Proximity Metrics Research Articles

Flood impact assessment in remote areas using machine learning, SAR, and GIS: a case study of Ngabang District, Indonesia

ABSTRACT Flooding in remote regions presents significant challenges due to data scarcity, complicating impact assessment and mitigation efforts. This research delineates an integrated methodology for quantifying flood impacts in such contexts. By leveraging machine-learning algorithms, Sentinel-1 synthetic aperture radar (SAR) imagery was combined with digital elevation model data and river proximity metrics to predict and accurately demarcate flood extents. Geographic information systems overlay techniques were then employed for spatial analysis of the floods’ impacts on population and infrastructural assets. The methodology was applied in a case study in Ngabang District, Indonesia, demonstrating its utility. Analysis using decision tree, random forest (RF), and gradient boosting machine models provided critical insights into flood prediction factors. The RF model was chosen as the best, successfully identified flood-prone regions, achieving an accuracy of 0.94 and a Kappa of 0.87 on the testing data, demonstrating its robustness. The flood map showed significant impacts, affecting 373.81 hectares, 10,706 people, 1,500 buildings, and 15 km of roads. This study highlights the importance of proximity, elevation, SAR imagery, and iterative model improvements in flood prediction, offering valuable insights for flood management and mitigation efforts in data-scarce regions.

Altered oxidative stress and antioxidant biomarkers concentrations in pregnant individuals exposed to oil and gas sites in Northeastern British Columbia.

Northeastern British Columbia is a region of prolific unconventional oil and gas (UOG) activity. UOG activity can release volatile organic compounds (VOCs) which can elevate oxidative stress and disrupt antioxidant activity in exposed pregnant individuals, potentially increasing the risk of adverse pregnancy outcomes. This study measured biomarkers of oxidative stress and antioxidant activity in pooled urine samples of 85 pregnant individuals living in Northeastern British Columbia, to analyze associations between indoor air VOCs, oil and gas well density and proximity metrics, and biomarker concentrations. Concentrations of catalase, superoxide dismutase (SOD), glutathione S-transferase, total antioxidant capacity, 6-hydroxymelatonin sulfate (aMT6s), malondialdehyde (MDA), 8-hydroxy-2'-deoxyguanosine (8-OHdG), and 8-isoprostane were measured using assay kits. Associations between exposure metrics and biomarker concentrations were determined using multiple linear regression models adjusted for biomarker-specific covariables. UOG proximity was associated with decreased SOD and 8-OHdG. Decreased 8-OHdG was associated with increased proximity to all wells. Decreased aMT6s were observed with increased indoor air hexanal concentrations. MDA was negatively associated with indoor air 1,4-dioxane concentrations. No statistically significant associations were found between other biomarkers and exposure metrics. Although some associations linked oil and gas activity to altered oxidative stress and antioxidant activity, the possibility of chance findings due to the large number of tests cannot be discounted. This study shows that living near UOG wells may alter oxidative stress and antioxidant activity in pregnant individuals. More research is needed to elucidate underlying mechanisms and to what degree UOG activity affects oxidative stress and antioxidant activity.

Abstract A035: Stromal composition, fibroblast heterogeneity and spatial organization in pancreatic adenocarcinoma

