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Underwater segmentation technology achieves effective monitoring of fish biological information through accurate identification of fish species and precise estimation of their quantities. It serves as an effective approach to enhance the informatization level of aquaculture and promote intelligent management in fisheries. However, the complex and diverse underwater environment, coupled with poor visibility, results in blurry and lower-quality underwater fish images. The current image segmentation methods, when applied to fish segmentation, exhibit low accuracy and inadequate generalization capabilities. This paper proposes an image segmentation model based on Atrous Spatial Pyramid Pooling (ASPP) to address these challenges. The model aims to improve fish feature extraction and enhance the segmentation precision of fish images in complex underwater environments, thereby enhancing the accuracy and generalization capabilities of existing fish segmentation models. First, a multiscale feature extraction module (triangular atrous spatial pyramid multifeature fusion) based on dilated convolutional spatial pyramid pooling, which enhances the extraction of high-level semantic features of images through the triangular combination of multilayer dilated convolutional pyramid pooling and the adaptive channel attention module, is proposed. Second, a spatial attention module based on strip pooling (atrous strip pooling) is proposed, which further expands the receptive field of the attention mechanism by combining different expansion rates, enhances the correlations between pixels, and effectively captures spatial information. Finally, a decoder module based on multilayer semantic feature fusion is proposed. Through the processing and fusion of medium-, low-, and high-level semantic features, the model understands image content and performs accurate pixel-level segmentation. The proposed model is evaluated using a VOC-compliant dataset created from underwater fish images and validated against public and specific underwater fish datasets. The results demonstrate the successful application of the feature extraction and feature fusion modules in underwater fish image segmentation, achieving an average Intersection over Union (MIoU) of 85.49% in segmentation tasks. Compared to conventional segmentation models, the proposed model shows significant improvements, with an average increase of 3.8% in MIoU and 2.5% in balanced F-score (F1-score). The accurate segmentation of underwater fish images and extraction of vital biological information by this model provide a solid foundation for intelligent monitoring, including fish length measurement, weight estimation, and analysis of growth and health status. Moreover, the model offers a scientific framework for decision-making in aquaculture, driving advancements in precision and intelligent management practices in the field. [ABSTRACT FROM AUTHOR]

In recent years, various stakeholders and political decision-makers have recognized the significance of high-quality urban sound environments, stressing the need for user-centered trajectories. Despite the rising interest in this field, the soundscape approach has not yet fully permeated urban planning and design, possibly due to a lack of comprehensible guidelines on how to implement and curate successful soundscape designs, attributed to on-going developments on this subject. In the course of the Catalogue of Soundscape Interventions (CSI) Project, a taxonomy of eight dimensions was developed to serve as an orientation aid for practitioners, describing important aspects of soundscape-related measures that can be used as a brief to facilitate communication between authorities, consultants, and researchers. This study describes the theoretical framework and, in particular, the sequential coding process involved in deriving these dimensions, which is based on grounded theory. It lists observations and limitations of the resulting taxonomy and builds upon these findings to critically review and revisit existing nomenclature and concepts. Finally, a qualitative distinction in the form of a design pyramid according to ascending levels of epistemic rigor is proposed, to differentiate between documented practices, which may serve as a reference point for future harmonization and standardization.

Gromov introduced two distance functions, the box distance and the observable distance, on the space of isomorphism classes of metric measure spaces and developed the convergence theory of metric measure spaces. We investigate several topological properties on the space equipped with these distance functions toward a deep understanding of convergence theory. [ABSTRACT FROM AUTHOR]

Five different solar stills (SSs) have been tested in this experimental study. Hemispherical SS, tubular SS, pyramid SS, double slope SS, and conventional SS are the five SS systems. The primary goal is to determine which SS will perform more effectively at the testing location. Initially, the SSs were evaluated with no changes and the outcomes were contrasted with those of the conventional SS. Secondly, exterior reflectors have been added to four SSs. Third, hemispherical SS and tubular SS with reflectors have been utilized with phase change material (PCM) combined with Ag-Nanoparticles. Lastly, tests using reflectors, fan, and external condensers have been conducted on the hemispherical SS and tubular SS. The findings showed that the increase in productivity for hemispherical SS, tubular SS, pyramid SS, and double slope SS without modifications are 107 %, 97 %, 66.5 %, and 30 % greater than that of conventional SS, respectively. Furthermore, the results depicted that employing reflectors improved the productivity rise for tubular SS, hemispherical SS, pyramid SS, and double slope SS to be higher than conventional SS's productivity by 168 %, 153 %, 113 %, and 85 %, respectively. Besides, the effect of employing PCM-Ag with hemispherical SS and tubular SS (with reflector) is increasing their productivity to be 202 % and 212 % higher than conventional SS's production, respectively. Also, the hemispherical SS and tubular SS with reflector, fan, and a condenser showed 217 % and 236 % higher productivity than conventional SS. The actual cost of producing water for the conventional SS, hemispherical SS, and tubular SS with reflectors and fan is 0.0244, 0.012, and 0.013 $/L, respectively.

