Spatial Sets Research Articles

The role of pleiotropy and population structure in the evolution of altruism through the greenbeard effect.

Several empirical examples and theoretical models suggest that the greenbeard effect may be an important mechanism in driving the evolution of altruism. However, previous theoretical models rely on assumptions such as spatial structure and specific sets of pleiotropic loci, the importance of which for the evolution of altruism has not been studied. Here, we develop a population-genetic model that clarifies the roles of extrinsic assortment (e.g., due to population viscosity) and pleiotropy in the maintenance of altruism through the greenbeard effect. We show that, when extrinsic assortment is too weak to promote the evolution of altruism on its own, the greenbeard effect can only promote altruism significantly if there is a pleiotropic locus controlling both altruism and signaling. Further, we show that indirect selection via genetic associations is too weak to have a noticeable impact on altruism evolution. We also highlight that, if extrinsic assortment is strong enough to promote the evolution of altruism on its own, it also favors the spread of alleles encoding the other functions of a greenbeard trait (signaling and discriminatory behavior), as well as genetic associations. This occurs despite the fact that the greenbeard effect did not favor the evolution of altruism in the first place. This calls for caution when inferring the causality between greenbeard traits and the evolution of altruism.

Deep spatio-temporal feature fusion learning for multi-step building cooling load forecasting

Accurate forecasting of building cooling load is essential for optimizing control strategies and energy efficiency in heating, ventilation, and air conditioning (HVAC) systems. However, existing machine learning and deep learning algorithms encounter challenges in addressing the dynamics and uncertain nature of cooling load demand, as they solely focus on temporal analysis. To tackle this issue, the paper proposes CRG-Informer, a deep learning approach that integrates spatio-temporal features to enhance multi-step predictive performance. Firstly, a time series spatial control relationship graph set is constructed based on the interconnected structure, static attributes, and control characteristics of the cooling system. Then, a graph neural network (GNN) is designed for graph embedding to extract spatial correlation information at each instance. Additionally, the Informer model with the ProbSparse self-attention mechanism is utilized to capture long-term temporal dependencies among input sequences. This integration of components enables feature fusion, and the proposed approach is evaluated using data from an office building in Hangzhou, China, over an entire cooling season. Extensive experiments were conducted to compare our method with five superior baseline models for 6, 12, 24, 72, and 144-step cooling load forecasting across four types of data acquisition intervals. The results demonstrate that the CRG-Informer, through spatio-temporal feature fusion, outperforms existing methods and provides a novel solution for improving cooling load forecasting performance.

Gel-assisted mass spectrometry imaging enables sub-micrometer spatial lipidomics

A technique capable of label-free detection, mass spectrometry imaging (MSI) is a powerful tool for spatial investigation of native biomolecules in intact specimens. However, MSI has often been precluded from single-cell applications due to the spatial resolution limit set forth by the physical and instrumental constraints of the method. By taking advantage of the reversible interaction between the analytes and a superabsorbent hydrogel, we have developed a sample preparation and imaging workflow named Gel-Assisted Mass Spectrometry Imaging (GAMSI) to overcome the spatial resolution limits of modern mass spectrometers. With GAMSI, we show that the spatial resolution of MALDI-MSI can be enhanced ~3-6-fold to the sub-micrometer level without changing the existing mass spectrometry hardware or analysis pipeline. This approach will vastly enhance the accessibility of MSI-based spatial analysis at the cellular scale.

Bidirectional scale-aware upsampling network for arbitrary-scale video super-resolution

