Search Results (184)

The monumental scale agricultural infrastructure systems built by Andean peoples during pre-Hispanic times have enabled intensive agriculture in the high-relief, arid/semi-arid landscape of the Southern Peruvian Andes. Large tracts of these labor-intensive systems have been abandoned, however, owing in large measure to a range of demographic, economic, and political crises precipitated by the Spanish invasion of the 16th century CE. This research seeks to better understand the dynamics of agricultural intensification and deintensification in the Andes by inventorying through the semantic segmentation of active and abandoned agricultural fields in satellite imagery across approximately 77,000 km² of the Southern Peruvian Highlands. While manual digitization of agricultural fields in satellite imagery is time-consuming and labor-intensive, deep learning-based semantic segmentation makes it possible to map and classify en masse Andean agricultural infrastructure. Using high resolution satellite imagery, training and validation data were manually produced in distributed sample areas and were used to transfer-train a convolutional neural network for semantic segmentation. The resulting dataset was compared to manual surveys of the region and results suggest that deep learning can generate larger and more accurate datasets than those generated by hand. Full article

The family Apiaceae, distributed throughout the Northern Hemisphere, is the largest family of angiosperms. However, little is known about the conservation status, diversity, and distribution of Apiaceae species in Mongolia. This study had two main aims: (1) to assess the national status of Apiaceae species under IUCN Red List Criterion B; (2) to evaluate the species diversity and richness of Apiaceae across Mongolia. We utilized ConR packages to assess the national Red List status of all known Mongolian Apiaceae species by analyzing their most comprehensive occurrence records. The results indicated that 27 species were classified as threatened, including 4 Critically Endangered (CR), 9 Endangered (EN), and 14 Vulnerable (VU) species. Meanwhile, 39 species were assessed as non-threatened, with 2 Near Threatened (NT) species and 37 species of Least Concern (LC). Furthermore, detailed distribution maps for 66 Apiaceae species in Mongolia were presented. We assessed the species diversity and Shannon and Simpson diversity indices of Apiaceae by analyzing all occurrence records using the iNext package. Overall, the Hill diversity estimates indicate that the sampling conducted in Mongolia adequately captured species occurrences. For species pattern analysis, we examined the species richness, weighted endemism, and the corrected weighted endemism index using Biodiverse v.4.1 software. Mongolia was portioned into 715 grid cells based on 0.5° × 0.5° grid sizes (equivalent to approximately 50 × 50 km²). There was a total of 3062 unique occurrences of all Apiaceae species across Mongolia. In the species richness analysis, we identified 10 grids that exhibited high species richness (18–29 species) and 36 grids with 11–17 species. For genus richness, we observed seven grids that exhibited a high genus richness of 16–22 genera. Furthermore, we analyzed species richness with a specific focus on threatened species, encompassing CR, EN, and VU species throughout Mongolia. A total of 92 grids contained at least one threatened species. There were six grids that had two to five threatened species, which were adequately covered by protected areas in western Mongolia. Overall, our results on species richness and conservation status will serve as important foundational research for future conservation and land management efforts in Mongolia. Full article

The success of developing and implementing Smart Cities (SC) projects depends on a varied set of factors, where the availability of a qualified technical workforce is a critical one. The combination of ICT requirements, like the effectiveness and quality of solutions merging IoT, cloud computing, sensors, and communications with the work from many varied disciplines (e.g., civil engineering, architecture, etc.), mixed with aspects of environmental and business sustainability, makes the management of these projects really challenging. Reports forecast a scarcity of qualified candidates, given this complexity and the growth of activity in SC projects. The European project SMACITE has addressed the requirements of the qualification of an ICT workforce with an analysis of multiples sources of information from the labor market, feedback from involved stakeholders, and the literature. The goal was the development of two occupational ICT profiles as a reference for training and for the availability of candidates for job vacancies. The result is two ICT role profiles for engineers and technicians, mapped with the European skills frameworks ESCO and EN16234. The profiles determined the whole set of requirements, including not only the technical areas and soft skills, but also additional technical areas and sustainability and managerial skills and the analysis of different sources of information. Our work has also determined which existing ESCO occupations are similar to the two reference profiles, so they are better adapted to SC projects. The training activities of SMACITE have also suggested the amount of training expected for a varied sample of candidates who want to be qualified for SC projects. Full article

