Spatial Framework Research Articles

3D printable spatial fractal structures undergoing auxetic elasticity

Currently simple and conventional structures are unable to meet increasingly critical and diverse applications, as well as establishment of constitutive relationship of complex structure is becoming a major challenge in materials and mechanics disciplines. To overcome these challenges, fractal structures have attracted much attention due to their abilities to establish constitutive relationships of complex structures. In this study, we design a spatial fractal framework by using high elasticity material and 3D printing method. The constitutive relationships of fractal framework are mathematically modeled. Effects of fractal levels, fractal dimension, and spatial pattern of structures on the mechanical behavior of the fractal framework and assembled spatial fractal structures are studied using both finite element analysis and experimental measurements. The constitutive relationships among stress, strain, fractal dimension, fractal levels, and spatial patterns of structures are proposed and discussed. This paper develops a novel method to establish constitutive relationships of complex structures, and provides theoretical and engineering basis for 3D printable spatial fractal structures undergoing auxetic elasticity.

A spatial framework for improved sanitation to support coral reef conservation

Coral reefs are one of the most valuable yet threatened ecosystems in the world. Improving human wastewater treatment could reduce land-based impacts on coral reefs. However, information on the quantity and spatial distribution of human wastewater pollution is lacking. Here, we develop a spatial model linking residential human wastewater pollution (nitrogen and phosphorus/year) and conservation sectors [coral reefs] to better understand the relative differences in the distribution and efficacy of different sanitation services and their potential implications for conservation monitoring and management. We apply our model to Fiji, where ongoing initiatives and investments in wastewater treatment for human health could be leveraged to cost-effectively improve coral reef condition. We estimate that wastewater treatment plants account for nearly 80% of human wastewater nutrients released into surface waters. Wasterwater nutrient pollution is widespread, affecting 95% of reefs, but is concentrated across a few watersheds. Our spatially explicit approach can be used to better understand potential benefits and trade-offs between sanitation service improvements and coral reef health, helping to bridge the sanitation and conservation sectors as well as inform and prioritize on the ground action.

Exploring Village Spatial Patterns for Sustainable Development: A Case Study of Diqing Prefecture

Alexander’s A Pattern Language is an important text and focuses on the theory of diverse environmental spatial sustainability. With the contemporary digital development of villages, it is urgent that village spatial patterns are analyzed in a scientific and quantitative way in order to determine heritage village diversity. The village settlements in the Diqing region are typical representatives, having a changeable terrain, being large in number, and being multi-ethnic in China; in recent years, they have also faced slow development and limited conditions. However, few studies have focused on the multiple quantitative analysis of the diverse spatial patterns of village settlements in an ethnic minority region. Therefore, this study selects 2486 village settlements in Diqing and, using KED, NNI SSIA, etc., proposes a spatial pattern analysis framework (SPAF) based on pattern language theory. According to the spatial influencing factors, spatial analysis criteria are constructed to analyze the village spatial pattern types and subtypes. The results show that the region’s topographic conditions are the dominant factors that form the diversified village spatial patterns existent in the Diqing Prefecture. Among them, the dominant pattern of building villages along slopes with a small-population scale and large-dispersed settlements achieves a healthy and sustainable living environment that is oriented well, cost-saving, and conforms to nature. Meanwhile, the dominant pattern is also the reason for the inhibition of development due to inconvenient transportation and difficult management. Therefore, sustainable strategies should strike a balance between the two opposites. Based on the SPAF, spatial patterns can be effectively extracted for diverse village spaces, providing digital and visual references for the regeneration of contemporary rural areas.

Introduction to special issue: “Scaled ethnographies of toxic flows”

