Layout Variations Research Articles

Effect of wing height layout on the aerodynamic performance ofhigh-speed train

PurposeThis paper aims to investigate the effect of different wing height layouts on the aerodynamic performance and flow structure of high-speed train, in a train-wing coupling method with multiple tandem wings installed on the train roof.Design/methodology/approachThe improved delayed detached eddy simulation method based on shear stress transport k-ω turbulence model has been used to conduct computational fluid dynamics simulation on the train with three different wing height layouts, at a Reynolds number of 2.8 × 106. The accuracy of the numerical method has been validated by wind tunnel experiments.FindingsThe wing height layout has a significant effect on the lift, while its influence on the drag is weak. There are three distinctive vortex structures in the flow field: wingtip vortex, train body vortex and pillar vortex, which are influenced by the variation in wing height layout. The incremental wing layout reduces the mixing and merging between vortexes in the flow field, weakening the vorticity and turbulence intensity. This enhances the pressure difference between the upper and lower surfaces of both the train and wings, thereby increasing the overall lift. Simultaneously, it reduces the slipstream velocity at platform and trackside heights.Originality/valueThis paper contributes to understanding the aerodynamic characteristics and flow structure of a high-speed train coupled with wings. It provides a reference for the design aiming to achieve equivalent weight reduction through aerodynamic lift synergy in trains.

Radiation Damage on Silicon Photomultipliers from Ionizing and Non-Ionizing Radiation of Low-Earth Orbit Operations.

Silicon Photomultipliers (SiPMs) are single photon detectors that gained increasing interest in many applications as an alternative to photomultiplier tubes. In the field of space experiments, where volume, weight and power consumption are a major constraint, their advantages like compactness, ruggedness, and their potential to achieve high quantum efficiency from UV to NIR makes them ideal candidates for spaceborne, low photon flux detectors. During space missions however, SiPMs are usually exposed to high levels of radiation, both ionizing and non-ionizing, which can deteriorate the performance of these detectors over time. The goal of this work is to compare process and layout variation of SiPMs in terms of their radiation damage effects to identify the features that helps reduce the deterioration of the performance and develop the next generation of more radiation-tolerant detectors. To do this, we used protons and X-rays to irradiate several Near Ultraviolet High-Density (NUV-HD) SiPMs with small areas (single microcell, 0.2 × 0.2 mm2 and 1 × 1 mm2) produced at Fondazione Bruno Kessler (FBK), Italy. We performed online current-voltage measurements right after each irradiation step, and a complete functional characterization before and after irradiation. We observed that the main contribution to performance degradation in space applications comes from proton damage in the form of an increase in primary dark count rate (DCR) proportional to the proton fluence and a reduction in activation energy. In this context, small active area devices show a lower DCR before and after irradiation, and we propose light or charge-focusing mechanisms as future developments for high-sensitivity radiation-tolerant detectors.

Syntactic analysis of traditional houses in urban kampung

This research investigates a syntactical study and comparative analysis of the statistical and spatial characteristics of traditional houses in an urban kampung settlement, focusing on a selected case study built by a participatory Arabic community in Palembang, South Sumatra, Indonesia. This research aims to determine a formal rule basis for spatial configurations to reveal identical sociospatial structures based on syntactical analysis. This experiment measures spatial layout variation through space syntax analysis to provide a better understand of how the correlation between spatial configuration and sociospatial structures in traditional houses can be deconstructed. This syntactical analysis applies four distinctive procedures: a selected case study, data collection, statistical and graphical analysis, and graph analysis. The results indicate that the spatiality of all traditional houses in this kampung settlement highlights the spatial hierarchy order as a formal rule-based system, and approximately an average of 10% of this community is concerned with designing intelligible layouts. Rumah Batu and other dwellings have a similarity and closeness. The main dwelling’s function involves more steps to separate public and private functional rooms, but a functional transformation from a dwelling into a public facility creates a short distance for easy access by users. Additionally, this separation affects occupants, especially in terms of spatial distribution activities, movement flows, and other social phenomena. This approach provides practical and tangible benefits for preservation values related to buildings; this strategy may also change how buildings are perceived in other built environments.

