Hasil untuk "Maps"

T. Terwilliger, P. D. Adams, Randy J. Read et al.

Sarah J. Becker, Nicole M. Wayant

Accurate identification of built-up land from remotely sensed imagery is essential for urban planning, environmental monitoring, and disaster response. However, binary built-up maps derived from single-date classifications often contain semantic noise—misclassified pixels resulting from shadows, bare soil confusion, or seasonal conditions. Common denoising methodologies, such as smoothing or filtering, are designed for continuous imagery and can distort small or fragmented features and fail to correct underlying classification errors. To overcome these limitations, this study evaluated a multi-date summation and thresholding workflow as a denoising alternative. Five Sentinel-2 images per site were classified as built-up maps, summed into a composite “built-up frequency” raster, and thresholded using Otsu, adaptive, and voting methods to produce refined binary maps. The results across nine international study sites show that the Otsu thresholding method outperformed the other methods in most locations when comparing their accuracies using the Matthews Correlation Coefficient (MCC), showing that using multiple images can improve identification of built-up land.

Tuyboyov Oybek, Baydullayev Azamat, Jeltuxin Andrey et al.

This study explores advanced methodologies for enhancing the precision and efficiency of CNC machining through the integration of experimental, computational, and simulation-based techniques. Utilizing tools such as laser interferometers, autocollimators, and predictive thermal simulations, the research addresses critical challenges in tool positioning accuracy caused by thermal fluctuations and geometric errors. Compensation strategies reduced thermal errors by 15%, while kinematic calibration mitigated angular discrepancies by 10%. Furthermore, the application of Setup-Maps (S-Maps) and Tolerance-Maps (T-Maps) optimized fixture design, reducing setup time by 25% and improving machining precision by 30%. Hybrid optimization techniques, including genetic algorithms, effectively balanced the trade-off between precision and productivity, achieving a 10% reduction in production time without sacrificing accuracy. Real-time calibration, IoT-enabled predictive maintenance, and collaborative robots (Cobots) further enhanced machining reliability, increasing operational uptime by 15% and reducing tool positioning errors by 18%. These advancements enabled sub-micron accuracy in high-precision applications, demonstrating their relevance to industries such as aerospace and medical devices. This research establishes a robust framework for improving CNC machining operations by addressing thermal and geometric errors, optimizing fixture design, and leveraging Industry 4.0 technologies. The proposed methods not only enhance machining precision but also contribute to sustainable manufacturing by minimizing downtime, energy consumption, and waste, paving the way for smarter and more efficient production systems.

Csilla Kalup, László Molnár

Interstellar extinction is a major obstacle in determining accurate stellar parameters from photometry near the Galactic disk. It is especially true for globular clusters at low galactic latitudes, which suffer from significant amounts of spatially variable reddening. Although differential reddening maps are available for tens of clusters, establishing and validating the absolute zero-point of relative maps is a challenge. In this study, we present a new approach to determine and evaluate absolute reddening zero-points for Galactic globular clusters by combining 3D reddening maps with Gaia DR3 RR Lyrae data. As a first case study, we investigate the low-latitude globular cluster M9. We compare the Gaia photometry and color data of the cluster member RR Lyrae stars to field RR Lyrae stars with accurate parallaxes and whose photometric metallicities match that of M9, as well as to theoretical models. We calculate the dereddened Gaia colors for the M9 stars based on three zero points. We confirm that the original SFD map by D. J. Schlegel et al. (1998) appears to be overcorrecting the reddening for at least some RR Lyrae stars, albeit not excessively. In contrast, the 3D Bayestar map and the recalibrated version of the SFD map provide physically plausible reddenings, which we accept as lower and upper limits for M9, respectively. Our results provide a physically motivated reddening range for M9 and outline a methodology that can be directly extended to other globular clusters that are accessible to the Gaia mission, and to other multicolor sky surveys, such as the Rubin Observatory.

