Haonan Li, Elizabeth A. Holzhausen, Devendra Paudel
et al.
Abstract This study investigates independent and joint effects of fine particulate matter (PM2.5) components on early childhood neurodevelopment and explores emission sources of key toxic components. We included 165 mother-infant dyads from Southern California. Annual average concentrations of 15 PM2.5 components, including carbonaceous components, secondary inorganic salts, and trace elements, were estimated for the birth year. Neurodevelopment across cognitive, language, motor, social-emotional, and adaptive behavior domains was assessed at age 2 using Bayley-III Scales. Mixture effects and key contributors were evaluated using weighted quantile sum (WQS) and Bayesian kernel machine regression (BKMR). Source inference was conducted through inter-component clustering and spatial analysis. Linear regression showed PM2.5, sulfate (SO4 2−), nitrate (NO3 −), ammonium (NH4 +), copper (Cu), nickel (Ni), lead (Pb), and vanadium (V) were inversely, while calcium (Ca) and zinc (Zn) were positively, associated with adaptive behavior scores (p < 0.05). WQS showed negative associations between the mixture and adaptive behavior (p = 0.02–0.06), with Ni, Cu, V, and SO₄²⁻ as key contributors. BKMR showed similar trends. Ni, V, and SO4 2− likely originate from heavy oil combustion, and Cu from brake wear. Findings suggest that PM2.5 components, particularly from traffic and marine fuel combustion, may adversely affect adaptive behavior in early childhood.
Landing methods have recently emerged in Riemannian matrix optimization as efficient schemes for handling nonlinear equality constraints without resorting to costly retractions. These methods decompose the search direction into tangent and normal components, enabling asymptotic feasibility while maintaining inexpensive updates. In this work, we provide a unifying geometric framework which reveals that, under suitable choices of Riemannian metric, the landing algorithm encompasses several classical optimization methods such as projected and null-space gradient flows, Sequential Quadratic Programming (SQP), and a certain form of the augmented Lagrangian method. In particular, we show that a quadratically convergent landing method essentially reproduces the quadratically convergent SQP method. These connections also allow us to propose a globally convergent landing method using adaptive step sizes. The backtracking line search satisfies an Armijo condition on a merit function, and does not require prior knowledge of Lipschitz constants. Our second key contribution is to analyze landing methods through a geometric parameterization of the metric in terms of fields of oblique projectors and associated metric restrictions. This viewpoint disentangles the roles of orthogonality, tangent and normal metrics, and elucidates how to design the metric to obtain explicit tangent and normal updates. For matrix optimization, this framework not only recovers recent constructions in the literature for problems with orthogonality constraints, but also provides systematic guidelines for designing new metrics that admit closed-form search directions.
Satellite foundation models produce dense embeddings whose physical interpretability remains poorly understood, limiting their integration into environmental decision systems. Using 12.1 million samples across the Continental United States (2017--2023), we first present a comprehensive interpretability analysis of Google AlphaEarth's 64-dimensional embeddings against 26 environmental variables spanning climate, vegetation, hydrology, temperature, and terrain. Combining linear, nonlinear, and attention-based methods, we show that individual embedding dimensions map onto specific land surface properties, while the full embedding space reconstructs most environmental variables with high fidelity (12 of 26 variables exceed $R^2 > 0.90$; temperature and elevation approach $R^2 = 0.97$). The strongest dimension-variable relationships converge across all three analytical methods and remain robust under spatial block cross-validation (mean $ΔR^2 = 0.017$) and temporally stable across all seven study years (mean inter-year correlation $r = 0.963$). Building on these validated interpretations, we then developed a Land Surface Intelligence system that implements retrieval-augmented generation over a FAISS-indexed embedding database of 12.1 million vectors, translating natural language environmental queries into satellite-grounded assessments. An LLM-as-Judge evaluation across 360 query--response cycles, using four LLMs in rotating generator, system, and judge roles, achieved weighted scores of $μ= 3.74 \pm 0.77$ (scale 1--5), with grounding ($μ= 3.93$) and coherence ($μ= 4.25$) as the strongest criteria. Our results demonstrate that satellite foundation model embeddings are physically structured representations that can be operationalized for environmental and geospatial intelligence.
Syafii Syafii, Krismadinata Krismadinata, Fahmi Fahmi
et al.
This study explores the integration of electric vehicles with photovoltaic systems in a building-level energy management framework, utilizing an Internet of Things-based system for real-time monitoring and optimization. The proposed system is implemented using a Raspberry Pi as the primary controller, interfacing with various sensors to track voltage, current, power, and energy consumption. A web-based platform is developed to enable seamless remote monitoring and control, ensuring efficient switching between solar power and the utility grid. The battery management system, incorporated within the framework, enhances operational reliability by optimizing charging and discharging cycles. Experimental validation demonstrates that the system effectively maintains voltage stability during source transitions while maximizing the utilization of solar energy. A case study in Indonesia further confirms the feasibility of this approach in promoting energy efficiency and sustainable charging infrastructure, contributing to broader clean energy adoption and reduced dependency on fossil fuels.