Tailoring the surface topography and crystallinity of EuMnO3 films: A study on sintering effects

We obtained EuMnO3 films through a sol-gel process at varying temperatures, aiming to investigate spatial layout variations influenced by different sintering temperatures. Our results indicate that films adopt an orthorhombic structure when sintered between 800 °C and 850 °C, accompanied by a reduction in nanocrystal size during annealing. Surface analysis reveals that films processed at higher temperatures exhibit increased roughness, negative asymmetry, heightened kurtosis, elevated peak densities, and more distinct peak forms. Furthermore, these films display surfaces with greater anisotropy and lower spatial frequencies compared to those sintered at lower temperatures. Nevertheless, fractal analysis uncovers that both the EuMnO800 and EuMnO850 samples showcase heightened spatial complexity, a more uniform nanotexture, optimal surface porosity, and improved topographic consistency. The findings suggest that films sintered at 800 °C display a spatial arrangement with superior topographic qualities, rendering them potentially valuable for the advancement of semiconductor films derived from perovskite rare-earth oxides for various technological applications.

UniSparse: An Intermediate Language for General Sparse Format Customization

The ongoing trend of hardware specialization has led to a growing use of custom data formats when processing sparse workloads, which are typically memory-bound. These formats facilitate optimized software/hardware implementations by utilizing sparsity pattern- or target-aware data structures and layouts to enhance memory access latency and bandwidth utilization. However, existing sparse tensor programming models and compilers offer little or no support for productively customizing the sparse formats. Additionally, because these frameworks represent formats using a limited set of per-dimension attributes, they lack the flexibility to accommodate numerous new variations of custom sparse data structures and layouts. To overcome this deficiency, we propose UniSparse, an intermediate language that provides a unified abstraction for representing and customizing sparse formats. Unlike the existing attribute-based frameworks, UniSparse decouples the logical representation of the sparse tensor (i.e., the data structure) from its low-level memory layout, enabling the customization of both. As a result, a rich set of format customizations can be succinctly expressed in a small set of well-defined query, mutation, and layout primitives. We also develop a compiler leveraging the MLIR infrastructure, which supports adaptive customization of formats, and automatic code generation of format conversion and compute operations for heterogeneous architectures. We demonstrate the efficacy of our approach through experiments running commonly-used sparse linear algebra operations with specialized formats on multiple different hardware targets, including an Intel CPU, an NVIDIA GPU, an AMD Xilinx FPGA, and a simulated processing-in-memory (PIM) device.

Anatomic attention regions via optimal anatomy modeling and recognition for DL-based image segmentation.

Organ segmentation is a crucial task in various medical imaging applications. Many deep learning models have been developed to do this, but they are slow and require a lot of computational resources. To solve this problem, attention mechanisms are used which can locate important objects of interest within medical images, allowing the model to segment them accurately even when there is noise or artifact. By paying attention to specific anatomical regions, the model becomes better at segmentation. Medical images have unique features in the form of anatomical information, which makes them different from natural images. Unfortunately, most deep learning methods either ignore this information or do not use it effectively and explicitly. Combined natural intelligence with artificial intelligence, known as hybrid intelligence, has shown promising results in medical image segmentation, making models more robust and able to perform well in challenging situations. In this paper, we propose several methods and models to find attention regions in medical images for deep learning-based segmentation via non-deep-learning methods. We developed these models and trained them using hybrid intelligence concepts. To evaluate their performance, we tested the models on unique test data and analyzed metrics including false negatives quotient and false positives quotient. Our findings demonstrate that object shape and layout variations can be explicitly learned to create computational models that are suitable for each anatomic object. This work opens new possibilities for advancements in medical image segmentation and analysis.

Assessing a GPS-Based 6-Minute Walk Test for People With Persistent Pain: Validation Study.

The 6-minute walk test (6MWT) is a commonly used method to assess the exercise capacity of people with many health conditions, including persistent pain. However, it is conventionally performed with in-person supervision in a hospital or clinic, therefore requiring staff resources. It may also be difficult when in-person supervision is unavailable, such as during the COVID-19 pandemic, or when the person is geographically remote. A potential solution to these issues could be to use GPS to measure walking distance. The primary aim of this study was to assess the validity of a GPS-based smartphone app to measure walking distance as an alternative to the conventional 6MWT in a population with persistent pain. The secondary aim of this study was to estimate the difference between the pain evoked by the 2 test methods. People with persistent pain (N=36) were recruited to complete a conventional 6MWT on a 30-m shuttle track and a 6MWT assessed by a smartphone app using GPS, performed on outdoor walking circuits. Tests were performed in random order, separated by a 15-minute rest. The 95% limits of agreement were calculated using the Bland-Altman method, with a specified maximum allowable difference of 100 m. Pain was assessed using an 11-point numerical rating scale before and after each walk test. The mean 6-minute walk distance measured by the GPS-based smartphone app was 13.2 (SD 46; 95% CI -2.7 to 29.1) m higher than that assessed in the conventional manner. The 95% limits of agreement were 103.9 (95% CI 87.4-134.1) m and -77.6 (95% CI -107.7 to -61) m, which exceeded the maximum allowable difference. Pain increased in the conventional walk test by 1.1 (SD 1.0) points, whereas pain increased in the app test by 0.8 (SD 1.4) points. In individuals with persistent pain, the 2 methods of assessing the 6MWT may not be interchangeable due to limited validity. Potential reasons for the differences between the 2 methods might be attributed to the variation in track layout (shuttle track vs continuous circuit); poor GPS accuracy; deviations from the 30-m shuttle track; human variability in walking speed; and the potential impact of a first test on the second test due to fatigue, pain provocation, or a learning effect. Future research is needed to improve the accuracy of the GPS-based approach. Despite its limitations, the GPS-based 6MWT may still have value as a tool for remote monitoring that could allow individuals with persistent pain to self-administer frequent assessments of their functional capacity in their home environment.

