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Underwater images tend to suffer from critical quality degradation, such as poor visibility, contrast reduction, and color deviation by virtue of the light absorption and scattering in water media. It is a challenging problem for these images to enhance visibility, improve contrast, and eliminate color cast. This paper proposes an effective and high-speed enhancement and restoration method based on the dark channel prior (DCP) for underwater images and video. Firstly, an improved background light (BL) estimation method is proposed to estimate BL accurately. Secondly, the R channel’s transmission map (TM) based on the DCP is estimated sketchily, and a TM optimizer integrating the scene depth map and the adaptive saturation map (ASM) is designed to refine the afore-mentioned coarse TM. Later, the TMs of G–B channels are computed by their ratio to the attenuation coefficient of the red channel. Finally, an improved color correction algorithm is adopted to improve visibility and brightness. Several typical image-quality assessment indexes are employed to testify that the proposed method can restore underwater low-quality images more effectively than other advanced methods. An underwater video real-time measurement is also conducted on the flipper-propelled underwater vehicle-manipulator system to verify the effectiveness of the proposed method in the real scene.

Background Ascochyta blight caused by Ascochyta fabae Speg. and broomrape ( Orobanche crenata) are among the economically most significant pathogens of faba bean. Several QTLs conferring resistance against the two pathogens have been identified and validated in different genetic backgrounds. The aim of this study was to saturate the most stable QTLs for ascochyta and broomrape resistance in two Recombinant Inbred Line (RIL) populations, 29H x Vf136 and Vf6 x Vf136, to identify candidate genes conferring resistance against these two pathogens. Results We exploited the synteny between faba bean and the model species Medicago truncatula by selecting a set of 219 genes encoding putative WRKY transcription factors and defense related proteins falling within the target QTL intervals, for genotyping and marker saturation in the two RIL populations. Seventy and 50 of the candidate genes could be mapped in 29H x Vf136 and Vf6 x Vf136, respectively. Besides the strong reduction of the QTL intervals, the mapping process allowed replacing previous dominant and pedigree-specific RAPD flanking markers with robust and transferrable SNP markers, revealing promising candidates for resistance against the two pathogens. Conclusions Although further efforts in association mapping and expression studies will be required to corroborate the candidate genes for resistance, the fine-mapping approach proposed here increases the genetic resolution of relevant QTL regions and paves the way for an efficient deployment of useful alleles for faba bean ascochyta and broomrape resistance through marker-assisted breeding.

Some major problems in the motor drive are the overshoot or undershoot of transient response characteristics and a parameter mismatch due to magnetic saturation. This study proposed a 3D inductance map combined with a maximum-torque-per-ampere (MTPA) map based on a finite-element (FE) motor model considering a cross-coupling magnetic saturation impact to overcome this problem. The proposed FE motor model has a high accuracy of no-load back electromotive force (e.m.f.) around 98.3% compared to the measurement results. Then, nine scenarios of vector control combinations of inductance maps and current supply variations of β 0°, 45°, and MTPA were investigated. As a result, the transient response improvement for β 0°, 45°, and MTPA without the map and with Ld and Lq maps is 63%, 10%, and 15%, respectively. Moreover, for the steady-state response, the average torque improvement between MTPA and Idref 0 A control is 9.21%, 8.97%, and 8.98% for the no-map, ave-map, and 3D-inductance-map conditions, respectively. The MTPA trajectory characteristic was also updated to illustrate the actual MTPA condition compared to the conventional MTPA control. In detail, the proposed method has reduced the parameter mismatch for the current control loop in the transient state and improved the MTPA control trajectory for the steady-state response. Finally, the improvement of vector control characteristics of the proposed method was verified by an FE simulation and experimental measurement results.

In watersheds, critical source areas (CSAs) result from the colocation of areas with high levels of nutrient availability with areas of high potential for nutrient movement. In this study, the ability of two simulation modes, the Soil and Water Assessment Tool (SWAT) and the Soil Moisture Distribution and Routing model (SMDR), were evaluated for their ability to identify CSAs of phosphorus loss. The input data were derived from a 39.5-ha upland watershed within the Valley and Ridge Province of east-central Pennsylvania. Descriptions are provided of the two models and their respective calibrations. Simulation results showed that SWAT predicted time-series streamflow much better than SMDR, although SWAT underpredicted streamflow during the early portion of the year and overpredicted flows toward the end of the year. Both models, however, showed the ability to represent runoff generation areas at a watershed scale, although neither allowed landscape-scale routing of surface runoff from the runoff generation areas to the stream. In terms of watershed management practices, SMDR allowed the practices to be altered within small portions of a field due to its operation on a grid basis, while management practices in SWAT could not be applied selectively.