Low-light image enhancement is a low-level vision task. Most of the existing methods are based on convolutional neural network(CNN). Transformer is a predominant deep learning model that has been widely adopted in various fields, such as natural language processing and computer vision. Compared with CNN, transformer has the ability to capture long-range dependencies to make full use of global contextual information. For low-light enhancement tasks, this capability can promote the model to learn the correct luminance, color and texture. We try to introduce transformer into the low-light image enhancement field. In this paper, we design a pyramid transformer with cross-shaped windows (CSwin-P). CSwin-P contains an encoder and decoder. Both the encoder and decoder contain several stages. Each stage contains several enhanced CSwin transformer blocks (ECTB). ECTB uses cross-shaped window self-attention and a feed-forward layer with spatial interaction unit. Spatial interaction unit can further capture local contextual information through gating mechanism. CSwin-P uses implicit positional encoding, and the model is unrestricted by the image size in the inference phase. Numerous experiments prove that our method is superior to the current state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In view of the current semantic segmentation can not accurately identify image edges and small objects, and simple fusion of multi-stage features will cause information redundancy, confusion and other problems, this paper proposed a Pre-guidanced multi-stage feature fusion network (PGMFFNet). PGMFFNet employed a encoder-decoder structure, at the encoder stage, which used a pre-guidance module to guide the information in each stage. Strengthened the relationship between the features of each stage, and solved the semantic confounding problems in the subsequent fusion process of the features of each stage. At the decoder stage, which used the multi-path up-pyramid sampling module to fuse high-level semantic features, and then used the improved dense void space pyramid pool module to further expand the sensory field of the fused features, and finally fused the feature information of high and low levels to make the segmentation effect of small objects better. This paper verified PGMFFNet on CityScapes open data set,and the mean intersection over union (MIoU) obtained to 78.38%, showing good segmentation effect. [ABSTRACT FROM AUTHOR]

Crowd counting provides an important foundation for public security and urban management. Due to the existence of small targets and large density variations in crowd images, crowd counting is a challenging task. Mainstream methods usually apply convolution neural networks (CNNs) to regress a density map, which requires annotations of individual persons and counts. Weakly-supervised methods can avoid detailed labeling and only require counts as annotations of images, but existing methods fail to achieve satisfactory performance because a global perspective field and multi-level information are usually ignored. We propose a weakly-supervised method, DTCC, which effectively combines multi-level dilated convolution and transformer methods to realize end-to-end crowd counting. Its main components include a recursive swin transformer and a multi-level dilated convolution regression head. The recursive swin transformer combines a pyramid visual transformer with a fine-tuned recursive pyramid structure to capture deep multi-level crowd features, including global features. The multi-level dilated convolution regression head includes multi-level dilated convolution and a linear regression head for the feature extraction module. This module can capture both low- and high-level features simultaneously to enhance the receptive field. In addition, two regression head fusion mechanisms realize dynamic and mean fusion counting. Experiments on four well-known benchmark crowd counting datasets (UCF_CC_50, ShanghaiTech, UCF_QNRF, and JHU-Crowd++) show that DTCC achieves results superior to other weakly-supervised methods and comparable to fully-supervised methods. [ABSTRACT FROM AUTHOR]

Abstract Crowd counting provides an important foundation for public security and urban management. Due to the existence of small targets and large density variations in crowd images, crowd counting is a challenging task. Mainstream methods usually apply convolution neural networks (CNNs) to regress a density map, which requires annotations of individual persons and counts. Weakly-supervised methods can avoid detailed labeling and only require counts as annotations of images, but existing methods fail to achieve satisfactory performance because a global perspective field and multi-level information are usually ignored. We propose a weakly-supervised method, DTCC, which effectively combines multi-level dilated convolution and transformer methods to realize end-to-end crowd counting. Its main components include a recursive swin transformer and a multi-level dilated convolution regression head. The recursive swin transformer combines a pyramid visual transformer with a fine-tuned recursive pyramid structure to capture deep multi-level crowd features, including global features. The multi-level dilated convolution regression head includes multi-level dilated convolution and a linear regression head for the feature extraction module. This module can capture both low- and high-level features simultaneously to enhance the receptive field. In addition, two regression head fusion mechanisms realize dynamic and mean fusion counting. Experiments on four well-known benchmark crowd counting datasets (UCF_CC_50, ShanghaiTech, UCF_QNRF, and JHU-Crowd++) show that DTCC achieves results superior to other weakly-supervised methods and comparable to fully-supervised methods.

