Spatial Adaptability Research Articles

Numerical Study of Two-Stage Temperature Control Strategy for High-Temperature Stability of Satellite-Borne Fiber Optic Gyroscope

Abstract Fiber optic gyroscopes (FOGs) are widely used in attitude control systems of spacecraft such as satellites and Mars rovers for their superior spatial adaptability. However, changes in ambient temperature can cause errors in the FOG and reduce its output accuracy. In this paper, a fuzzy two-stage temperature control strategy applied to a satellite-borne three-axis integrated fiber optic gyroscope (TAIFOG) is proposed. And the control rules of the fuzzy two-stage temperature controller are described in detail. A thermodynamic model that can quickly and accurately respond to the dynamic thermal characteristics of the satellite-borne TAIFOG is also constructed based on the lumped method. The effectiveness of the proposed fuzzy two-stage temperature control strategy in improving the temperature stability of the satellite-borne TAIFOG in orbit is verified through numerical studies. Numerical results show that this fuzzy two-stage active temperature control strategy can fundamentally improve the thermal state of the satellite-borne TAIFOG and ensure that its sensitive element fiber optic coils maintain high-temperature stability while the TAIFOG is in orbit. In addition, the startup time of TAIFOG is reduced to 308 s compared to the case without active temperature control, which is a reduction of 96.27%. This active temperature control strategy is well suited for engineering applications to improve FOG output accuracy.

Analysis of the Spatial Adaptability of Gross Ecosystem Production, Gross Domestic Production, and Population Density in Chinese Mainland

As China is currently in a critical period of transition to green development, a quantitative analysis of the coupled synergy between economic growth, population migration, and environmental protection performance can provide a rational reference for the formulation of macro-policies in relevant regions. Based on these objectives, this study built a matching analysis framework between gross domestic production (GDP) density (GD), population density (PD), and gross ecosystem production (GEP) density (ED) to analyze the spatial heterogeneity of these three indicators among 362 municipal units in different regions of the Chinese Mainland from 2000 to 2020 based on satellite remote sensing images and statistical data. The spatial adaptability between them was explored by employing a center-of-gravity model. The findings of this study show that: (1) the GD, PD, and ED on the Chinese Mainland exhibited varying degrees of spatial heterogeneity on both sides of the Hu Line during the investigation period, with the general feature of being higher in the southeastern region and lower in the northwestern region; (2) the centers of gravity of GD, PD, and ED were all located in Hubei Province during the investigation period. The centers of gravity of PD and GD shifted 79.39 km and 109.72 km to the southwest, respectively, whereas the center of gravity of ED, remained relatively stable during the investigation period; and (3) the center of gravity distances between PD–GD, ED–PD, and ED–GD in 2020 were 99.31 km, 247.52 km, and 346.27 km, respectively, and the percentages of highly matched units ranked among the 362 samples were 72.93%, 23.48%, and 25.69% for GD–PD, GD–ED, and ED–PD, respectively. This study concluded that a synergistic spatial pattern of the population, economic layout, and land use on the Chinese Mainland has not yet been formed. Therefore, this study suggests that future policies should be committed to promoting the northwest Chinese Mainland movement of the center of gravity of GD and PD, as well as the southeast movement of the ED center.

High-Precision Dynamic Traffic Noise Mapping Based on Road Surveillance Video

High-precision dynamic traffic noise maps can describe the spatial and temporal distributions of noise and are necessary for actual noise prevention. Existing monitoring point-based methods suffer from limited spatial adaptability, and prediction model-based methods are limited by the requirements for traffic and environmental parameter specifications. Road surveillance video data are effective for computing and analyzing dynamic traffic-related factors, such as traffic flow, vehicle speed and vehicle type, and environmental factors, such as road material, weather and vegetation. Here, we propose a road surveillance video-based method for high-precision dynamic traffic noise mapping. First, it identifies dynamic traffic elements and environmental elements from videos. Then, elements are converted from image coordinates to geographic coordinates by video calibration. Finally, we formalize a dynamic noise mapping model at the lane level. In an actual case analysis, the average error is 1.53 dBA. As surveillance video already has a high coverage rate in most cities, this method can be deployed to entire cities if needed.

