achieve good conformity at the nanoscale and address spatial complexity to ensure stable and accurate signal acquisition. Finally, we consider future directions in MIP-based nanosensor development. Full article

In football, accurately pinpointing key events like passes is vital for analyzing player and team performance. Despite continuous technological advancements, existing tracking systems still face challenges in accurately synchronizing events and positional data accurately. This is a case study that proposes a new method to synchronize events and positional data collected during football matches. Three datasets were used to perform this study: a dataset created by applying a custom algorithm that synchronizes positional and event data, referred to as the optimized synchronization dataset (OSD); a simple temporal alignment between positional and event data, referred to as the raw synchronization dataset (RSD); and a manual notational data (MND) from the match video footage, considered the ground truth observations. The timestamp of the pass in both synchronized datasets was compared to the ground truth observations (MND). Spatial differences in OSD were also compared to the RSD data and to the original data from the provider. Root mean square error (RMSE) and mean absolute error (MAE) were utilized to assess the accuracy of both procedures. More accurate results were observed for optimized dataset, with RMSE values of RSD = 75.16 ms (milliseconds) and OSD = 72.7 ms, and MAE values RSD = 60.50 ms and OSD = 59.73 ms. Spatial accuracy also improved, with OSD showing reduced deviation from RSD compared to the original event data. The mean positional deviation was reduced from 1.59 ± 0.82 m in original event data to 0.41 ± 0.75 m in RSD. In conclusion, the model offers a more accurate method for synchronizing independent datasets for event and positional data. This is particularly beneficial for applications where precise timing and spatial location of actions are critical. In contrast to previous synchronization methods, this approach simplifies the process by using an automated technique based on patterns of ball velocity. This streamlines synchronization across datasets, reduces the need for manual intervention, and makes the method more practical for routine use in applied settings. Full article

Background/Objectives: Neoadjuvant radiochemotherapy and perioperative chemotherapy are both well-established treatment strategies for locally advanced adenocarcinoma of the esophagus (EAC) and the esophagogastric junction (AEGJ). However, recent knowledge controversially discusses whether neoadjuvant radiotherapy or perioperative chemotherapy represents superior therapeutic options to prolong survival or cause less toxicity. Methods: We retrospectively analyzed 76 patients with locally advanced EAC or AEGJ treated at our tertiary cancer center between January 2015 and March 2023. Patients received either perioperative FLOT chemotherapy (n = 36) or neoadjuvant radiochemotherapy following the CROSS protocol (n = 40), followed by surgical resection and standardized follow-up. We compared survival outcomes, toxicity profiles, treatment compliance, and surgical results between the two groups. Results: There were no statistically significant differences between FLOT and CROSS treatments in five-year loco-regional controls (LRC: 61.5% vs. 68.6%; p = 0.81), progression-free survival (PFS: 33.9% vs. 42.8%; p = 0.82), overall survival (OS: 60.2% vs. 63.4%; p = 0.91), or distant controls (DC: 42.1% vs. 56.5%; p = 0.39). High-grade hematologic toxicities did not significantly differ between groups (p > 0.05). Treatment compliance was lower in the FLOT group, with 50% (18/36) not completing all the planned chemotherapy cycles, compared to 17.5% (7/40) in the CROSS group. All the patients in the CROSS group received the full radiotherapy dose. Surgical outcomes and post-surgical tumor status were comparable between the groups. Conclusions: Although perioperative chemotherapy with FLOT has recently become a standard of care for locally advanced EAC and AEGJ, neoadjuvant radiochemotherapy per the CROSS protocol remains a well-tolerated alternative. In appropriately selected patients, both approaches yield comparable oncological outcomes. Full article

