Massive slums have become major features of cities in many low-income and middle-income countries. Here, in the first in a Series of two papers, we discuss why slums are unhealthy places with especially high risks of infection and injury. We show that children are especially vulnerable, and that the combination of malnutrition and recurrent diarrhoea leads to stunted growth and longer-term effects on cognitive development. We find that the scientific literature on slum health is underdeveloped in comparison to urban health, and poverty and health. This shortcoming is important because health is affected by factors arising from the shared physical and social environment, which have effects beyond those of poverty alone. In the second paper we will consider what can be done to improve health and make recommendations for the development of slum health as a field of study.
The fundamental topology of manufacturing has not undergone a paradigm-level transformation since Henry Ford's moving assembly line in 1913. Every major innovation of the past century, from the Toyota Production System to Industry 4.0, has optimized within the Fordist paradigm without altering its structural logic: centralized mega-factories, located near labor pools, producing at scale. We argue that embodied intelligence is poised to break this century-long stasis, not by making existing factories more efficient, but by triggering phase transitions in manufacturing economic geography itself. When embodied AI capabilities cross critical thresholds in dexterity, generalization, reliability, and tactile-vision fusion, the consequences extend far beyond cost reduction: they restructure where factories are built, how supply chains are organized, and what constitutes viable production scale. We formalize this by defining a Capability Space C = (d, g, r, t) and showing that the site-selection objective function undergoes topological reorganization when capability vectors cross critical surfaces. Through three pathways, weight inversion, batch collapse, and human-infrastructure decoupling, we show that embodied intelligence enables demand-proximal micro-manufacturing, eliminates "manufacturing deserts," and reverses geographic concentration driven by labor arbitrage. We further introduce Machine Climate Advantage: once human workers are removed, optimal factory locations are determined by machine-optimal conditions (low humidity, high irradiance, thermal stability), factors orthogonal to traditional siting logic, creating a production geography with no historical precedent. This paper establishes Embodied Intelligence Economics, the study of how physical AI capability thresholds reshape the spatial and structural logic of production.
Accurate daily mapping of 30-m fractional snow cover (FSC) is critical for hydrological modeling and disaster assessment. Frequent cloud cover and satellite revisit cycles create significant data gaps in high-resolution optical imagery (e.g., Landsat, Sentinel-2), hindering the continuous monitoring of rapid snow dynamics. To address these limitations, we propose the Time-series-based Adaptive snow-Fraction Fusion (TAFF) framework for generating seamless daily 30-m FSC over large scales. The core of TAFF is a dual-path fusion strategy that adapts to the physical state of the snowpack. First, a time-series-based snow stability assessment gauges the magnitude of temporal FSC change. This assessment then directs the fusion process: stable snow is processed using time-weighted fusion, while rapidly changing snow is handled by a pixel-level regression. Evaluated over the Qinghai-Tibet Plateau, TAFF demonstrates robust improvements over established spatiotemporal fusion algorithms, particularly under cloudy conditions. Independent validation against 215 Landsat 8 images yielded strong performance (R2 = 0.76, RMSE = 19.58 %). Further validation against 46 in-situ snow depth stations indicated a high binary classification accuracy of 0.91. As a robust and practical method for large-scale FSC mapping, TAFF shows promise for integrating additional data sources, such as geostationary and microwave sensors, to enhance the high-resolution monitoring of ephemeral snow.
Machine learning is rapidly making its pathway across all of the natural sciences, including physical sciences. The rate at which ML is impacting non-scientific disciplines is incomparable to that in the physical sciences. This is partly due to the uninterpretable nature of deep neural networks. Symbolic machine learning stands as an equal and complementary partner to numerical machine learning in speeding up scientific discovery in physics. This perspective discusses the main differences between the ML and scientific approaches. It stresses the need to develop and apply symbolic machine learning to physics problems equally, in parallel to numerical machine learning, because of the dual nature of physics research.
Esta investigación explora una experiencia didáctica diseñada para integrar los Objetivos de Desarrollo Sostenible (ODS) en la materia de Geografía en 2º de Bachillerato de adultos. El objetivo fue promover el conocimiento y la comprensión de los ODS mediante actividades vinculadas al bloque de Geografía Física (lluvia de ideas, mapas conceptuales, comentario de mapas temáticos, análisis de imágenes geográficas y elaboración de infografías). Se utilizó una metodología mixta, empleando como principal instrumento una rúbrica de evaluación. Los resultados evidenciaron una fiabilidad sólida de las actividades (Alfa de Cronbach: 0,77; Omega de McDonald: 0,85). El análisis de correlación de Pearson mostró relaciones significativas, destacando una fuerte correlación entre las actividades 1 y 3 (r = 0,80) y 2 y 3 (r = 0,69). El análisis cualitativo con MAXQDA identificó fortalezas y debilidades en las producciones y reflejó un conocimiento inicial bajo hacia niveles intermedios y altos, destacando el impacto positivo en la comprensión geográfica y los ODS.