Background: The TactiFlex™ ablation catheter, Sensor Enabled™ (Abbott, Minneapolis, MN, USA), is an open-irrigation radiofrequency (RF) ablation catheter with flexible tip technology. This catheter delivers high-power short-duration (HPSD) RF ablations and has been adopted for atrial fibrillation (AF) ablation. HPSD is well-established not only in pulmonary vein isolation (PVI) but also when targeting extra-pulmonary vein (PV) targets. This study aims to determine the safety, effectiveness, and acute outcomes of PVI plus posterior wall isolation (PWI) in patients with persistent atrial fibrillation (Pe-AF) using HPSD and the TactiFlex™ ablation catheter. Methods: Consecutive patients who underwent the ablation of Pe-AF in our centre between February 2023 and February 2024 were prospectively enrolled in the study. All patients underwent PVI plus PWI using TactiFlex™ and the HPSD strategy. The RF parameters were 50 W on all the PV segments and the roof, and within the posterior wall (PW). Left atrial mapping was performed with the EnSite X mapping system and the high-density multipolar Advisor HD Grid, Sensor Enabled™ mapping catheter. We compared the procedural data using HPSD with TactiFlex™ (n = 52) vs. a historical cohort of patients who underwent PVI plus PWI using HPSD settings and the TactiCath ablation catheter (n = 84). Results: Fifty-two consecutive patients were included in the study. PVI and PWI were achieved in all patients in the TactiFlex™ group. First-pass PVI was achieved in 97.9% of PVs (n = 195/199). PWI was obtained in all cases by delivering extensive RF lesions within the PW. There were no significant differences compared to the TactiCath group: first-pass PVI was achieved in 96.3% of PVs (n = 319/331). Adenosine administration revealed PV reconnection in 5.7% of patients, and two reconnections of the PW were documented. Procedure and RF time were significantly shorter in the TactiFlex™ group compared to the TactiCath group, 73.1 ± 12.6 vs. 98.5 ± 16.3 min, and 11.3 ± 1.5 vs. 23.5 ± 3.6 min, respectively, p < 0.001. The fluoroscopy time was comparable between both groups. No intraprocedural and periprocedural complications related to the ablation catheter were observed. Patients had an implantable loop recorder before discharge. At the 6-month follow-up, 76.8% of patients remained free from atrial arrhythmia, with no significant differences between groups. Conclusions: HPSD PVI plus PWI using the TactiFlex™ ablation catheter is effective and safe. Compared to a control group, the use of TactiFlex™ to perform HPSD PVI plus PWI is associated with a similar effectiveness but with a significantly shorter procedural and RF time. Full article

With the increasing number of ongoing space missions for Earth Observation (EO), there is a need to enhance data products by combining observations from various remote sensing instruments. We introduce a new Transformer-based approach for data fusion, achieving up to a 10- to-30-fold increase in the spatial resolution of our hyperspectral data. We trained the network on a synthetic set of Sentinel-2 (S2) and Sentinel-3 (S3) images, simulated from the hyperspectral mission EnMAP (30 m resolution), leading to a fused product of 21 bands at a 30 m ground resolution. The performances were calculated by fusing original S2 (12 bands, 10, 20, and 60 m resolutions) and S3 (21 bands, 300 m resolution) images. To go beyond EnMap’s ground resolution, the network was also trained using a generic set of non-EO images from the CAVE dataset. However, we found that training the network on contextually relevant data is crucial. The EO-trained network significantly outperformed the non-EO-trained one. Finally, we observed that the original network, trained at 30 m ground resolution, performed well when fed images at 10 m ground resolution, likely due to the flexibility of Transformer-based networks. Full article

This paper presents a new third-order symmetric difference equation transformed into a 3D discrete symmetric map. The nonlinear dynamics and symmetry of the proposed map are analyzed with two initial conditions for exploring the sensitivity of the map and highlighting the influence of the map parameters on its behaviors, thus comparing the findings. Moreover, the stability of the zero fixed point and symmetry are examined by theoretical analysis, and it is proved that the map generates diverse nonlinear traits comprising multistability, chaos, and hyperchaos, which is confirmed by phase attractors in 2D and 3D space, Lyapunov exponents ($L{E}_s$) analysis and bifurcation diagrams; also, 0-1 test and sample entropy (SampEn) are used to confirm the existence and measure the complexity of chaos. In addition, a nonlinear controller is introduced to stabilize the symmetry map and synchronize a duo of unified symmetry maps. Finally, numerical results are provided to illustrate the findings. Full article