Background/Objectives: Orbital reconstruction remains one of the most demanding procedures in maxillofacial surgery. It requires not only precise anatomical knowledge but also poses multiple intraoperative challenges. Limited surgical visibility—especially in transconjunctival or transcaruncular approaches—demands exceptional precision from the surgeon. At the same time, the complex anatomical structure of the orbit, its rich vascularization and innervation, and the risk of severe postoperative complications—such as diplopia, sensory deficits, impaired ocular mobility, or in the most serious cases, post-traumatic blindness due to nerve injury or orbital compartment syndrome—necessitate the highest level of surgical accuracy. In this context, patient-specific implants (PSIs), commonly fabricated from zirconium oxide or ultra-high-density polyethylene, have become invaluable. Within CAD-based reconstructive planning, especially for orbital implants, critical factors include the implant’s anatomical fit, passive stabilization on intact bony structures, and non-interference with orbital soft tissues. Above all, precise replication of the orbital dimensions is essential for optimal clinical outcomes. This study compares the morphological accuracy of orbital structures based on anthropometric measurements from 3D models generated from fan-beam computed tomography (FBCT) and cone-beam computed tomography (CBCT). Methods: A cohort group of 500 Caucasian patients aged 8 to 88 years was analyzed. 3D models of the orbits were generated from FBCT and CBCT scans. Anthropometric measurements were taken to evaluate the morphological accuracy of the orbital structures. The assessed parameters included orbital depth, orbital width, the distance from the infraorbital rim to the infraorbital foramen, the distance between the piriform aperture and the infraorbital foramen, and the distance from the zygomatico-orbital foramen to the infraorbital rim. Results: Statistically significant differences were observed between virtual models derived from FBCT and those based on CBCT in several key parameters. Discrepancies were particularly evident in measurements of orbital depth, orbital width, the distance from the infraorbital rim to the infraorbital foramen, the distance between the piriform aperture and the infraorbital foramen, and the distance from the zygomatico-orbital foramen to the infraorbital rim. Conclusions: The statistically significant discrepancies in selected orbital dimensions—particularly in regions of so-called thin bone—demonstrate that FBCT remains the gold standard in the planning and design of CAD/CAM patient-specific orbital implants. Despite its advantages, including greater accessibility and lower radiation dose, CBCT shows limited reliability in the context of orbital and infraorbital reconstruction planning. Full article

The eruption of the Cumbre Vieja volcano on 19 September 2021 resulted in the deposition of over 20 million cubic meters of tephra, posing significant environmental and logistical challenges in the affected areas. This study aimed to explore the valorization of volcanic ash (VA) by evaluating its potential use in producing sustainable mortar by incorporating it as a replacement for cement or sand. Various experimental mixtures were prepared with different proportions of VA which substituted either cement or sand, and these mixes were characterized through a mechanical and microstructural campaign. Additionally, shrinkage was evaluated for the mixtures which showed good mechanical results. The results suggest that partially replacing cement with up to 15% ground VA as well as substituting sand with up to 25% VA are promising strategies for the production of sustainable mortar mixes. This research contributes to the understanding of the influence of VA in cementitious matrices and offers a novel approach for integrating locally available geomaterials into infrastructure design in volcanic active regions. Full article

Background: Atrial fibrosis is a key contributor to atrial cardiomyopathy and can be assessed invasively using mean left atrial voltage (MLAV) from electroanatomical mapping. However, the invasive nature of this procedure limits its clinical applicability. Machine learning (ML), particularly regression tree-based models, may offer a non-invasive approach for predicting MLAV using clinical and echocardiographic data, improving non-invasive atrial fibrosis characterisation beyond current dichotomous classifications. Methods: We prospectively included and followed 113 patients with paroxysmal or persistent atrial fibrillation (AF) undergoing pulmonary vein isolation (PVI) with ultra-high-density voltage mapping (uHDvM), from whom MLAV was estimated. Standardised two-dimensional transthoracic echocardiography was performed before ablation, and clinical and echocardiographic variables were analysed. A regression tree model was constructed using the Classification and Regression Trees—CART-algorithm to identify key predictors of MLAV. Results: The regression tree model exhibited moderate predictive accuracy (R² = 0.63; 95% CI: 0.55–0.71; root mean squared error = 0.90; 95% CI: 0.82–0.98), with indexed minimum LA volume and passive emptying fraction emerging as the most influential variables. No significant differences in AF recurrence-free survival were found among MLAV tertiles or model-based generated groups (log-rank p = 0.319 and p = 0.126, respectively). Conclusions: We present a novel ML-based regression tree model for non-invasive prediction of MLAV, identifying minimum LA volume and passive emptying fraction as the most significant predictors. This model offers an accessible, non-invasive tool for refining atrial cardiomyopathy characterisation by reflecting the fibrotic substrate as a continuum, a crucial advancement over existing dichotomous approaches to guide tailored therapeutic strategies. Full article

