How body asymmetries determine accessibility in spatial frameworks.

Ecological network resilience, the core capacity of ecosystems to maintain functional stability under external disturbances, is of great significance for ensuring regional ecological security and promoting sustainable development. This study takes the Wuhan Metropolitan Area as the research object. Based on multi-temporal data from 2000 to 2020, we innovatively constructed a comprehensive evaluation system of "water resources–soil conservation–ecosystem quality" to identify ecological source areas. Combined with a three-dimensional resistance surface model of "natural environment–human activities–physical barriers," the Minimum Cumulative Resistance (MCR) model was applied to extract ecological corridors, forming a "source–corridor" spatial frame-work. The gravity model was used to construct the ecological network and analyze its topological structure. Finally, a robustness model was employed to assess the dynamic changes in network resilience. The key findings include: (1) The number of ecological source areas exhibited a "rise-then-decline" trend, with spatial distribution shifting from fragmented to clustered and the global control of core nodes transitioning from decentralized to highly centralized. (2) Ecological corridors displayed a "dense-south-sparse-north, dense-periphery-sparse-center" spatial pattern. Although the number of corridors decreased, the interaction strength and species migration efficiency significantly improved. (3) The ecological network evolved from "expansion" to "quality enhancement," with significant improvements in network density and clustering coefficients, ultimately forming an efficient and stable structure. Based on these findings, strategic recommendations are proposed to optimize the ecological network and enhance environmental quality in the Wuhan Metropolitan Area's composite ecosystem.

We describe the methodological development and preliminary results of a new spatial modelling framework to support the evaluation and design of novel Food Value Chains (FVC). The sustainability of future FVCs will depend on how effectively these can be adapted to environmental (e.g., climate change) and socio-economic (e.g., resource access and dietary preferences) changes projected for coming decades. Our approach aims to account for the spatial and temporal complexity inherent to both biophysical (e.g., climate, genotypes and soils) and techno-economic (e.g., processing technologies and markets) components of FVCs to optimise supply- (e.g., production areas) and demand- (processing-plant locations) across landscapes. For that, we integrated georeferenced biophysical outputs of a process-based agricultural model (Agricultural Production Systems sIMulator, APSIM-NextGeneration) into a spatial techno-economic model (IIASA-BeWhere). We test the approach through a case-study to evaluate a novel (hypothetical) FVC to produce plant-based proteins from lucerne crops (Medicago sativa) across New Zealand’s agricultural landscapes. Results highlighted spatial protein production patterns driven by changes in crop canopy expansion and net carbon assimilation, with lower yields estimated in cooler and dryer environments, particularly when water supply was limited under rain-fed (non-irrigated) conditions with soils of low water holding capacity. Spatial variability in protein yields, production costs and emissions estimated by APSIM-NG running in the ATLAS framework were then used as inputs by BeWhere to optimise the location of production areas and protein-processing plants. We discuss potentials, limitations, and future development areas of this approach.

Polarity and early human embryo development

The longitude, latitude and depth of the hypocenter in 3-D space and the date and time of rupture makes an earthquake a “point” in a spatio-temporal point pattern, observed over a region and months, years or decades. The magnitude of earthquakes marks the point pattern, as would hypocenter depth do if only the longitude and latitude of epicenters were used for location in 2-D space. Stochastic declustering, based on a space–time ETAS model (ETAS: epidemic-type aftershock sequence), is a procedure that can be applied in the preliminary stage of an earthquake catalog data analysis. Stochastic declustering procedures have underlying assumptions, such as the time independence of the background intensity function whether the spatial framework is 2-D or 3-D, and a separate treatment of hypocenter depth from longitude and latitude when the spatial framework is 3-D. Cyclical processes in the Earth, including tides and seasonal surface water loads, can introduce periodic behavior in earthquake occurrence and related variables. The effects of ETAS-based 2-D and 3-D declustering on the outcome of periodicity analyses performed from the resulting earthquake data catalogs are studied. The research objectives and statistical challenges include the detection of periodicities for hypocenter depth in addition to monthly earthquake number, and the risk of missing observations for hypocenter depth when the monthly earthquake number after declustering is zero. A version of the method of multi-frequential periodogram analysis (MFPA) that allows for missing observations in the input temporal series is presented in detail, and applied to hypocenter depth (monthly mean and median) for central and northern California from January 2006 to December 2014. The results obtained for the 2-D and 3-D declustered catalogs are compared with those for the original catalog for this region. A semiannual periodicity in hypocenter depth is detected for the original and 2-D declustered catalogs, and fitted with the goal of relating it to periodicities found in the time series of monthly earthquake numbers. Using these results for central and northern California earthquakes, some of the assumptions on the intensity function of the spatio-temporal point process in stochastic declustering are discussed and future research perspectives are proposed.