The third part of the article proceeds from the starting points in the decision-making problem (DMP) specified in the first stages of research [1, 2]. Here, we continue to compare the predominance (first of all, quality) of autoclave and non-autoclave alternative technological processes (ATP) as part of the stages of TP(e) with a linear algorithm of end-to-end action in the manufacture of carbon fiber (CF) aircraft structures (AC) such as highly loaded wing stringer panels (HLS) of B787, A350, MC-21, CSeries mainline aircraft as a component of hierarchical systems. To describe and study them, the following were involved: initial technological and verbal models, technological and mathematical model of an autonomous dynamic system (ADS) and a number of two-dimensional manifolds (topology) to it in the form of technological and geometric models. The article continues the study of development in this direction by approximating manifolds by polyhedra. Including co-cellular and double-formed structures - pentagonal pyramids and bipyramids, as well as bodies of revolution around them - cone and bicon. Examples of schematic and technological interpretive modeling are presented. The methodological basis is the main provisions of decision-making theory, factor analysis and system-process approach with the involvement of practice results (expert analysis), first of all, the definition of technological factors of processes with criterion assessments of the advantages, components of alternatives in their competitive strategies. [ABSTRACT FROM AUTHOR]

We prove that eight dihedral angles in a pyramid with an arbitrary quadrilateral base always sum up to a number in the interval (3π, 5π). Moreover, for any number in (3π, 5π) there exists a pyramid whose dihedral angle sum is equal to this number, which means that the lower and upper bounds are tight. Furthermore, the improved (and tight) upper bound 4π is derived for the class of pyramids with parallelogramic bases. This includes pyramids with rectangular bases, often used in finite element mesh generation and analysis. [ABSTRACT FROM AUTHOR]

Video super-resolution(videoSR) usually involves several steps: motion estimation, motion compensation, fusion, and upsampling. Here, we propose a novel architecture for video SR. First, in place of motion estimation and compensation, this architecture is based on a specially designed deformable convolution shared-assignment network. The model does not require warp operation and uses a three-layer pyramid deformable convolution network. Second, inspired by the idea of back-projection and Encoder-Decoder structure, we propose a deep recursive fusion network that fuses multi-frame information for the target frame. The fusion network adopts a Decoder-Encoder structure with shared weights to construct the back-projection network, and concatenates the output of each back-projection layer. This design not only reduces the network requirements, but also deepens the network structure so that it can extract deeper image features and achieve fusion. Extensive evaluations and comparisons with previous methods validate the strengths of this approach and demonstrate that the proposed framework is able to significantly outperform the current state of the art. [ABSTRACT FROM AUTHOR]

Background: Evidence-based medicine (EBM), as originally conceived, used all types of peer-reviewed evidence to guide medical practice and decision-making. During the SARS-CoV-2 Coronavirus disease (COVID-19) pandemic, the standard usage of EBM, modeled by the Evidence-Based Medicine Pyramid, undermined EBM by incorrectly using pyramid levels to assign relative quality. The resulting pyramid-based thinking is biased against reports both in levels beneath randomized control trials (RCTs) and those omitted from the pyramid entirely. Thus, much of the evidence was ignored. Our desire for a more encompassing and effective medical decision-making process to apply to repurposed drugs led us to develop an alternative to the EBM Pyramid for EBM. Herein, we propose the totality of evidence (T-EBM) wheel. Objectives: To create an easily understood graphic that models EBM by incorporating all peer-reviewed evidence that applies to both new and repurposed medicines, and to demonstrate its potential utility using ivermectin as a case study. Methods: The graphics were produced using Microsoft Office Visio Professional 2003 except for part of the T-EBM wheel sunburst chart, which was produced using Microsoft 365 Excel. For the case study, PubMed® was used by searching for peer-reviewed reports containing “ivermectin” and either “covid” or “sars” in the title. Reports were filtered for those using ivermectin-based protocols in the treatment of COVID-19. The resulting 265 reports were evaluated for their study design types and treatment outcomes. The three-ringed graphical T-EBM wheel was composed of two inner rings showing all types of reports and an outer ring showing outcomes for each type. Findings-Conclusions: The T-EBM wheel avoids the biases of the EBM Pyramid and includes all types of reports in the pyramid along with reports such as population and mechanistic studies. In both early and late stages of medical emergencies, pyramid-based thinking may overlook indications of efficacy in regions of the T-EBM wheel beyond RCTs. This is especially true when searching for ways to prevent and treat a novel disease with repurposed therapeutics before RCTs, safety assessments, and mechanisms of action of novel therapeutics are established. As such, T-EBM Wheels should replace the EBM Pyramids in medical decision-making and education. T-EBM Wheels can be expanded upon by implementing multiple outer rings, one for each different kind of outcome (efficacy, safety, etc.). A T-EBM Wheel can be created for any proprietary or generic medicine. The ivermectin (IVM) T-EBM Wheel displays the efficacy of IVM-based treatments of COVID-19 in a color-coded graphic, visualizing each type of evidence and the proportions of each of their outcomes (positive, inconclusive, negative).