As artificial intelligence and robotics increasingly reshape the global labor market, understanding public perceptions of these technologies becomes critical. We examine how these perceptions have evolved across Latin America, using survey data from the 2017, 2018, 2020, and 2023 waves of the Latinobarómetro. Drawing on responses from over 48,000 individuals across 16 countries, we analyze fear of job loss due to artificial intelligence and robotics. Using statistical modeling and latent class analysis, we identify key structural and ideological predictors of concern, with education level and political orientation emerging as the most consistent drivers. Our findings reveal substantial temporal and cross-country variation, with a notable peak in fear during 2018 and distinct attitudinal profiles emerging from latent segmentation. These results offer new insights into the social and structural dimensions of AI anxiety in emerging economies and contribute to a broader understanding of public attitudes toward automation beyond the Global North.
Deep learning (DL)-based general circulation models (GCMs) are emerging as fast simulators, yet their ability to replicate extreme events outside their training range remains unknown. Here, we evaluate two such models -- the hybrid Neural General Circulation Model (NGCM) and purely data-driven Deep Learning Earth System Model (DL\textit{ESy}M) -- against a conventional high-resolution land-atmosphere model (HiRAM) in simulating land heatwaves and coldwaves. All models are forced with observed sea surface temperatures and sea ice over 1900-2020, focusing on the out-of-sample early-20th-century period (1900-1960). Both DL models generalize successfully to unseen climate conditions, broadly reproducing the frequency and spatial patterns of heatwave and cold wave events during 1900-1960 with skill comparable to HiRAM. An exception is over portions of North Asia and North America, where all models perform poorly during 1940-1960. Due to excessive temperature autocorrelation, DL\textit{ESy}M tends to overestimate heatwave and cold wave frequencies, whereas the physics-DL hybrid NGCM exhibits persistence more similar to HiRAM.
Echoes of the Land is an interactive installation that transforms seismic dynamics into a multisensory experience through a scientifically grounded spring-block model. Simulating earthquake recurrence and self-organized criticality, the work generates real-time sound and light via motion capture and concatenative granular synthesis. Each block acts as an agent, producing emergent audiovisual cascades that visualize the physics of rupture and threshold behavior. This work exemplifies the amalgamation of scientific knowledge and artistic practice, opening new avenues for novel forms of musical instrument and narrative medium, while inviting further investigation into the intersection of emergent complexity, aesthetics and interactivity.
China’s Grand Canal was the world’s most extensive civil engineering project before the Industrial Revolution. This interview explores how the process of applying for and achieving World Heritage status has led to the improvement of the environment surrounding the Grand Canal and encouraged collaboration among canal cities spanning eight provincial administrations. It highlights the role of water heritage as a catalyst for improving the protection of historic landscapes and waterscapes as well as the Grand Canal’s cultural heritage. It also addresses how these efforts have supported the integrated development of canal cities. The Grand Canal remains a vital link that promotes balanced cultural, ecological and economic development, contributing to the sustainability of various canal cities across northern and southern China.
Mahdi Pourbafrani, Hossein Ghadamian, Mohammad Aminy
et al.
Evacuated tube solar collectors (ETSC) are widely utilized in both domestic and industrial solar water heaters (SWH) due to their commendable thermal performance and straightforward installation. However, a significant challenge associated with ETSC lies in the fact that half of the collector remains unexposed to sunlight. To overcome this limitation, parabolic reflectors can be employed as a viable solution. The primary objective of this study is to assess the performance of a compound parabolic concentrator (CPC) in conjunction with ETSC, taking into account a specific ratio between the areas of the CPC and ETSC. To achieve the desired configuration, the CPC was meticulously designed, fabricated, installed, and subsequently tested. Moreover, the energy performance of the absorber tube was scrutinized both with and without the integration of a parabolic trough collector. The experiments and data collection were conducted on two selected days for both the conventional ETSC device and the system incorporating the CPC. Meteorological data and operational conditions were measured and digitally stored for subsequent analysis. A noteworthy outcome of the study is the revelation that the energy efficiency of the system with a concentrator exhibited a notable improvement of 2.8% compared to the conventional system. Offline results further indicated that the performance of a single absorber tube with a concentrator increased by approximately 2.7 times when compared to the standard system. This suggests that the energy performance of the solar water heater, with a capacity of about 200 liters and featuring 7 absorber tubes with a concentrator, is comparable to that of the conventional system equipped with 18 absorber tubes.