Abstract Background: The pancreatic ductal adenocarcinoma (PDAC) microenvironment is characterized by highly abundant stromal fibroblasts that are hypothesized to influence tumor behavior. While recent studies have identified multiple populations of stromal fibroblasts with divergent roles in PDAC biology, stromal composition remains incompletely characterized in human tumors, including the landscape of fibroblast subtypes and their spatial organization. Design: We designed multiplex immunofluorescence assays to quantify tumor cells (cytokeratin), immune cells (CD45), fibroblasts (αSMA, CD74, FAP, LaminA), other cells (CD31, CD56, CALD1, NG2), and collagen I in fixed tissue sections. Combinatorial fibroblast marker expression identified myofibroblasts (myCAF), inflammatory fibroblasts (iCAF), antigen-presenting fibroblasts (apCAF), and other hybrid fibroblast subtypes. We characterized fibroblast populations in up-front resected and neoadjuvant-treated cohorts of patients with localized PDAC and in patients with biopsies of metastatic lesions. Using digital imaging, supervised machine learning, and spatial proximity metrics, we quantified cell phenotypes, fibroblast subtype composition and spatial organization at the single-cell level and in the context of tumor clinicopathologic features and patient outcomes. Results: Across 301 up-front resected tumors, we cumulatively analyzed 1.7 million cells, including 611,000 fibroblasts. Stromal fibroblast density ranged from 632 to 4150 cells/mm2. Five main fibroblast subtypes constituted more than 85% of the fibroblast population in these tumors: αSMA+ (myCAF), FAP+αSMA+ (FAP+ myCAF), LaminA+ (iCAF), αSMA+ LaminA+ (myCAF/iCAF), and FAP+ αSMA+ LaminA+ (FAP+ myCAF/iCAF). Unexpectedly, 39% of fibroblasts showed co-expression of markers previously considered distinct for major fibroblast subtypes. By unsupervised clustering of fibroblast subtype proportions, we identified 3 fibroblast-defined tumor subgroups, including tumors rich in FAP+ fibroblasts, hybrid fibroblasts, and single positive fibroblasts (myCAFs, iCAFs, and apCAFs), which were associated with patient survival. Furthermore, fibroblast subtypes were spatially organized, with preferential localization of FAP+ myCAFs near tumor cells and iCAFs more distant from tumor cells yet closer to immune cells. FAP+ myCAFs were more abundant in basal-like tumors than in classical tumors, and FAP+ myCAFs were located closer to basal-like tumor cells than classical tumor cells within individual tumors. Compared to up-front resected primary tumors, neoadjuvant FOLFIRINOX treated tumors and metastatic lesions harbored distinct fibroblast profiles. Conclusion: Fibroblasts in the PDAC microenvironment are heterogeneous both in abundance and subtype composition, exhibit distinct spatial organization and are associated with patient outcomes. Treatment with FOLFIRINOX alters the composition of fibroblast subtypes in primary PDAC, and metastatic PDAC lesions have a different fibroblast composition compared to primary tumors. Citation Format: Dalia Elganainy, Andressa Dias Costa, Alexander Jordan, Suryun Kim, Sara A. Väyrynen, Hannah L. Williams, Chen Yuan, Douglas A. Rubinson, Kimberly Perez, Harshabad Singh, Richard F. Dunne, Thomas E. Clancy, David C. Linehan, Daniel T. Chang, Aram F. Hezel, Albert C. Koong, Andrew Aguirre, Brian M. Wolpin, Jonathan A. Nowak. Stromal composition, fibroblast heterogeneity and spatial organization in pancreatic adenocarcinoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr A035.

The role of street network metrics in shaping distance distributions in a residential neighbourhood

Walkability studies often rely on physical proximity metrics for destination accessibility. However, some recent studies argue that configuration (topological) distance provides a more accurate reflection of the actual distances experienced by individuals. This study therefore aims to test the extent to which an association exists between network-metric proximity to several destinations and two main measures of space syntax – integration and choice – in a dendriform network structure such as Putrajaya city (Malaysia). Using GIS, multiple buffer-service areas were generated around each housing unit (N = 2,392). Interpolation and space syntax analyses were conducted to assess metric and topological distance at local and global levels for each buffer. The statistical analysis showed that distances to commercial areas, transportation and average distances to all destinations were strongly affected by the syntactical properties of the neighbourhood. Moreover, global measures were more powerful in detecting metric-distance changes compared to local measures. Aligned with natural movement theory, these results support the idea that the spatial properties of urban form have a significant impact on distance distributions within residential settings, specifically distances to commercial areas. Therefore, policies that promote mixed-use development, especially in areas with good transportation access, should consider the impact of syntactical accessibility of the network.