A novel wafer-scale silicon fractal fabrication method is presented here for forming pyramids only in the lateral direction using the crystal orientation of silicon. Fractals are fabricated in silicon by masking only the corners (corner lithography) of a cavity in silicon with silicon nitride, where the shape is determined by the crystal {111} planes of the silicon. The octahedral cavity shaped by the {111} planes was previously only used for forming octahedral fractals in all directions, but by using a planar silicon dioxide hard-mask on a silicon (100) wafer, the silicon octahedral cavity is "cut in half". This creates a pyramid with sharper edges and vertices at its base than those determined by just the {111} planes. This allows selective corner lithography patterning at the vertices of the base while leaving the apex unpatterned, leading to lateral growing of pyramidal fractals. This selective patterning is shown mathematically and then demonstrated by creating a fractal of four generations, with the initial pyramid being 8 µm and the two final generations being of submicron size. [ABSTRACT FROM AUTHOR]

Named entity recognition (NER) is one of the widely studied natural language processing tasks in recent years. Conventional solutions treat the NER as a sequence labeling problem, but these approaches cannot handle nested NER. This is due to the fact that nested NER refers to the case where one entity contains another entity and it is not feasible to tag each token with a single tag. The pyramid model stacks L flat NER layers for prediction, which subtly enumerates all spans with length less than or equal to L. However, the original model introduces a block consisting of a convolutional layer and a bidirectional long short-term memory (Bi-LSTM) layer as the decoder, which does not consider the dependency between adjacent inputs and the Bi-LSTM cannot perform parallel computation on sequential inputs. For the purpose of improving performance and reducing the forward computation, we propose a Multi-Head Adjacent Attention-based Pyramid Layered model. In addition, when constructing a pyramid structure for span representation, the information of the intermediate words has more proportion than words on the two sides. To address this imbalance in the span representation, we fuse the output of the attention layer with the features of head and tail words when doing classification. We conducted experiments on nested NER datasets such as GENIA, SciERC, and ADE to validate the effectiveness of our proposed model. [ABSTRACT FROM AUTHOR]

To solve the ghosting artifacts problemin dynamic scenemulti-scale exposure fusion, an improvedmulti-exposure fusionmethod has been proposed without ghosting based on the exposure fusion framework and the color dissimilarity feature of this study. This fusion method can be further applied to power system monitoring and unmanned aerial vehicle monitoring. In this study, first, an improved exposure fusion framework based on the camera responsemodel was applied to preprocess the input image sequence. Second, the initial weight map was estimated bymultiplying four weight items. In removing the ghosting weight term, an improved color dissimilarity feature was used to detect the object motion features in dynamic scenes. Finally, the improved pyramid model as adopted to retain detailed information about the poor exposure areas. Experimental results indicated that the proposed method improves the performance of images in terms of sharpness, detail processing, and ghosting artifacts removal and is superior to the five existing multi-exposure image fusion (MEF) methods in quality evaluation. [ABSTRACT FROM AUTHOR]

Recent transformer-based models, especially patch-based methods, have shown huge potentiality in vision tasks. However, the split fixed-size patches divide the input features into the same size patches, which ignores the fact that vision elements are often various and thus may destroy the semantic information. Also, the vanilla patch-based transformer cannot guarantee the information communication between patches, which will prevent the extraction of attention information with a global view. To circumvent those problems, we propose an Efficient Attention Pyramid Transformer (EAPT). Specifically, we first propose the Deformable Attention, which learns an offset for each position in patches. Thus, even with split fixed-size patches, our method can still obtain non-fixed attention information that can cover various vision elements. Then, we design the Encode-Decode Communication module (En-DeC module), which can obtain communication information among all patches to get more complete global attention information. Finally, we propose a position encoding specifically for vision transformers, which can be used for patches of any dimension and any length. Extensive experiments on the vision tasks of image classification, object detection, and semantic segmentation demonstrate the effectiveness of our proposed model. Furthermore, we also conduct rigorous ablation studies to evaluate the key components of the proposed structure. [ABSTRACT FROM AUTHOR]