Minimization of the edge modes and near fields of a travelling wave array antenna for WEST

The travelling wave array (TWA) antenna has been proposed as a promising alternative for ion cyclotron resonant heating in future fusion reactors. In this study, the possibility to make a TWA compatible with a tungsten environment like the WEST tokamak is assessed. For this purpose, two aspects of the antenna are investigated: the power spectrum and the near fields excited by the antenna. The sensitivity of these parameters to load and capacitor layout variations is taken into account while satisfying a proper antenna frequency response. The sensitivity of the power spectrum to frequency variation is also investigated to allow the possibility of fast feedback of the power deposition into the core plasma of WEST. The high resilience of the TWA to these variations is demonstrated and the main parameters of the TWA expected in WEST are compared to a current WEST antenna for the same loading. Possible optimizations of the TWA antenna are discussed. The present study is fully transferable to a fusion reactor like DEMO or ARC.

Reassessing Roman and Late Antique ‘Marbles Lanes’: One Game or Many?

Abstract Characterised by the presence of multiple depressions or pockets in a variety of arrangements, and, in some cases, the presence of a single, double, or triple ‘start line’ carved into horizontal stone surfaces, marble lanes in their variety of forms open a window onto ancient play that few have looked through. Thought to be a playing surface for some kind of throwing or rolling game which involved the use of glass or ceramic spheres, Roman marble lanes have received comparatively little attention in the recent upswing of scholarship on ancient play, partially as a result of the relative dearth of textual and iconographic sources discussing or depicting their usage, but these playing surfaces nevertheless represent a major corpus of ludic material. This contribution summarises past work on marble lanes before exploring the limited textual and iconographic source material related to playing with marbles. It offers a tentative new typology by which to categorise marble lanes and a non-exhaustive list of these playing surfaces recorded at archaeological sites around the Mediterranean. It then moves onto a discussion of the game/games that may be played on these boards, arguing that the wide variations in the different layouts for marble lanes may indicate that they were used not for one tightly-defined game, but more likely facilitated the playing of a loosely connected family of games, with implicaitons for how we think about communities of play in the past.

Space standards in affordable housing in England

ABSTRACT This paper examines the state of affordable housing in England, with a focus on regional variations in space standards and standardized dwelling layouts widely used by volume housebuilders. Space standards are not statutory and therefore adopted inconsistently across development types or building typologies in England. The study draws on data obtained from planning applications, analysing 153 housing developments and 9876 newly constructed affordable housing units from different regions in England that were completed and marketed in 2021. Based on this, the study compares space standards and their effectiveness as well as the use of standardized unit types. The analysis reveals that apart from London, the most recent Nationally Described Space Standard (2015) is not yet widely used. Instead, Housing Quality Indicators that preceded the new national standards continue to be the norm for houses built outside of London. The findings demonstrate that there is a high level of standardization in affordable housing in terms of dwelling size and layout, with widely used standard house types often determining the design and size of dwellings more than space standards.

A generative deep learning for exploring layout variation on visual poster design

Layout variation is an essential concept in design and allows designers to create a sense of depth and complexity in their work. However, manually creating layout variations can be time-consuming and limit a designer's creativity. The use of generative art as a tool for creating visual poster designs that emphasize layout variety is explored in this study. Deep learning through generative art offers a solution by using an algorithm to generate layout variations automatically. This paper uses the VQGAN and CLIP approach to describe a generative art system, which renders images via a text prompt and produces a series of variations based on the zoom parameter 0.95 and shifts the y-axis 5 pixels. Our experiment shows that one frame can be generated roughly in 10.108±0.226 seconds, significantly faster than the conventional method for creating layouts on poster design. The model achieved a good quality image, scoring 4.248 using an inception score evaluation. The layout variations can be used as a basis for poster design visuals, allowing designers to explore different visual representations of layouts. This paper demonstrates the potential of generative art to explore layout variation in visual design, offering designers a new approach to creating dynamic and engaging visual designs.