ENHANCING HUMAN-ROBOT INTERACTION THROUGH GROUP EMOTION RECOGNITION

Abstract - This article explores within the field of Human-Robot Interaction (HRI), focusing on the complicated relationship between emotions, decision-making, and robot behaviors. Emotions are essential to effective communication and interaction, requiring the development of emotion recognition systems in robots. The article explores both individual and group emotion recognition, including microexpressions and macroexpressions. Group emotion dynamics, encompassing phenomena like emotional contagion, convergence, and social influence, are separated to understand how emotions combine within collective settings. A concept, Group Emotion Recognition (GER), is introduced, providing a framework for recognizing emotions within groups. GER involves proximity metrics, emotion classification, and entropy-based analysis to quantify emotion diversity. The article also outlines how GER can enhance user engagement, personalize interactions, improve group dynamics, and foster social acceptance in various human-robot interaction scenarios. Decision-making based on GER, driven by positive or negative emotion labels, is discussed, highlighting the adaptability and sensitivity required for effective human-robot interactions. Ethical considerations regarding the use of emotion recognition technology are addressed throughout the article, emphasizing responsible implementation. Overall, this work lays a solid foundation for advancing the field of HRI by integrating emotion recognition and decision-making to create emotionally intelligent and socially aware robots.

A new proximity metric based on optimality conditions for single and multi-objective optimization: Method and validation

With the widespread application of Evolutionary Algorithms (EAs), their performance needs to be evaluated using more than the usual performance metrics. In the EA literature, various metrics assess the convergence ability of these algorithms. However, many of them require prior knowledge of the Pareto-optimal front. Recently, two Karush–Kuhn–Tucker Proximity Metrics (KKTPMs) have been introduced to measure convergence without needing prior knowledge. One relies on the Augmented Achievement Scalarization Function (AASF) method (AASF-KKTPM), and the other on Benson’s method (B-KKTPM). However, both require specific parameters and reference points, making them computationally expensive. In this paper, we introduce a novel version of KKTPM applicable to single-, multi-, and many-objective optimization problems, utilizing the Penalty-based Boundary Intersection (PBI) method (PBI-KKTPM). Additionally, we introduce an approximate approach to reduce the computational burden of solving PBI-KKTPM optimization problems. Through extensive computational experiments across 23 case studies, our proposed metric demonstrates a significant reduction in computational cost, ranging from 20.68% to 60.03% compared to the computational overhead associated with the AASF-KKTPM metric, and from 16.48% to 61.15% compared to the computational overhead associated with the B-KKTPM metric. Noteworthy features of the proposed metric include its independence from knowledge of the true Pareto-optimal front and its applicability as a termination criterion for EAs. Another feature of the proposed metric is its ability to deal with black box problems very efficiently.

Diversity, solidarity and the construction of the ingroup among (post)colonial migrants in The Netherlands, 1945–1968

ABSTRACT Concerns about the impact of immigration (‘diversity’) on welfare states (‘solidarity’) are widespread among political economists. This article presents an alternative theoretical framework for understanding their relationship. Using social and cultural theory, I argue that it is tautological to suggest that diversity and solidarity covary; both emerge out of the same ideological and material efforts to construct an ‘ingroup.’ I probe this theory with a historical case study of the inclusion of (post)colonial migrants in the Netherlands from 1945 to 1968. Complementing secondary literature with original archival research, I show key state and non-state agents of the emergent Dutch welfare state constructing racial categories by fixing ‘Westernness’ or ‘rootedness’ as a salient determinant of ingroup membership, locating (post)colonial migrants in relation to it, and distributing entry, citizenship and social rights accordingly. An elusive metric of cultural proximity, ‘Westernness’ was under construction at the same time as it was in use, as state officials, social workers, and private charities negotiated its meaning with the public. The article not only compels European welfare scholarship to acknowledge race and racism in its recent past, but also builds theory regarding the influence of identity on redistributive outcomes.

Long-term exposure to traffic-related air pollution and stroke: A systematic review and meta-analysis.

Stroke remains the second cause of death worldwide. The mechanisms underlying the adverse association of exposure to traffic-related air pollution (TRAP) with overall cardiovascular disease may also apply to stroke. Our objective was to systematically evaluate the epidemiological evidence regarding the associations of long-term exposure to TRAP with stroke. PubMed and LUDOK electronic databases were searched systematically for observational epidemiological studies from 1980 through 2019 on long-term exposure to TRAP and stroke with an update in January 2022. TRAP was defined according to a comprehensive protocol based on pollutant and exposure assessment methods or proximity metrics. Study selection, data extraction, risk of bias (RoB) and confidence assessments were conducted according to standardized protocols. We performed meta-analyses using random effects models; sensitivity analyses were assessed by geographic area, RoB, fatality, traffic specificity and new studies. Nineteen studies were included. The meta-analytic relative risks (and 95% confidence intervals) were: 1.03 (0.98-1.09) per 1μg/m3 EC, 1.09 (0.96-1.23) per 10μg/m3 PM10, 1.08 (0.89-1.32) per 5μg/m3 PM2.5, 0.98 (0.92; 1.05) per 10μg/m3 NO2 and 0.99 (0.94; 1.04) per 20μg/m3 NOx with little to moderate heterogeneity based on 6, 5, 4, 7 and 8 studies, respectively. The confidence assessments regarding the quality of the body of evidence and separately regarding the presence of an association of TRAP with stroke considering all available evidence were rated low and moderate, respectively. The available literature provides low to moderate evidence for an association of TRAP with stroke.