Copyright of Bulletin of the Center Papyrological Studies (BCPS) is the property of Ain Shams University, Faculty of Archaeology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Imago (22549633) is the property of IMAGO Revista de Emblematica y Cultura Visual and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The potential solution offered by floating solar still (FSS) to water scarcity confronts challenges associated with condensate droplet loss and structural instability under oceanic and swaying condition. A novel pyramid FSS with reduced wettability surfaces (silicon nano-coated) was proposed, utilizing central symmetry structure of pyramid to reinforce the stability and adopting nano-coated surfaces to enhance the condensation. A 4-day outdoor experiment was conducted to evaluate the performance. The highest yield of nano-coated FSS was 1.017 kg/m2/d under an accumulated irradiation of 15.166 MJ/m2/d, which was 16.76 % higher than that of uncoated FSS. Furthermore, the yield of nano-coated FSS was 0.949 kg/m2/d in the river experiment, showing a significant improvement. Next, the enhancement of nano-coated surface on droplet collection was numerically analyzed, showing that the condensation droplets could retain their complete shape until detachment. In contrast, the detachment process on the uncoated surface involved a separation of neck, leaving a residue of liquid behind. Quantitative analysis showed longer movement distances and faster travel velocities for droplets on the nano-coated surface, and these two improvements could facilitate condensate collection. Furthermore, the cost analysis reveals a CPL of 0.129 $/L for the proposed FSS, suggesting its affordability for off-grid communities. [ABSTRACT FROM AUTHOR]

Damage identification in post-earthquake reinforced concrete (RC) structures based on semantic segmentation has been recognized as a promising approach for rapid and non-contact damage localization and quantification. In damage segmentation tasks, damage regions are often set against complex backgrounds, featuring irregular geometric boundaries and intricate textures, posing significant challenges to model segmentation performance. Additionally, the absence of public datasets exacerbates these challenges, hindering advancements in this field. In this paper, a pyramid Haar wavelet downsampling attention UNet (PHA-UNet) semantic segmentation network is proposed, and a database containing 1400 images of damaged RC components (PEDRC-Dataset) with pixel-level annotations is established. In the proposed PHA-UNet, attention mechanisms, multiscale feature fusion, Haar wavelet downsampling, and transfer learning are introduced to address above challenges. Finally, the proposed PHA-UNet is compared with four existing image segmentation architectures on both the Cityspace and the PEDRC-Dataset. • A pyramid Haar wavelet downsampling attention UNet (PHA-UNet) semantic segmentation network is proposed. • A database containing 1400 images of damaged RC components with pixel-level annotations is established. • In the proposed PHA-UNet, attention mechanisms, multiscale feature fusion, and Haar wavelet downsampling are introduced. • Ablation studies highlight the importance of attention mechanism in damage segmentation task of RC components. [ABSTRACT FROM AUTHOR]

At schools, many teachers generally applied a conventional way of teaching mathematics. Students were only required to achieve cognitive learning success. This study aimed to develop mathematics learning media for lessons related to pyramids using the Reflective Pedagogical Paradigm (RPP) and Van Hiele theory, including the development steps and student and teacher responses to the learning process. The researchers used the research and development procedures by Borg and Gall that include (1) Preliminary studies; (2) Planning Research; (3) Design Development; (4) Limited Trial; (5) Revision of Limited Field Test Results. The learning tools developed were syllabus, lesson plans, modules, worksheets, formative tests, and attitude assessments. The data collection techniques used were observation, distributing questionnaires, interviews, and tests. Based on the validation results, the learning media got 4.19, thus categorized as "good," and students' response to the learning process reached 128.23, thus categorized as "good" as well. Students and teachers carried out learning happily and actively.

Background: The emergence of wireless capsule endoscopy (WCE) has presented a viable non-invasive mean of identifying gastrointestinal diseases in the field of clinical gastroenterology. However, to overcome its extended time of manual inspection, a computer aided automatic detection system is getting vast popularity. In this case, major challenges are low resolution and lack of regional context in images extracted from WCE videos. Methods: For tackling these challenges, in this paper a convolution neural network (CNN) based architecture, namely RAt-CapsNet, is proposed that reliably employs regional information and attention mechanism to classify abnormalities from WCE video data. The proposed RAt-CapsNet consists of two major pipelines: Compression Pipeline and Regional Correlative Pipeline. In the compression pipeline, an encoder module is designed using a Volumetric Attention Mechanism which provides 3D enhancement to feature maps using spatial domain condensation as well as channel-wise filtering for preserving relevant structural information of images. On the other hand, the regional correlative pipeline consists of Pyramid Feature Extractor which operates on image driven feature vectors to generalize and propagate local relationships of pixels from WCE abnormalities with respect to the normal healthy surrounding. The feature vectors generated by the pipelines are then accumulated to formulate a classification standpoint. Results: Promising computational accuracy of mean 98.51% in binary class and over 95.65% in multi-class are obtained through extensive experimentation on a highly unbalanced public dataset with over 47 thousand labelled. Conclusion: This outcome in turn supports the efficacy of the proposed methodology as a noteworthy WCE abnormality detection as well as diagnostic system.

