Explore the vast range of titles available.

Outlier detection algorithms are intimately connected with robust statistics that down-weight some observations to zero. We define a number of outlier detection algorithms related to the Huber-skip and least trimmed squares estimators, including the one-step Huber-skip estimator and the forward search. Next, we review a recently developed asymptotic theory of these. Finally, we analyse the gauge, the fraction of wrongly detected outliers, for a number of outlier detection algorithms and establish an asymptotic normal and a Poisson theory for the gauge.

The boss of a skip business operating seven HGVs has pleaded guilty to breaching environmental laws after storing skips overflowing with rubbish at his Bralntree site. Roy Brett Ignored repeated warnings about the amount of skips and waste built up at the site through his company. RJ Brett Contracts, which also admitted breaking the law. The Environment Agency said Brett claimed he \"didn't do emails\" so missed written instructions to remove the waste. Investigators found Brett stored too much wood, metal. textiles and builders' rubbish in relation to the size of his yard. There was barely any free space, with skips overflowing with more rubbish, including mattresses and soil. By January 2025 almost 50 skips filled the yard, some stacked on top of others.

Some recent studies show that filters in convolutional neural networks (CNNs) have low color selectivity in datasets of natural scenes such as Imagenet. CNNs, bio-inspired by the visual cortex, are characterized by their hierarchical learning structure which appears to gradually transform the representation space. Inspired by the direct connection between the LGN and V4, which allows V4 to handle low-level information closer to the trichromatic input in addition to processed information that comes from V2/V3, we propose the addition of a long skip connection (LSC) between the first and last blocks of the feature extraction stage to allow deeper parts of the network to receive information from shallower layers. This type of connection improves classification accuracy by combining simple-visual and complex-abstract features to create more color-selective ones. We have applied this strategy to classic CNN architectures and quantitatively and qualitatively analyzed the improvement in accuracy while focusing on color selectivity. The results show that, in general, skip connections improve accuracy, but LSC improves it even more and enhances the color selectivity of the original CNN architectures. As a side result, we propose a new color representation procedure for organizing and filtering feature maps, making their visualization more manageable for qualitative color selectivity analysis.

Ehlers-Danlos Syndrome (EDS) is a genetic condition characterized characterized by join hypermobility, skin hyperextension, and tissue fragility that affects the connective tissue and collagen structures in the human body. The prevalence has been reported as in 1 in 5000 births and affects equally in both sexes. EDS has no racial proportions. There are several types of EDS, that are based on the 2017 International Ehlers-Danlos Syndrome Classification. Thin and fragile mucosa, bleeding tendency, periodontal tissue injuries, and also tongue ghorlin syndrome has been reported as the intraoral manifestations in EDS. Another manifestation is hypermobile temporomandibular joint with high incidence of subluxation and dislocation. The mechanism of Ehlers-Danlos Syndrome is connected to collagen biosyhntesis, originating with nucleus transcription to aggregate collagen heterotrimers into large fibrils. Mutations have been found in collagen-encoding genes for several of these forms, or in genes encoding collagen-modifying enzymes. One of the most common type of EDS is classical EDS which is having type V collagen deficiency. This is caused by mutation in type V collagen-encoding gene, COL5A1 dan COL5A2. Type V collagen is a regulatory collagen fibril that forms the basis of the fibrils in bony, cartilaginous, fibrous, and tubular structures. The majority of mutations have been reported are nonsense mutations; splice site mutations leading to exon skips, missense mutations causing glycine substitutions, and frameshift mutation. As a clinician, the knowledge about the etiology, clinical sign, oral manifestation, and the genetic aspect of this syndrome is crucial for making correct diagnoses and proper treatment planning. In this review, the author will explain further about the genetic aspects of Ehlers-Danlos Syndrome.