Multi-Hazard property buyouts: Making a case for the acquisition of flood and contaminant-prone residential properties in Galena Park, TX

The aftermath of extreme flood events can be particularly devastating for residential communities in proximity to flood-prone petrochemical facilities, as they are likely to experience multiple impacts from a single flood event. Hazard impacts could be from flood inundation to their properties, and floodwaters carrying contaminants from nearby facilities into their homes. While property acquisition or buyouts have been effectively used as a flood mitigation strategy, current buyout selection processes only factor in flood exposure, thereby ignoring other hazards such as exposure of properties to potential chemical substance transfer. In this paper, we identify properties that are eligible for flood buyouts but are also at a high risk of contaminant transferal during extreme flood events. We apply a benefit–cost analysis methodology to assess the economic viability of buyouts and proximity metrics to prioritize buyouts of contaminant-prone properties in Galena Park, Texas. Results indicate that, in selected flood-only property acquisition scenarios, cumulative avoided flood losses exceed the cost of property acquisition. However, although with lower cost-benefit values, a selection criterion that factors a combination of flood and contaminant-prone properties as buyouts results in multiple properties being removed from harm’s way. Our findings emphasize the potential economic benefits of applying a multi-hazard selection criterion in dealing with flood property buyouts, especially in socially vulnerable communities with high exposure to both flooding and contaminant transfer.

Evaluating the Utility of High-Resolution Proximity Metrics in Predicting the Spread of COVID-19

High resolution mobility datasets have become increasingly available in the past few years and have enabled detailed models for infectious disease spread including those for COVID-19. However, there are open questions on how such mobility data can be used effectively within epidemic models and for which tasks they are best suited. In this paper, we extract a number of graph-based proximity metrics from high resolution cellphone trace data from X-Mode and use it to study COVID-19 epidemic spread in 50 land grant university counties in the US. We present an approach to estimate the effect of mobility on cases by fitting an ordinary differential equation based model and performing multivariate linear regression to explain the estimated time varying transmissibility. We find that, while mobility plays a significant role, the contribution is heterogeneous across the counties, as exemplified by a subsequent correlation analysis. We also evaluate the metrics’ utility for case surge prediction defined as a supervised classification problem, and show that the learnt model can predict surges with 95% accuracy and an 87% F1-score.

How does the structure of data impact cell-cell similarity? Evaluating how structural properties influence the performance of proximity metrics in single cell RNA-seq data.

Accurately identifying cell-populations is paramount to the quality of downstream analyses and overall interpretations of single-cell RNA-seq (scRNA-seq) datasets but remains a challenge. The quality of single-cell clustering depends on the proximity metric used to generate cell-to-cell distances. Accordingly, proximity metrics have been benchmarked for scRNA-seq clustering, typically with results averaged across datasets to identify a highest performing metric. However, the 'best-performing' metric varies between studies, with the performance differing significantly between datasets. This suggests that the unique structural properties of an scRNA-seq dataset, specific to the biological system under study, have a substantial impact on proximity metric performance. Previous benchmarking studies have omitted to factor the structural properties into their evaluations. To address this gap, we developed a framework for the in-depth evaluation of the performance of 17 proximity metrics with respect to core structural properties of scRNA-seq data, including sparsity, dimensionality, cell-population distribution and rarity. We find that clustering performance can be improved substantially by the selection of an appropriate proximity metric and neighbourhood size for the structural properties of a dataset, in addition to performing suitable pre-processing and dimensionality reduction. Furthermore, popular metrics such as Euclidean and Manhattan distance performed poorly in comparison to several lessor applied metrics, suggesting that the default metric for many scRNA-seq methods should be re-evaluated. Our findings highlight the critical nature of tailoring scRNA-seq analyses pipelines to the dataset under study and provide practical guidance for researchers looking to optimize cell-similarity search for the structural properties of their own data.

