Wind-forced near-inertial waves (NIWs) propagate through a sea of mesoscale eddies, which can fundamentally alter their evolution. The nature of this NIW--mesoscale interaction depends on how dispersive the waves are. For weakly dispersive waves, ray tracing suggests that the NIW frequency should be shifted by $\frac{1}{2}ζ$, where $ζ$ is the mesoscale vorticity, and that the waves are refracted into anticyclones. Strongly dispersive waves, in contrast, retain the large-scale structure of the wind forcing and exhibit a small negative frequency shift. Previous in situ observational studies have indeed revealed varying degrees of NIW--mesoscale interaction. Here, observations of NIWs from drifters are used to map the geography of NIW--mesoscale interactions globally, and idealized simulations and a simple model are used to identify the underlying physical processes. Almost everywhere in the ocean, with the notable exception of the North Pacific, the NIW frequency is strongly modulated by the mesoscale vorticity, with the slope of the frequency shift vs. vorticity taking values of approximately $0.4$. Concentration of NIW energy into anticyclones is a common feature throughout the ocean. Other aspects of the observations, however, show signatures of strongly dispersive waves: a negative frequency shift and weaker concentration into anticyclones in regions with strong eddies as well as weak modulation of the NIW frequency by mesoscale eddies in the North Pacific. The signatures of both weakly and strongly dispersive NIW behavior can be rationalized by the geography of the wave dispersiveness and the fact that wind forcing excites multiple vertical modes with different wave dispersiveness. These results have implications for NIW-induced mixing in the upper-ocean.
Oracle bone inscriptions (OBIs) are the earliest known form of Chinese characters and serve as a valuable resource for research in anthropology and archaeology. However, most excavated fragments are severely degraded due to thousands of years of natural weathering, corrosion, and man-made destruction, making automatic OBI recognition extremely challenging. Previous methods either focus on pixel-level information or utilize vanilla transformers for glyph-based OBI denoising, which leads to tremendous computational overhead. Therefore, this paper proposes a fast attentive denoising framework for oracle bone inscriptions, i.e., OBIFormer. It leverages channel-wise self-attention, glyph extraction, and selective kernel feature fusion to reconstruct denoised images precisely while being computationally efficient. Our OBIFormer achieves state-of-the-art denoising performance for PSNR and SSIM metrics on synthetic and original OBI datasets. Furthermore, comprehensive experiments on a real oracle dataset demonstrate the great potential of our OBIFormer in assisting automatic OBI recognition. The code will be made available at https://github.com/LJHolyGround/OBIFormer.
<p>This work presents Mexico's High Resolution Climate Database (MexHiResClimDB), which is a newly developed gridded, high-resolution climate dataset comprised of daily, monthly and yearly precipitation and temperature (<span class="inline-formula"><i>T</i><sub>min</sub></span>, <span class="inline-formula"><i>T</i><sub>max</sub></span>, <span class="inline-formula"><i>T</i><sub>avg</sub></span>). This new database provides the largest temporal coverage of the aforementioned climate variables at the highest spatial resolution (20 arcsec, or 560 m on Mexico's CCL projection) when compared to the other currently available gridded datasets for Mexico and its development has allowed for the analysis of the country's climate extremes for the 1951–2020 period. By comparing the spatial distribution of precipitation from the MexHiResClimDB with other gridded data (Daymet, L15, CHIRPS and PERSIANN CDR), it was found that the precipitation provided by this new dataset adequately represents the spatial variation of extreme precipitation events, in particular for the precipitation that occurred during 15–16 September 2013, caused by the presence of Tropical storm Manuel in the Pacific Ocean and Hurricane Ingrid (Cat 1) in the Gulf of Mexico. Using data from 61 days retrieved from Automated Weather Stations located throughout Mexico – and correspoding to the two months with the largest precipitation in Mexico – it was found that precipitation data from MexHiResClimDB has the lowest MAE (8.7 mm), compared to those of L15 (9.5 mm), Daymet (10.1 mm) and CHIRPS (11.7 mm). For <span class="inline-formula"><i>T</i><sub>min</sub></span> and <span class="inline-formula"><i>T</i><sub>max</sub></span>, the lowest MAE was obtained with MexHiResClimDB (1.7 and 1.8 °C, respectively), followed by Daymet (2.0 °C for both temperatures) and L15 (2.4 and 2.5 °C). With this new database an analysis of the extreme events of precipitation and temperature in Mexico