Road network extraction is one of the significant assignments for disaster emergency response, intelligent transportation systems, and real-time updating road network. Road extraction base on high-resolution remote sensing images has become a hot topic. Presently, most of the researches are based on traditional machine learning algorithms, which are complex and computational because of impervious surfaces such as roads and buildings that are discernible in the images. Given the above problems, we propose a new method to extract the road network from remote sensing images using a DenseUNet model with few parameters and robust characteristics. DenseUNet consists of dense connection units and skips connections, which strengthens the fusion of different scales by connections at various network layers. The performance of the advanced method is validated on two datasets of high-resolution images by comparison with three classical semantic segmentation methods. The experimental results show that the method can be used for road extraction in complex scenes.

Balancing the exploration and exploitation in any nature-inspired optimization algorithm is an essential task, while solving the real-world global optimization problems. Therefore, the search agents of an algorithm always try to explore the unvisited domains of a search space in a balanced manner. The sine cosine algorithm (SCA) is a recent addition to the field of metaheuristics that finds the solution of an optimization problem using the behavior of sine and cosine functions. However, in some cases, the SCA skips the true solutions and trapped at sub-optimal solutions. These problems lead to the premature convergence, which is harmful in determining the global optima. Therefore, in order to alleviate the above-mentioned issues, the present study aims to establish a comparatively better synergy between exploration and exploitation in the SCA. In this direction, firstly, the exploration ability of the SCA is improved by integrating the social and cognitive component, and secondly, the balance between exploration and exploitation is maintained through the grey wolf optimizer (GWO). The proposed algorithm is named as SC-GWO. For the performance evaluation, a well-known set of benchmark problems and engineering test problems are taken. The dimension of benchmark test problems is varied from 30 to 100 to observe the robustness of the SC-GWO on scalability of problems. In the paper, the SC-GWO is also used to determine the optimal setting for overcurrent relays. The analysis of obtained numerical results and its comparison with other metaheuristic algorithms demonstrate the superior ability of the proposed SC-GWO.

Ski-interacting protein (SKIP) is a bifunctional regulator of gene expression that works as a splicing factor as part of the spliceosome and as a transcriptional activator by interacting with EARLY FLOWERING 7 (ELF7). MOS4-Associated Complex 3A (MAC3A) and MAC3B interact physically and genetically with SKIP, mediate the alternative splicing of c. 50% of the expressed genes in the Arabidopsis genome, and are required for the splicing of a similar set of genes to that of SKIP. SKIP interacts physically and genetically with splicing factors and Polymerase-Associated Factor 1 complex (Paf1c) components. However, these splicing factors do not interact either physically or genetically with Paf1c components. The SKIP-spliceosome complex mediates circadian clock function and abiotic stress responses by controlling the alternative splicing of pre-mRNAs encoded by clock- and stress tolerance-related genes. The SKIP-Paf1c complex regulates the floral transition by activating FLOWERING LOCUS C (FLC) transcription. Our data reveal that SKIP regulates floral transition and environmental fitness via its incorporation into two distinct complexes that regulate gene expression transcriptionally and post-transcriptionally, respectively. It will be interesting to discover in future studies whether SKIP is required for integration of environmental fitness and growth by control of the incorporation of SKIP into spliceosome or Paf1c in plants.

Among researchers using traditional and new machine learning and deep learning techniques, 2D medical image segmentation models are popular. Additionally, 3D volumetric data recently became more accessible, as a result of the high number of studies conducted in recent years regarding the creation of 3D volumes. Using these 3D data, researchers have begun conducting research on creating 3D segmentation models, such as brain tumor segmentation and classification. Since a higher number of crucial features can be extracted using 3D data than 2D data, 3D brain tumor detection models have increased in popularity among researchers. Until now, various significant research works have focused on the 3D version of the U-Net and other popular models, such as 3D U-Net and V-Net, while doing superior research works. In this study, we used 3D brain image data and created a new architecture based on a 3D U-Net model that uses multiple skip connections with cost-efficient pretrained 3D MobileNetV2 blocks and attention modules. These pretrained MobileNetV2 blocks assist our architecture by providing smaller parameters to maintain operable model size in terms of our computational capability and help the model to converge faster. We added additional skip connections between the encoder and decoder blocks to ease the exchange of extracted features between the two blocks, which resulted in the maximum use of the features. We also used attention modules to filter out irrelevant features coming through the skip connections and, thus, preserved more computational power while achieving improved accuracy.