Martin Lotze

Functional imaging has helped to understand the role of the human insula as a major processing network for integrating input with the current state of the body. However, these studies remain at a correlative level. Studies that have examined insula damage show lesion-specific performance deficits. Case reports have provided anecdotal evidence for deficits following insula damage, but group lesion studies offer a number of advances in providing evidence for functional representation of the insula. We conducted a systematic literature search to review group studies of patients with insula damage after stroke and identified 23 studies that tested emotional processing performance in these patients. Eight of these studies assessed emotional processing of visual (most commonly IAPS), auditory (e.g., prosody), somatosensory (emotional touch) and autonomic function (heart rate variability). Fifteen other studies looked at social processing, including emotional face recognition, gaming tasks and tests of empathy. Overall, there was a bias towards testing only patients with right-hemispheric lesions, making it difficult to consider hemisphere specificity. Although many studies included an overlay of lesion maps to characterise their patients, most did not differentiate lesion statistics between insula subunits and/or applied voxel-based associations between lesion location and impairment. This is probably due to small group sizes, which limit statistical comparisons. We conclude that multicentre analyses of lesion studies with comparable patients and performance tests are needed to definitively test the specific function of parts of the insula in emotional processing and social interaction.

Zhen Zhang, Zhen Zhang, Zhen Zhang et al.

Efficient and precise thinning during the orchard blossom period is a crucial factor in enhancing both fruit yield and quality. The accurate recognition of inflorescence is the cornerstone of intelligent blossom equipment. To advance the process of intelligent blossom thinning, this paper addresses the issue of suboptimal performance of current inflorescence recognition algorithms in detecting dense inflorescence at a long distance. It introduces an inflorescence recognition algorithm, YOLOv7-E, based on the YOLOv7 neural network model. YOLOv7 incorporates an efficient multi-scale attention mechanism (EMA) to enable cross-channel feature interaction through parallel processing strategies, thereby maximizing the retention of pixel-level features and positional information on the feature maps. Additionally, the SPPCSPC module is optimized to preserve target area features as much as possible under different receptive fields, and the Soft-NMS algorithm is employed to reduce the likelihood of missing detections in overlapping regions. The model is trained on a diverse dataset collected from real-world field settings. Upon validation, the improved YOLOv7-E object detection algorithm achieves an average precision and recall of 91.4% and 89.8%, respectively, in inflorescence detection under various time periods, distances, and weather conditions. The detection time for a single image is 80.9 ms, and the model size is 37.6 Mb. In comparison to the original YOLOv7 algorithm, it boasts a 4.9% increase in detection accuracy and a 5.3% improvement in recall rate, with a mere 1.8% increase in model parameters. The YOLOv7-E object detection algorithm presented in this study enables precise inflorescence detection and localization across an entire tree at varying distances, offering robust technical support for differentiated and precise blossom thinning operations by thinning machinery in the future.

Jhansi Rani Challapalli, Nagaraju Devarakonda

Multi-person pose estimation (MPE) remains a significant and intricate issue in computer vision. This is considered the human skeleton joint identification issue and resolved by the joint heat map regression network lately. Learning robust and discriminative feature maps is essential for attaining precise pose estimation. Even though the present methodologies established vital progression via feature map’s interlayer fusion and intralevel fusion, some studies show consideration for the combination of these two methodologies. This study focuses upon three phases of pre-processing stages like occlusion elimination, suppression strategy, and heat map methodology to lessen noise within the database. Subsequent to pre-processing errors will be eliminated by employing the quantization phase by embracing the pose detector. Lastly, Image-Guided Progressive Graph Convolution Network (IGP-GCN) has been built for MPE. This IGP-GCN consistently learns rich fundamental spatial information by merging features inside the layers. In order to enhance high-level semantic information and reuse low-level spatial information for correct keypoint representation, this also provides hierarchical connections across feature maps of the same resolution for interlayer fusion. Furthermore, a missing connection between the output high level information and low-level information was noticed. For resolving the issue, the effectual shuffled attention mechanism has been proffered. This shuffle intends to support the cross-channel data interchange between pyramid feature maps, whereas attention creates a trade-off between the high level and low-level representations of output features. This proffered methodology can be called Occlusion Removed_Image Guided Progressive Graph Convolution Network (OccRem_IGP-GCN), and, thus, this can be correlated with the other advanced methodologies. The experimental outcomes exhibit that the OccRem_IGP-GCN methodology attains 98% of accuracy, 93% of sensitivity, 92% of specificity, 88% of f1-score, 42% of relative absolute error, and 30% of mean absolute error.

Chi-Min Chiu, Laurence Zsu-Hsin Chuang, Wei-Liang Chuang et al.