for the 1951–2020 period was undertaken: the wettest year was 1958, the wettest day 26 September 1970, and September of 2013 the wettest month. It was also found that eight out of the ten days with the highest <span class="inline-formula"><i>T</i><sub>min</sub></span> occurred in 2020, the two months with the highest <span class="inline-formula"><i>T</i><sub>min</sub></span> were July and August of 2020 and that the six years with the highest <span class="inline-formula"><i>T</i><sub>min</sub></span> were 2015–2020. When <span class="inline-formula"><i>T</i><sub>max</sub></span> was analysed, it was found that the hottest day was 15 June 1998, while June of 1998 was the hottest month and 2020 the hottest year, and that the four hottest years occurred between 2011–2020. Nationwide (and considering 1961–1990 as the baseline period), <span class="inline-formula"><i>T</i><sub>min</sub></span>, <span class="inline-formula"><i>T</i><sub>avg</sub></span> and <span class="inline-formula"><i>T</i><sub>max</sub></span> have increased, with their anomalies drastically increasing in recent years and reaching values above 1.0 °C in 2020. At the same time, precipitation has also decreased in recent years – which combined with the increase in temperature will have severe impacts on water availability. This new database provides a tool to quantify – in detail – the spatio-temporal variability of climate throughout Mexico.</p>
<p>The MexHiResClimDB entire dataset is available on Figshare (<a href="https://doi.org/10.6084/m9.figshare.c.7689428.v2">https://doi.org/10.6084/m9.figshare.c.7689428.v2</a>, <span class="cit" id="xref_altparen.1"><a href="#bib1.bibx16">Carrera-Hernández</a>, <a href="#bib1.bibx16">2025</a><a href="#bib1.bibx16">a</a></span>).</p>
Fiona Preston-Whyte , Toshka Barnardo , Danica Marlin
et al.
Data gaps limit solutions and policy development for environmental issues. Citizen science offers a possible solution to reduce data gaps at a limited cost while enhancing environmental education (EE). While highly effective in the latter, citizen science campaigns rarely produce reliable, comparable, and meaningful data. This often results from fragmented awareness, varying data collection methods, and little training prior to data collection. This article explores how Massive Open Online Courses (MOOCs) can be used to train citizen scientists, increase the value of citizen science data, and ensure that resources invested in citizen science initiatives are used more efficiently. We use a beach macrolitter monitoring course developed by Sustainable Seas Trust (SST) (NGO/NPO) and GRID-Arendal (a research foundation) as a case study in Africa, since the marine litter issue has widespread public support, and beaches are pleasant locations that attract potential citizen scientists. Beach macrolitter surveys utilise everyday equipment, and monitoring methods are simple if individuals are supported with appropriate training. This is especially relevant in Africa, where plastic pollution is forecasted to increase faster than other regions, and resources for research can be limited. This article gives a modified problemsolution model (mPSM) perspective, considering the challenges and solutions of MOOC development by two organisations working in the same space with limited resources. Challenges to inclusivity for online training in Africa included language barriers and limited technological access. Using Africa as a case study, we show that by combining professional abilities, inclusive digital education can be achieved using data-light MOOCs, offline engagement and other inclusive strategies to overcome the challenges of m- (mobile) and e- (electronic) learning. This kind of EE can be a powerful tool in developing reliable data while enhancing citizens’ agency in working towards Sustainable Development Goals (SDGs).
O ensino de Geografia tem como finalidade fazer o aluno aprender para crescer, viver e conviver em sociedade. No entanto, persistem desafios no processo de ensino, como a desconexão entre os conteúdos e a realidade dos estudantes e a falta de conhecimento pleno das bases epistemológicas da disciplina por parte de alguns professores. É necessário pensar em metodologias didáticas para que os conteúdos promovam raciocínio crítico e análise geográfica eficaz, uma vez que ele deve proporcionar aos alunos uma compreensão reflexiva sobre o espaço em que vivem e as dinâmicas sociais e ambientais que o moldam. Este estudo discute o papel do professor, as reflexões sobre o ensino de Geografia e a implementação de propostas complementares, como jogos e maquetes, no contexto educacional. O embasamento desta pesquisa deu-se a partir do registro disponível, decorrente de pesquisas anteriores, em documentos impressos, como livros, artigos, teses etc. Utiliza-se de dados ou de categorias teóricas já trabalhados por outros pesquisadores e devidamente registrados.
