20 research outputs found
Risk factors for the development of avascular necrosis after femoral neck fractures in children
No full textAims The aim of this study was to clarify the factors that predict the development of avascular necrosis (AVN) of the femoral head in children with a fracture of the femoral neck. Patients and Methods We retrospectively reviewed 239 children with a mean age of 10.0 years (sd 3.9) who underwent surgical treatment for a femoral neck fracture. Risk factors were recorded, including age, sex, laterality, mechanism of injury, initial displacement, the type of fracture, the time to reduction, and the method and quality of reduction. AVN of the femoral head was assessed on radiographs. Logistic regression analysis was used to evaluate the independent risk factors for AVN. Chi-squared tests and Student’s t-tests were used for subgroup analyses to determine the risk factors for AVN. Results We found that age (p = 0.006) and initial displacement (p = 0.001) were significant independent risk factors. Receiver operating characteristic (ROC) curve analysis indicated that 12 years of age was the cut-off for increasing the rate of AVN. Severe initial displacement (p = 0.021) and poor quality of reduction (p = 0.022) significantly increased the rate of AVN in patients aged 12 years or greater, while in those aged less than 12 years, the rate of AVN significantly increased only with initial displacement (p = 0.048). A poor reduction significantly increased the rate of AVN in patients treated by closed reduction (p = 0.026); screw and plate fixation was preferable to cannulated screw or Kirschner wire (K-wire) fixation for decreasing the rate of AVN in patients treated by open reduction (p = 0.034). Conclusion The rate of AVN increases with age, especially in patients aged 12 years or greater, and with the severity of displacement. In patients treated by closed reduction, anatomical reduction helps to decrease the rate of AVN, while in those treated by open reduction, screw and plate fixation was preferable to fixation using cannulated screws or K-wires
On the Deskins index complex of a maximal subgroup of a finite group
No full textAbstractLet M be a maximal subgroup of a finite group G. A subgroup C of G is said to be a completion of M in G if C is not contained in M while every proper subgroup of C which is normal in G is contained in M. The set, I(M), of all completions of M is called the index complex of M in G. Set P(M) = {C ϵ I(M) ¦ C} is maximal in I(M) and G = CM. The purpose of this note is to prove: A finite group G is solvable if and only if, for each maximal subgroup M of G, P(M) contains element C with CK(C) nilpotent
Deep Learning Segmentation and Classification for Urban Village Using a Worldview Satellite Image Based on U-Net
No full textUnplanned urban settlements exist worldwide. The geospatial information of these areas is critical for urban management and reconstruction planning but usually unavailable. Automatically characterizing individual buildings in the unplanned urban village using remote sensing imagery is very challenging due to complex landscapes and high-density settlements. The newly emerging deep learning method provides the potential to characterize individual buildings in a complex urban village. This study proposed an urban village mapping paradigm based on U-net deep learning architecture. The study area is located in Guangzhou City, China. The Worldview satellite image with eight pan-sharpened bands at a 0.5-m spatial resolution and building boundary vector file were used as research purposes. There are ten sites of the urban villages included in this scene of the Worldview image. The deep neural network model was trained and tested based on the selected six and four sites of the urban village, respectively. Models for building segmentation and classification were both trained and tested. The results indicated that the U-net model reached overall accuracy over 86% for building segmentation and over 83% for the classification. The F1-score ranged from 0.9 to 0.98 for the segmentation, and from 0.63 to 0.88 for the classification. The Interaction over Union reached over 90% for the segmentation and 86% for the classification. The superiority of the deep learning method has been demonstrated through comparison with Random Forest and object-based image analysis. This study fully showed the feasibility, efficiency, and potential of the deep learning in delineating individual buildings in the high-density urban village. More importantly, this study implied that through deep learning methods, mapping unplanned urban settlements could further characterize individual buildings with considerable accuracy
Phylogenetic analysis of C, M, N, and U genomes and their relationships with <i>Triticum</i> and other related genomes as revealed by LMW-GS genes at <i>Glu-3</i> loci