Societal concerns over ‘toxic’ substances have become ubiquitous as human and more-than-human entanglements with toxicity are ever-increasing; people are now living in a ‘permanently polluted world’. Ethnographies that focus specifically on making visible the uneven geographies of ‘toxic flows’ expand interdisciplinary research on the spatialization of a ‘toxic’ politics. By highlighting the structural inequalities of poisonous exposure in everyday life, the papers in this edited collection underscore the dynamic material conditions and interrelations of toxicity inherent in the global ‘frictions’ of late-stage capitalism. Focusing on embodied experiences of toxic exposure in differentiated landscapes among working-class peoples, scheduled castes, ethnic minorities and other-than-human populations, the articles in this themed issue use ‘toxic’ with ‘flows’ as a spatial and conceptual framework to reframe and challenge ideas of the political. The spatial politics of seeking to contain flows of toxic substances are fraught in tensions in ways that may open up possibilities for, as well as limit, political agency. Here, we recognize both intimate activism and individual coping strategies as well as how resignation and denial of toxic effects may thwart efforts of political mobilization while operating to naturalize the violence of toxicity. The ethnographies in this special issue help to visualize and reconceptualize geographies of structural inequalities while they also offer glimpses of what non-toxic geographies might look like.

Integrate memory mechanism in multi-granularity deep framework for driver drowsiness detection

Driver drowsiness detection is a critical task for early warning of safe driving, while existing spatial feature-based methods face the challenges of large variations of head pose. This paper proposes a novel approach to integrate the memory mechanism in a multi-granularity deep framework to detect driver drowsiness, and the temporal dependencies over sequential frames are well integrated with the spatial deep learning framework on the frontal faces. The proposed approach includes two steps. First, the spatial Multi-granularity Convolutional Neural Network is designed to utilize a group of parallel Convolutional Neural Network extractors on well-aligned facial patches of different granularities and extract facial representations effectively for large variations of head pose. Furthermore, it can flexibly fuse detailed appearance clues of the main parts and local-to-global spatial constraints. Second, the memory mechanism is set up using a deep long short-term memory network of facial representations to explore long-term relationships with variable length over sequential frames, which is capable of distinguishing the states with temporal dependencies, such as blinking and closing eyes. The proposed approach achieves 90.05% accuracy and about 37 frames per second (FPS) speed on the evaluation set of the National Tsing Hua University Driver Drowsiness Detection dataset, which is applied to the intelligent vehicle for driver drowsiness detection. A dataset named Forward Instant Driver Drowsiness Detection is also built and will be publicly accessible to speed up the study of driver drowsiness detection.

A new dynamic spatial information design framework for AR-HUD to evoke drivers’ instinctive responses and improve accident prevention

Driver’s instinctive responses and skill-based behaviors enable them to react faster and better control their vehicle in dangerous situations. This study incorporated dynamic spatial information design (DSID) in an augmented reality head-up display (AR-HUD) under manual driving conditions. By integrating the skill, rule, and knowledge (SRK) taxonomy and situation awareness (SA) theory, our AR-HUD successfully evoked drivers’ instinctive responses and improved driving safety. First, we converted symbol and sign information processed at the knowledge-based and rule-based levels, respectively, into signal information processed at the skill-based level. Then we developed four AR-HUD interfaces with different dynamic designs for use in a hazardous scenario at an intersection. Finally, we investigated each design’s impact on drivers’ SA and driving performance. Experimental results demonstrated that our DSID enhanced drivers’ SA and accident-avoidance capabilities while reducing their cognitive workload. Among the four AR-HUD interfaces, the one that incorporated all three information elements under study (i.e., lateral warning, dynamic driving space, and speedometer) performed the best. This indicates that our proposed framework has potential applications in other similar dangerous driving scenarios, thus contributing to the development of safer and more efficient driving environments.

Isma‘ili Continuity and Social Change: Chronotopes and Practicing Taqiyya within the Sulaymani Community of Saudi Arabia

This paper examines discourses around the religious and social practice of taqiyya among members of the Sulaymani Isma‘ili community in Saudi Arabia. Isma‘ilism, in the context of 1200 years of anti-Shi‘a discrimination, cultivated the practice known as taqiyya (Arabic, ‘circumscription’) as a tool for self-preservation, which was then further rationalized and reinforced by the sect’s esoteric theology. Taqiyya consists of concealing religious identity, public avoidance of certain rituals, and, in some instances, claiming to be a member of the unmarked Sunni majority. Sweeping changes in Saudi society in the last several years have meant a growing ambivalence about taqiyya and its continued utility. This is significant since taqiyya for many of our interlocutors in this study is not merely a survival tactic. Instead, it is better understood as an embodied disposition cultivated against the backdrop of household privacy. This disposition intimately links everyday comportment to the esoteric cosmology of Isma‘ilism, which is the distinctive and iconic feature of the faith. Our interlocutors’ narratives demonstrated how the invocation of different spatial and temporal frameworks provides a basic heuristic by which to interpret these individuals’ accounts of taqiyya. For some of these individuals, taqiyya is an essential and timeless practice, while for others, the meaning has been reshaped by the recent socio-political reforms.