Muhammad Afaq Hussain, Zhanlong Chen, Biswajeet Pradhan
et al.
Study region: The National Highways 85 and 50, key routes of the China–Pakistan Economic Corridor (CPEC) in Balochistan, Pakistan. Study focus: Flooding is a natural disaster that is becoming increasingly frequent and severe. The National Highways 85 and 50 are vulnerable, necessitating accurate flood susceptibility mapping (FSM). Current machine learning (ML) models for FSM often suffer from low efficiency and overfitting. This study introduces an innovative hybrid FSM approach using four heterogeneous ensemble learning (HEL) techniques combined with three ML models: Random Forest (RF), Support Vector Machine (SVM), and Light Gradient Boosting Machine (LGBM). The proposed method was tested using satellite data from Sentinel-1, Sentinel-2, and Landsat-8, analyzing 1371 flood locations and 12 contributing variables. RF, variable importance factors (VIF), and information gain ratio (IGR) were applied to assess multicollinearity. The dataset was split (70:30) for model training and testing, with HEL-based models achieving superior performance over single ML models. New hydrological insights for the region: The stacking model yielded the highest AUROC (0.98), Kappa (0.82), accuracy (0.927), precision (0.963), Matthew’s correlation coefficient (0.820), and F1-score (0.950). HEL-based models proved more stable and resistant to overfitting. IGR analysis identified slope and distance from streams as key factors in FSM. The resulting flood-prone maps provide insights for disaster management adaptation strategies, demonstrating the broader applicability of the developed approach to enhance FSM accuracy and reliability.
Oleksandr Karasov, Evelyn Uuemaa, Olle Järv
et al.
In the context of urbanisation and growing disparities, timely and detailed spatial data on income inequality in cities is essential. We combined satellite imagery with streetlevel photographs provided by Google Street View to reveal the spatial distribution of household income. For this, we suggest a harmonised framework for median household income modelling based on deconstructing landscape patterns using a machine-learning approach, applied across three ’global cities’: Amsterdam, New York, and Sydney. First, we classified Sentinel-1 and Sentinel-2 mosaics and Google Street View scenes to detect functional elements of the built environment. Second, we calculated spatial indices for Sentinel imagery and visual indices for Google Street View scenes to characterise the urban landscape. Third, by combining various indicators, we trained city-specific income prediction models according to ground truth census data. The correlation between actual and predicted income in New York and Sydney reached 0.76 and 0.78, respectively. The accuracy of income prediction in Amsterdam reached 51.13%. We revealed relationships between spatial indicators of landscape patterns and spatial income distribution and recommend using Sentinel-1 and Sentinel-2 imagery as the primary data choice for income modelling in datarestricted regions. Google Street View data can be used complementarily when available.
Study region: Beijing plain in the eastern of Beijing, China. Study focus: Over the past three decades, more than 2 billion m3 of groundwater have been pumped annually in the Beijing Plain, resulting in approximately 431 km2 of land subsidence of more than 50 mm annually. While most studies have identified a correlation between land subsidence and groundwater overexploitation, quantifying their relationship has been challenging. In this paper, the land subsidence data were obtained based on the persistent scatterer interferometric synthetic aperture radar (PS-InSAR, PSI) and the least square (LS) method. The parameter of inelastic skeletal storativity (Si) of the confined aquifer was used as a quantitative indicator to describe the relationship between land subsidence and groundwater exploitation in the Beijing Plain. New hydrology insights: Moreover, the paper found a robust correlation between groundwater overexploitation and land subsidence in the deep confined aquifer through groundwater monitoring data. From 2005–2016, Si showed a gradual and continuous increase in a specific range. The abnormal change in Si value during 2014–2015 may be associated with the recharge of shallow confined and unconfined aquifers in the Beijing Plain by the South-to-North Water Diversion Project. The Si estimated in the study area can be utilized to accurately deduce the regional water level shifts, thereby aiding in the efficient and sustainable management of groundwater resources.
This paper proposes a two blocks and three regions economic geography model that can account for the most salient stylized facts experienced by Eastern European transition economies during the period 1990 2005. In contrast to the existing literature, which has favored technological explanations, trade liberalization is the only driving force. The model correctly predicts that in the first half of the period, trade liberalization led to divergence in GDP per capita, both between the West and the East and within the East. Consistent with the data, in the second half of the period, this process was reversed and convergence became the dominant force.