The performance of video super-resolution (VSR) has significantly improved. However, the current methods only focus on a single scale factor, treating the VSR of different scale factors independently and disregarding video super-resolution of arbitrary-scale factors. To address this issue, we propose a model, the Bidirectional Scale-Aware Upsampling Network for Arbitrary-Scale Video Super-Resolution, which eliminates the need for multiple models for various scale factors. We design a Bidirectional Scale-Aware Upsampling module in the proposed model, consisting of a Bidirectional Scale-Aware Module (BSAM) and a Spatial Pyramid Upsampling section. The BSAM extracts feature for various scale factors and allows feature information of different scales to interact bidirectionally. Additionally, we propose a Spatial Pyramid Loss that optimizes the network based on upsampling and maps the results of different scales to a unified spatial set to find the arbitrary-scale factor's loss. Along with this, we introduce an Explicit Feature Pyramid module, which uses Spatial Pyramid Upsampling to learn arbitrary-scale factor details explicitly. Finally, we demonstrate the extensibility of the model through a VSR algorithm integration with the Bidirectional Scale-Aware Upsampling, ensuring high-resolution results of arbitrary-scale factors without affecting the performance. Our comprehensive experiments on public benchmarks show promising results for video super-resolution of arbitrary-scale factors.

Gaussian-kernel weighted neighborhood preserving embedding algorithm and its application in fault detection

Fault detection in industrial processes is essential for enhancing production safety. Despite the application of the neighborhood preserving embedding (NPE) algorithm in fault detection as a manifold learning technique, a notable limitation exists-NPE overlooks local geometric structure, leading to suboptimal fault detection and occasional false alarms. This paper introduces the Gaussian kernel weighted NPE (KW-NPE) algorithm to address this challenge. Specifically designed for precise weight assignment in local structures, KW-NPE strategically employs the Gaussian kernel method to project the spatial neighborhood set and capture comprehensive local structural characteristics. The weight assignment, dependent on feature values, enhances the retention of intrinsic structure during dimensionality reduction. A novel objective function further augments this process.To assess performance, a comprehensive composite index is introduced in a case study, amalgamating the false alarm rate and fault detection rate. The effectiveness of the KW-NPE algorithm is demonstrated through extensive simulations and its application to the Tennessee Eastman process dataset, highlighting its superiority over conventional approaches.

Data collection for assessment of the natural capital at the regional level: case study of LTSER Trnava region

ContextThe landscape provides not only a living space for all life forms, including humans, but also a spatial base and set of resources for the implementation of individual human activities. Inappropriate implementation of human activities, disrespecting the properties of the landscape's natural resources, causes the degradation of natural resources and, consequently, the human living.ObjectivesThe aim of this paper is to develop new methodological procedures and algorithms for effective assessment of natural capital based on the geosystem approach.MethodsEach territorial unit (geosystem) represents a unique combination of natural assets that create a certain potential for the development of individual activities and eco-stabilization functions. In this study, we developed a new approach and algorithms to assess the natural capital of landscapes for sustainable use. This involves selecting indicators and their functional interpretation, as well as collecting available spatial data and statistics for GIS analysis, synthesis, and modeling.ResultsThe methodological procedure consists of the determination of indicators for natural capital assessment, the determination of their functional values and weighting coefficients, the determination of the suitability of the geosystem for the implementation of individual activities based on the value of natural capital, and the determination of restrictions and limiting factors. The set of data on landscape assets can be categorized into abiotic, land cover and biotic, and socio-economic indicators, which can either support human activities or limit them. Options for sustainable use of natural capital were split into two groups of potential activities: (I) natural capital for landscape planning activities and (II) specific activities or functions (e.g., natural capital for energy use, recreation, regulation services). The modeling of eco-stabilizing natural capital in Trnava LTSER pointed to low spatial ecological stability, mainly in the central part of the district.DiscussionDiscussion pointed to strength, novelty and opportunities of implementing methodological approach to natural capital assessment.ConclusionsAs an output of this methodological approach, a comprehensive digital spatial database of landscape-ecological data for the assessment of natural capital and the suitability of its use for socio-economic activities has been created in Slovakia. The database represents a set of consistent spatial information on natural capital assets and other indicators, including land cover and socio-ecological indicators. The methodological approach can be applicable to any territory on the basis of a modification.