This study aims to establish a comprehensive workflow for developing emergency response plans for both actual and scenario oil spill incidents in the Taiwan waters while addressing the resource allocation for oil spill containment as well. This workflow comprises two vital components. The first component involves the integration of numerical tools and observational data, which includes the incorporation of wind data from sources such as the National Centers for Environmental Prediction (NCEP) or meteorological stations. Additionally, it incorporates ocean current data simulated by the semi-implicit cross-scale hydroscience integrated system model (SCHISM) into the general NOAA operational modeling environment (GNOME) model, which is a new approach for this purpose. In order to assess the efficacy of this component, two distinct case studies were conducted. The first case study focused on an incident in a northern coastal area of Taiwan under open sea conditions, whereas the second case study examined an incident within a major commercial harbor in central Taiwan. The second component of this workflow involves creating oil risk maps by integrating the results from the first component with specific geographical factors into Google Earth. These oil risk maps serve multiple purposes. They offer real-time information to emergency response commanders regarding oil spill hazard prediction, and they also enable the effective development of emergency response strategies and disposal plans for potential oil spill incidents. This is achieved by generating risk maps for various scenarios using the approach outlined in the first component. Additionally, these maps assist in the assessment and planning of resource allocation for oil containment.

Osvaldo Villarreal, Daniela Ahumada-C., Leonardo Delgado-Santa

Colombia is a biodiverse country with 1,486 protected areas covering almost 50 million hectares, yet little is known about the biodiversity they harbor, particularly in terms of harvestmen (Arachnida: Opiliones). This study provides a comprehensive updated summary of the armored harvestmen (Laniatores) species found in Colombia with a list of 173 nominal species, focusing on the Laniatores fauna found in protected areas and its diversity is detailed and analyzed. Maps with all records associated with ecoregions and protected areas in Colombia are presented. Additionally, three new Laniatores species are described here: Phalangodus andresi sp. nov. from the department of Cundinamarca, and Ventrifurca phallaina sp. nov. and Ampycella fortunata sp. nov. from the department of Valle del Cauca; and a new family assignment is proposed for Paraphalangodus Roewer, 1915, placing it in the family Nomoclastidae. Information available on Laniatores in the National System of Protected Areas is still scarce and promoting strategies to facilitate the regulatory procedures for collecting specimens in these areas and increasing investment in basic science projects, are suggested to improve the understanding and study of the Laniatores fauna and other invertebrates in Colombia. Finally, a chronicle and timeline set of figures of species of Laniatores from Colombia, described by various authors during three periods, is given.

B. Paschal, H. S. Shpetner, R. Vallee

We observe that one of the high molecular mass microtubule-associated proteins (MAPs) from brain exhibits nucleotide-dependent binding to microtubules. We identify the protein as MAP IC, which was previously described in this laboratory as a minor component of standard microtubule preparations (Bloom, G.S., T. Schoenfeld, and R.B. Vallee, 1984, J. Cell Biol., 98:320-330). We find that MAP 1C is enriched in microtubules prepared in the absence of nucleotide. Kinesin is also found in these preparations, but can be specifically extracted with GTP. A fraction highly enriched in MAP 1C can be prepared by subsequent extraction of the microtubules with ATP. Two activities cofractionate with MAP 1C upon further purification, a microtubule-activated ATPase activity and a microtubule-translocating activity. These activities indicate a role for the protein in cytoplasmic motility. MAP 1C coelectrophoreses with the beta heavy chain of Chlamydomonas flagellar dynein, and has a sedimentation coefficient of 20S. Exposure to ultraviolet light in the presence of vanadate and ATP results in the production of two large fragments of MAP 1C. These characteristics suggest that MAP 1C may be a cytoplasmic analogue of axonemal dynein.