Real-world vibration signal acquisition of automotive machines often results in imbalanced sample sets due to restricted test conditions, adversely impacting fault diagnostic accuracy. To address this problem, we propose fractional diversity entropy (FrDivEn) and incorporate it into the classifier-guided diffusion model (CGDM) to synthesize high-quality samples. Additionally, we present a corresponding imbalanced fault diagnostic method. This method first converts vibration data to Gramian angular field (GAF) image samples through GAF transformation. Then, FrDivEn is mapped to the gradient scale of CGDM to trade off the diversity and fidelity of synthetic samples. These synthetic samples are mixed with real samples to obtain a balanced sample set, which is fed to the fine-tuned pretrained ConvNeXt for fault diagnosis. Various sample synthesizers and fault classifiers were combined to conduct imbalanced fault diagnosis experiments across bearing, gearbox, and rotor datasets. The results indicate that for the three datasets, the diagnostic accuracies of the proposed CGDM using FrDivEn at an imbalance ratio of 40:1 are 91.22%, 87.90%, and 98.89%, respectively, which are 7.32%, 11.59%, and 3.48% higher than that of the Wasserstein generative adversarial network (WGAN), respectively. The experimental results across the three datasets validated the validity and generalizability of the proposed diagnostic method. Full article

Sea surface target detection is a key stage in a typical target detection system and directly influences the performance of the whole system. As an effective discriminator, the one-class support vector machine (OCSVM) has been widely used in target detection. In OCSCM, training samples are first mapped to the hypersphere in the kernel space with the Gaussian kernel function, and then, a linear classification hyperplane is constructed in each cluster to separate target samples from other classes of samples. However, when the distribution of the original data is complex, the transformed data in the kernel space may be nonlinearly separable. In this situation, OCSVM cannot classify the data correctly, because only a linear hyperplane is constructed in the kernel space. To solve this problem, a novel one-class classification algorithm, referred to as ensemble one-class support vector machine (En-OCSVM), is proposed in this paper. En-OCSVM is a hybrid model based on $k$-means clustering and OCSVM. In En-OCSVM, training samples are clustered in the kernel space with the $k$-means clustering algorithm, while a linear decision hyperplane is constructed in each cluster. With the combination of multiple linear classification hyperplanes, a complex nonlinear classification boundary can be achieved in the kernel space. Moreover, the joint optimization of the $k$-means clustering model and OCSVM model is realized in the proposed method, which ensures the linear separability of each cluster. The experimental results based on the synthetic dataset, benchmark datasets, IPIX datasets, and SAR real data demonstrate the better performance of our method over other related methods. Full article

On 14 November 2021, a doublet earthquake, each event of which had an Mw of 6.1, struck near Fin in the Simply Folded Belt (SFB) in southern Iran. The first quake occurred at 12:07:04 UTC, followed by a second one just a minute and a half later. The SFB is known for its blind thrust faults, typically not associated with surface ruptures. These earthquakes are usually linked to the middle and lower layers of the sedimentary cover. Identifying the faults that trigger earthquakes in the region remains a significant challenge and is subject to high uncertainty. This study aims to identify and determine the fault(s) that may have caused the doublet earthquake. To achieve this goal, we utilized the DInSAR method using Sentinel-1 to detect deformation, followed by finite-fault inversion and magnetic interpretation to determine the location, geometry, and slip distribution of the fault(s). Bayesian probabilistic joint inversion was used to model the earthquake sources and derive the geometric parameters of potential fault planes. The study presents two potential fault solutions—one dipping to the north and the other to the south. Both solutions showed no significant difference in strike and fault location, suggesting a single fault. Based on the results of the seismic inversion, it appears that a north-dipping fault with a strike, dip, and rake of 257°, 74°, and 77°, respectively, is more consistent with the geological setting of the area. The fault plane has a width of roughly 3.6 km, a length of 13.4 km, and a depth of 5.6 km. Our results revealed maximum displacements along the radar line of sight reaching values of up to −360 mm in the ascending orbit, indicating an unknown fault with horizontal displacements at the surface ranging from −144 to 170 mm and maximum vertical displacements between −204 and 415 mm. Aeromagnetic data for Iran were utilized with an average flight-line spacing of 7.5 km. The middle of the data observation period was considered to apply the RTP filter, and the DRTP method was used. We calculated the gradient of the residual anomaly in the N-S direction due to the direction of the existing faults and folds. The gradient map identified the fault and potential extension of the observed anomalies related to a fault with an ENE-WSW strike, which could extend to the ~ E-W. We suggest that earthquakes occur in the sedimentary cover of the SFB where subsurface faulting is involved, with Hormuz salt acting as an important barrier to rupture. The multidisciplinary approach used in this study, including InSAR and magnetic data, underscores the importance of accurate fault characterization. These findings provide valuable insights into the seismic hazard of the area. Full article