Voxel-Wise Comparison of Co-Registered Quantitative CT and Hyperpolarised Gas Diffusion-Weighted MRI Measurements in IPF

The patterns of idiopathic pulmonary fibrosis (IPF) lung disease that directly correspond to elevated hyperpolarised gas diffusion-weighted (DW) MRI metrics are currently unknown. This study aims to develop a spatial co-registration framework for a voxel-wise comparison of hyperpolarised gas DW-MRI and CALIPER quantitative CT patterns. Sixteen IPF patients underwent 3He DW-MRI and CT at baseline, and eleven patients had a 1-year follow-up DW-MRI. Six healthy volunteers underwent 129Xe DW-MRI at baseline only. Moreover, 3He DW-MRI was indirectly co-registered to CT via spatially aligned 3He ventilation and structural 1H MRI. A voxel-wise comparison of the overlapping 3He apparent diffusion coefficient (ADC) and mean acinar dimension (LmD) maps with CALIPER CT patterns was performed at baseline and after 1 year. The abnormal lung percentage classified with the LmD value, based on a healthy volunteer 129Xe LmD, and CALIPER was compared with a Bland–Altman analysis. The largest DW-MRI metrics were found in the regions classified as honeycombing, and longitudinal DW-MRI changes were observed in the baseline-classified reticular changes and ground-glass opacities regions. A mean bias of −15.3% (95% interval −56.8% to 26.2%) towards CALIPER was observed for the abnormal lung percentage. This suggests DW-MRI may detect microstructural changes in areas of the lung that are determined visibly and quantitatively normal by CT.

Semi-Automatic Geographic Information System Framework for Creating Photo-Realistic Digital Twin Cities to Support Autonomous Driving Research

Digital twin cities are frequently used in vehicle and traffic simulations to render realistic on-road driving scenarios under various traffic and environmental conditions. These digital twins provide a high-fidelity replica of the physical world (e.g., buildings, roads, infrastructures, traffic) to create three-dimensional (3D) virtual-physical environments to support various emerging vehicle and transportation technologies such as connected and automated vehicles. These virtual environments provide a cost-effective digital proving ground to evaluate, validate, and test emerging technologies that include control algorithms, localization, perception, and sensors. Replicating a real-world traffic scenario in a digital twin using a traditional 3D modeling approach is a time-consuming and labor-intensive effort. This paper presents a semi-automated spatial framework to construct realistic 3D digital twin cities to support autonomous driving research using readily available geographic information system (GIS) data and 3D prefabricated (prefab) models. We start with a comprehensive review of geospatial data sources of essential digital entities required in a 3D digital twin city and present an integrated GIS-3D modeling pipeline using customized QGIS/GDAL and Blender scripting in Python. The pipeline outputs are realistic 3D digital twin cities compatible with common vehicle simulation software, such as CARLA and IPG CarMaker. The paper closes with a showcase to demonstrate the quality and usability of a digital twin city created to replicate the Shallowford Road corridor in Chattanooga in both Unity and Unreal engine-based virtual environment. The generated digital twin city can be applied to a hardware-in-the-loop simulation environment with an actual testing vehicle to facilitate autonomous driving research.

A Weighted k-Nearest-Neighbors-Based Spatial Framework of Flood Inundation Risk for Coastal Tourism—A Case Study in Zhejiang, China

