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Purposes: Developing the cultivation and utilization of tiger nuts on marginal land is an effective approach to tackle the shortage of arable land in northern China. However, key information on their growth is unclear. This study aims to reveal the adaption potential of tiger nuts to on marginal land to help improve and adjust management practices. Methods: Three treatments were arranged to plant tiger nut in marginal land: no fertilization (N:P = 0:0), traditional fertilization (N:P = 15:15), and additional nitrogen fertilizer (N:P = 60:15). Functional traits of tiger nut and soil nutrients over three growth periods were investigated. Results: Under no fertilization, tiger nuts extended roots and toughened leaves to maximize nutrient absorption, and then quickly transported those nutrients to tubers. However, the lack of available phosphorus in the soil prevented the expansion of tubers. With the addition of fertilizers, tiger nuts produced more individuals, and their abundant belowground tissues resulted in soil organic matter increase. Under traditional fertilization, the tuber remains the main reproductive organ for tiger nuts, and phosphorus has a significant effect on the expansion of tubers. But under additional nitrogen fertilizer, the main reproductive organ changed to the stolon, nitrogen and phosphorus acted on stolons to promote their extension. Conclusion: Tiger nuts outstandingly adapt to marginal land environments and adjust their adaptation strategies according to varying soil nutrient conditions, Best tuber production will be achieved in soils with higher phosphorus availability, and a significant nitrogen proportion increase in fertilizers is necessary for enhancing aboveground production. [ABSTRACT FROM AUTHOR]

Objective: This study aims to explore the feasibility of employing convolutional neural networks for detecting and localizing implant cutouts on anteroposterior pelvic radiographs.The research involves the development of two Deep Learning models. Initially, a model was created for image-level classification of implant cutouts using 40191 pelvic radiographs obtained from a single institution. The radiographs were partitioned into training, validation, and hold-out test datasets in a 6/2/2 ratio. Performance metrics including the area under the receiver operator characteristics curve (AUROC), sensitivity, and specificity were calculated using the test dataset. Additionally, a second object detection model was trained to localize implant cutouts within the same dataset. Bounding box visualizations were generated on images predicted as cutout-positive by the classification model in the test dataset, serving as an adjunct for assessing algorithm validity.The classification model had an accuracy of 99.7%, sensitivity of 84.6%, specificity of 99.8%, AUROC of 0.998 (95% CI: 0.996, 0.999) and AUPRC of 0.774 (95% CI: 0.646, 0.880). From the pelvic radiographs predicted as cutout-positive, the object detection model could achieve 95.5% localization accuracy on true positive images, but falsely generated 14 results from the 15 false-positive predictions.The classification model showed fair accuracy for detection of implant cutouts, while the object detection model effectively localized cutout. This serves as proof of concept of using a deep learning-based approach for classification and localization of implant cutouts from pelvic radiographs.Materials and methods: This study aims to explore the feasibility of employing convolutional neural networks for detecting and localizing implant cutouts on anteroposterior pelvic radiographs.The research involves the development of two Deep Learning models. Initially, a model was created for image-level classification of implant cutouts using 40191 pelvic radiographs obtained from a single institution. The radiographs were partitioned into training, validation, and hold-out test datasets in a 6/2/2 ratio. Performance metrics including the area under the receiver operator characteristics curve (AUROC), sensitivity, and specificity were calculated using the test dataset. Additionally, a second object detection model was trained to localize implant cutouts within the same dataset. Bounding box visualizations were generated on images predicted as cutout-positive by the classification model in the test dataset, serving as an adjunct for assessing algorithm validity.The classification model had an accuracy of 99.7%, sensitivity of 84.6%, specificity of 99.8%, AUROC of 0.998 (95% CI: 0.996, 0.999) and AUPRC of 0.774 (95% CI: 0.646, 0.880). From the pelvic radiographs predicted as cutout-positive, the object detection model could achieve 95.5% localization accuracy on true positive images, but falsely generated 14 results from the 15 false-positive predictions.The classification model showed fair accuracy for detection of implant cutouts, while the object detection model effectively localized cutout. This serves as proof of concept of using a deep learning-based approach for classification and localization of implant cutouts from pelvic radiographs.Results: This study aims to explore the feasibility of employing convolutional neural networks for detecting and localizing implant cutouts on anteroposterior pelvic radiographs.The research involves the development of two Deep Learning models. Initially, a model was created for image-level classification of implant cutouts using 40191 pelvic radiographs obtained from a single institution. The radiographs were partitioned into training, validation, and hold-out test datasets in a 6/2/2 ratio. Performance metrics including the area under the receiver operator characteristics curve (AUROC), sensitivity, and specificity were calculated using the test dataset. Additionally, a second object detection model was trained to localize implant cutouts within the same dataset. Bounding box visualizations were generated on images predicted as cutout-positive by the classification model in the test dataset, serving as an adjunct for assessing algorithm validity.The classification model had an accuracy of 99.7%, sensitivity of 84.6%, specificity of 99.8%, AUROC of 0.998 (95% CI: 0.996, 0.999) and AUPRC of 0.774 (95% CI: 0.646, 0.880). From the pelvic radiographs predicted as cutout-positive, the object detection model could achieve 95.5% localization accuracy on true positive images, but falsely generated 14 results from the 15 false-positive predictions.The classification model showed fair accuracy for detection of implant cutouts, while the object detection model effectively localized cutout. This serves as proof of concept of using a deep learning-based approach for classification and localization of implant cutouts from pelvic radiographs.Conclusion: This study aims to explore the feasibility of employing convolutional neural networks for detecting and localizing implant cutouts on anteroposterior pelvic radiographs.The research involves the development of two Deep Learning models. Initially, a model was created for image-level classification of implant cutouts using 40191 pelvic radiographs obtained from a single institution. The radiographs were partitioned into training, validation, and hold-out test datasets in a 6/2/2 ratio. Performance metrics including the area under the receiver operator characteristics curve (AUROC), sensitivity, and specificity were calculated using the test dataset. Additionally, a second object detection model was trained to localize implant cutouts within the same dataset. Bounding box visualizations were generated on images predicted as cutout-positive by the classification model in the test dataset, serving as an adjunct for assessing algorithm validity.The classification model had an accuracy of 99.7%, sensitivity of 84.6%, specificity of 99.8%, AUROC of 0.998 (95% CI: 0.996, 0.999) and AUPRC of 0.774 (95% CI: 0.646, 0.880). From the pelvic radiographs predicted as cutout-positive, the object detection model could achieve 95.5% localization accuracy on true positive images, but falsely generated 14 results from the 15 false-positive predictions.The classification model showed fair accuracy for detection of implant cutouts, while the object detection model effectively localized cutout. This serves as proof of concept of using a deep learning-based approach for classification and localization of implant cutouts from pelvic radiographs. [ABSTRACT FROM AUTHOR]