: A curve number based model, Soil and Water Assessment Tool (SWAT), and a physically based model, Soil Moisture Distribution and Routing (SMDR), were applied in a headwater watershed in Pennsylvania to identify runoff generation areas, as runoff areas have been shown to be critical for phosphorus management. SWAT performed better than SMDR in simulating daily streamflows over the four‐year simulation period (Nash‐Sutcliffe coefficient: SWAT, 0.62; SMDR, 0.33). Both models varied streamflow simulations seasonally as precipitation and watershed conditions varied. However, levels of agreement between simulated and observed flows were not consistent over seasons. SMDR, a variable source area based model, needs further improvement in model formulations to simulate large peak flows as observed. SWAT simulations matched the majority of observed peak flow events. SMDR overpredicted annual flow volumes, while SWAT underpredicted the same. Neither model routes runoff over the landscape to water bodies, which is critical to surface transport of phosphorus. SMDR representation of the watershed as grids may allow targeted management of phosphorus sources. SWAT representation of fields as hydrologic response units (HRUs) does not allow such targeted management.

OpenStreetMap, a crowdsourced geographic database, provides the only global-level, openly licensed source of geospatial road data, and the only national-level source in many countries. However, researchers, policy makers, and citizens who want to make use of OpenStreetMap (OSM) have little information about whether it can be relied upon in a particular geographic setting. In this paper, we use two complementary, independent methods to assess the completeness of OSM road data in each country in the world. First, we undertake a visual assessment of OSM data against satellite imagery, which provides the input for estimates based on a multilevel regression and poststratification model. Second, we fit sigmoid curves to the cumulative length of contributions, and use them to estimate the saturation level for each country. Both techniques may have more general use for assessing the development and saturation of crowd-sourced data. Our results show that in many places, researchers and policymakers can rely on the completeness of OSM, or will soon be able to do so. We find (i) that globally, OSM is ∼83% complete, and more than 40% of countries-including several in the developing world-have a fully mapped street network; (ii) that well-governed countries with good Internet access tend to be more complete, and that completeness has a U-shaped relationship with population density-both sparsely populated areas and dense cities are the best mapped; and (iii) that existing global datasets used by the World Bank undercount roads by more than 30%.

Accurately predicting future ocean acidification (OA) conditions is crucial for advancing OA research at regional and global scales, and guiding society's mitigation and adaptation efforts. This study presents a new model‐data fusion product covering 10 global surface OA indicators based on 14 Earth System Models (ESMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6), along with three recent observational ocean carbon data products. The indicators include fugacity of carbon dioxide, pH on total scale, total hydrogen ion content, free hydrogen ion content, carbonate ion content, aragonite saturation state, calcite saturation state, Revelle Factor, total dissolved inorganic carbon content, and total alkalinity content. The evolution of these OA indicators is presented on a global surface ocean 1° × 1° grid as decadal averages every 10 years from preindustrial conditions (1750), through historical conditions (1850–2010), and to five future Shared Socioeconomic Pathways (2020–2100): SSP1‐1.9, SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5. These OA trajectories represent an improvement over previous OA data products with respect to data quantity, spatial and temporal coverage, diversity of the underlying data and model simulations, and the provided SSPs. The generated data product offers a state‐of‐the‐art research and management tool for the 21st century under the combined stressors of global climate change and ocean acidification. The gridded data product is available in NetCDF at the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information: https://www.ncei.noaa.gov/data/oceans/ncei/ocads/metadata/0259391.html, and global maps of these indicators are available in jpeg at: https://www.ncei.noaa.gov/access/ocean-carbon-acidification-data-system/synthesis/surface-oa-indicators.html. Plain Language Summary A new data product, based on the latest computer simulations and observational data, offers improved projections of ocean acidification (OA) conditions from the start of the Industrial Revolution in 1750 to the end of the 21st century. These projections will support OA research at regional and global scales, and provide essential information to guide OA mitigation and adaptation efforts for various sectors, including fisheries, aquaculture, tourism, marine resource decision‐makers, and the general public. Key Points This study presents the evolution of 10 ocean acidification (OA) indicators in the global surface ocean from 1750 to 2100 By leveraging 14 Earth System Models (ESMs) and the latest observational data, it represents a significant advancement in OA projections This inter‐model comparison effort showcases the overall agreements among different ESMs in projecting surface ocean carbon variables