Abstract 1163: Increased spatial coupling of integrin and collagen IV in the immunoresistant clear cell renal cell carcinoma tumor microenvironment

Abstract Immunotherapy (IO) has improved the survival of patients with clear cell renal cell carcinoma (ccRCC); however, most patients will not have a durable complete response and will die from their disease. Our understanding of the development of IO resistance for ccRCC is very limited. We employed spatial transcriptomics (ST) with single cell resolution on patients with IO naïve tumors and IO exposed tumors to characterize spatial properties and differences between these two populations. Fourteen matched tumor and adjacent stromal fields of view (FOV) from surgically resected primary kidney specimens were analyzed using CosMx Spatial Molecular Imager (SMI). Non-tumor cell phenotyping was performed using the Kidney Cell Atlas. A generalized linear model was developed to identify malignant tumor cells in kidney tissues and validated by a pathologist. Differential cell abundance, clustering, and gene expression for each cell phenotype were compared between the two groups. Spatial gene set enrichment (GSEA) of modified hallmark gene sets were also compared. CD8+ T cells were more abundant in IO exposed vs IO naïve tumor FOVs. YES1 expression in the malignant tumor cells was significantly higher in IO exposed samples compared to IO naïve samples (false discovery rate [FDR] = 0.084). Spatial GSEA showed significant clustering of many gene sets (cells of high enrichment closer together than expected by chance), most notably epithelial-mesenchymal transition (EMT) pathway. Next, a post-hoc analysis of genes belonging to known ligand-receptor pairs in the EMT pathway was completed using bivariate Moran’s I to estimate spatial autocorrelation. We found significantly higher spatial autocorrelation of ITGAV-COL4A1 in the stroma of IO exposed FOVs than IO naïve FOVs (FDR = 0.008). Cell types with the highest expression of ITGAV and COL4A1 were found to be fibroblasts, endothelial, and tumor cells. Protein validation with multiplex immunofluorescence (mIF) confirmed cells positively stained for anti-integrin alpha-V were more abundance on stromal FOVs (p < 0.001) in IO exposed samples. Fibroblast (SMA+) cells in stromal FOVs were more likely to be positively stained for anti-integrin alpha-V (p < 0.001) in IO exposed than IO naïve FOVs. ITGAV protein is involved in migration from primary to metastatic disease in several cancers, and its inhibition increases T cell killing indicating ccRCC endothelial and tumor cells may be using this signaling for immune evasion and disease progression. Further, collagen and cancer associated fibroblasts (CAFs) play a role in tumor growth. To our knowledge, this is the first study to leverage single-cell resolution ST to identify potential ways by which ccRCC tumors become resistant to IO therapy. Citation Format: Alex C. Soupir, Mitchell T. Hayes, Taylor C. Peak, Oscar E. Ospina, Nicholas H. Chakiryan, Anders E. Berglund, Paul A. Stewart, Jonathan Nguyen, Carlos M. Moran-Segura, Natasha L. Francis, Paola M. Ramos-Echevarria, Jad Chahoud, Roger Li, Kenneth Y. Tsai, Jodi A. Balasi, Yamila Caraballo-Perez, Jasreman Dhillon, Lindsey A. Martinez, Warren E. Gloria, Nathan Schurman, Sean Kim, Mark Gregory, James J. Mule, Brooke L. Fridley, Brandon J. Manley. Increased spatial coupling of integrin and collagen IV in the immunoresistant clear cell renal cell carcinoma tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1163.

On matched asymptotic expansion solutions of a bucklewave propagation problem

AbstractIn the present paper, the method of matched asymptotic expansions is used to derive approximate analytical solutions to a model problem related to bucklewave propagation in undersea pipelines. The model problem, introduced by Chater, Hutchinson and Neale (in: Collapse – The Buckling of Structures in Theory and Practice (Thompson, J.M.T., Hunt, G.W., eds.) Cambridge Univ. Press, Cambridge, 1983), consists of a linear elastic beam resting on a nonlinear elastic foundation, with inclusion of axial tension and inertia, related to a propagating buckle. When tension dominates over inertia, it is found that the asymptotic solution describing a propagating buckle has the character of nonoscillatory motion. When tension and inertia forces are of equal magnitude, or when inertia forces dominate over tension, the solutions are found to be oscillatory, but still localized around the bucklewave front. Finally, it is shown that when the propagation speed of the buckle exceeds a critical value, a spatial instability sets in, with exponentially growing amplitude of the beam deflection.