The requirement for high-performance and energy-saving materials motivated the researchers to develop novel composite materials. This investigation focuses on utilizing aluminum alloy (A383) as the matrix material to produce hybrid metal matrix composites (HMMCs) incorporating boron carbide (B₄C) and multi-walled carbon nanotube (MWCNT) through a cost-effective stir casting technique. The synthesis of HMMCs involved varying the weight fractions of B₄C (2%, 4%, and 6%) and MWCNT (0.5%, 1%, and 1.5%). The metallographic study was carried out by field emission scanning electron microscopy (FESEM) mapped with EDS analysis. The results indicated a uniform dispersion and robust interfacial interaction between aluminum and the reinforced particles, significantly enhancing the mechanical properties. Micro-hardness and wear characteristics of the fabricated HMMCs were investigated using Vickers microhardness testing and the pin-on-disc tribometer setup. The disc is made of hardened chromium alloy EN 31 steel of hardness 62 HRC. The applied load was varied as 10N, 20N, 30N with a constant sliding speed of 1.5 m/s for different sliding distances. The micro-hardness value of composites reinforced with 1.5 wt% MWCNT and 6 wt% B₄C improved by 61% compared to the base alloy. Additionally, the wear resistance of the composite material improved with increasing reinforcement content. Incorporating 1.5% CNT and 6% B₄C as reinforcements results in the composite experiencing about a 40% reduction in wear loss compared to the unreinforced aluminum alloy matrix. Furthermore, the volumetric wear loss of the HMMCs was critically analyzed with respect to different applied loads and sliding distances. This research underscores the positive impact of varying the reinforcement content on the mechanical and wear properties of aluminum alloy-based hybrid metal matrix composites. Full article

The new generation of satellite hyperspectral (HS) sensors provides remarkable potential for regional-scale mineralogical mapping. However, as with any satellite sensor, mapping results are dependent on a typically complex correction procedure needed to remove atmospheric, topographic and geometric distortions before accurate reflectance spectra can be retrieved. These are typically applied by the satellite operators but use different approaches that can yield different results. In this study, we conduct a comparative analysis of PRISMA, EnMAP, and EMIT hyperspectral satellite data, alongside airborne data acquired by the HyMap sensor, to investigate the consistency between these datasets and their suitability for geological mapping. Two sites in Namibia were selected for this comparison, the Marinkas-Quellen and Epembe carbonatite complexes, based on their geological significance, relatively good exposure, arid climate and data availability. We conducted qualitative and three different quantitative comparisons of the hyperspectral data from these sites. These included correlative comparisons of (1) the reflectance values across the visible-near infrared (VNIR) to shortwave infrared (SWIR) spectral ranges, (2) established spectral indices sensitive to minerals we expect in each of the scenes, and (3) spectral abundances estimated using linear unmixing. The results highlighted a notable shift in inter-sensor consistency between the VNIR and SWIR spectral ranges, with the VNIR range being more similar between the compared sensors than the SWIR. Our qualitative comparisons suggest that the SWIR spectra from the EnMAP and EMIT sensors are the most interpretable (show the most distinct absorption features) but that latent features (i.e., endmember abundances) from the HyMap and PRISMA sensors are consistent with geological variations. We conclude that our results reinforce the need for accurate radiometric and topographic corrections, especially for the SWIR range most commonly used for geological mapping. Full article