Combining multi-indicators with machine-learning algorithms for maize yield early prediction at the county-level in China

• Multi-indicators were jointly used for maize yield estimation. • Random Forest algorithm had a good performance in estimating maize yield. • Surface temperature had the best accuracy among single indicator in estimating yield. • The yield can be predicted within 24 d before maturity with a good accuracy. • More informative indicators can improve the spatial adaptability of the model. The accurate and timely prediction of crop yield at a large scale is important for food security and the development of agricultural policy. An adaptable and robust method for estimating maize yield for the entire territory of China, however, is currently not available. The inherent trade-off between early estimates of yield and the accuracy of yield prediction also remains a confounding issue. To explore these challenges, we employ indicators such as GPP, ET, surface temperature (Ts), LAI, soil properties and maize phenological information with random forest regression (RFR) and gradient boosting decision tree (GBDT) machine learning approaches to provide maize yield estimates within China. The aims were to: (1) evaluate the accuracy of maize yield prediction obtained from multimodal data analysis using machine-learning; (2) identify the optimal period for estimating yield; and (3) determine the spatial robustness and adaptability of the proposed method. The results can be summarized as: (1) RFR estimated maize yield more accurately than GBDT; (2) Ts was the best single indicator for estimating yield, while the combination of GPP, Ts, ET and LAI proved best when multi-indicators were used ( R 2 = 0.77 and rRMSE = 16.15% for the RFR); (3) the prediction accuracy was lower with earlier lead time but remained relatively high within at least 24 days before maturity ( R 2 > 0.77 and rRMSE <16.92%); and (4) combining machine-learning algorithms with multi-indicators demonstrated a capacity to cope with the spatial heterogeneity. Overall, this study provides a reliable reference for managing agricultural production.

SuperFormer: Enhanced Multi-Speaker Speech Separation Network Combining Channel and Spatial Adaptability

Speech separation is a hot topic in multi-speaker speech recognition. The long-term autocorrelation of speech signal sequences is an essential task for speech separation. The keys are effective intra-autocorrelation learning for the speaker’s speech, modelling the local (intra-blocks) and global (intra- and inter- blocks) dependence features of the speech sequence, with the real-time separation of as few parameters as possible. In this paper, the local and global dependence features of speech sequence information are extracted by utilizing different transformer structures. A forward adaptive module of channel and space autocorrelation is proposed to give the separated model good channel adaptability (channel adaptive modeling) and space adaptability (space adaptive modeling). In addition, at the back end of the separation model, a speaker enhancement module is considered to further enhance or suppress the speech of different speakers by taking advantage of the mutual suppression characteristics of each source signal. Experiments show that the scale-invariant signal-to-noise ratio improvement (SI-SNRi) of the proposed separation network on the public corpus WSJ0-2mix achieves better separation performance compared with the baseline models. The proposed method can provide a solution for speech separation and speech recognition in multi-speaker scenarios.

Fixture-free omnidirectional prestretching fabrication and integration of crumpled in-plane micro-supercapacitors.

Multidimensional folded structures with elasticity could provide spatial charge storage capability and shape adaptability for micro-supercapacitors (MSCs). Here, highly crumpled in-plane MSCs with superior conformality are fabricated in situ and integrated by a fixture-free omnidirectional elastic contraction strategy. Using carbon nanotube microelectrodes, a single crumpled MSC holds an ultrahigh volumetric capacitance of 9.3 F cm−3, and its total areal capacitance is 45 times greater than the initial state. Experimental and theoretical simulation methods indicate that strain-induced improvements of adsorption energy and conductance for crumpled microelectrodes are responsible for the prominent enhancement of electrochemical performance. With outstanding morphological randomicity, the integrated devices can serve as smart coatings in moving robots, withstanding extreme mechanical deformations. Notably, integration on a spherical surface is possible by using a spherical mask, in which a small area of the microdevice array (3.9 cm2) can produce a high output voltage of 100 V.