Let $α$ be a map from the set of all knot types ${\mathcal K}$ to a set $X$. Let $β$ be a map from ${\mathcal K}$ to a set $Y$. We define the relation between $α$ and $β$ to be the image of a map $(α,β)$ from ${\mathcal K}$ to $X\times Y$ sending an element $K$ of ${\mathcal K}$ to $(α(K),β(K))$. We determine the relations between $α$ and $β$ for certain $α$ and $β$ such as crossing number, unknotting number, bridge number, braid index, genus and canonical genus. This is a study of geography problem in knot theory.
In recent years, several efforts have been aimed at improving the robustness of vision models to domains and environments unseen during training. An important practical problem pertains to models deployed in a new geography that is under-represented in the training dataset, posing a direct challenge to fair and inclusive computer vision. In this paper, we study the problem of geographic robustness and make three main contributions. First, we introduce a large-scale dataset GeoNet for geographic adaptation containing benchmarks across diverse tasks like scene recognition (GeoPlaces), image classification (GeoImNet) and universal adaptation (GeoUniDA). Second, we investigate the nature of distribution shifts typical to the problem of geographic adaptation and hypothesize that the major source of domain shifts arise from significant variations in scene context (context shift), object design (design shift) and label distribution (prior shift) across geographies. Third, we conduct an extensive evaluation of several state-of-the-art unsupervised domain adaptation algorithms and architectures on GeoNet, showing that they do not suffice for geographical adaptation, and that large-scale pre-training using large vision models also does not lead to geographic robustness. Our dataset is publicly available at https://tarun005.github.io/GeoNet.
Deep neural networks (DNNs) have demonstrated high vulnerability to adversarial examples, raising broad security concerns about their applications. Besides the attacks in the digital world, the practical implications of adversarial examples in the physical world present significant challenges and safety concerns. However, current research on physical adversarial examples (PAEs) lacks a comprehensive understanding of their unique characteristics, leading to limited significance and understanding. In this paper, we address this gap by thoroughly examining the characteristics of PAEs within a practical workflow encompassing training, manufacturing, and re-sampling processes. By analyzing the links between physical adversarial attacks, we identify manufacturing and re-sampling as the primary sources of distinct attributes and particularities in PAEs. Leveraging this knowledge, we develop a comprehensive analysis and classification framework for PAEs based on their specific characteristics, covering over 100 studies on physical-world adversarial examples. Furthermore, we investigate defense strategies against PAEs and identify open challenges and opportunities for future research. We aim to provide a fresh, thorough, and systematic understanding of PAEs, thereby promoting the development of robust adversarial learning and its application in open-world scenarios to provide the community with a continuously updated list of physical world adversarial sample resources, including papers, code, \etc, within the proposed framework
Arindam Saha, Maziar Ghorbani, Diana Suleimenova
et al.
Agent-based models are widely used to predict infectious disease spread. For these predictions, one needs to understand how each input parameter affects the result. Here, some parameters may affect the sensitivities of others, requiring the analysis of higher order coefficients through e.g. Sobol sensitivity analysis. The geographical structures of real-world regions are distinct in that they are difficult to reduce to single parameter values, making a unified sensitivity analysis intractable. Yet analyzing the importance of geographical structure on the sensitivity of other input parameters is important because a strong effect would justify the use of models with real-world geographical representations, as opposed to stylized ones. Here we perform a grouped Sobol's sensitivity analysis on COVID-19 spread simulations across a set of three diverse real-world geographical representations. We study the differences in both results and the sensitivity of non-geographical parameters across these geographies. By comparing Sobol indices of parameters across geographies, we find evidence that infection rate could have more sensitivity in regions where the population is segregated, while parameters like recovery period of mild cases are more sensitive in regions with mixed populations. We also show how geographical structure affects parameter sensitivity changes over time.
Quantitative differentiation of climate and human activities on runoff is important for water resources management and future water resources trend prediction. In recent years, runoff in the middle reaches of the Yellow River (MRYR) has decreased dramatically. Many studies have analyzed the causes of runoff reduction, but there is still a lack of understanding of the spatial differences in runoff contributions and their causes. Therefore, this study quantitatively distinguishes the contributions of climate and human activities to runoff changes in nine sub-basins of the MRYR based on the Budyko framework and analyses the differences in the contributions of different basins and their causes. The results show that the runoff in the nine sub-basins decreases significantly and the precipitation increases from northwest to southeast. The contribution of human activities to runoff is greater than that of climate change, especially in the Huangfuchuan (HF) River and Kuye (KY) River basins, where the contribution of human activities to runoff exceeds 90%. The greater impact of human activities in HF River and KY River is due to the significantly higher water use growth rate and normalized vegetation index trends than in other areas.