Purpose. Digital transformation of education determines the need to rethink the goals and meanings of the educational process and, at present, is associated with the problems of our time, the expansion of the role of artificial intelligence. The paper is devoted to the presentation of the taxonomy of educational goals from the standpoint of a mental model of thinking, which allows designing new educational results in the digital transformation of education. Methodology and methods. Unlike Bloom’s taxonomy, the main goal of modern education is the formation and development of computational thinking. The basis for constructing the taxonomy of educational goals is a mental approach, which is a set of principles and strategies for organizing the educational process aimed at forming and developing the cognitive abilities of the student. To identify the essence of computational thinking, a mental model of thinking is used, in which cognitive functions are determined by the mechanisms of perception, memorization, structuring and retrieval of information. In this model, knowledge is a structural set of mental images; mental schemes and mental models based on the first, second, and third signal systems. Results. Consideration of the mental characteristics of a modern person in a digital society, definition of the 3rd signal system in the “person + ICT + artificial intelligence” link, in which mental schemes and mental models play the main role, made it possible to formulate the stages of the educational process target settings in the form of the taxonomy of educational goals. The taxonomy of educational goals begins with the assignment of mental entropy - as a subject field of the future professional activity of a specialist, containing questions, tasks and problems. They can be formulated by traditional educational goals in the form of: know, be able to, master. The specification of these goals leads to target guidelines for the composition, volume and content of subject mental images, mental schemes and mental models. The set of mental images, schemes and models represent a knowledge block. In parallel, an ascending target block of universal cognitive operations is set, that a modern specialist must master. The pinnacle of the taxonomy is computational thinking. Based on the mental model of thinking, the essence of computational thinking is revealed and a set of universal cognitive operations is compiled. The basic composition of mental models that determine human cognitive behavior in a digital society is formed. Computational thinking is a type of thinking with the activation of conceptual-abstract and conceptual-machine superstructures of mental images, mental schemes and models in the circuit of the third signal system. Conclusion. The proposed mental taxonomy of educational goals determines the vector of the direction of digital transformation of education to achieve the main result - computational thinking. The stages of formation and the quality of computational thinking are determined by the formation of a set of universal cognitive operations, the volume and content of mental images, mental schemes and mental models of the subject area.

Since the work of Downs (1957), spatial models of elections have been a mainstay of research in political science and public choice. Despite the plethora of theoretical and empirical research involving spatial models, researchers have not considered in great detail the complexity of the decision task that a candidate confronts. Two facets of a candidate’s decision process are investigated here, using a set of laboratory experiments where subjects face a fixed incumbent in a two-dimensional policy space. First, we analyze the effect that the complexity of the electoral landscape has on the ability of the subject to defeat the incumbent. Second, we analyze the impact that a subject’s “mental model” (which we infer from a pre-experiment questionnaire) has on her performance. The experimental results suggest that the complexity of a candidate’s decision task and her perception of the task may be important factors in electoral competition.

Individuals' decisions and behaviour can play a critical role in determining both the probability and severity of injury. Behavioural decision research studies peoples' decision-making processes in terms comparable to scientific...

Caching is a well-known approach to achieve good performance and scalability in mobile computing environments. Using this technique, the query response time and the overall system performance can be extremely improved by decreasing the volume of data transferred between the server and the mobile device. However, the effectiveness of caching techniques depend greatly on the nature of the data processed by the application and the data access patterns which are specific for this type of analysis. Cache management techniques for Geographical Information Systems (GIS) deviate substantially from existing methods used with relational data, since GIS map navigation operations (e.g. panning, zooming in/out) have their own unique access patterns that differ greatly from its relational equivalents. This paper presents a spatial caching framework that can efficiently handle spatial objects and it is tailored toward the typical map navigation operations seen in GIS systems. As a result, heavyweight map operations (e.g. labeling and editing) which require multiple round-trips to the data source can significantly benefit from the use of our proposed framework. The goal of this paper is to serve as a proof of concept and to demonstrate the efficiency and scalability of the proposed spatial caching framework and its applicability in a production commercial system (Esri's ArcGIS).

Spatial multi-omics sequencing enables the simultaneous profiling of transcriptomics, proteomics, and epigenomics at a spatial resolution, offering insights into complex tissue organization and molecular regulation. However, the effective integration of multiple omics modalities in a spatial context remains a major challenge. Here, we present SpaDDM, a spatial multi-omics integration framework based on directional diffusion models (DDMs), which supports spatial pattern identification, cross-omics alignment, and inter-and intracellular signaling flow analysis. SpaDDM employs DDM-based graph networks to learn omics-specific representations by jointly incorporating spatial coordinates and molecular measurements within each modality, followed by an attention mechanism to align features across modalities. We benchmarked SpaDDM on diverse spatial multi-omics datasets, including transcriptomics-epigenomics and transcriptomics-proteomics combinations across multiple tissues and species. SpaDDM consistently outperformed existing methods by more accurately deciphering spatial tissue patterns and effectively reducing the boundary noise between spatial regions. Moreover, the learned low-dimensional coembedded representations of individual cells serve as integral mediators for inferring the signaling flows that underlie spatial patterning. Finally, we demonstrated that SpaDDM alignment of complementary information across multi-omics layers facilitates cross-omics translation and significantly improves the prediction of cell state alignments.