No full text Phylogenetic relationships between the C, U, N, and M genomes of Aegilops species and the genomes of common wheat and other related species were investigated by using three types of low-molecular-weight glutenin subunit (LMW-GS) genes at Glu-3 loci. A total of 20 LMW-GS genes from Aegilops and Triticum species were isolated, including 11 LMW-m type and 9 LMW-i type genes. Particularly, four LMW-m type and three LMW-i type subunits encoded by the genes on the C, N, and U genomes possessed an extra cysteine residue at conserved positions, which could provide useful information for understanding phylogenetic relationships among Aegilops and Triticum genomes. Phylogenetic trees constructed by using either LMW-i or the combination of LMW-m and LMW-s, as well as analysis of all the three types of LMW-GS genes together, demonstrated that the C and U genomes were closely related to the A genome, whereas the N and M genomes were closely related to the D genome. Our results support previous findings that the A genome was derived from Triticum uratu, the B genome was from Aegilops speltoides, and the D genome was from Aegilops tauschii. In addition, phylogenetic relationships among different genomes analysed in this study support the concept that Aegilops is not monophyletic. </jats:p
Effects of legume-cover crop rotations on soil pore characteristics and particulate organic matter distributions in Vertisol based on X-ray computed tomography
No full textCover crops have been used as an effective soil management practice to improve soil pore structure and promote particulate organic matter (POM) accumulation. However, the effects of different cover crop rotations on soil pore characteristics and the spatial distributions for POM remain poorly understood. This study examined the influence of five cropping systems (spring maize monoculture (M), wheat-maize rotation (WM), wheat-soybean rotation (WS), wheat-Cassia occidentalis rotation (WCo), and wheat-Cassia tora Linn. rotation (WCt)) on soil POM fractions, pore structure, and soil physical properties, and nutrient availability in a Vertisol. The results showed that, compared to M treatment, all four rotation systems significantly increased image-based porosity, 120–1000 μm porosity, pore surface area density, and connection probability (P < 0.05). The WCo and WCt treatments led to an increase in connected porosity by 207 % and 130 % (P < 0.05). They also significantly increased fresh POM by 90.4 % and 55.5 %, and total POM by 75.4 % and 52.4 %, respectively (P < 0.05). These treatments also significantly increased air permeability (Ka), relative gas diffusivity (Ds/D0), and saturated hydraulic conductivity (Ks) (P < 0.05). Strong correlations were observed between fresh POM and pore structural parameters (image-based porosity, connected porosity, 500–1000 μm porosity, hydraulic radius), as well as soil physical properties (Ks, Ka, Ds/D0). The positive feedback between pore structure and POM accumulation facilitated soil structural improvement under leguminous cover crop-based rotations (WCo, WCt). These findings suggest that integrating Cassia occidentalis and Cassia tora Linn. into rotation systems can effectively enhance soil structure and promote organic matter sequestration in Vertisols
Atomic understanding of the evolutionary mechanism of fused glass densification generation during single particle scratching
No full textThe densification of fused glass during processing has a significant impact on the performance and application of fused glass components. However, the precise atomic mechanisms underlying densification remain elusive. In this study, we explore the atomic mechanisms responsible for densification in fused glass during single particle scratching, with a focus on the scratching depths and environmental humidity. We employ reactive force field molecular dynamics (ReaxFF MD) simulations for our investigation. We subjected models to scratching under various humidity conditions using a spherical virtual indenter with a 20 Å radius. The scratching depths were set at 10 Å and 15 Å, respectively, with a constant scraping speed of 40 m/s. Our findings indicate that water molecules impede lateral atom movement on the fused glass surface while enhancing vertical flow. Furthermore, water molecules facilitate the volume recovery of fused glass following scratching. The transfer of hydrogen (H) atoms within the fused glass, facilitated by Si–O–H⋯O–Si structures, plays a crucial role in promoting volume recovery. The ultimate density distribution of fused glass results from a combination of atomic displacement during scratching and subsequent volume recovery. This study enhances our atomic-level understanding of densification generation in fused glass
Phylogenetic analysis of C, M, N, and U genomes and their relationships with Triticum and other related genomes as revealed by LMW-GS genes at Glu-3 loci
No full textStudy of provenance character on ancient porcelain of Yue Kiln at Silongkou with NAA
No full textChemical composition is an important information of studying the provenance character of ancient pottery and porcelain. The ancient celadon samples produced in Later Tang (850-907 A.D.) to Former Southern Song (1127-1279 A.D.) have been analyzed with NAA. Its provenance characteristic was compared with that of Hongzhou Kiln of Jiangxi Province and Yaozhou Kiln of Shanxi Province in this paper. The experimental data were studied with statistic methods. The results indicated that the chemical compositions of ancient porcelain body samples made in three kilns were different. The difference is able to be identified. The porcelain body materials of both Silongkou Yue Kiln and Hongzhou Kiln were similar. The samples of Yaozhou kiln in north of China existed obvious difference
Estimación neuronal no paramétrica eficiente de la densidad y su aplicación a la detección de valores atípicos y anomalías
No full textilustraciones, diagramasThe main goal of this thesis is to propose efficient non-parametric density estimation methods