Megaproject social responsibility (MSR) has received a great deal of attention from both academics and practitioners. However, as a very broad and complex concept, MSR requires an in-depth investigation of its components, driving forces, and contingent factors. Thus, this study aims to explore the climbing process across different levels of MSR from the perspectives of external stakeholders and project complexity. This study first establishes a pyramid framework for analyzing the different components and levels of MSR by leveraging stakeholder theory. Then, drawing upon attention- and capability-based views, theoretical development and empirical analyses are carried out to validate the influence of external stakeholders on participating organizations' MSR and the moderating effects of project complexity. Using a set of survey data from Chinese megaprojects, the empirical findings demonstrate that the positive influence of external stakeholders and the negative moderating effect of project complexity become salient when ascending the pyramid of MSR. The value of this study lies in the way in which it considers the climbing process based on a hierarchical framework of MSR. The theoretical framework and empirical findings offer both project managers and policy makers with new insights into how to govern the diverse social responsibility issues in megaproject construction and management. [ABSTRACT FROM AUTHOR]

Summary: Polyhydride electrocatalysts integrated directly into metal substrates have attracted attention because they can provide high electrochemical specific surface area and excellent mechanical properties. In this work, through the classical hydrothermal method and subsequent phosphating procedure, we synthesized for the first time an electrocatalyst with a pyramid‐shaped MnCO3 as the main structure, P and Co as co‐doping elements, named Co, P‐MnCO3, and used it as hydrogen evolution reaction (HER). The experimental results show that the excellent catalytic activity in alkaline media, with current densities of 10 and 200 mA∙cm−2 at 61 mV and 158 mV and low overpotentials, which are better than P‐Co (108/234 mV) and P‐MnCO3 (175 mV/340 mV), etc. it has more electrochemically active sites, faster charge transfer rate and lower resistance. The excellent electrochemical properties benefit from the unique highly active binary Co, P‐MnCO3 pyramid brick sites shortened the ion transfer distance and optimized synergistic effect between the doped metal elements (Co,P between MnCO3), the porous connection between Co, P‐MnCO3 and the high conductivity substrate, increased the electron‐transfer efficiency between the substrate and material, accelerating the conversion of H+ to H2; facilitated the release of bubbles and provided excellent mechanical properties for improved catalyst stability. This work provides a new idea for the synthesis of multi‐element electrocatalysts. [ABSTRACT FROM AUTHOR]

Local maxima and minima of a Dyck path are called peaks and valleys, respectively. A Dyck path is called restricted d -Dyck if the difference between any two consecutive valleys is at least d (right-hand side minus left-hand side) or if it has at most one valley. In this paper, we use several techniques to enumerate some statistics over this new family of lattice paths. For instance, we use the symbolic method, the Chomsky–Schűtzenberger methodology, Zeilberger's creative telescoping method, recurrence relations, and bijective relations. We count, for example, the number of paths of length 2 n , the number of peaks, the number of valleys, the number of peaks of a fixed height, and the area under the paths. We also give a bijection between the restricted d -Dyck paths and a family of binary words. [ABSTRACT FROM AUTHOR]

This study aims to develop and produce a mathematics student worksheet based on guided discovery to facilitate the understanding concept and the curiosity of eighth grade students on prism and pyramid material. The type of research is research and development using the Richey and Klein development procedure, which consists of the planning, production, and evaluation. Mathematics student worksheet assessment for material and media experts. Material and media experts fill out the mathematics student worksheet assessment. The research data analysis technique is product quality analysis. According to the evaluation of material experts and media experts, the mathematics student worksheet is very good. Based on the results of the study, it found that the quality of the mathematics student worksheet based on guided discovery was classified as very good with the specification of the average score of the material expert validator assessment of 194.5 from the ideal maximum score of 208 and classified in the very good category with the specification of the average score of the media expert validator assessment of 57 from the ideal maximum score of 72. Based on this, the product of the guided discovery-based math worksheets is declared valid and can use in the learning process.

The most effective way to improve the optical properties of silicon-based solar cells is to form the textures on their surface. In this paper, the authors studied the influence of geometric sizes of periodical pyramidal textures, which are formed on the surface of a silicon-based solar cell, on its photoelectric properties. Through optics theories, it was determined that the angle at the base of the pyramid should be equal to 73°7ʹ12ʺ. But, using the Sentaurus TCAD program, it was found that the angle at the base of pyramid should be 70°21ʹ0ʺ, in order to reach the maximum efficiency. Because the model takes into account all the electric, optic and thermic properties of the solar cell. The modeling identified that the output power of the simple planar silicon-based solar cell was equal to 6.13 mW/cm2, the output power of the solar cell, which was covered with the pyramidal texture with height of 1.4 μm, was equal to 10.62 mW/cm2. It was found that the efficiency of the solar cell increases by 1.6 times, when it is covered with pyramids with the angle at the base of pyramid equal to 70°21ʹ0ʺ.