Distributed temporal–spatial neighbourhood enhanced variational autoencoder for multiunit industrial plant‐wide process monitoring

AbstractProcess monitoring plays an essential role in ensuring the safe and efficient operation of multi‐unit plantwide industrial processes. However, complicated nonlinear dynamics of these processes pose enormous challenges to the plantwide process monitoring approaches. In this paper, a novel distributed temporal–spatial neighbourhood enhanced variational autoencoder (DTS‐VAE) monitoring scheme is proposed. The scheme selects the temporal and spatial neighbourhood sets of a current sample from a local unit. Time‐series correlation is utilized to construct temporal representative samples to obtain process dynamic characteristics. Likewise, spatial similarity is employed to construct spatial representative samples to obtain spatial patterns in the data. Then the reconstructed and current samples serve as the inputs for a variational autoencoder to extract features from the current sample and its neighbourhoods. Subsequently, a distributed monitor that considers the temporal–spatial characteristics is established for each unit. Finally, a comprehensive evaluation index is developed using Bayesian fusion strategy to improve monitoring performance. The proposed DTS‐VAE monitoring scheme was effectively verified on the Tennessee Eastman benchmark process and a wastewater treatment plant.

Multiscale lung nodule segmentation based on 3D coordinate attention and edge enhancement

<abstract> <p>An important prerequisite for improving the reliability of lung cancer surveillance and clinical interventions is accurate lung nodule segmentation. Although deep learning is effective at performing medical image segmentation, lung CT image heterogeneity, nodule size, shape, and location variations, convolutional localized feature extraction characteristics, the receptive field limitations of continuous downsampling, lesion edge information losses, fuzzy boundary segmentation challenges, and the low segmentation accuracy achieved when segmenting lung CT images using deep learning remain. An edge-enhanced multiscale Sobel coordinate attention-atrous spatial convolutional pooling pyramid V-Net (SCA-VNet) algorithm for lung nodule segmentation was proposed to solve these problems. First, a residual edge enhancement module was designed, which was used to enhance the edges of the original data. Using an edge detection operator in combination with a residual module, this module could reduce data redundancy and alleviate the gray level similarity between the foreground and background. Then, a 3D atrous spatial convolutional pooling pyramid module set different expansion rates, which could obtain feature maps under different receptive fields and capture the multiscale information of the segmentation target. Finally, a three-dimensional coordinate attention network (3D CA-Net) module was added to the encoding and decoding paths to extract channel weights from multiple dimensions. This step propagated the spatial information in the coding layer to the subsequent layers, and it could reduce the loss of information during the forward propagation process. The proposed method achieved a Dice coefficient of 87.50% on the lung image database consortium and image database resource initiative (LIDC-IDRI). It significantly outperformed the existing lung nodule segmentation models (UGS-Net, REMU-Net, and multitask models) and compared favorably with the Med3D, CENet, and PCAM_Net segmentation models in terms of their Dice coefficients, which were 3.37%, 2.2%, and 1.43%, respectively. The experimental results showed that the proposed SCA-VNet model attained improved lung nodule segmentation accuracy and laid a good foundation for improving the early detection rate of lung cancer.</p> </abstract>

SVFR: A novel slice-to-volume feature representation framework using deep neural networks and a clustering model for the diagnosis of Alzheimer's disease

Deep neural networks (DNNs) have been effective in classifying structural magnetic resonance imaging (sMRI) images for Alzheimer's disease (AD) diagnosis. In this study, we propose a novel two-phase slice-to-volume feature representation (SVFR) framework for AD diagnosis. Specifically, we design a slice-level feature extractor to automatically select informative slice images and extract their slice-level features, by combining DNN and clustering models. Furthermore, we propose a joint volume-level feature generator and classifier to hierarchically aggregate the slice-level features into volume-level features and to classify images, by devising a spatial pyramid set pooling module and a fusion module. Experimental results demonstrate the superior performance of the proposed SVFR, surpassing the majority of the state-of-the-art methods and achieving comparable results to the best-performing approach. Experimental results also showcase the efficacy of the slice-level feature extractor in the selection of informative slice images, as well as the effectiveness of the volume-level feature generator and classifier in the integration of slice-level features for image classification. The source code for this study is publicly available at https://github.com/gll89/SVFR.