Flood inundation causes socioeconomic losses for coastal tourism under climate extremes, progressively attracting global attention. Predicting, mapping, and evaluating the flood inundation risk (FIR) is important for coastal tourism. This study developed a spatial tourism-aimed framework by integrating a Weighted k Nearest Neighbors (WkNN) algorithm, geographic information systems, and environmental indexes, such as precipitation and soil. These model inputs were standardized and weighted using inverse distance calculation and integrated into WkNN to infer the regional probability and distribution of the FIR. Zhejiang province, China, was selected as a case study. The evaluation results were mapped to denote the likelihood of an FIR, which was then validated by the historical Maximum Inundation Extent (MIE) extracted from the World Environment Situation Room. The results indicated that 80.59% of the WkNN results reasonably confirmed the MIE. Among the matched areas, 80.14%, 90.13%, 65.50%, and 84.14% of the predicted categories using WkNN perfectly coincided with MIE at high, medium, low, and very low risks, respectively. For the entire study area, approximately 2.85%, 64.83%, 10.8%, and 21.51% are covered by a high, medium, low, and very low risk of flood inundation. Precipitation and elevation negatively contribute to a high-medium risk. Drainage systems positively alleviate the regional stress of the FIR. The results of the evaluation illustrate that in most inland areas, some tourism facilities are located in high-medium areas of the FIR. However, most tourism facilities in coastal cities are at low or very low risk, especially from Hangzhou-centered northern coastal areas to southern Wenzhou areas. The results can help policymakers make appropriate strategies to protect coastal tourism from flood inundation. Moreover, the evaluation accuracy of WkNN is higher than that of kNN in FIR. The WkNN-based framework provides a reasonable method to yield reliable results for assessing FIR. The framework can also be extended to other risk-related research under climate change.

Direct and indirect impacts of natural disasters on banks: A spatial framework

We examine the direct and indirect impacts of natural disasters on deposit rates of U.S. bank branches from 2008 to 2017. We capture the indirect impact by the spatial spillover effects of disasters, from branches directly exposed to such disasters to neighboring branches. We theoretically motivate our spatial framework by local competition for deposits among branches and provide empirical evidence consistent with this model. We find that indirect effects contribute to at least two-thirds of the total impact for deposit rate-setting branches. Rate-setting branches in affected counties, on average, raise their deposit rates on 12-month CDs by 1.5 basis points directly due to the disaster shock. However, there is an additional indirect increase of 2.7 – 4.3 basis points for all rate-setting branches, including those in adjacent but unaffected counties, due to the local geographical competition for deposits. We also confirm that the spillover effect occurs among branches across counties via an overlooked social connectedness. Moreover, and importantly, online and one-county banks are more likely to rely on the information channel embedded in the social connectedness effect in response to natural disasters. Branches in less concentrated local markets also respond more to the nature disaster and rate adjustments of neighboring branches.

TMIC-86. SPATIAL ORGANIZATION OF THE PEDIATRIC HIGH-GRADE GLIOMA TUMOR-IMMUNE LANDSCAPE

Abstract BACKGROUND Advances in cellular immunotherapies have shown promising results for brain tumors, including pediatric-type diffuse high-grade gliomas (pHGG). A comprehensive understanding of the tumor-immune microenvironment (TME) is essential for efficient target identification and novel therapeutic strategies. The interplay between cells and their spatial localization within the TME may affect immune cell phenotype and function. We characterized these interactions and phenotypes by single-cell (SC) spatial proteomic analysis. METHODS Thirty-two tissue biopsies of pHGGs were collected from the Princess Máxima Center biobank consisting of 14 diffuse midline gliomas, H3K27-altered (DMG) and 18 other pHGGs. Three cores per biopsy were assembled in a tissue micro-array (TMA). A cyclical multiplex immunofluorescence antibody staining technique by means of chemical antibody-removal was applied to the TMA sections. This resulted in hyperplexed images enabling detailed mapping of the spatial organization. Survival data of patients was collected retrospectively. Clusters of interacting cells were detected using cellular neighborhood (CN) analysis and SC distance analysis. RESULTS SC analysis revealed a large fraction of myeloid cells in the TME, up to 21% of total cells within a tumor, characterized by expression of IBA1 and CD68. We also identified some significant differences in the composition and spatial organization of DMGs compared to other pHGGs. More specifically, DMG biopsies showed lower abundance of CD163+/CD206+ immunosuppressive myeloid cells compared to other pHGGs (p< 0.001). Additionally, the presence of T cells was significantly lower in DMGs compared to other pHGGs (p< 0.001). CN analysis revealed a higher occurrence of myeloid/astrocyte-like neighborhoods in pHGGs other than DMG. The presence of this cluster in these tumors is associated with higher risk of dying (p< 0.05). Thus, the combination of our spatial proteomic SC technology framework and unique patient sample collection will offer essential insights into the cellular interplay in the TME, and its influence on patient outcomes.