The acquisition, utilization, and extension of a spatial mental model were assessed for young and older adults. The model consisted of a map with nine buildings. In acquisition, 91% of the young adults and 62% of the older adults were able to acquire the mental model as determined by a strict criterion. There were age differences in trials required to reach criterion. However, once acquired, the older adults were able to utilize their mental model as well as the young adults, with the exception of questions that were taxing on working memory. Both age groups were also able to extend their mental model to 16 buildings, and then utilize this extended model. Individual differences in working memory and fluid intelligence were related to successful acquisition of the mental model.

Space is important. The recent global financial crisis has vividly pointed to spatial patterns in economies’ reactions to the global economic shocks. This paper focuses on labor market responses and its interactions with criminal activities in a causal and spatial framework. we study the case of Germany as evidently this country’s economy has demonstrated resilience during the whirl of economic crisis. Our contribution is twofold: first, we lay down a parsimonious labor market model with search frictions, criminal opportunities, and, unlike earlier analyzes, productivity shocks which are important in explaining empirical regularity of criminal engagement. Second, we seek empirical support using data on the 402 German districts for 2009-2010, the years following the global financial crisis, in a setting that allows not only crime spatial multipliers but also inherent endogeneity of unemployment. Adverse income shocks clearly unfold a spatial nexus between unemployment and crime rates. More specifical ly, we find that youth unemployment plays a prominent role in explaining property crime, namely housing burglary. Our results are in line with previous research: neglecting endogeneity of unemployment understates its impact and employing the youth unemployment share instead of rate points to distinctive effects. The analysis offers important implications for countries that are currently undergoing fiscal consolidation and are experiencing high unemployment rates.

Spatial modeling is an integral component of most geographic information systems (GISs). However, conventional GIS modeling techniques can require substantial processing time and storage space and have limited statistical and machine learning functionality. To address these limitations, many have parallelized spatial models using multiple coding libraries and have applied those models in a multiprocessor environment. Few, however, have recognized the inefficiencies associated with the underlying spatial modeling framework used to implement such analyses. In this paper, we identify a common inefficiency in processing spatial models and demonstrate a novel approach to address it using lazy evaluation techniques. Furthermore, we introduce a new coding library that integrates Accord.NET and ALGLIB numeric libraries and uses lazy evaluation to facilitate a wide range of spatial, statistical, and machine learning procedures within a new GIS modeling framework called function modeling. Results from simulations show a 64.3% reduction in processing time and an 84.4% reduction in storage space attributable to function modeling. In an applied case study, this translated to a reduction in processing time from 2247 h to 488 h and a reduction is storage space from 152 terabytes to 913 gigabytes.

With the development of the consumption era, the research on spatial production in urban spatial renewal has turned to spatial consumption. However, the injustice caused by the urban renewal activities with capital and power as the main body also makes the research heat of spatial justice remain high. This paper explores the integrated analytical framework of “space production, space consumption, and spatial justice” in urban renewal, aiming to deeply understand the complexity and multidimensionality of contemporary urban spaces. The article first argues the necessity of constructing a tripartite spatial theory analysis framework, introduces the concepts and main contents of space production, space consumption, and spatial justice, and discusses their interactions and influences. Secondly, based on the original theoretical content, a tripartite spatial theory analysis framework is constructed. Finally, starting from the absence of spatial justice, the limitations of traditional spatial production models, and the differentiation of space consumption, the paper proposes urban renewal strategies guided by the tripartite spatial theory, emphasizing the importance of policy intervention, mode innovation, and social integration to achieve more equitable and sustainable urban spatial development.

Évolution comparée des tests cliniques et posturographiques lors de la rééducation des arthroplasties totales de hanche: Contrôle de l’équilibre après arthroplastie de hanche

This study presents an application of the Ricardian approach to explore the impact of climate change on farmland values in Nepal. The Ricardian approach is estimated using a panel fixed effects model, and the outcome is compared against two separate models that account for spatial correlation: a spatial autoregressive (SAR) model; and a spatial error model (SEM). The findings suggest that Nepalese farmlands are sensitive to climate change, and this result was consistent in both the non-spatial and the spatial frameworks. The inclusion of the spatial effects, however, revealed the presence of positive spatial autocorrelation and produced conservative estimates of climate change impacts. The net effect of annual increases in average temperature was negative; while the net effect of higher annual average precipitation was a positive outcome on farmland values. In particular, we found that the marginal effect of every degree increase in average annual temperature was Rs.180 /hectare ($1.80) reduction in farmland values. Likewise, for rainfall, it was found that 1 mm increase in average annual rainfall would positively affect farmland value by Rs.225/hectare ($2.25). Finally, the study findings suggested that extreme weather events could also impact the agricultural productivity and the farmland values in Nepal.   Key words: Climate change, ricardian approach, spatial panel data analysis, Nepalese agriculture, environmental valuation.