G. Rohrer, L. Alexander, Zhi-Liang Hu et al.

We report the highest density genetic linkage map for a livestock species produced to date. Three published maps for Sus scrofa were merged by genotyping virtually every publicly available microsatellite across a single reference population to yield 1042 linked loci, 536 of which are novel assignments, spanning 2286.2 cM (average interval 2.23 cM) in 19 linkage groups (18 autosomal and X chromosomes, n = 19). Linkage groups were constructed de novo and mapped by locus content to avoid propagation of errors in older genotypes. The physical and genetic maps were integrated with 123 informative loci assigned previously by fluorescence in situ hybridization (FISH). Fourteen linkage groups span the entire length of each chromosome. Coverage of chromosomes 11, 12, 15, and 18 will be evaluated as more markers are physically assigned. Marker-deficient regions were identified only on 11q1.7-qter and 14 cen-q1.2. Recombination rates (cM/Mbp) varied between and within chromosomes. Short chromosomal arms recombined at higher rates than long arms, and recombination was more frequent in telomeric regions than in pericentric regions. The high-resolution comprehensive map has the marker density needed to identify quantitative trait loci (QTL), implement marker-assisted selection or introgression and YAC contig construction or chromosomal microdissection.

L. Kruglyak

H. Jacob, Donna M. Brown, R. Bunker et al.

T. Youd, D. Perkins

Zachary R Sailer, Sarah H Shafik, Robert L Summers et al.

Understanding evolution requires detailed knowledge of genotype-phenotype maps; however, it can be a herculean task to measure every phenotype in a combinatorial map. We have developed a computational strategy to predict the missing phenotypes from an incomplete, combinatorial genotype-phenotype map. As a test case, we used an incomplete genotype-phenotype dataset previously generated for the malaria parasite's 'chloroquine resistance transporter' (PfCRT). Wild-type PfCRT (PfCRT3D7) lacks significant chloroquine (CQ) transport activity, but the introduction of the eight mutations present in the 'Dd2' isoform of PfCRT (PfCRTDd2) enables the protein to transport CQ away from its site of antimalarial action. This gain of a transport function imparts CQ resistance to the parasite. A combinatorial map between PfCRT3D7 and PfCRTDd2 consists of 256 genotypes, of which only 52 have had their CQ transport activities measured through expression in the Xenopus laevis oocyte. We trained a statistical model with these 52 measurements to infer the CQ transport activity for the remaining 204 combinatorial genotypes between PfCRT3D7 and PfCRTDd2. Our best-performing model incorporated a binary classifier, a nonlinear scale, and additive effects for each mutation. The addition of specific pairwise- and high-order-epistatic coefficients decreased the predictive power of the model. We evaluated our predictions by experimentally measuring the CQ transport activities of 24 additional PfCRT genotypes. The R2 value between our predicted and newly-measured phenotypes was 0.90. We then used the model to probe the accessibility of evolutionary trajectories through the map. Approximately 1% of the possible trajectories between PfCRT3D7 and PfCRTDd2 are accessible; however, none of the trajectories entailed eight successive increases in CQ transport activity. These results demonstrate that phenotypes can be inferred with known uncertainty from a partial genotype-phenotype dataset. We also validated our approach against a collection of previously published genotype-phenotype maps. The model therefore appears general and should be applicable to a large number of genotype-phenotype maps.

M. J. Kraak, F. Ormeling

A. Alonso, I. Grundke‐Iqbal, H. S. Barra et al.

J. Staub, F. Serquén, Manju M. Gupta

N. Rulkov

A simple model that replicates the dynamics of spiking and spiking-bursting activity of real biological neurons is proposed. The model is a two-dimensional map that contains one fast and one slow variable. The mechanisms behind generation of spikes, bursts of spikes, and restructuring of the map behavior are explained using phase portrait analysis. The dynamics of two coupled maps that model the behavior of two electrically coupled neurons is discussed. Synchronization regimes for spiking and bursting activities of these maps are studied as a function of coupling strength. It is demonstrated that the results of this model are in agreement with the synchronization of chaotic spiking-bursting behavior experimentally found in real biological neurons.

Bashir Ahmad, Sotiris K. Ntouyas, Ahmed Alsaedi

In this paper, we develop the existence theory for a new kind of nonlocal three-point boundary value problems for differential equations and inclusions involving both left Caputo and right Riemann–Liouville fractional derivatives. The Banach and Krasnoselskii fixed point theorems and the Leray–Schauder nonlinear alternative are used to obtain the desired results for the singlevalued problem. The existence of solutions for the multivalued problem concerning the upper semicontinuous and Lipschitz cases is proved by applying nonlinear alternative for Kakutani maps and Covitz and Nadler fixed point theorem. Examples illustrating the main results are also presented.