“The Great Game” — confrontation between the Anglo-Saxon and Russian world

This article is devoted to understanding the essence, defining the spatial and time frames, as well as describing the most significant episodes of the development of The Great Game — the policy of containing the development of Russia by Anglo-Saxon elites and powers, dating back more than two centuries. Perceiving Russia as the main obstacle to achieving its global geopolitical hegemony, Anglo-Saxon elites and powers actively waged large-scale diplomatic, economic, information wars and battles against Russia, military operations, conducted operations to organize coups d’etat and “revolutions”, trying to destroy Russia both by actions from outside and undermining it from within. The author of the article draws attention to the origin of the very concept of “Great Game”, as well as its use in a narrow and broad sense. In a narrow sense, this concept is used to denote the active geopolitical and economic confrontation between Great Britain and the Russian Empire for control over Central Asia throughout the 19th century (or according to a number of authors, starting in 1856) until 1907. Broadly, to indicate the global geopolitical confrontation between the Anglo-Saxon and Russian world from thebeginning of the 19th century and continuing to the present.The author believes that the interpretation of the “Great Game” in a narrow sense is incorrect, politically biased and deliberately aimed at hiding the knowledge of the “Great Game” from the mass audience.The author considers various points of view of domestic and foreign authors to determine the starting and final point, time and spatial framework of the “Great Game”. He refers to the vivid episodes of The “Great Game”: the Palace Coup of 1801 — the assassination of Emperor Paul I; “Napoleonic” wars; December putsch of 1825; Confrontation with England in Turkestan, the Caucasus, other regions; Russo-Persian and Russo-Turkish Wars, Crimean War, Russo-Japanese War; the Caucasian War and events in Central Asia and Turkestan; Financing and organization in the Russian Empire of the “fifth column”; Revolutionary events of 1905–1907; World War I; 1917 Revolution; Russian Civil War; Support for “intra-party” opposition; Bringing A. Hitler to power in Germany; World War II organization; “Cold War”, Dismemberment of the USSR; Chechen Wars; “Color revolutions” in the post-Soviet space; The 2008 war and Georgia’s “coercion” to peace; 2014 coup d’état, the subsequent civil war in Ukraine, start of Special Military Operation.

Spatial Frameworks of Comparison: Planning Western India’s Free Ports and Free Trade Zones, 1830s–1980s

ABSTRACT Ports offer key vantage points from which to write a global history, as their trade connects people, goods, and capital to far reaching parts of the world. Instead of focusing on trade connections, this article proposes studying the spatial frameworks of comparison in free port and free zone planning. Using reports from ports, ministries, and chambers of commerce, this article analyses these shifting frameworks for comparison in Bombay’s port planning from the 1830s to the 1980s. Port planners, merchants, and ministry experts placed these ports and zones within shifting spatial frameworks, which determined which zones or ports could and could not be compared. These comparisons informed policy changes. Uncovering India’s free port and zone debates reveals the plurality of concepts and policy models, not a single original zone that has spread around the world. This article asks how these actors understood the world, its changing spatial formats, and the role of free ports or free zones in these constellations.

Exposing low-quality deepfake videos of Social Network Service using Spatial Restored Detection Framework

The increasing abuse of facial manipulation methods, such as FaceSwap, Deepfakes etc., seriously threatens the authenticity of digital images/videos on the Internet. Therefore, it is of great importance to identify the facial videos to confirm the contents and avoid fake news or rumours. Many researchers have paid great attention to the detection of deepfakes and put forward a number of deep-learning-based detection models.The existing approaches mostly face the performance degradation in detecting low-quality(LQ) videos, i.e. heavily compressed or low-resolution videos through some SNS (Social Network Service), resulting in the limitation in real applications. To address this issue, in this paper, a novel Spatial Restore Detection Framework(SRDF) is proposed for improving the detection performance for LQ videos by restoring spatial features. We designed a feature extraction-enhancement block and a mapping block inspired by super-resolution methods, to restore and enhance texture features. An attention module was introduced to guide the texture features restoration and enhancement stage attending to different local areas and restoring the texture features. Besides, an improved isolated loss was put forward to prevent the expansion of a single area concerned. Moreover, we adopted a regional data augmentation strategy to prompt feature restore and enhancement in the region attended. Extensive experiments conducted on two deepfake datasets have validated the superiority of the proposed method compared to the state-of-the-art, especially in the scenarios of detecting low-quality deepfake videos.