Accurately monitoring disaster effects is a crucial task in relief efforts (e.g. typhoon rescue). Social media data plays a vital role in disaster management, while deep learning-based methods gain more attention in typhoon disaster research. However, most existing classification methods for typhoon disasters are limited to multi-class but single-label levels, contradicting the reality that a social media text may correspond to multiple types of disaster damage. This paper proposes a siamese text classification network (SiamTCN) for multi-class multi-label information extraction from typhoon disasters based on Sina Weibo data. The SiamTCN leverages a dual-path architecture with shared weights, utilizing multi-head self-attention and convolution to extract hidden features from texts. A novel multi-class multi-label contrastive loss function is proposed to optimize the model. Additionally, address information is extracted through address matching and check-in locations. The spatio-temporal characteristics provide actionable insights for disaster management, enabling timely and targeted responses to affected regions. Experiments are conducted on Sina Weibo texts collected from six typical typhoon land-falls in Chinese coastal regions from 2018 to 2023. The results demonstrate that the accuracy achieved by the proposed method is 0.9454, 0.9391, and 0.9422, respectively. The code for this paper is available at https://github.com/SiamTCN.
Abstract This paper examines the aspirations, imaginaries and utopias of designers of an AI-based mental health app in India. By looking at automated therapy as both technological fix and sociotechnical object, I ask, What can we learn from engaging with psy technologists’ imaginaries and practices of health care futures? What are the assumptions they encode in the app? How does automated therapy reconfigure the geographies and temporalities of care? While automated therapy as instantiated by Wysa provides, I argue, a modest mental health intervention, the scalar aspirations of designers are anything but small. The paper proceeds in three steps. First, it turns to designers’ imaginaries of what it means to care for current mental health needs in digitally saturated lifeworlds and how they inscribe them into the app. It identifies nonjudgmental listening, anonymity, acceptance, reframing, and agency as key ideas encoded in Wysa’s sociotechnical algorithms, along with a congruence between entrepreneurial and encoded ethics of care. Second, it situates automated therapy within anthropological scholarship on ‘little’ technical devices in global health to argue that automated therapy devices such as Wysa articulate dreams for minimalist interventions with macro effects. Finally, it explores the new geographies and temporalities of care that automated therapy spurs, tracing the ways the app bridges various spatial and temporal gaps and obstacles of human therapy and upends common global health pathways. This paper contributes to recent scholarship on aspirations, dreams and utopias and on digitization and datafication in global health.
Recent advances in single-image 3D face reconstruction have shown remarkable progress in various applications. Nevertheless, prevailing techniques tend to prioritize the global facial contour and expression, often neglecting the nuanced dynamics of the eye region. In response, we introduce an Eye Landmark Adjustment Module, complemented by a Local Dynamic Loss, designed to capture the dynamic features of the eyes area. Our module allows for flexible adjustment of landmarks, resulting in accurate recreation of various eye states. In this paper, we present a comprehensive evaluation of our approach, conducting extensive experiments on two datasets. The results underscore the superior performance of our approach, highlighting its significant contributions in addressing this particular challenge.
The \textit{slope} of a fibered $3$-folds $f:X \to B$ is a relative numerical invariant defined by $λ(f) := K_{f}^{3}/\mathrm{deg}(f_{\ast}ω_{f})$, where $K_{f}$ is the relative canonical divisor and $ω_{f}$ is the relative dualizing sheaf. Establishing slope inequalities is a fundamental problem in the geography of fibered spaces. In this paper, we introduce a new invariant called the \textit{minimal covering degree} as a gonality-type invariant and study a lower bound of the slope increasing with the covering gonality and the minimal covering degree of the general fiber of $f$.