that can be integrated with deep learning architectures, for instance, convolutional neural
networks and transformers. A recent approach to non-parametric density estimation is neural
density estimation. One advantage of these methods is that they can be integrated with deep
learning architectures and trained using gradient descent. Most of these methods are based
on neural network implementations of normalizing flows which transform an original simpler
distribution to a more complex one. The approach of this thesis is based on a different idea
that combines random Fourier features with density matrices to estimate the underlying
distribution function. The method can be seen as an approximation of the popular kernel
density estimation method but without the inherent computational cost. Density estimation methods can be applied to different problems in statistics and machine learning. They
may be used to solve tasks such as anomaly detection, generative models, semi-supervised
learning, compression, text-to-speech, among others. This thesis explores the application of
the method in anomaly and outlier detection tasks such as medical anomaly detection, fraud
detection, video surveillance, time series anomaly detection, industrial damage detection,
among others. (Texto tomado de la fuente)El objetivo principal de esta tesis es proponer m´etodos eficientes de estimaci´on de densidad no param´etrica que puedan integrarse con arquitecturas de aprendizaje profundo, por
ejemplo, redes neuronales convolucionales y transformadores. Una aproximaci´on reciente a
la estimaci´on no param´etrica de la densidad es la estimaci´on de la densidad usando redes
neuronales. Una de las ventajas de estos m´etodos es que pueden integrarse con arquitecturas de aprendizaje profundo y entrenarse mediante gradiente descendente. La mayor´ıa de
estos m´etodos se basan en implementaciones de redes neuronales de flujos de normalizaci´on
que transforman una distribuci´on original m´as simple en una m´as compleja. El enfoque
de esta tesis se basa en una nueva idea diferente que combina caracter´ısticas aleatorias de
Fourier con matrices de densidad para estimar la funci´on de distribuci´on subyacente. El
m´etodo puede considerarse una aproximaci´on al popular m´etodo kernel density estimation,
pero sin el coste computacional inherente. Los m´etodos de estimaci´on de la densidad pueden
aplicarse a diferentes problemas en estad´ıstica y aprendizaje autom´atico. Pueden ser utilizados para resolver tareas como la detecci´on de anomal´ıas, modelos generativos, aprendizaje
semi-supervisado, compresi´on, texto a habla, entre otros. El presente trabajo se centra
principalmente en la aplicaci´on del m´etodo en tareas de detecci´on de anomal´ıas y valores
at´ıpicos como la detecci´on de anomal´ıas m´edicas, la detecci´on de fraudes, la videovigilancia,
la detecci´on de anomal´ıas en series temporales, la detecci´on de da˜nos industriales, entre otras.DoctoradoDoctor en IngenieríaMachine Learnin
A typology for urban Green Infrastructure, to guide multifunctional planning of nature-based solutions
No full textThis is the author accepted manuscript. The final version is available on open access from Elsevier via the DOI in this record Data Availability:
No data was used for the research described in the article.Urban Green Infrastructure (GI) provides multiple benefits to city inhabitants and can be an important component in nature-based solutions (NBS), but the ecosystem services that underpin those benefits are inconsistently quantified in the literature. There remain substantial knowledge gaps about the level of service supported by less studied GI types, e.g. cemeteries, or less-studied ecosystem services, e.g. noise mitigation. Decision-makers and planners in cities often face conflicting or incomplete information on the effectiveness of GI, particularly on their ability to provide a suite of co-benefits. Here, we describe a feature-based typology of GI which combines elements of land cover, land use and both ecological and social function. It is consistent with user requirements on mapping, and with the needs of models which can conduct more detailed ecosystem service assessments which can guide NBS design. We provide an evidence synthesis based on published literature, which scores the ability of each GI type to deliver a suite of ecosystem services. In the multivariate analysis of the typology scores, the main axis of variation differentiates between constructed (or hybrid) GI types designed primarily for water flow management (delivering relatively few services) and more natural green GI with trees, or blue GI such as lakes and the sea, which deliver a more multi-functional set of regulating services. The most multi-functional GI on this axis also score highest for biodiversity. The second element of variation separates those GI which support very few cultural services and those which score highly in enabling physical wellbeing and social interaction and, to a lesser extent, restoring capacities. Together the typology and multi-functionality matrix provide a much needed assessment for less studied GI types, and allow planners and decision-makers to make a-priori assessments of the relative ability of different GI as part of NBS to address urban challenges.European CommissionMinistry of Science and Technology of ChinaNatural Environment Research Council (NERC