ORNAMENTAL VEGETABLE GARDEN DESIGN POSSIBILITIES USING MODULAR RAISED BEDS

Ornamental vegetable gardens have many benefits. They are aesthetic, educational, ecological, economical, sanitary, and are a source of fresh food. Small allotments in urban and peri-urban areas do not allow the implementation of utilitarian gardens. Thus, ornamental vegetable gardens are the best choice. Raised beds can be used to enhance the decorative effect of the entire vegetable garden. This type of construction presents, in turn, several advantages both for the user and crop: reduced effort for maintenance work, early heating of the substrate, loose substrate, reduced volume of weeds, efficient use of space for plants, etc. Establishing ornamental vegetable gardens on raised beds is easily achieved using geometric modules, as they can be adapted to various designs. This paper presents several design options for a decorative vegetable garden on private properties. The case studies presented showed different methods of using modular raised beds depending on the property's area, the owner's wishes, and the desired area to be planted. Also, depending on the results obtained from the experimental batch, three other variations of modular layouts with different purposes were proposed: focal point, physically closed but visually open space, and relaxation space.

Impact of Device Layout on Thermal Parameters and RF Performance of 90-nm SiGe HBTs

The present work investigates the impact of transistor layout on both the RF and thermal performance of 90-nm SiGe heterojunction bipolar transistors (HBTs). The resulting performance shows only slight degradation in small-signal RF figures-of-merit, while large-signal RF figures-of-merit relevant to power amplifiers (PAs) are improved. Thermal resistance and capacitance were extracted for five different SiGe HBT layouts with the same total emitter area. Additionally, a favorable trade-off exists between RF performance figures-of-merit and thermal parameters; namely, the thermal time constant can be reduced by up to 37% with optimal layout, with only slight degradation (up to 8%) in RF performance. These optimal device layout variations can help circuit designers mitigate thermal memory effects at the device level, thereby improving the overall large-signal linearity of PAs.

Navigating the narrative: An eye‐tracking study of readers' strategies when Reading comic page layouts

AbstractIn multimedia stimuli (e.g., comics), the reader must follow a narrative in which text and image both contribute information, and artists may use more irregular layouts which must still be followed correctly. While previous work has found that the external structure (outlines) of panels is a major contributor to navigation decisions in comics, other studies have shown that panel content can affect reading order. The present studies use eye‐tracking to investigate these contributions further. In Experiment 1, the reading behaviors on six layout variations were compared. The influence of the external structure was replicated, but an effect of text location was also found for one layout type. Experiment 2 focused on variations of this particular layout, manipulating the location of text within critical panels. Panel content was a consistent effect for all variations. While most navigation decisions are made using the external structure, content becomes key when resolving ambiguous layouts.

Multi-granularity scenarios understanding network for trajectory prediction

Understanding agents’ motion behaviors under complex scenes is crucial for intelligent autonomous moving systems (like delivery robots and self-driving cars). It is challenging duo to the inherent uncertain of future trajectories and the large variation in the scene layout. However, most recent approaches ignored or underutilized the scenario information. In this work, a Multi-Granularity Scenarios Understanding framework, MGSU, is proposed to explore the scene layout from different granularity. MGSU can be divided into three modules: (1) A coarse-grained fusion module uses the cross-attention to fuse the observed trajectory with the semantic information of the scene. (2) The inverse reinforcement learning module generates optimal path strategy through grid-based policy sampling and outputs multiple scene paths. (3) The fine-grained fusion module integrates the observed trajectory with the scene paths to generate multiple future trajectories. To fully explore the scene information and improve the efficiency, we present a novel scene-fusion Transformer, whose encoder is used to extract scene features and the decoder is used to fuse scene and trajectory features to generate future trajectories. Compared with the current state-of-the-art methods, our method decreases the ADE errors by 4.3% and 3.3% by gradually integrating different granularity of scene information on SDD and NuScenes, respectively. The visualized trajectories demonstrate that our method can accurately predict future trajectories after fusing scene information.