Hjort and Poulsen (2019) frames the staggered arrival of submarine Internet cables on the shores of Africa circa 2010 as a difference-in-differences natural experiment. The paper finds positive impacts of broadband on individual- and firm-level employment -- with a bias toward skilled employment -- and on nighttime light emissions. These results largely are not robust to alternative geocoding of survey locations, to correcting for a satellite changeover at end-2009, and to revisiting a definition of the treated zone that has no clear technological basis, is narrower than the spatial resolution of nearly all the data sources, and is empirically suboptimal as a representation of the geography of broadband.
The rates of tropical deforestation remain high, resulting in carbon emissions, biodiversity loss, and impacts on local communities. To design effective policies to tackle this, it is necessary to know what the drivers behind deforestation are. Since drivers vary in space and time, producing accurate spatially explicit maps with regular temporal updates is essential. Drivers can be recognized from satellite imagery but the scale of tropical deforestation makes it unfeasible to do so manually. Machine learning opens up possibilities for automating and scaling up this process. In this study, we developed and trained a deep learning model to classify the drivers of any forest loss—including deforestation—from satellite image time series. Our model architecture allows understanding of how the input time series is used to make a prediction, showing the model learns different patterns for recognizing each driver and highlighting the need for temporal data. We used our model to classify over $588^{^{\prime}}000$ sites to produce a map detailing the drivers behind tropical forest loss. The results confirm that the majority of it is driven by agriculture, but also show significant regional differences. Such data is a crucial source of information to enable targeting specific drivers locally and can be updated in the future using free satellite data.
Alisa Luangrath, Joorim Na, Pandi Kalimuthu
et al.
Biodegradable plastics (BPs) are widely used as alternatives to non-BPs due to their inherent ability to undergo facile degradation. However, the ecotoxicological impact of biodegradable microplastics (MPs) rarely remains scientific documented especially to aquatic ecosystem and organisms compared to conventional microplastics. Therefore, this study aimed to investigate the ecotoxicity of biodegradable polylactic acid (PLA) MPs to Daphnia magna with that of conventional polyethylene (PE) MPs with and without ultraviolet (UV) treatment (4 weeks). The acute toxicity (48 h) of PLA MPs was significantly higher than that of PE MPs, potentially attributable to their elevated bioconcentration resulting from their higher density. UV treatment notably reduced the particle size of PLA MPs and induced new hydrophilic functional groups containing oxygen. Thus, the acute lethal toxicity of PLA MPs exhibited noteworthy increase, compared to before UV treatment after UV treatment, which was greater than that of UV-PE MPs. In addition, UV-PLA MPs showed markedly elevated reactive oxygen species concentration in D. magna compared to positive control. However, there was no significant increase in the level of lipid peroxidation, possibly due to successful defense by antioxidant enzymes (superoxide dismutase and catalase). These findings highlight the ecotoxicological risks of biodegradable MPs to aquatic organisms, which require comprehensive long-term studies.
La gentrificación es reconocida como uno de los procesos urbanos, a nivel planetario, tendiente a agudizar las desigualdades socioespaciales y a cercenar el derecho a la ciudad para grupos de menores ingresos. El papel decisivo del Estado en este proceso ha recibido creciente atención, y la investigación latinoamericana sobre el tema ha aportado evidencias clave en ese sentido. A partir de una revisión de la literatura especializada sobre gentrificación, gestión de la ciudad y políticas urbanas neoliberales, el presente artículo propone una categorización de diferentes mecanismos concretos a través de los cuales se pone en práctica la gentrificación liderada por el Estado. Se detallan los siguientes mecanismos: el marco regulatorio urbano y la “excepcionalidad sistemática”; la institucionalización de la gobernanza urbana empresarial; la (re)inversión selectiva en el territorio; y las estrategias de (re)activación territorial. El artículo ilustra estos mecanismos a partir de la experiencia argentina, con el fin de sentar las bases para operacionalizar diversas variables capaces de captar cada uno de ellos.