ACO intelligent task scheduling algorithm based on Q-learning optimization in a multilayer cognitive radio platform

With the rapid development of cognitive radio technology, multilayer heterogeneous cognitive radio computing platforms with large computing, high-throughput, ultralarge bandwidth and ultralow latency have become a research hotspot. Aiming at the core scheduling problems of multilayer heterogeneous computing platforms, this paper abstracts the bidirectional interconnection topology, node computing capacity, and internode communication capability of the heterogeneous computing platform into an undirected graph model and abstracts the nodes with dependencies, nodes’ computing requirements, and internode communication requirements in streaming tasks into a directed acyclic graph (DAG) model so as to transform the task-scheduling problem into a deployment-scheduling problem from DAG to undirected graph. To efficiently solve this graph model, this paper calculates and forms a component scheduling sequence based on the dependencies of functional components in streaming domain tasks. Then, according to the scheduling sequence, ant colony optimization (ACO) algorithms, such as ant colonies and Q-learning select functional components, deploy components to different computing nodes, calculate the scheduling cost, guide the solution space search of agents, and complete the scenario migration adaptation of the scheduling algorithms to intelligent scheduling of domain tasks. So, this paper proposes the ACO field task intelligent scheduling algorithm based on Q-learning optimization (QACO). QACO uses the Q-table matrix of Q-learning as the initial pheromone of the ant colony algorithm, which not only solves the dimensional disaster of the Q-learning algorithm but also accelerates the convergence speed of the ant colony intelligent scheduling algorithm, reduces the task scheduling length, and further enhances the search ability of the existing scheduling algorithm to solve the spatial set. Based on the randomly generated DAG domain task map, three experimental test scenarios are designed to verify the algorithm performance. The experimental results show that compared with the Q-learning, ACO, and genetic algorithms (GA) algorithms, the proposed algorithm improves the convergence speed of the solution by 72.3%, 63.4%, and 64% on average, reduces the scheduling length by 2.8%, 2.2%, and 0.9% on average, and increases the parallel acceleration ratio by 2.8%, 2.1%, and 0.9% on average, respectively. The practical application value of the algorithm is analyzed through typical radar task simulation, but the load balancing of the algorithm needs to be further improved.

Light-field spectral decomposition with a spatial–angular consistency prior for disparity estimation

Modeling the spatial–angular relationship is the theoretical basis for accurate disparity estimation from the light-field data. In this paper, the spatial–angular coupling relationship of light-field is established under the assumption of constant radiance along rays, and the spatial–angular consistency model of light-field is established under the assumption of Lambertian radiance. Based on the spatial–angular consistency prior of light-field, the light-field spectral decomposition based on disparity layers can be modeled as the ill-posed inverse problem. Based on the spectral decomposition, the disparity estimation is transformed into the process of searching for the spatial domain support set in the scene disparity hierarchy. The normalized cross-correlation function is utilized to estimate the disparity based on spectral decomposition under the spatial–angular consistency of the light-field. The performance on both simulated and real captured data show that the proposed algorithm is effective at high-precision and dense disparity estimation.

Detecting drought stress occurrence using synergies between Sun induced fluorescence and vegetation surface temperature spatial records