Mutant-specific inhibitors of KRASG12C, such as AMG510 (sotorasib) and MRTX849 (adagrasib), offer the unprecedented opportunity to inhibit KRAS, the most frequently mutated and heretofore undruggable oncoprotein. While clinical data are still limited, on-target mutations in KRASG12C at position 12 and other sites are emerging as major drivers of clinical relapse. We identified additional mutations in KRASG12C that impact inhibitor sensitivity through a saturation mutagenesis screen in the KRASG12C NCI-H358 non–small-cell lung cancer (NSCLC) cell line. We also identified individuals in population genetic databases harboring these resistance mutations in their germline and in tumors, including a subset that co-occur with KRASG12C , indicating that these mutations may preexist in patients treated with KRASG12C inhibitors. Notably, through structural modeling, we found that one such mutation (R68L) interferes with the critical protein–drug interface, conferring resistance to both inhibitors. Finally, we uncovered a mutant (S17E) that demonstrated a strong sensitizing phenotype to both inhibitors. Functional studies suggest that S17E sensitizes KRASG12C cells to KRASG12C inhibition by impacting signaling through PI3K/AKT/mTOR but not the MAPK signaling pathway. Our studies highlight the utility of unbiased mutation profiling to understand the functional consequences of all variants of a disease-causing genetic mutant and predict acquired resistant mutations in the targeted therapeutics.

Benchmarks are crucial to measuring and steering progress in artificial intelligence (AI). However, recent studies raised concerns over the state of AI benchmarking, reporting issues such as benchmark overfitting, benchmark saturation and increasing centralization of benchmark dataset creation. To facilitate monitoring of the health of the AI benchmarking ecosystem, we introduce methodologies for creating condensed maps of the global dynamics of benchmark creation and saturation. We curate data for 3765 benchmarks covering the entire domains of computer vision and natural language processing, and show that a large fraction of benchmarks quickly trends towards near-saturation, that many benchmarks fail to find widespread utilization, and that benchmark performance gains for different AI tasks are prone to unforeseen bursts. We analyze attributes associated with benchmark popularity, and conclude that future benchmarks should emphasize versatility, breadth and real-world utility. Recent studies raised concerns over the state of AI benchmarking, reporting issues such as benchmark overfitting, benchmark saturation and increasing centralization of benchmark dataset creation. To facilitate monitoring of the health of the AI benchmarking ecosystem, the authors introduce methodologies for creating condensed maps of the global dynamics of benchmark.

Wheat is one among the significant crops for humans. Significant fungal illnesses of wheat are brought on by multiple pathogens. Wheat output could be enhanced by the early identification of wheat leaf disease. Thus, a novel hyperparameter tanh-based convolutional neural network (HT-CNN)-based wheat leaf disease prediction is proposed with its severity level. Here, initially, the red, green, and blue (RGB) images are converted into a hue saturation value (HSV) image. Next, the small probability space filtering is applied to the V component. Afterward, the contrast of the V component has been enhanced. The obtained HSV image is converted into the RGB image. Then, by employing weighted Canberra distance-based K-means (WCD-K means), the affected and normal regions are segmented. Next, the image is binarized. Afterward, for tracing a boundary around disease-affected region, the hex directional vertex chain code (Hex-D-VCC) is applied over the binarized image, and then the features are extracted. By employing baker’s map-based Harris hawks optimization (BM-HHO), the optimal features are selected. For classifying disease, the selected features are further given into the HT-CNN, and the severity level is calculated to minimize the yield loss. As per the experimental result, the proposed model shows higher accuracy and efficacy when analogized to the other methods.