There are several theories in economics regarding the roots or causes of prosperity in a society. One of these theories or hypotheses -- named geography hypothesis -- mentions that the reason why some countries are prosperous and some others are poor is the geographical location of the countries in the world as makes their climate and environment favorable or unfavorable regarding natural resources. Another competing hypothesis states that man-made institutions particularly inclusive political institutions are the reasons why some countries are prosperous and some others are poor. On the other hand, there is a specific political theory developed for the long-term social development in Iran -- named Arbitrary Rule and Aridisolatic Society which particularly emphasizes on the role of aridity to shape arbitrary political and economical institutions in Iran, without any functional social classes in the society. In this paper, by extending the AI-Economist -- a recently developed two-level multi-agent reinforcement learning environment -- I show that when the central planner is ruling the environment by arbitrary rules, the society evolves through different paths in different environments. In the environment having band-like vertical isolated patches of natural resources, all mobile agents are equally exploited by the central planner and the central planner is also not gaining any income, while in the society having more uniformly distributed natural resources, the productivity and Maximin are higher and the society generates a heterogeneous stratified social structure. All these findings provide a partial answer to the above debate and reconcile the role of geography and political institutions on the long-term development in a region.
Origin-destination (OD) flow modeling is an extensively researched subject across multiple disciplines, such as the investigation of travel demand in transportation and spatial interaction modeling in geography. However, researchers from different fields tend to employ their own unique research paradigms and lack interdisciplinary communication, preventing the cross-fertilization of knowledge and the development of novel solutions to challenges. This article presents a systematic interdisciplinary survey that comprehensively and holistically scrutinizes OD flows from utilizing fundamental theory to studying the mechanism of population mobility and solving practical problems with engineering techniques, such as computational models. Specifically, regional economics, urban geography, and sociophysics are adept at employing theoretical research methods to explore the underlying mechanisms of OD flows. They have developed three influential theoretical models: the gravity model, the intervening opportunities model, and the radiation model. These models specifically focus on examining the fundamental influences of distance, opportunities, and population on OD flows, respectively. In the meantime, fields such as transportation, urban planning, and computer science primarily focus on addressing four practical problems: OD prediction, OD construction, OD estimation, and OD forecasting. Advanced computational models, such as deep learning models, have gradually been introduced to address these problems more effectively. Finally, based on the existing research, this survey summarizes current challenges and outlines future directions for this topic. Through this survey, we aim to break down the barriers between disciplines in OD flow-related research, fostering interdisciplinary perspectives and modes of thinking.
Detecting travel modes from global navigation satellite system (GNSS) trajectories is essential for understanding individual travel behavior and a prerequisite for achieving sustainable transport systems. While studies have acknowledged the benefits of incorporating geospatial context information into travel mode detection models, few have summarized context modeling approaches and analyzed the significance of these context features, hindering the development of an efficient model. Here, we identify context representations from related work and propose an analytical pipeline to assess the contribution of geospatial context information for travel mode detection based on a random forest model and the SHapley Additive exPlanation (SHAP) method. Through experiments on a large-scale GNSS tracking dataset, we report that features describing relationships with infrastructure networks, such as the distance to the railway or road network, significantly contribute to the model's prediction. Moreover, features related to the geospatial point entities help identify public transport travel, but most land-use and land-cover features barely contribute to the task. We finally reveal that geospatial contexts have distinct contributions in identifying different travel modes, providing insights into selecting appropriate context information and modeling approaches. The results from this study enhance our understanding of the relationship between movement and geospatial context and guide the implementation of effective and efficient transport mode detection models.