The realization of FDI and DDI from January to December 2022 reached Rp1,207.2 trillion. The largest FDI investment realization by sector was led by the Basic Metal, Metal Goods, Non-Machinery, and Equipment Industry sector, followed by the Mining sector and the Electricity, Gas, and Water sector. The uneven amount of FDI investment realization in each industry and the impact of the COVID-19 pandemic in Indonesia are the main issues addressed in this study. This study aims to identify the factors that influence the entry of FDI into industries in Indonesia and measure the extent of these factors' influence on the entry of FDI. In this study, classical assumption tests and hypothesis tests are conducted to investigate whether the research model is robust enough to provide strategic options nationally. Moreover, this study uses the ordinary least squares (OLS) method. The results show that the electricity factor does not influence FDI inflows in the three industries. The Human Development Index (HDI) factor has a significant negative effect on FDI in the Mining Industry and a significant positive effect on FDI in the Basic Metal, Metal Goods, Non-Machinery, and Equipment Industries. However, HDI does not influence FDI in the Electricity, Gas, and Water Industries in Indonesia.
The aim of this scoping review was to identify and consolidate existing empirical evidence on consumer behavior research regarding traditional foods in Africa, with a view to contributing to the advancement of further research in the area. More specifically, the study sought to map the extent of available evidence, establish the nature of research topics and identify theories and models upon which identified studies were based. While results showed a general scarcity of empirical studies on consumer behavior towards traditional foods in Africa, the last five years have recorded a sustained increase in research. Nigeria, South Africa and Kenya are the main contributors to this research. Most of the research focused on sensory attributes of traditional foods and how they were perceived, evaluated and liked/disliked by consumers. There was also significant research exploring cognitive aspects underlying consumer behavior. However, the use of established theory or models in framing existing research was minimal.
Antika Fardilla, Rifta Septiavi, Ratna Juwita T
et al.
Land use change is an important issue for urban and regional planners and policy makers, but it is also very useful in conservation planning, food security, and hydrological modeling. Data, information and analysis tools become obstacles in detecting changes in land use. With increasing access to data and current technology, it is hoped that land use observations can be carried out in a simple way but have more accurate results. This study aimed to analyze land cover changes in Padang City 2018-2022, using Landsat Imagery and Geographic Information System (GIS) analysis. Firstly, observations on ESRI Land Cover which displays a global map of land use or land cover (LULC) derived from ESA Sentinel-2 Imagery at a resolution of 10 m. The results showed that the area of forest cover has decreased and the built-up area has increased in the 2017-2018 and 2021-2022. Secondly, using the EO Browser, namely Sentinel-2, that was done in one to search for and compare images using high resolution at these sources, there were 19 land cover changes, such as increasing residential land use, while forest land allotment decreased.
Iosto Fodde, Jinglang Feng, Massimiliano Vasile
et al.
ESA's Hera mission aims to visit binary asteroid Didymos in late 2026, investigating its physical characteristics and the result of NASA's impact by the DART spacecraft in more detail. Two CubeSats on-board Hera plan to perform a ballistic landing on the secondary of the system, called Dimorphos. For these types of landings the translational state during descent is not controlled, reducing the spacecrafts complexity but also increasing its sensitivity to deployment maneuver errors and dynamical uncertainties. This paper introduces a novel methodology to analyse the effect of these uncertainties on the dynamics of the lander and design a trajectory that is robust against them. This methodology consists of propagating the uncertain state of the lander using the non-intrusive Chebyshev interpolation (NCI) technique, which approximates the uncertain dynamics using a polynomial expansion, and analysing the results using the pseudo-diffusion indicator, derived from the coefficients of the polynomial expansion, which quantifies the rate of growth of the set of possible states of the spacecraft over time. This indicator is used here to constrain the impact velocity and angle to values which allow for successful settling on the surface. This information is then used to optimize the landing trajectory by applying the NCI technique inside the transcription of the problem. The resulting trajectory increases the robustness of the trajectory compared to a conventional method, improving the landing success by 20 percent and significantly reducing the landing footprint.
Autonomous landing of UAVs in high sea states requires the UAV to land exclusively during the ship deck's "rest period," coinciding with minimal movement. Given this scenario, determining the ship's "rest period" based on its movement patterns becomes a fundamental prerequisite for addressing this challenge. This study employs the Long Short-Term Memory (LSTM) neural network to predict the ship's motion across three dimensions: longi-tudinal, transverse, and vertical waves. In the absence of actual ship data under high sea states, this paper employs a composite sine wave model to simulate ship deck motion. Through this approach, a highly accurate model is established, exhibiting promising outcomes within various stochastic sine wave combination models.
Abdullahi Saka, Ridwan Taiwo, Nurudeen Saka
et al.