Drought stress occurrence and recovery from drought can be detected using a single spatial set of simultaneous observations of SIF and canopy temperature records. Temporal and spatial responses to drought and heat stresses by plant stands of a drought-adapted diverse grassland ecosystem were studied using sun induced fluorescence (SIF,O2A and O2B bands) and further ecophysiological (canopy temperature (Tsurf), spatially modeled evapotranspiration, vegetation reflectance spectra) variables collected along spatial sampling grids while also utilizing eddy covariance measured carbon dioxide (net ecosystem exchange: NEE, gross primary production: GPP) and water flux (evapotranspiration: ET) data. The grids were of 0.5 and 5 ha spatial extents and contained 78 sampling points. Data were collected in four spatial sampling campaigns, two under drought (early summer) and another two during and after recovery (midsummer) at both spatial resolutions.Small values of spatial SIF_A averages (around 0.5 mW m−2 nm−1 sr−1) under strong early summer drought increased (to around 2 mW m−2 nm−1 sr−1) due recovery upon rain arrivals, showing high (R2: 0.8–0.88) positive temporal correlations to eddy covariance measured carbon (GPP, NEE) and water (ET) fluxes. Spatial averages of LAI, vegetation indices (NDVI, NIRv) and modeled ET followed similar temporal patterns.While SIF was depressed by drought, it showed higher values in high canopy temperature vegetation patches than in vegetation patches with lower Tsurf. The spatial pattern of higher SIF in higher Tsurf patches was persistent (2 weeks) under drought. The positive SIF_A-Tsurf spatial correlation turned into negative/not significant after recovery of the grassland from the drought, while hot summer weather persisted. It is proposed that, by using a single set of simultaneously measured spatial SIF and Tsurf data it is possible to infer whether the studied vegetation is under drought (and heat) stress while it could not be decided on the base of SIF data alone. Evaluation of the slope of the above relationship seems therefore beneficial before e.g. starting the (stress) classification procedure based on SIF.

Data Analysis of Linguistic Landscape in the Context of “Internationalization +” Urban Construction

Abstract With the rapid development of internationalization and urban construction, the urban linguistic landscape has become an area of great interest. The purpose of this paper is to explore the data analysis method of urban visual linguistic landscape based on line-of-sight orientation and to optimize the design of urban linguistic landscape based on an improved genetic algorithm. The basic visual analysis unit and the viewable area analysis unit of the linguistic landscape are proposed to quantify the visual characteristic indexes and the optimal view distance of the linguistic landscape. At the same time, the viewable spatial point set analysis unit is used to obtain the view accessibility of the language landscape. Finally, the linguistic landscape optimization model was evaluated and analyzed for application effects. The results show that through the optimized design of 10 linguistic spots in H City National Forest Park, the average score of each spot evaluation is 3.48, and the full score ratio is 63.18%. The urban language landscape optimization model proposed in this paper can effectively improve the aesthetics and visual sensitivity of the urban language landscape.

Interference Analysis of NGSO Constellation to GEO Satellite Communication System Based on Spatio–Temporal Slices

The co-frequency interference (CFI) of the non-geostationary orbit (NGSO) constellation to the geostationary earth orbit (GEO) satellite internet of things (IoT) system is an nonnegligible problem. In this paper, based on the calculation method of the NGSO constellation satellite distribution state period, we propose a global characteristic method to analyze the CFI of the NGSO constellation to the GEO satellite IoT system by spatio-temporal slices. In order to analyze and evaluate the global characteristic and overall situation of interference, the temporal and spatial cut set of interference is established by fragmenting time and gridding space. The correctness of the proposed NGSO constellation satellite distribution state period is verified by taking the OneWeb constellation’s interference to the SINOSAT-5 satellite communication system as the simulation scenario. Furthermore, the global interference characteristic of the OneWeb constellation to the SINOSAT-5 satellite communication system is analyzed with the method mentioned above in the range of 80∘ 140∘E and 0∘ 50∘N. The results show that the analysis method can reflect the overall situation of the CFI of the NGSO constellation to the GEO satellite IoT system.

Wind Power Group Prediction Model Based on Multi-Task Learning

Large-scale wind power grid connection increases the uncertainty of the power system, which reduces the economy and security of power system operations. Wind power prediction technology provides the wind power sequence for a period of time in the future, which provides key technical support for the reasonable development of the power generation plan and the arrangement of spare capacity. For large-scale wind farm groups, we propose a cluster model of wind power prediction based on multi-task learning, which can directly output the power prediction results of multiple wind farms. Firstly, the spatial and temporal feature matrix is constructed based on the meteorological forecast data provided by eight wind farms, and the dimensionality of each attribute is reduced by the principal component analysis algorithm to form the spatial fusion feature set. Then, a network structure with bidirectional gated cycle units is constructed, and a multi-output network structure is designed based on the Multi-gate Mixture-of-Experts (MMoE) framework to design the wind power group prediction model. Finally, the data provided by eight wind farms in Jilin, China, was used for experimental analysis, and the predicted average normalized root mean square error is 0.1754, meaning the prediction precision meets the scheduling requirement, which verifies the validity of the wind power prediction model.