Front and back cover caption, volume 39 issue 5IBN KHALDUN AND RE‐TRIBALIZATIONA bust of Ibn Khaldun (1332‐1406), at the entrance of the Kasbah of Bejaia, Algeria. As you gaze upon this scholar, who first delved into the cyclical dynamics of tribes and civilizations, you are not just looking at history — you are looking at a mirror reflecting our modern world. Khaldun's pioneering insights into tribal cohesion (asabiyyah) and its impact on societal rise and fall are not relics of the past; they are prophetic echoes reverberating in today's global landscape.In an increasingly interconnected yet paradoxically fragmented world, the concept of ‘tribalism’ is making a surprising comeback.No longer confined to anthropology textbooks or remote communities, tribalism resurfaces in our political dialogues, social affiliations, and even international relations. But this is not your grandfather's tribalism; it is ‘re‐tribalization’, a modern reimagining of ancient affiliations and loyalties shaping nations and rewriting global equations.In this issue, the first of a two‐part article by Ahmed et al., ‘Re‐tribalization in the 21st century’, peels back the layers of this complex phenomenon. It challenges the conventional wisdom that pits ‘tribalism’ against ‘civilization’, revealing instead a dynamic interplay that influences everything from state governance to globalization. Whether it is the UK Brexit vote, the rise of ethnonationalism in various countries or the enduring conflicts in the Middle East, the fingerprints of tribalism — and its modern avatar, re‐tribalization — are unmistakably present.As we navigate the complexities of a world that is both a ‘global village’ and a patchwork of evolving tribal identities, the concept of re‐tribalization serves as an analytical lens. This resurgence of tribal affiliations is a complex adaptation to the challenges and opportunities of a globalized world. The ancient codes of tribalism are being reinterpreted in the context of modern geopolitics and digital communication. While the old and the new may seem to be in tension, they are part of a complex dynamic that requires scrutiny. The ancient and the modern coexist in a world as fraught with conflict as it is ripe for cooperation.FOOTBALL AND CLIMATE CHANGEOn the dwindling sands of Ariyallur Beach in the coastal hamlet of Ottummal, Malabar, India, children passionately kick a football around, savouring the shrinking space that remains for their cherished sport. Their laughter and shouts echo against a backdrop of rising tides and eroding shores, a poignant reminder of the impermanence of their playground.In this issue, Muhammed Haneefa delves into the heart of this coastal community to explore how the relentless rise in sea levels is not just a geographical alteration but a transformation of a way of life. He uncovers the erosion of subaerial beaches — once the lifeblood of the community's social and cultural fabric — and its devastating impact on leisure activities, most notably the deeply ingrained pastime of football.Haneefa also scrutinizes the local government's ‘managed retreat’ strategy, a well‐intentioned but complex proposal that involves relocating these vulnerable communities away from their endangered coastal homes. While the plan may offer a temporary respite from the encroaching waters, it fails to account for the fisherfolk's profound emotional and cultural ties to their land and traditions.This article serves as a lens through which we can view climate change from the ground up. While satellite images and climatological data may provide a bird's‐eye view of the planet's changing face, it is through the worm's‐eye view of anthropologists and ethnographers like Haneefa that we truly understand the human cost. Here, climate change is not just a statistic or a future projection; it is a lived reality that is reshaping communities, altering identities and challenging the very essence of cultural heritage.
Abstract Forest areas, like other ecosystems, provide a range of ecosystem services. The most common classification of ecosystem services distinguishes them into four categories: provisioning, regulating, supporting, and cultural. As is well known, the two categories of services (provisioning and cultural) provide direct benefits to society. The paper focuses on a selected element of Cultural Ecosystem Services, namely recreation in forested areas. The study aimed to determine the spatial and temporal distribution of recreational activities in forest areas in Poland using Volunteered Geographic Information data from the Flickr portal from 2010 to 2018. In addition, the influence of 48 variables, assigned to three groups of variables (tree stand; topographic-spatial; socio-economic), on the attractiveness of forest areas and their actual use was analysed. In this paper, Boosted Regression Trees was used to identify the variables with the most significant influence on the use of forest spaces (photo availability). Five different model variants were developed based on the specified groups of variables. The results show that, depending on the explaining variables, the models are characterised by accuracy ranging from 0.63 (a model based on socio-economic variables) to 0.92 (a model constructed using all variables). The share of topographic-spatial variables in this model is 52.30%, of tree stand variables – 25.18%, and of demographic variables – 22.52%. The time distribution of activity shows that forest areas are most frequently visited during free time, on the weekends, and in the summer. The main factor contributing to this differentiation is leisure time availability, which is directly reflected in a more significant number of photos taken in the late afternoon and evening hours and on weekends and summer months. Analysis of the Flickr data distribution made it possible to identify areas with the highest recreational use intensity (conurbation, Forest Promotional Complexes, mountain areas). Flickr data allowed partial inferences about the reason for the user's visit to the forest. Due to privacy restrictions, these data only allow a small part of the user to be identified. Information about the spatial and temporal distribution of recreational visitation is an important characteristic feature of Flickr data. Thus, data from the Flickr site can become a real tool for decision-makers who manage forest areas when making decisions regarding such places in the context of forest use for recreation and forest management. In order to discover the full preferences and expectations of society regarding the recreational function of forests, additional information is needed, such as that obtained from survey-type studies.