This study explores the perceptions of future educators, specifically Early Childhood Education students at the Universities of Cádiz and Gdansk, regarding the interconnections between critical thinking and sustainability. The work aims to provide valuable insights into general teacher training, examining how these students’ experiences are contextualized within their respective educational systems and cultural contexts. To achieve this, eleven group interviews (three in Cádiz, eight in Gdansk) were conducted using a structured and expert-validated script. The transcribed data were qualitatively analyzed using QDA MINER v.6 software. Key findings reveal divergent perceptions of critical thinking among pre-service teachers: while Spanish students leaned towards a subjective understanding, Polish students emphasized an objective, data-driven approach. This distinction has significant implications for the conceptualization and teaching of critical thinking in educator training. Despite these differences, both groups of participants highlighted the necessity of implementing active methodologies in higher education (such as cooperative learning, problem-solving, and debates) to foster critical thinking, both for their own development and for preparing for their future practice with young children. This study also identified an excessive emphasis on theoretical aspects of sustainability in these future teachers’ training and a limited understanding of their practical application in the classroom. Furthermore, explicit connections between critical thinking and sustainability were scarce in student responses, highlighting a gap in current educator training in these areas. Collectively, the results suggest significant weaknesses in current teacher training efforts regarding the development of critical thinking and its effective integration with sustainability competencies. Full article

Colorectal cancer (CRC) constitutes a major global health concern, ranking as the third most common cancer and the second leading cause of cancer-related mortality. The current review explores sex-based differences in CRC epidemiology, risk factors, tumor biology, and clinical outcomes. Males exhibit a higher incidence and mortality rate, with left-sided (distal) CRC predominating, while females are more frequently diagnosed with right-sided (proximal) tumors, which tend to be more aggressive and less responsive to conventional chemotherapy. Genetic disparities, including microsatellite instability and X-chromosome tumor suppressor genes, contribute to sex-specific differences in tumor progression and treatment response. Immune variations also influence disease outcomes, with females exhibiting stronger immune surveillance but higher exhaustion markers. Lifestyle factors such as body mass index (BMI), smoking, and hormonal influences further modulate CRC risk. While males are more vulnerable to obesity-related CRC, central obesity (waist-to-hip ratio) emerges as a stronger predictor in females. Additionally, smoking increases CRC risk differentially by tumor location. These findings underscore the importance of sex-specific approaches in CRC prevention, screening, and treatment, advocating for personalized medicine strategies tailored to gender-based biological and clinical distinctions. Full article

This work contextualizes the possibility of deriving a unifying artificial intelligence framework by walking in the footsteps of General, Explainable, and Verified Artificial Intelligence (GEVAI): by considering explainability not only at the level of the results produced by a specification but also considering the explicability of the inference process as well as the one related to the data processing step, we can not only ensure human explainability of the process leading to the ultimate results but also mitigate and minimize machine faults leading to incorrect results. This, on the other hand, requires the adoption of automated verification processes beyond system fine-tuning, which are essentially relevant in a more interconnected world. The challenges related to full automation of a data processing pipeline, mostly requiring human-in-the-loop approaches, forces us to tackle the framework from a different perspective: while proposing a preliminary implementation of GEVAI mainly used as an AI test-bed having different state-of-the-art AI algorithms interconnected, we propose two other data processing pipelines, LaSSI and EMeriTAte+DF, being a specific instantiation of GEVAI for solving specific problems (Natural Language Processing, and Multivariate Time Series Classifications). Preliminary results from our ongoing work strengthen the position of the proposed framework by showcasing it as a viable path to improve current state-of-the-art AI algorithms. Full article