Effects of design features: The determinant for hospital performance in southwest, Nigeria

This study examined the effects of design features on the user as a determinants for hospital performance in Southwest, Nigeria. The design features to proactively improve the hospital environment should be put in place to increase the hospital performance and users ’satisfaction. This study was carried out at the first generation Federal University Teaching Hospitals (FUTH) in southwest, Nigeria. Methodologically, it employed mixed methods of data collection. The primary source involves the use of multiple-choice structured questionnaire to collect data from the sample population. Also, semi-structured interview (SSI) was used to support the information gathered through the questionnaire in the study area. Sample size which amounted to 575 respondents and were randomly taken from the staff, inpatients and outpatients across the FUTH. However, Respondents were asked to rate the design features such as cleanness of the healthcare environment, natural lighting and ventilation, design for regular hygiene and handwashing policy, noise level within the environment and ward spaces, adequate air quality within wards, offices, and treatment spaces, location of the nursing station to oversee patients without obstructions and design for spatial flexibility and adaptability in order of preferences as a major determinants for hospital building performance.

Research on the Aging-Friendly Kitchen Based on Space Syntax Theory.

As a result of aging populations globally, a growing number of older adults prefer to age in place; therefore, it is essential to study the spatial adaptability of the house. This study aimed to explore the relationship between the efficiency of daily activities and the spatial layout of home kitchens in the elderly population, and to assess the moderating role of cognitive function. Twenty-one elderly participants completed the experiment, including non-cognitively impaired (n = 12) and cognitively impaired groups (n = 9). Their home kitchen space was partitioned in plan and elevation based on space syntax theory. They were required to complete three tasks (i.e., an easy task, a medium task, and a difficult task) in their respective kitchens. The relationship between the efficiency of different tasks’ completion and the corresponding kitchen space attributes (integration, mean depth) was examined. The results showed a significant association between the home kitchen space layout of homebound older adults and their kitchen activity efficiency. In addition, a positive moderating effect of cognitive ability was found in the association between moderate and difficult tasks (p < 0.05), and its effect appeared to be stronger under challenging tasks (p < 0.01). The results of this study may help provide a basis for future design and optimization of aging-friendly residential spaces.

Creating Sustainable Buildings: Structural Design Based on the Criterion of Social Benefits for Building Users

Sustainable building involves reducing negative environmental impacts with a simultaneous increase in life quality. The aim is to optimize building performances while considering all aspects of sustainability: environmental, economic, and social. The building structure determines the building’s performances, and it should be designed and evaluated as a subsystem of the building, in line with the objectives of the system–building. This paper investigates structural design based on integrated design objectives within the criterion of social benefits for users throughout the use phase of the building, focusing on protection and safety, aspects of comfort, spatial organization, spatial adaptability, and maintenance. The problem was studied using integrative literature review methodology and system theory. The main findings of the research are a review and critical analysis of the representative literature and the derived conceptual framework for structural design based on the criterion of social benefits for building users, which should support more comprehensive and more efficient decision-making during systemic design and optimization of buildings. The presented integrated literature review indicates the need for the application of a systemic approach to structural design in order to create sustainable buildings.

New spatial practices in organised sport following COVID-19: the Swedish case

Following the outbreak of COVID-19 in 2020, the majority of European countries imposed lockdowns, whereas Sweden introduced comparatively moderate constraints. The Public Health Agency of Sweden stated that sports and physical activity could continue if conducted ‘safely’ and recommended outdoor activities. This article aims to identify new spatial practices and strategies developed by organised recreational sport providers (i.e. sport clubs, commercial providers) following the outbreak of the pandemic. Further, it identifies obstacles and opportunities for new spatial solutions, as well as possible long-term (lasting) effects of the strategies imposed. Also, it discusses these obstacles and opportunities in relation to contemporary sport facility challenge. The data draws upon 24 semi-structured interviews with sport coaches and leaders. All interviews were transcribed verbatim and analysed using a thematic analysis. The results suggest that most respondents show adaptability by moving their activities outdoors. Generally, for-profit organisations have been more innovative and adaptive. Obstacles for new spatial solutions include localisation, weather, (perceived) need of equipment, leadership, competing interests in public space, and notably the perception of what sport is. Positive examples of spatial adaptability show that there is a potential for sport providers to open up for different spatial solutions and more flexibility in relation to their activities, in order to ease the pressure on formal indoor (and, to some extent, outdoor) sport facilities while amending the perception of sport activities. Research methods The data draws upon 24 semi-structured interviews with sport coaches and leaders. All interviews were transcribed verbatim and analysed using a thematic analysis. Results and findings The results suggest that most respondents show adaptability by moving their activities outdoors. Generally, for-profit organisations have been more innovative and adaptive. Obstacles for new spatial solutions include localisation, weather, (perceived) need of equipment, leadership, competing interests in public space, and notably the perception of what sport is. Implications Positive examples of spatial adaptability show that there is a potential for sport providers to open up for different spatial solutions and more flexibility in relation to their activities, in order to ease the pressure on formal indoor (and, to some extent, outdoor) sport facilities while amending the perception of sport activities.