Large Language Models(LLMs) trained on large data sets came into prominence in 2018 after Google introduced BERT. Subsequently, different LLMs such as GPT models from OpenAI have been released. These models perform well on diverse tasks and have been gaining widespread applications in fields such as business and education. However, little is known about the opportunities and challenges of using LLMs in the construction industry. Thus, this study aims to assess GPT models in the construction industry. A critical review, expert discussion and case study validation are employed to achieve the study objectives. The findings revealed opportunities for GPT models throughout the project lifecycle. The challenges of leveraging GPT models are highlighted and a use case prototype is developed for materials selection and optimization. The findings of the study would be of benefit to researchers, practitioners and stakeholders, as it presents research vistas for LLMs in the construction industry.
Economists, historians, and social scientists have long debated how open-access areas, frontier regions, and customary landholding regimes came to be enclosed or otherwise transformed into private property. This paper analyzes decentralized enclosure processes using the theory of aggregative games, examining how population density, enclosure costs, potential productivity gains, and the broader physical, institutional, and policy environment jointly determine the property regime. Changes to any of these factors can lead to smooth or abrupt changes in equilibria that can result in inefficiently high, inefficiently low, or efficient levels of enclosure and associated technological transformation. Inefficient outcomes generally fall short of second-best. While policies to strengthen customary governance or compensate displaced stakeholders can realign incentives, addressing one market failure while neglecting others can worsen outcomes. Our analysis provides a unified framework for evaluating mechanisms emphasized in Neoclassical, Neo-institutional, and Marxian interpretations of historical enclosure processes and contemporary land formalization policies.
Land-use monitoring is fundamental for spatial planning, particularly in view of compound impacts of growing global populations and climate change. Despite existing applications of deep learning in land use monitoring, standard convolutional kernels in deep neural networks limit the applications of these networks to the Euclidean domain only. Considering the geodesic nature of the measurement of the earth's surface, remote sensing is one such area that can benefit from non-Euclidean and spherical domains. For this purpose, we designed a novel Graph Neural Network architecture for spatial and spatio-temporal classification using satellite imagery to acquire insights into socio-economic indicators. We propose a hybrid attention method to learn the relative importance of irregular neighbors in remote sensing data. Instead of classifying each pixel, we propose a method based on Simple Linear Iterative Clustering (SLIC) image segmentation and Graph Attention Network. The superpixels obtained from SLIC become the nodes of our Graph Convolution Network (GCN). A region adjacency graph (RAG) is then constructed where each superpixel is connected to every other adjacent superpixel in the image, enabling information to propagate globally. Finally, we propose a Spatially driven Attention Graph Neural Network (SAG-NN) to classify each RAG. We also propose an extension to our SAG-NN for spatio-temporal data. Unlike regular grids of pixels in images, superpixels are irregular in nature and cannot be used to create spatio-temporal graphs. We introduce temporal bias by combining unconnected RAGs from each image into one supergraph. This is achieved by introducing block adjacency matrices resulting in novel Spatio-Temporal driven Attention Graph Neural Network with Block Adjacency matrix (STAG-NN-BA). SAG-NN and STAG-NN-BA outperform graph and non-graph baselines on Asia14 and C2D2 datasets efficiently.
Francesco Roscia, Michele Focchi, Andrea Del Prete
et al.
Quadruped robots are machines intended for challenging and harsh environments. Despite the progress in locomotion strategy, safely recovering from unexpected falls or planned drops is still an open problem. It is further made more difficult when high horizontal velocities are involved. In this work, we propose an optimization-based reactive Landing Controller that uses only proprioceptive measures for torque-controlled quadruped robots that free-fall on a flat horizontal ground, knowing neither the distance to the landing surface nor the flight time. Based on an estimate of the Center of Mass horizontal velocity, the method uses the Variable Height Springy Inverted Pendulum model for continuously recomputing the feet position while the robot is falling. In this way, the quadruped is ready to attain a successful landing in all directions, even in the presence of significant horizontal velocities. The method is demonstrated to dramatically enlarge the region of horizontal velocities that can be dealt with by a naive approach that keeps the feet still during the airborne stage. To the best of our knowledge, this is the first time that a quadruped robot can successfully recover from falls with horizontal velocities up to 3 m/s in simulation. Experiments prove that the used platform, Go1, can successfully attain a stable standing configuration from falls with various horizontal velocity and different angular perturbations.
This paper studies the problem of designing a certified vision-based state estimator for autonomous landing systems. In such a system, a neural network (NN) processes images from a camera to estimate the aircraft relative position with respect to the runway. We propose an algorithm to design such NNs with certified properties in terms of their ability to detect runways and provide accurate state estimation. At the heart of our approach is the use of geometric models of perspective cameras to obtain a mathematical model that captures the relation between the aircraft states and the inputs. We show that such geometric models enjoy mixed monotonicity properties that can be used to design state estimators with certifiable error bounds. We show the effectiveness of the proposed approach using an experimental testbed on data collected from event-based cameras.