The growing need for rapid screening of adsorption energies in organic materials has driven substantial progress in developing various architectures of equivariant graph neural networks (eGNNs). This advancement has largely been enabled by the availability of extensive Density Functional Theory (DFT)-generated datasets, sufficiently large to train complex eGNN models effectively. However, certain material groups with significant industrial relevance, such as aromatic compounds, remain underrepresented in these large datasets. In this work, we aim to bridge the gap between limited, domain-specific DFT datasets and large-scale pretrained eGNNs. Our methodology involves creating a specialized dataset by segregating aromatic compounds after a targeted ensemble extraction process, then fine-tuning a pretrained model via approaches that include full retraining and systematically freezing specific network sections. We demonstrate that these approaches can yield accurate energy and force predictions with minimal domain-specific training data and computation. Additionally, we investigate the effects of augmenting training datasets with chemically related but out-of-domain groups. Our findings indicate that incorporating supplementary data that closely resembles the target domain, even if approximate, would enhance model performance on domain-specific tasks. Furthermore, we systematically freeze different sections of the pretrained models to elucidate the role each component plays during adaptation to new domains, revealing that relearning low-level representations is critical for effective domain transfer. Overall, this study contributes valuable insights and practical guidelines for efficiently adapting deep learning models for accurate adsorption energy predictions, significantly reducing reliance on extensive training datasets. Full article

Background: Positive identification is at the forefront of tasks for forensic practitioners when a set of remains is discovered. Standard means of identification include fingerprints, dental, and DNA analyses; however, additional methods are utilized by forensic practitioners to identify remains when these primary methods of identification are not applicable. Comparative radiography has become a frequently employed approach for positive identification, specifically focused on individualizing characteristics evident in human skeletal variation. Regions that display wide ranges of morphological variation within the human skeleton include the cranium as well as the thorax. With regard to the cranium specifically, paranasal sinuses have been recognized as unique features and are valuable for identification purposes. Objectives: This paper explores the basic information of the anatomy and development, range of variation, and the importance of paranasal sinuses in forensic contexts. Results: This article discusses how practitioners can best use the morphological information contained in the paranasal sinuses and how to compare the antemortem and postmortem datasets involving different imaging modalities for positive identification purposes, in order to provide practical concepts that may assist in cases where paranasal sinuses may be used for forensic human identification. Conclusions: Understanding the development of paranasal sinuses, the imaging techniques applied for their visualization, as well as the principles of identification, is key to conducting proper antemortem vs. postmortem comparisons and effectively utilizing paranasal sinuses in forensic identification contexts. Full article

The modern livestock industry incorporates widely used antibiotic growth promoters into animal feed at sub-therapeutic levels to enhance growth performance and feed efficiency. However, this practice contributes to the emergence of antibiotic-resistant pathogens in livestock, which may be transmitted to humans through the food chain, thereby diminishing the efficacy of antibiotics in treating bacterial infections. Current research explores the potential of essential oils from derived medicinal plants as alternative phytobiotics. This review examines modern encapsulation strategies that incorporate essential oils into natural-origin matrices to improve their stability and control their release both in vitro and in vivo. We discuss a range of encapsulation approaches utilizing polysaccharides, gums, proteins, and lipid-based carriers. This review highlights the increasing demand for antibiotic alternatives in animal nutrition driven by regulatory restrictions, and the potential benefits of essential oils in enhancing feed palatability and stabilizing the intestinal microbiome in monogastric animals and ruminants. Additionally, we address the economic viability and encapsulation efficiency of different matrix formulations. Full article