Attention-Based and Staged Iterative Networks for Pansharpening of Remote Sensing Images

The pansharpening method combines complementary features from panchromatic (PAN) images and multispectral (MS) images to provide high-resolution MS images. Therefore, how to extract the features completely and reconstruct the image with high quality is the key link to obtain the ideal fusion image. We propose an attention-based and staged iterative network (ASIN) framework, which considers each subnetwork of the iterative network as a multistage process of pansharpening and carries out feature extraction and image reconstruction in each stage. Using the advantages of an iterative network for cross-stage depth features for the hierarchical extraction of refined features of MS images and PAN images for image reconstruction, we use the large kernel attention (LKA) module and the cascaded asymmetric coupling representation module to build the framework for feature extraction and the attention fusion module (AFM) to fuse the features of PAN and MS in the image reconstruction stage. LKA has channel and spatial adaptability, as well as strong long-range dependency establishment capability, which makes the feature extraction more complete. Asymmetric coupled representation module (ACRM) outputs refined spectral and spatial features by learning the hybrid correlation of MS and PAN images. AFM effectively utilizes the spectral and spatial features of the input, enabling the network to reduce information loss and retain important information. On the QuickBird (QB), WorldView-2 (WV2), and Gaofen-2 (GF-2) datasets, the superior performance of our method over the contrasting methods is demonstrated by quantitative comparison and qualitative analysis.

A regeneration process-matching scaffold with appropriate dynamic mechanical properties and spatial adaptability for ligament reconstruction

Ligament regeneration is a complicated process that requires dynamic mechanical properties and allowable space to regulate collagen remodeling. Poor strength and limited space of currently available grafts hinder tissue regeneration, yielding a disappointing success rate in ligament reconstruction. Matching the scaffold retreat rate with the mechanical and spatial properties of the regeneration process remains challenging. Herein, a scaffold matching the regeneration process was designed via regulating the trajectories of fibers with different degradation rates to provide dynamic mechanical properties and spatial adaptability for collagen infiltration. This core-shell structured scaffold exhibited biomimetic fiber orientation, having tri-phasic mechanical behavior and excellent strength. Besides, by the sequential material degradation, the available space of the scaffold increased from day 6 and remained stable on day 24, consistent with the proliferation and deposition phase of the native ligament regeneration process. Furthermore, mature collagen infiltration and increased bone integration in vivo confirmed the promotion of tissue regeneration by the adaptive space, maintaining an excellent failure load of 67.65% of the native ligament at 16 weeks. This study proved the synergistic effects of dynamic strength and adaptive space. The scaffold matching the regeneration process is expected to open new approaches in ligament reconstruction.

Improving the understanding of cytoneme-mediated morphogen gradients by in silico modeling.

Morphogen gradients are crucial for the development of organisms. The biochemical properties of many morphogens prevent their extracellular free diffusion, indicating the need of an active mechanism for transport. The involvement of filopodial structures (cytonemes) has been proposed for morphogen signaling. Here, we describe an in silico model based on the main general features of cytoneme-meditated gradient formation and its implementation into Cytomorph, an open software tool. We have tested the spatial and temporal adaptability of our model quantifying Hedgehog (Hh) gradient formation in two Drosophila tissues. Cytomorph is able to reproduce the gradient and explain the different scaling between the two epithelia. After experimental validation, we studied the predicted impact of a range of features such as length, size, density, dynamics and contact behavior of cytonemes on Hh morphogen distribution. Our results illustrate Cytomorph as an adaptive tool to test different morphogen gradients and to generate hypotheses that are difficult to study experimentally.