HIGHLIGHTS
Spatial differences in the causes of runoff variation in nine small watersheds in the middle reaches of the Yellow River were analyzed.;
The influence of NDVI and human water extraction cannot be ignored.;
River, lake, and water-supply engineering (General), Physical geography
Water detection from SAR imagery has significant values, such as the flood monitoring and environmental protection. Nowadays, significant progress has been achieved in water detection using deep neural network (DNN) methods, but the blackbox behavior incurs many doubts in the performance of deep learning techniques, which undermines its trustworthiness in water detection from SAR imagery. By integrating SAR domain knowledge, DNN and eXplainable Artificial Intelligence (XAI), an explainable DNN framework for surface water detection is proposed for the first time. This framework includes three parts: the water extraction network containing four backbone networks, the Local and Global Mixed Attribution (LGMA) module for performance evaluation of backbone network, and the Semantic Specific-class Activation Mapping (SSAM) module, which performs geo-visualization for the output layers of high-level features. In the experiment, SAR images from different resolutions and frequency-bands are utilized, which are from millimeter-wave and Sentinel-1 systems. The attribution maps and heatmaps of four backbone networks are assessed towards the final water extraction results. The experiment indicates that the proposed framework can glass-box the decision-making process of DNN in water detection and offer corresponding attribution analytics for given input SAR imagery. This work encourages other scholars to conduct extensive research on the explanation of DNN in SAR domain, gradually establish the trustworthiness of DNN, and promote the development of DNN in SAR images analytics.
We develop a two-region economic geography model with vertical innovations that improve the quality of manufactured varieties produced in each region. The chance of innovation depends on the \emph{related variety}, i.e. the importance of interaction between researchers within the same region rather than across different regions. As economic integration increases from a low level, a higher related variety is associated with more agglomerated spatial configurations. However, if the interaction with foreign scientists is relatively more important for innovation, economic activities may (completely) re-disperse after an initial phase of agglomeration due to the increase in the relative importance of a higher chance of innovation in the less industrialized region. This non-monotonic relationship between economic integration and spatial imbalances may exhibit very diverse qualitative properties, not yet described in the literature.
In this paper I review the history and geography of the pentaquark searches and discuss the current situation surrounding these searches performed at different facilities around the world. The possibility of the existence of multiquark states like tetraquarks and pentaquarks was already predicted by Gell-Mann based on the Constituent Quark Model (CQM), however more than half a century efforts in a wide range of experiments led to controversial situation, when the fate of the light quark pentaquarks is almost decided to not exist. The recent LHCb results on the observation of the charm pentaquarks in the invariant mass of $pJ/ψ$ from the $Λ_b\to K^- p J/ψ$ decay created a new wave of excitement and rises the question about the existence of the light pentaquarks. The main question which still remains to be clarified is whether already acquired evidences are sufficient to completely disregard the light pentaquarks and leave it out as an example of the scientific curiosity or there are still rooms for further, more dedicated efforts and scrupulous analyses to answer the question of the existence or non existence of the light pentaquarks made of $u, d$ and $s$ quarks.
The present exposures of the (Mesoproterozoic to Neoproterozoic) Purana basins of Peninsular India occupy nearly 1.5 × 105 km2 area in the Indian subcontinent, with an equal area being concealed under younger cover or lost to ensuing erosion. These extensional basins evolved on the margins (& with basement) of the existing cratonic blocks during the ‘boring billion years’ of Earth history. They host nearly 0.8 million km3 of epicratonic compacted and lithified sediments derived by weathering and erosion of the adjoining cratonic blocks and deposited on their fringes.The volumetric contents of these basins and their temporal distribution are compiled. The relative distribution and secular variations of sediment contents from these basins appear to synchronise with global Proterozoic supercontinental assembly and dispersal cycles. A comparison of mass-transfer by the erosion of the provenance area and sedimentation in continent margin basins shows that the volumes preserved in the Purana basins are at least 2 magnitudes larger than what can be derived from adjoining cratonic areas within the Indian Subcontinent. Much wider continental masses as well as exhumation (aided by uplift) of km-scale magnitudes of the provenance areas are required to reconcile their volume. Possible linkages with other cratonic blocks within the contemporary supercontinental assemblies are required to resolve this discrepancy.