This work deals with real conditions testing of two Fresnel lens-based optical concentrators for concentrator photovoltaic systems (CPV). Particular attention is paid to validate the performances of the secondary optical elements (SOEs), namely pyramid and cone, both made from highly transparent fused silica and mounted with a poly methyl methacrylate (PMMA) Fresnel lens as a primary optical element (POE). The effet of the focal distance of the POE on the main optical characteristics is analyzed by simulation and the corresponding results and behavior are discussed in details. Prototypes based on Fresnel lens having 75 and 100mm in diameter and pyramid- and cone-based secondary optical elements have been simulated, fabricated and tested. Optical and electrical characterization procedures are described in details. The optical efficiency, acceptance angle, spatial homogeneity of the output power and electrical performances of the fabricated CPV units are measured and are in a good agreement with simulation data. Results show that the pyramid-type concentrator gives the best optical and electrical performances. The electric efficiency achieved by the pyramid-based concentrators reaches 30% which make it able to reach the highest standards of CPV technology performances. A large acceptance angle of 1.35° is measured as one of the highest value reported in the literature for systems with pyramid as a SOE. A good agreement between simulation and experiment results has been obtained, confirming the performance expected from a CPV system having the same secondary optical element with a larger primary optical element.

Although graph convolutional networks (GCNs) have shown their demonstrated ability in skeleton-based action recognition, both the spatial and the temporal connections rely too much on the predefined skeleton graph, which imposes a fixed prior knowledge for the aggregation of high-level semantic information via the graph-based convolution. Some previous GCN-based works introduced dynamic topology (vertex connection relationships) to capture flexible spatial correlations from different actions. Then, the local relationships from both the spatial and temporal domains can be captured by diverse GCNs. This paper introduces a more straightforward and more effective backbone to obtain the spatial-temporal correlation between skeleton joints with a local-global alternation pyramid architecture for skeleton-based action recognition, namely the pyramid spatial-temporal graph transformer (PGT). The PGT consists of four stages with similar architecture but different scales: graph embedding and transformer blocks. We introduce two kinds of transformer blocks in our work: the spatial-temporal transformer block and joint transformer block. In the former, spatial-temporal separated attention (STSA) is proposed to calculate the connection of the global nodes of the graph. Due to the spatial-temporal transformer block, self-attention can be performed on skeleton graphs with long-range temporal and large-scale spatial aggregation. The joint transformer block flattens the tokens in both the spatial and temporal domains to jointly capture the overall spatial-temporal correlations. The PGT is evaluated on three public skeleton datasets: the NTU RGBD 60, NTU RGBD 120 and NW-UCLA datasets. Better or comparable performance with the state of the art (SOTA) shows the effectiveness of our work. [ABSTRACT FROM AUTHOR]

Although transformer has achieved great progress on computer vision tasks, the scale variation in dense image prediction is still the key challenge. Few effective multi-scale techniques are applied in transformer and there are two main limitations in the current methods. On the one hand, self-attention module in vanilla transformer fails to sufficiently exploit the diversity of semantic information because of its rigid mechanism. On the other hand, it is difficult to build attention and interaction among different levels due to the heavy computational burden. To alleviate this problem, we first revisit multi-scale problem in dense prediction, verifying the significance of diverse semantic representation and multi-scale interaction, and exploring the adaptation of transformer to pyramidal structure. Inspired by these findings, we propose a novel Semantic-aware Decoupled Transformer Pyramid (SDTP) for dense image prediction, consisting of Intra-level Semantic Promotion (ISP), Cross-level Decoupled Interaction (CDI) and Attention Refinement Function (ARF). ISP explores the semantic diversity in different receptive space through more flexible self-attention strategy. CDI builds the global attention and interaction among different levels in decoupled space which also solves the problem of heavy computation. Besides, ARF is further added to refine the attention in transformer. Experimental results demonstrate the validity and generality of the proposed method, which outperforms the state-of-the-art by a significant margin in dense image prediction tasks. Furthermore, the proposed components are all plug-and-play, which can be embedded in other methods. [ABSTRACT FROM AUTHOR]

Fast and accurate MRI image reconstruction from undersampled data is crucial in clinical practice. Deep learning based reconstruction methods have shown promising advances in recent years. However, recovering fine details from undersampled data is still challenging. In this paper, we introduce a novel deep learning based method, Pyramid Convolutional RNN (PC-RNN), to reconstruct images from multiple scales. Based on the formulation of MRI reconstruction as an inverse problem, we design the PC-RNN model with three convolutional RNN (ConvRNN) modules to iteratively learn the features in multiple scales. Each ConvRNN module reconstructs images at different scales and the reconstructed images are combined by a final CNN module in a pyramid fashion. The multi-scale ConvRNN modules learn a coarse-to-fine image reconstruction. Unlike other common reconstruction methods for parallel imaging, PC-RNN does not employ coil sensitive maps for multi-coil data and directly model the multiple coils as multi-channel inputs. The coil compression technique is applied to standardize data with various coil numbers, leading to more efficient training. We evaluate our model on the fastMRI knee and brain datasets and the results show that the proposed model outperforms other methods and can recover more details. The proposed method is one of the winner solutions in the 2019 fastMRI competition. [ABSTRACT FROM AUTHOR]