A new attention-based CNN approach for crop mapping using time series Sentinel-2 images

Accurate crop mapping is of great importance for agricultural applications, and deep learning methods have been applied on multi-temporal remotely sensed images to classify crops. However, due to the geographic heterogeneity, the spectral profiles of the same crop can vary spatially, and thus using the spectral features alone can limit the model performance in mapping crops in large scales. Moreover, it is a challenge for traditional deep learning models to accurately capture the important information from a large number of features. To address these issues, in this study, we developed a novel attention-based convolutional neural network (CNN) approach (Geo-CBAM-CNN) for crop classification using time series Sentinel-2 images. Specifically, geographic information of crops was first integrated into an advanced attention module, Convolutional Block Attention Module (CBAM) to form a Geo-CBAM module which can help mitigate the impacts of geographic heterogeneity and restrain unnecessary information. Then, the developed Geo-CBAM module was embedded into a CNN model to boost the model’s attention both spectrally and spatially. The proposed Geo-CBAM-CNN model was validated on four main crops over six counties with different geographic environments in the U.S. Also, it was compared to three other state-of-the-art machine learning approaches, including CBAM-CNN, CNN and Random Forest (RF). The results showed that the proposed model achieved the best performance, reaching 97.82% overall accuracy, 96.82% Kappa coefficient and 96.96% Macro-average F1 score. Moreover, the developed Geo-CBAM-CNN model showed strong spatial adaptability, indicating its superior performance in large scale applications. Furthermore, by visualizing the structure of the Geo-CBAM-CNN, we found that the model automatically allocated different weights to the features, and generally, the red-edge features in the middle of the year obtained more attention.

Spatiotemporal variation of soil moisture in Northern China based on climate change initiative data

AbstractIn view of the sparse, uneven distribution and poor data continuity of measured soil moisture sites in China and the lack of large‐scale research, the soil moisture data set of the European Space Agency (ESA) Climate Change Initiative (CCI) was applied to carry out spatial‐temporal variation analysis of soil moisture in five agricultural ecological zones in Northern China. The Pearson correlation coefficient and multiple‐component metric Kling–Gupta efficiency were used to verify the validity of remote sensing data and to analyze the temporal and spatial variation of soil moisture with the concept of temporal and spatial persistence. The ESA CCI remote sensing soil moisture data had good spatial and temporal adaptability for the whole year in the Huang‐Huai‐Hai region, in summer in Northeast China, in spring in Northwest China, in summer and autumn in the Loess Plateau, and in summer and autumn in the Inner Mongolian Plateau. The annual mean soil volume water content in Northern China from 1991 to 2016 was 0.06–0.39 m3 m−3. Spatially, soil moisture in Northern China has been gradually increasing from west to east. In this area, seasonally, soil moisture was the highest in summer, followed by autumn and then winter and spring. Northeast China, the Loess Plateau, and the Huang‐Huai‐Hai region are important grain‐producing areas in China. The soil moisture in these areas fluctuates widely with seasonal changes and shows an obvious drying trend.

Using GPS Trajectories to Adaptively Plan Bus Lanes

Since bus prioritization policies can help mitigate urban traffic jams, the planning of bus lanes has drawn considerable attention. Existing methods suffer from a common limitation, which is that the limited spatial adaptability resulting from certain road condition information cannot be directly specified. Many bus GPS trajectories have been accumulated and can be contiguously gathered if needed. This paper proposes a trajectory-based bus lane planning method. First, we formulize the bus lane planning problem as a multiobjective optimization problem in which the road conditions, traffic flow, connectivity of bus lanes, and construction cost are organized as four constraints, and road utilization and bus punctuality are modeled as two objectives. Then, an evolutionary algorithm-based method is presented to solve the problem. We tested the model in the Nanshan District, Shenzhen City, China. Through a comparison with existing survey-based methods, the parameters associated with road conditions in this method are directly extracted from GPS trajectories, and this method is more effectively deployed than other methods. Since GPS trajectories can cover a wide area if needed, and because the proposed method can be effectively executed, this method can be adapted to large urban scales.