Comparing modelled with self-reported travel time and the used versus the nearest facility: modelling geographic accessibility to family planning outlets in Kenya

IntroductionGeographic access to family planning (FP) services has been characterised through a variety of proximity metrics. However, there is little evidence on the validity of women’s self-reported compared with modelled...

Learning about Spatial and Temporal Proximity using Tree-Based Methods

AbstractLearning about the relationship between distance to landmarks and events and phenomena of interest is a multi-faceted problem, as it may require taking into account multiple dimensions, including: spatial position of landmarks, timing of events taking place over time, and attributes of occurrences and locations. Here I show that tree-based methods are well suited for the study of these questions as they allow exploring the relationship between proximity metrics and outcomes of interest in a non-parametric and data-driven manner. I illustrate the usefulness of tree-based methods vis-à-vis conventional regression methods by examining the association between: (i) distance to border crossings along the US-Mexico border and support for immigration reform, and (ii) distance to mass shootings and support for gun control.

Signed network representation with novel node proximity evaluation

Currently, signed network representation has been applied to many fields, e.g., recommendation platforms. A mainstream paradigm of network representation is to map nodes onto a low-dimensional space, such that the node proximity of interest can be preserved. Thus, a key aspect is the node proximity evaluation. Accordingly, three new node proximity metrics were proposed in this study, based on the rigorous theoretical investigation on a new distance metric - signed average first-passage time (SAFT). SAFT derives from a basic random-walk quantity for unsigned networks and can capture high-order network structure and edge signs. We conducted network representation using the proposed proximity metrics and empirically exhibited our advantage in solving two downstream tasks — sign prediction and link prediction. The code is publicly available.

Оценка актуальных угроз безопасности информации с помощью технологии трансформеров

Purpose: development of an automated system for assessing current threats to the security of software of industrial automation systems based on the technology of Transformers. Methods: comparison of the set of identified software vulnerabilities, corresponding tactics (techniques) and relevant threats to information security by assessing the semantic proximity metrics of their text descriptions using Text Mining technology based on transformers models. Practical relevance: an automated system for assessing current software security threats has been developed, which makes it possible to compare and rank information and cyber security threats for identified vulnerabilities from the FSTEC of Russia Information Security Threats Databank, to automate the selection of techniques and tactics for constructing threat scenarios. The results of the comparative analysis show that the use of this system makes it possible to simplify the procedure for selecting potential threats and comparing vulnerabilities to them, in addition, a possible set of tactics and techniques is automatically generated, which makes it possible to reduce the time spent on building scenarios for the implementation of threats.

Model validation for inferred hot-spot conditions in National Ignition Facility experiments

Progress toward ignition requires accurately diagnosing current conditions and assessing proximity metrics for implosion experiments on the National Ignition Facility. Hot-spot conditions are not directly measured, but rather inferred, often using simple 0- and 1D models [P. Patel et al., Phys. Plasmas 27, 050901 (2020)]. Here, we present a detailed accuracy validation exercise using a set of ∼20 000 2D simulations encompassing a variety of performance and degradation levels. We find good agreement between the model-inferred pressure and the simulated burn-weighted pressure at peak neutron production and also present results on the precision of inferred quantities using a Markov-Chain Monte Carlo algorithm.

Measurement of Nontechnical Skills During Robotic-Assisted Surgery Using Sensor-Based Communication and Proximity Metrics

This cohort study uses sensor-based communication and proximity metrics to assess surgeon nontechnical skills during robotic-assisted surgery.