Soybean cyst nematode (SCN, Heterodera glycines Ichinohe) is one of the most economically destructive pathogens. The soybean line Zhongpin03-5373 (ZP), which combines resistance genes from several donors, is highly resistant to SCN race 3 (SCN3). In our previous study, two QTL (rhg1 and GmSNAP11) were identified in a population of recombinant inbred lines derived from a cross between ZP and the susceptible parent Zhonghuang 13. The two QTL explained around one-third of the resistance, suggesting the presence of further QTL contributing to SCN resistance. In the present study, we used an improved version of the genetic map comprising the previously applied 1062 molecular markers and 47 newly developed InDel (insertion-deletion) markers. The improved map revealed a novel locus contributing to SCN3 resistance: qSCN3-1, flanked by InDel marker InDel1-7 and SNP marker Map-0047, explained 4.55% of the phenotypic variance for resistance to SCN3 and was not involved in digenic epistatic interaction with rhg1 and GmSNAP11. Haplotypes of Map-0047_CAPS (a CAPS marker developed for Map-0047) and InDel1-7 were significantly associated with SCN3 resistance in a panel of 209 resistant and susceptible accessions. Using further allele-combination analysis for three functional markers representing three cloned resistance genes (rhg1, Rhg4, and GmSNAP11) and two markers flanking qSCN3-1, we found that adding the resistance allele of qSCN3-1 greatly increased soybean resistance to SCN, even in diverse genetic backgrounds. The qSCN3-1 locus will be useful for marker-assisted polygene pyramid breeding and should be targeted for the future identification of candidate genes.

The efficiency of solar cells strongly depends on their thickness. Therefore, it is important to study the dependence of the photoelectric parameters of solar cells on the thickness. In the work, silicon solar cells with various pyramidal textures are chosen as objects of study, since they are most often used in industry. Silicon solar cells of three types are compared: planar, with pyramidal texture with base angles of 54° and 70.4°. It has been found that the efficiency of a textured silicon solar cell (70.4°) can reach a maximum value of 21% at a thickness of 190 µm. A solar cell (70.4°) with a thickness of 40 µm can reach 19.56% of the efficiency of a textured silicon solar cell (54°) with a thickness of 250 µm. This means that if we form textures with a 70.4° of angle at the base of pyramid on the surface of silicon solar cell, we can reduce its cost by a factor of five. [ABSTRACT FROM AUTHOR]

The wheat multi-pest resistance genes Lr67 and Lr34 are similar in that they both condition resistance to many diseases, in a non-race-specific manner, and code for cellular transporters. Lr34 plays a critical role in breeding wheat for disease resistance in large part because it interacts with other resistance genes to result in effective and durable resistance. To determine if Lr67 interacts with other resistance genes in a similar manner as Lr34 six different doubled haploid populations were developed which segregated for either Lr67 or Lr34 along with a second resistance gene, either Lr13 , Lr16 , or Lr32. The presence or absence of each of these genes in the progeny lines was determined by molecular marker analysis. These six populations were tested for leaf rust field resistance in the same environments to compare the effects of Lr34 and Lr67 alone, and in combination with Lr13 , Lr16 or Lr32. Lr67 and Lr34 significantly reduced the levels of rust severity, Lr34 showed a significant interaction with Lr13 but Lr67 did not. Both genes interacted with Lr16 , and Lr67 had a significant interaction with Lr32. This analysis demonstrates the similar effect of Lr67 , as seen with Lr34 , on the interaction with other resistance genes to give a better level of resistance than with single resistance genes. While Lr67 is not widely deployed in agriculture, it could play an important role in disease resistance in future wheat cultivars. [ABSTRACT FROM AUTHOR]

Monocular depth estimation is an undirected problem, so constructing a network to predict better image depth information is an important research topic. This paper proposes a mixed-scale Unet network (MAPUnet) with a dense atrous pyramid based on the coder-decoder structure widely used in computer vision. We innovatively introduce the Unet++ structure of the image segmentation network for depth estimation. We reset the number of convolutional layers of the network under the framework of the Unet++ network and innovatively connect the decoders densely. Moreover, by choosing the appropriate size of the atrous radius, we form a dense atrous pyramid based on different feature layers to better connect the features in the deep and shallow layers of the network. To verify the effectiveness of the proposed algorithm, we test the network on the KITTI dataset and the NYU Depth V2 dataset. We compare the network with the current state-of-the-art methods. The proposed method has higher accuracy and has steadily improved relative to the threshold of accuracy and root-mean-square error. We also conduct ablation studies, studies targeting the effectiveness of the network framework, and discussions on the convergence time and parameter complexity of the network. We will open-source the code at https://github.com/yang-yi-fan/MAPUnet.