Uncertainty Modeling with Second-Order Transformer for Group Re-identification

Group re-identification (G-ReID) focuses on associating the group images containing the same persons under different cameras. The key challenge of G-ReID is that all the cases of the intra-group member and layout variations are hard to exhaust. To this end, we propose a novel uncertainty modeling, which treats each image as a distribution depending on the current member and layout, then digs out potential group features by random samplings. Based on potential and original group features, uncertainty modeling can learn better decision boundaries, which is implemented by two modules, member variation module (MVM) and layout variation module (LVM). Furthermore, we propose a novel second-order transformer framework (SOT), which is inspired by the fact that the position modeling in the transformer is coped with the G-ReID task. SOT is composed of the intra-member module and inter-member module. Specifically, the intra-member module extracts the first-order token for each member, and then the inter-member module learns a second-order token as a group feature by the above first-order tokens, which can be regarded as the token of tokens. A large number of experiments have been conducted on three available datasets, including CSG, DukeGroup and RoadGroup. The experimental results show that the proposed SOT outperforms all previous state-of-the-art methods.

WARGEAR: ‘Real time’ generation of detailed layout plans of surface warships during early stage design

Generating detailed warship layouts is crucial to check technical feasibility and performance consistent with emergent requirement elucidation during early stage design. However, generating feasible detailed layouts is a complex and time consuming task.Even today, detailed layout plans are often manually drawn using CAD software, taking up to 150 work hours to complete a single feasible layout plan, as found by the Netherlands Defence Materiel Organisation (DMO). As a result, the number of layout variations that can be generated and analysed is limited. This typically means that further detailed layout generation is postponed, increasing the risk of costly sizing and integration issues later in the design process. Therefore, a method that enables rapid insight into layout sizing issues is required.This paper elaborates on the mathematical working mechanisms of the WARship GEneral ARrangement (WARGEAR) tool, that has been developed to support naval architects in detailing ship arrangements to space level in a matter of minutes. Contributions are: (1) a probabilistic staircase placement algorithm, (2) a network-based approach combined with probabilistic selection for allocation of spaces to compartments, (3) the use of cross-correlation to quickly arrange spaces, and (4) a ‘carving’-based approach to ensure connectivity. A representative WARGEAR application case study is presented. This test shows how WARGEAR is able to confirm the feasibility of future warship arrangements at a high level of detail within minutes.

The Grass Is Not Greener on the Other Side: The Role of Attention in Voting Behaviour

The effect of ranking and the effect of attention both increase the chances that candidates running in the top positions of electoral lists will win voters’ support. We exploit a variation in ballot layout (the location of the break between the first and second sides of the ballot) in the 2006–2017 Czech parliamentary elections to disentangle these effects and identify the effect of attention. We show that being listed on the reverse side of the ballot paper decreases electoral support by at least 50%.

Multiregion Scale-Aware Network for Building Extraction From High-Resolution Remote Sensing Images

Building extraction is an essential task due to its relevance to urban planning and automatic surveying mapping activities. Despite the existing convolutional neural network-based methods that have achieved remarkable progress on building extraction from remote sensing images, the accurate extraction of buildings with extremely large variations of scales and layouts is still challenging. In this study, a novel multiregion scale-aware network is proposed to address these issues. The network consists of two key components. First, a multiregion attention module is proposed to capture long-range context dependencies and exploit different regions’ attention information, alleviating the interference of cluttered backgrounds and variations in building layouts. With the multiscale features generated by a backbone network as input, a graph-based scale-aware structure is designed to model and reason the interactions between different scale features to enable a better understanding of multiscale features. Extensive experiments conducted on three datasets demonstrate that the proposed method achieves superior performance compared with the other state-of-the-art methods.

TableDet: An end-to-end deep learning approach for table detection and table image classification in data sheet images

Global supply chains are kept viable through the information shared through billions of electronic documents, many of which extensively use tables to display critical information. Making effective supply chain decisions requires the extraction of data from these tables which is hindered by the variations in layouts and styles of tables. In this paper, we propose Table Det: a deep learning based methodology to solve table detection and table image classification in data sheet images in a single inference as the first stage of the table text extraction pipeline. TableDet utilizes Cascade R-CNN with Complete IOU (CIOU) loss and a deformable convolution backbone as its underlying architecture to capture the variations in scales and orientations of tables. It also detects text and figures to enhance its table detection performance. We demonstrate the effectiveness of training TableDet with a dual-step transfer learning process and fine-tuning it with Table Aware Cutout (TAC) augmentation strategy. We achieved the highest F1 score for table detection against state-of-the-art solutions on ICDAR 2013 (complete set), ICDAR 2017 (test set) and ICDAR 2019 (test set) with 100%, 99.3% and 95.1% respectively. For the table image classification task we attained 100% recall and above 85% precision on three test sets. This classification capability ensures that all images with tables would be promoted to the next step in the table text extraction pipeline, with a small number of images without tables making it through.