An introduction to Random Forests for modeling tasks - theory and practice in python with public health case studies

Abstract RFs are a powerful, volatile, and easy to use method, making it an excellent benchmark method for different types of analysis, notably in the public health domain. RFs offer outstanding performance with minimal effort from the user (given its low sensitivity to its hyperparameters) and can be used for many different modeling tasks (classification, regression, clustering, outlier detection). In practice, RFs are easily applicable through libraries in Python and R languages, allowing users to benefit from their capabilities with minimal coding knowledge. In addition to their pure performance abilities, RFs have some practical advantages when compared to many classical statistical models: it does not require any normalization of the data, handles very large datasets (in population or variables) and all kinds of data types (e.g., binary, categorical, continuous); it handles outliers and is insensitive to multicollinearity within the input variables. Their main limitation is their less straightforward interpretation of the final model they build. However, they offer additional tools, such as variable importance and proximity metrics, that improve the understanding of their results and potentially provide insights that traditional models cannot. Finally, its construction based on decision trees grants it additional capabilities, notably the possibility to extract prediction intervals and to handle efficiently imbalanced data problems with a variant of the algorithm called Balanced Random Forests.

Comparison Analysis of Gene Expression Profiles Proximity Metrics

The problems of gene regulatory network (GRN) reconstruction and the creation of disease diagnostic effective systems based on genes expression data are some of the current directions of modern bioinformatics. In this manuscript, we present the results of the research focused on the evaluation of the effectiveness of the most used metrics to estimate the gene expression profiles’ proximity, which can be used to extract the groups of informative gene expression profiles while taking into account the states of the investigated samples. Symmetry is very important in the field of both genes’ and/or proteins’ interaction since it undergirds essentially all interactions between molecular components in the GRN and extraction of gene expression profiles, which allows us to identify how the investigated biological objects (disease, state of patients, etc.) contribute to the further reconstruction of GRN in terms of both the symmetry and understanding the mechanism of molecular element interaction in a biological organism. Within the framework of our research, we have investigated the following metrics: Mutual information maximization (MIM) using various methods of Shannon entropy calculation, Pearson’s χ2 test and correlation distance. The accuracy of the investigated samples classification was used as the main quality criterion to evaluate the appropriate metric effectiveness. The random forest classifier (RF) was used during the simulation process. The research results have shown that results of the use of various methods of Shannon entropy within the framework of the MIM metric disagree with each other. As a result, we have proposed the modified mutual information maximization (MMIM) proximity metric based on the joint use of various methods of Shannon entropy calculation and the Harrington desirability function. The results of the simulation have also shown that the correlation proximity metric is less effective in comparison to both the MMIM metric and Pearson’s χ2 test. Finally, we propose the hybrid proximity metric (HPM) that considers both the MMIM metric and Pearson’s χ2 test. The proposed metric was investigated within the framework of one-cluster structure effectiveness evaluation. To our mind, the main benefit of the proposed HPM is in increasing the objectivity of mutually similar gene expression profiles extraction due to the joint use of the various effective proximity metrics that can contradict with each other when they are used alone.

Local deforestation spillovers induced by forest moratoria: Evidence from Indonesia

Moratoria on commodities produced in deforestation-risk areas have been shown to be highly effective in reducing deforestation within targeted areas. Various studies have shown, however, that such policies are prone to large local spillover effects, i.e., non-trivial changes (reductions or increases) in the amount of deforestation in areas just outside the direct scope of the moratorium. Little is known about the direction and magnitude of local spillover effects that may have been induced by the Indonesian forest moratorium, an anti-deforestation policy enacted in 2011 that covers around a third of Indonesia’s terrestrial area and that is of high importance in meeting international deforestation goals. Here, we empirically assess the evidence of spillover effects near the Indonesian moratorium boundaries, using several proximity metrics and a panel dataset spanning the years 2001–2018. Based on our negative binomial fixed effects regressions, we estimate that the moratorium induced 1324 km2 of deforestation in areas located within 10 km of the targeted areas in the period 2011–2018, most of which occurred near conservation and protection forests. Evidence of spillover effects is also strong within concession areas slated for development. This suggests that companies may have shifted their planned production activities from areas targeted by the moratorium to neighbouring concession areas, resulting in additional forest loss. To minimize or halt such spillover effects, the scope of the Indonesian moratorium could be expanded to high-deforestation risk areas, such as forest areas outside mountainous regions, with relatively high GDP per capita and high agro-ecological suitability for oil palm plantations. In addition, a higher uptake of certification schemes and increased international finance would complement the moratorium, helping to reduce incentives to deforest both within and outside the moratorium areas.