Model Development for Optimized Land Usage in Urban Agglomeration Zone Using Bee Colony Algorithms

This research work was developed to increase the output of urban zone regions and also to bring out the best use of the space of the urban zone regions. The problems associated with the current research work is basically poor accuracy, slow convergence speed and low rationality of evaluation index. We have proposed to use Bee Colony Algorithm which optimizes the usage areas of land in urban zone regions. The work that we developed a model using the bee colony algorithm for best optimization. The various evaluation units are divided by grid method. By this algorithm principle the weighted summation method of indexes is used to calculate the evaluation units. The the evaluation model is developed by using the above calculated evaluation value of land usage spatial adaptability in the required regions. By the evaluation function of urban land suitability, the cost function and spatial coordination function of urban land usage and the spatial optimization model of land use in urban agglomeration development zone is constructed. The model is developed by the bee colony algorithm and best fitting solution of land usage in urban agglomeration development zone is obtained. The research results prove that the evaluation index of the proposed method is reasonable, the accuracy of the evaluation results is high, and the convergence speed is fast.

Adaptively Meshed Video Stabilization

Video stabilization is essential for improving the visual quality of shaky videos. Current video stabilization methods usually take feature trajectories in the background to estimate one global transformation matrix or several transformation matrices based on a fixed mesh, and warp shaky frames into their stabilized views. However, these methods may not model the shaky camera motion well in complicated scenes, such as scenes containing large foreground objects or strong parallax, and may result in notable visual artifacts in the stabilized videos. To resolve the above issues, this paper proposes an adaptively meshed method to stabilize a shaky video based on all of its feature trajectories and an adaptive blocking strategy. More specifically, we first extract the feature trajectories of the shaky video and then generate a triangle mesh according to the distribution of the feature trajectories in each frame. Then, the transformations between shaky frames and their stabilized views over all triangular grids of the mesh are calculated to stabilize the shaky video. Since more feature trajectories can usually be extracted from all of the regions, including both the background and foreground regions, a finer mesh will be obtained and provided for camera motion estimation and frame warping. We estimate the mesh-based transformations of each frame by solving a two-stage optimization problem. Moreover, foreground and background feature trajectories are no longer distinguished and both contribute to the estimation of the camera motion in the proposed optimization problem, yielding better estimation performance than previous works, particularly in challenging videos with large foreground objects or strong parallax. To further enhance the robustness of our method, we propose two adaptive weighting mechanisms to improve its spatial and temporal adaptability. Experimental results demonstrate the effectiveness of our method in producing visually pleasing stabilization effects in various challenging videos.

The spatial evolution process, characteristics and driving factors of traditional villages from the perspective of the cultural ecosystem: A case study of Chengkan Village

The traditional village research is a popular topic in rural geography. The traditional village space contains rich human settlement cultural ideas, and thus, revealing its formation and evolutionary law is a practical need for the protection and development of a traditional village. Based on the theoretical analysis, this paper constructs a cultural-ecological environmental framework to analyse the spatial evolutionary process and characteristics of traditional villages. The Chengkan traditional village space displays the evolutionary process of formation, development, prosperity, decay, and regeneration. The natural, economic, and social environments together are regarded as an ecological environment support system that interacts with the traditional village space, drives the spatial evolutionary process. The spatial evolution of the traditional village is an orderly and dynamic process of spatial adaptability transformation that reflects the wisdom of the creation of the ancient Chinese living environment and provides useful inspiration for contemporary village planning and traditional village activation and utilization. The results show that it is necessary to grasp the spatial evolution process, characteristics and driving factors analysed from the perspective of the cultural ecosystem.

Lockdown urbanism: COVID-19 lifestyles and liveable futures opportunities in Wuhan and Manchester

ABSTRACT Based on the authors’ personal experiences, this commentary discusses contrasting urban contexts and lockdown measures in twinned cities Wuhan, China and Manchester, UK, to examine spatial reach under COVID-19 restrictions in both places. Focusing on latency, the capacity of space to fit new occupation patterns and uses, the role of architecture and urban design is considered, to identify lessons applicable to physical and digital environment design, in scales and media that can absorb shock, supporting flexible, creative resilient approaches and patterns of future liveability. With massive externally induced change, what stays, what shifts, what disappears? This paper considers spatial adaptability, spatial resilience in two comparative, yet different contexts to identify design-based questions and propose thematic responses addressing resilient liveable future urbanism. This reflects on the similarities and differences between lockdown in China and the UK, the concept of mental as well as physical lockdown and how this has played out in these two countries.