Spatial pilot-aided fast-adapted framework for stable image transmission over long multi-mode fiber.

Multi-mode fiber (MMF) has emerged as a promising platform for spatial information transmission attributed to its high capacity. However, the scattering characteristic and time-varying nature of MMF pose challenges for long-term stable transmission. In this study, we propose a spatial pilot-aided learning framework for MMF image transmission, which effectively addresses these challenges and maintains accurate performance in practical applications. By inserting a few reference image frames into the transmitting image sequence and leveraging a fast-adapt network training scheme, our framework adaptively accommodates to the physical channel variations and enables online model update for continuous transmission. Experimented on 100 m length unstable MMFs, we demonstrate transmission accuracy exceeding 92% over hours, with pilot frame overhead around 2%. Our fast-adapt learning scheme requires training of less than 2% of network parameters and reduces the computation time by 70% compared to conventional tuning approaches. Additionally, we propose two pilot-insertion strategies and elaborately compare their applicability to a wide range of scenarios including continuous transmission, burst transmission and transmission after fiber re-plugging. The proposed spatial pilot-aided fast-adapt framework opens up the possibility for MMF spatial transmission in practical complicated applications.

Quantifying the health impacts of alternative urban development scenarios using the HAUS tool

Abstract Local authorities face a capacity challenge to balance policy considerations when master-planning an area for redevelopment. Effects of redevelopment on residents’ health comprise one competing priority alongside financial viability, sustainability and employment provision. The challenge is not a lack of data, but a lack of accessible tools to help LAs synthesise and interpret complex information about health effects. We address this by creating a health-economic valuation model which allows the user to understand health trade-offs entailed in alternative scenarios and relative to other costs and benefits. The model applies evidence to quantify and monetise the potential impact on health outcomes related to characteristics of the urban environment. The model is driven by a set of 200+ environment-health impact pathways supported by systematic reviews of evidence relating to building design, natural environment, community infrastructure and transport. We estimate the societal value of changes to health using a database of economic values for 70+ physical and mental health outcomes. Values include costs of treatment, productivity loss and pain & suffering associated with ill health, and are disaggregated according to who bears the costs, e.g. NHS, police. Values are modelled for a synthetic population which can be tailored to local contexts, allowing for aggregation. We apply the model to a regeneration site in Bristol, UK, identifying health outcomes resulting from alternative land use scenarios for the site. Model outputs were used to inform the process of developing a Spatial Regeneration Framework and preferred land use scenarios within the regeneration site. Our research helped to widen out the number of health impacts considered by the design team, and to build a strong case for healthy policy options such as enhanced green space within the strategic framework, ensuring that health data can be built into negotiations with developers in the implementation phase. Key messages • Economic valuation of health impacts of urban environment leads to better public health decision-making. • The HAUS model allows better-informed trade-offs between health and urban investment to be made.

The influence of collaborative learning and self-organisation on medical students’ academic performance in anatomy

BackgroundIn 2021, university teaching at the Faculty of Medicine Tuebingen was transferred to a digital setting. Due to the discontinuation of face-to-face teaching, students lacked the temporal and spatial framework of the curriculum and the possibility to socialise with fellow students was extremely limited. Increased demands were placed on the students' self-organisation, and the possibility of collaborative learning was drastically limited. The present study investigated the extent to which these study conditions affected the academic performance of medical students in the oral exam after the 60-hour introductory anatomy lecture. Specifically, collaborative, and organisational learning strategies were considered. MethodsLearning strategies of 146 students were assessed by means of a questionnaire. The anatomy exam was used to assess the academic performance. In addition, students were asked to rate their own oral exam performance. ResultsStudents used strategies of collaborative learning (M = 3.30, SD = 0.88) and self-organisation (M = 3.28, SD = 0.72) about equally as often. A significant correlation was found between the use of collaborative learning strategies and the ability for realistic self-assessment (ρ = −0.22, P =.02). Collaborative behaviour could be divided into Initiative Collaborative Learning and Subordinate Collaborative Learning. Only the former had an influence on the ability for realistic self-assessment. Neither organisational learning strategies nor collaborative learning strategies had an influence on academic performance. Discussion and conclusionInitiative collaborative learners showed a higher degree of realistic self-assessment. This could be explained through a more distinct social referencing, as fellow students’ knowledge levels can be related early on to their own academic performance. There was no correlation between the learning strategies used and the exam result.