Inspired by an open question by Chemin, Zhang and Zhang about the regularity of the 3D Navier–Stokes equations with one initially small component, we investigate symmetry breaking and symmetry preservation. Our results fall in three classes. First we prove strong symmetry breaking. Specifically, we demonstrate third component norm inflation (3rdNI) and Isotropic Norm Inflation (INI) starting from zero third component. Second we prove symmetry breaking for initially zero third component, even in the presence of a favorable initial pressure gradient. Third we study certain symmetry preserving solutions with a shear flow structure. Specifically, we give applications to the inviscid limit and exhibit explicit solutions that inviscidly damp to the Kolmogorov flow. [ABSTRACT FROM AUTHOR]

The development of biodegradable materials from biomass is a crucial strategy for leveraging biomass resources. A key component in this process is 2,5-bis(hydroxymethyl)furan (BHMF), a significant biomass-derived platform chemical. However, producing BHMF with high selectivity through the selective hydrogenation of 5-hydroxymethylfurfural, while avoiding the creation of unwanted byproducts, is a complex challenge. In our research, we have developed a novel catalyst featuring a magnetic core. This catalyst, which consists of platinum loaded onto a graphene-like shell, has been applied to the catalytic hydrogenation of HMF in an aqueous environment. Under the optimized conditions of 373 K, 3 MPa H2, and 1 h reaction time, a BHMF yield exceeding 99% was achieved. This method presents a promising and effective approach for the high-value conversion of biomass waste into polymer materials and pharmaceutical chemicals.Graphical Abstract: The development of biodegradable materials from biomass is a crucial strategy for leveraging biomass resources. A key component in this process is 2,5-bis(hydroxymethyl)furan (BHMF), a significant biomass-derived platform chemical. However, producing BHMF with high selectivity through the selective hydrogenation of 5-hydroxymethylfurfural, while avoiding the creation of unwanted byproducts, is a complex challenge. In our research, we have developed a novel catalyst featuring a magnetic core. This catalyst, which consists of platinum loaded onto a graphene-like shell, has been applied to the catalytic hydrogenation of HMF in an aqueous environment. Under the optimized conditions of 373 K, 3 MPa H2, and 1 h reaction time, a BHMF yield exceeding 99% was achieved. This method presents a promising and effective approach for the high-value conversion of biomass waste into polymer materials and pharmaceutical chemicals. [ABSTRACT FROM AUTHOR]