As an unprecedented stream of decametric hyperspectral observations becomes available from recent and upcoming spaceborne missions, effective algorithms are required to retrieve vegetation biophysical and biochemical variables such as leaf area index (LAI) and canopy chlorophyll content (CCC). In the context of missions such as the Environmental Mapping and Analysis Program (EnMAP), Precursore Iperspettrale della Missione Applicativa (PRISMA), Copernicus Hyperspectral Imaging Mission for the Environment (CHIME), and Surface Biology Geology (SBG), several retrieval algorithms have been developed based upon the turbid medium Scattering by Arbitrarily Inclined Leaves (SAIL) radiative transfer model. Whilst well suited to cereal crops, SAIL is known to perform comparatively poorly over more heterogeneous canopies (including forests and row-structured crops). In this paper, we investigate the application of hybrid radiative transfer models, including a modified version of SAIL (rowSAIL) and the Invertible Forest Reflectance Model (INFORM), to such canopies. Unlike SAIL, which assumes a horizontally homogeneous canopy, such models partition the canopy into geometric objects, which are themselves treated as turbid media. By enabling crown transmittance, foliage clumping, and shadowing to be represented, they provide a more realistic representation of heterogeneous vegetation. Using airborne hyperspectral data to simulate EnMAP observations over vineyard and deciduous broadleaf forest sites, we demonstrate that SAIL-based algorithms provide moderate retrieval accuracy for LAI (RMSD = 0.92–2.15, NRMSD = 40–67%, bias = −0.64–0.96) and CCC (RMSD = 0.27–1.27 g m⁻², NRMSD = 64–84%, bias = −0.17–0.89 g m⁻²). The use of hybrid radiative transfer models (rowSAIL and INFORM) reduces bias in LAI (RMSD = 0.88–1.64, NRMSD = 27–64%, bias = −0.78–−0.13) and CCC (RMSD = 0.30–0.87 g m⁻², NRMSD = 52–73%, bias = 0.03–0.42 g m⁻²) retrievals. Based on our results, at the canopy level, we recommend that hybrid radiative transfer models such as rowSAIL and INFORM are further adopted for hyperspectral biophysical and biochemical variable retrieval over heterogeneous vegetation. Full article

This paper concerns the Eurocode 3 Part 1-3 (EN 1993-1-3) methods for calculating the distortional buckling (bifurcation) load of cold-formed steel-lipped channels subjected to axial force, major and minor axis bending. More specifically, the paper presents the results of a parametric study that assesses the accuracy of the simplified method in EN 1993-1-3, which relies on direct/iterative hand calculations and an approximate mechanical model, through comparison with “exact” numerical results, obtained using semi-analytical linearized buckling analyses based on Generalized Beam Theory, which are also allowed by the code. Isoline error maps are presented for a wide range of geometric and material parameters, covering common commercial profiles and corresponding to a dataset of more than 24,000 cases. These maps make it possible to identify the parameter ranges leading to an acceptable error and, even though they strongly depend on the loading, general remarks concerning the expected error pertaining to the simplified method are drawn. Full article

This paper aims to simplify the application of optical coherence tomography (OCT) for the examination of subsurface morphology in the oral cavity and reduce barriers towards the adoption of OCT as a biopsy guidance device. The aim of this work was to develop automated software tools for the simplified analysis of the large volume of data collected during OCT. Imaging and corresponding histopathology were acquired in-clinic using a wide-field endoscopic OCT system. An annotated dataset (n = 294 images) from 60 patients (34 male and 26 female) was assembled to train four unique neural networks. A deep learning pipeline was built using convolutional and modified u-net models to detect the imaging field of view (network 1), detect artifacts (network 2), identify the tissue surface (network 3), and identify the presence and location of the epithelial–stromal boundary (network 4). The area under the curve of the image and artifact detection networks was 1.00 and 0.94, respectively. The Dice similarity score for the surface and epithelial–stromal boundary segmentation networks was 0.98 and 0.83, respectively. Deep learning (DL) techniques can identify the location and variations in the epithelial surface and epithelial–stromal boundary in OCT images of the oral mucosa. Segmentation results can be synthesized into accessible en face maps to allow easier visualization of changes. Full article

To perform quality assessments of both metal alloys and many other engineering materials, measurements of the volume fractions of phases or microstructure components are utilized. For this purpose, quantitative analysis of the evaluated components’ distribution on metallographic specimens is often employed. Phases or components of the microstructure are identified based on the variation in signal received in the band of light seen. Problems with the correct identification of measurement results in this spectral band can be caused by the inhomogeneity of the etching when the alloy components are segregated. Additional uncertainty arises when the analyzed image pixel contains a boundary between grains of different phases. This article attempts to use the results of local chemical composition measurements as a source signal for quantitative evaluation of phase composition. For this purpose, quantitative maps of elemental concentration distributions, obtained with a Tescan Mira GMU high-resolution scanning electron microscope in QuantMap mode, were used as input data for the phase composition evaluation of an EN AC 46000 alloy sample. The X-ray microanalysis signal generation area may contain grains of more than one phase. Therefore, evaluation of the phase fractions in areas of individual measurements were calculated by looking for the minimum of the objective function, calculated as the sum of the squares of the deviations of the results of measurements of the concentration of individual elements from the weighted average values of solubilities of these elements in the phases. Full article