MRI with generalized diffusion encoding reveals damaged white matter in patients previously hospitalized for COVID-19 and with persisting symptoms at follow-up.

There is mounting evidence of the long-term effects of COVID-19 on the central nervous system, with patients experiencing diverse symptoms, often suggesting brain involvement. Conventional brain MRI of these patients shows unspecific patterns, with no clear connection of the symptomatology to brain tissue abnormalities, whereas diffusion tensor studies and volumetric analyses detect measurable changes in the brain after COVID-19. Diffusion MRI exploits the random motion of water molecules to achieve unique sensitivity to structures at the microscopic level, and new sequences employing generalized diffusion encoding provide structural information which are sensitive to intravoxel features. In this observational study, a total of 32 persons were investigated: 16 patients previously hospitalized for COVID-19 with persisting symptoms of post-COVID condition (mean age 60 years: range 41-79, all male) at 7-month follow-up and 16 matched controls, not previously hospitalized for COVID-19, with no post-COVID symptoms (mean age 58 years, range 46-69, 11 males). Standard MRI and generalized diffusion encoding MRI were employed to examine the brain white matter of the subjects. To detect possible group differences, several tissue microstructure descriptors obtainable with the employed diffusion sequence, the fractional anisotropy, mean diffusivity, axial diffusivity, radial diffusivity, microscopic anisotropy, orientational coherence (Cc) and variance in compartment's size (CMD) were analysed using the tract-based spatial statistics framework. The tract-based spatial statistics analysis showed widespread statistically significant differences (P < 0.05, corrected for multiple comparisons using the familywise error rate) in all the considered metrics in the white matter of the patients compared to the controls. Fractional anisotropy, microscopic anisotropy and Cc were lower in the patient group, while axial diffusivity, radial diffusivity, mean diffusivity and CMD were higher. Significant changes in fractional anisotropy, microscopic anisotropy and CMD affected approximately half of the analysed white matter voxels located across all brain lobes, while changes in Cc were mainly found in the occipital parts of the brain. Given the predominant alteration in microscopic anisotropy compared to Cc, the observed changes in diffusion anisotropy are mostly due to loss of local anisotropy, possibly connected to axonal damage, rather than white matter fibre coherence disruption. The increase in radial diffusivity is indicative of demyelination, while the changes in mean diffusivity and CMD are compatible with vasogenic oedema. In summary, these widespread alterations of white matter microstructure are indicative of vasogenic oedema, demyelination and axonal damage. These changes might be a contributing factor to the diversity of central nervous system symptoms that many patients experience after COVID-19.

Spatial multicriteria framework for sustainable wind-farm planning – Accounting for conflicts

Considerable pressure is placed on wind power in Sweden due to the country's goal of generating 100% of its electricity from fossil free resources. The aim was to develop the REWIND methodological framework to support wind power planning, built on spatial multi-criteria analysis (SMCA). In addition, the purpose was to develop a conflict score as a novel component of this framework, for handling goal conflicts. This framework includes the scoping, design and evaluation stages. It was applied in a case study of Västernorrland County with extensive involvement of stakeholders throughout the process.The conflict score allows a separate analysis of trade-offs between factors, highlighting potential conflicts across the landscape to increase transparency. Thus, users are allowed to decide on a threshold on how much conflict among factors should be allowed for areas to qualify as planning alternatives. Critical issues that will need further attention concern quality and availability of data, creation of representative spatial indicators for the factors, weighting methods, and uncertainty analysis. The REWIND framework is open-ended and allows for further development to provide planning support that gives more control of factors and conflicts to be acceptable in real-world planning. Capacity building involving stakeholders in the design of planning alternatives are crucial. In Sweden, it can promote a more proactive planning process in the municipalities, supported by the regional actors, leading to a more predictable permitting process for developers. This will be useful for inclusive wind power planning in any country, since it is applicable on different scales.