Input data resolution affects the conservation prioritization outcome of spatially sparse biodiversity features.

Detailed spatial data are an essential part of land use planning and decision-making. Their spatial resolution sets limitations to their use, as coarse datasets are not suitable for detecting small-scale phenomena. In this study, we explored the effects of spatial resolution on the ecological outcome of a conservation prioritization process in Zonation software. Our study area was in Evo, southern Finland, covering a mosaic of managed and conserved forests. We produced the feature layers describing the forest characteristics using high-resolution remote sensing datasets, object-based mapping methods, and forest site type data. We found that increasing the resolution above the 16 m baseline resolution resulted in substantial errors. The conservation errors were the highest for rare features related to European Aspen, whereas the common features related to dominant tree species could benefit from the growth of cell size. We conclude that adequate spatial resolution is a prerequisite for efficient conservation prioritization, and that the size and spatial distribution of the features affect the resolution requirements.

1H- and 2H-1,2,3-triazoles hybrids: Comparative study of photophysical properties

The photophysical properties of three types of hybrid molecules, containing two 1,2,3-triazole isomers with different spatial arrangements and set of surrounding substituents, were investigated. The effect of the heterocyclic scaffold construction and the nature and position of the substituents were revealed by spectral and computational investigations. UV–Vis absorption spectra showed maxima with high molar extinction coefficients. Moderate blue emission (QYs up to 42.2%) and average fluorescence life-time of 1.05–4.53 ns were determined. Positive solvatochromism and intramolecular charge transfer were detected and confirmed by the quantum mechanical calculations. A peculiar effect of a hydrogen bond formation was registered in MeOH, EtOH, i-PrOH, ethylene glycol (EG) solutions and water mixtures. The compounds exhibited an Aggregation Induced Emission Enhancement (AIEE) effect in DMSO-H2O mixtures with a 1.3–3.1 fold increase of QYs and a 1.1–2.8 fold increase of fluorescence life-time. Molecular geometries and electronic characteristics were studied at (Time Dependent) Density Functional Theory ((TD-)DFT) level. The biological behavior of 2H-bis[1,2,3]triazolo)[5,1-a:4′,5′-c]isoquinolines was studied by means of confocal microscopy experiments, which showed that these compounds successfully penetrate the cell membranes, accompanied by bright emission in the cytoplasmic region of the confocal micrographs.

An inquiry on rebel cities: How spatial morphology sets the stage for urban movements

The common thread to urban movements happening worldwide in recent years is the fact that urban public space is used as a significant setting by city dwellers for expressing their “objections”. What has been experienced throughout urban movements when public spaces have been occupied enables us to grasp the meaning of occupied spaces in the city thus allowing us to get to know societies and cities. Therefore, this research has investigated the impact of urban public space on the consciousness, interaction and gathering of city dwellers as well as urban movements. Within the scope of the research, eight “rebel cities” have been analyzed, and have interviews with participants of urban movements from these cities. These are Tunis, Cairo, Barcelona, London, New York, Dublin, Paris, and Hamburg, respectively. The places where urban movements were visible in urban space and their surroundings have been analyzed using the Space Syntax method, and the gathering/unification/integration potential of public space has been spatially investigated by determining the characteristics of urban patterns. Accordingly, the city affects the formation of urban movements with its spatial pattern. In the case of Merida city, which constitutes the control sample and which was not affected by the urban movements that spread to the whole world, this finding is also supported. With the results obtained in the research, the significance of public space, as an essential element contributing to the formation of urban movements, has been proven. This study further reveals the possibility of urban spaces allowing social encounters and its importance in terms of democracy.