Calum J. Pritchard, Nabeil K. G. Salama, Iain Berrill
et al.
Abstract Minimum landing sizes are a fisheries management tool conventionally used to exclude juveniles from fisheries. Harvest slot limits (HSL) are an alternative fisheries management tool used to exclude both juveniles and larger individuals from a fishery through the implementation of both minimum and maximum landing sizes. However, fishery‐dependent data from HSL‐managed fisheries are only representative of a small portion of the population. These data do not meet the requirements for conventional stock assessments nor harvest control rules, so these fisheries cannot be assessed without additional and expensive fishery‐independent data. The objective of this research was to develop a novel harvest control rule able to produce catch advice for fisheries managed by HSL using fishery‐dependent data. The SlotLim method, and corresponding R package, were developed and applied to the data‐limited Scottish live ballan wrasse Labrus bergylta fishery. Within SlotLim, the advised catch is a product of the previous catch and two multipliers: the targeted biomass adjustment (TBA) and size adherence multiplier (SAM). The TBA assesses the rate of change in an abundance or biomass index, adjusted for the proportion of the population targeted by HSL. The SAM assesses fishers' compliance with HSL. The methodology follows a simple premise: the advised catch increases with increasing abundance/biomass indices and adherence to HSL. The minimum data requirements are two consecutive years of catch, length frequency and an abundance or biomass index (all from fishery‐dependent sources), species‐specific growth rate coefficients and the natural mortality rate. The SlotLim method advised catch for a reduction in catch by 17% for the Scottish ballan wrasse fishery due to an observed reduction in abundances indices and non‐adherence to maximum landing sizes. Solution. The SlotLim method allows HSL‐managed fisheries to be assessed at limited expense, contributing to the continued sustainable use of these resources. HSL may also be considered a viable strategy for other data‐limited fisheries upon the availability of this harvest control rule.
PyGALAX is a Python package for geospatial analysis that integrates automated machine learning (AutoML) and explainable artificial intelligence (XAI) techniques to analyze spatial heterogeneity in both regression and classification tasks. It automatically selects and optimizes machine learning models for different geographic locations and contexts while maintaining interpretability through SHAP (SHapley Additive exPlanations) analysis. PyGALAX builds upon and improves the GALAX framework (Geospatial Analysis Leveraging AutoML and eXplainable AI), which has proven to outperform traditional geographically weighted regression (GWR) methods. Critical enhancements in PyGALAX from the original GALAX framework include automatic bandwidth selection and flexible kernel function selection, providing greater flexibility and robustness for spatial modeling across diverse datasets and research questions. PyGALAX not only inherits all the functionalities of the original GALAX framework but also packages them into an accessible, reproducible, and easily deployable Python toolkit while providing additional options for spatial modeling. It effectively addresses spatial non-stationarity and generates transparent insights into complex spatial relationships at both global and local scales, making advanced geospatial machine learning methods accessible to researchers and practitioners in geography, urban planning, environmental science, and related fields.
Eleni Oikonomaki, Belivanis Dimitris, Kakderi Christina
The geography of innovation offers a framework to understand how territorial characteristics shape innovation, often via spatial and cognitive proximity. Empirical research has focused largely on national and regional scales, while urban and sub-regional geographies receive less attention. Local studies typically rely on limited indicators (e.g., firm-level data, patents, basic socioeconomic measures), with few offering a systematic framework integrating urban form, mobility, amenities, and human-capital proxies at the neighborhood scale. Our study investigates innovation at a finer spatial resolution, going beyond proprietary or static indicators. We develop the Local Innovation Determinants (LID) database and framework to identify key enabling factors across regions, combining traditional government data with publicly available data via APIs for a more granular understanding of spatial dynamics shaping innovation capacity. Using exploratory big and geospatial data analytics and random forest models, we examine neighborhoods in New York and Massachusetts across four dimensions: social factors, economic characteristics, land use and mobility, morphology, and environment. Results show that alternative data sources offer significant yet underexplored potential to enhance insights into innovation dynamics. City policymakers should consider neighborhood-specific determinants and characteristics when designing and implementing local innovation strategies.
Sjoerd Halmans, Lavinia Paganini, Alexander Serebrenik
et al.
Hackathons are time-bound collaborative events that often target software creation. Although hackathons have been studied in the past, existing work focused on in-depth case studies limiting our understanding of hackathons as a software engineering activity. To complement the existing body of knowledge, we introduce HackRep, a dataset of 100,356 hackathon GitHub repositories. We illustrate the ways HackRep can benefit software engineering researchers by presenting a preliminary investigation of hackathon project continuation, hackathon team composition, and an estimation of hackathon geography. We further display the opportunities of using this dataset, for instance showing the possibility of estimating hackathon durations based on commit timestamps.
Any oriented $4$-dimensional Einstein metric with semi-definite sectional curvature satisfies the pointwise inequality \[ \frac{|s|}{\sqrt{6}}\geq|W^+|+|W^-|, \] where $s$, $W^+$ and $W^-$ are respectively the scalar curvature, the self-dual and anti-self-dual Weyl curvatures. We give a complete characterization of closed $4$-dimensional Einstein metrics with semi-definite sectional curvature saturating this pointwise inequality. We then present further consequences of this circle of ideas, in particular to the study of the geography of non-positively curved closed Einstein and Kaehler-Einstein $4$-manifolds. In the Kaehler-Einstein case, we obtain a sharp Gromov-Lueck type inequality.
If an active citizen should increasingly be a computationally enlightened one, replacing the autonomy of reason with the heteronomy of algorithms, then I argue in this article that we must begin teaching the principles of critiquing the computal through new notions of what we might call digital Bildung. Indeed, if civil society itself is mediated by computational systems and media, the public use of reason must also be complemented by skills for negotiating and using these computal forms to articulate such critique. Not only is there a need to raise the intellectual tone regarding computation and its related softwarization processes, but there is an urgent need to attend to the likely epistemic challenges from computation which, as presently constituted, tends towards justification through a philosophy of utility rather than through a philosophy of care for the territory of the intellect. We therefore need to develop an approach to this field that uses concepts and methods drawn from philosophy, politics, history, anthropology, sociology, media studies, computer science, and the humanities more generally, to try to understand these issues - particularly the way in which software and data increasingly penetrate our everyday life and the pressures and fissures that are created. We must, in other words, move to undertake a critical interdisciplinary research program to understand the way in which these systems are created, instantiated, and normatively engendered in both specific and general contexts.
Social Cyber Geography is the space in the digital cyber realm that is produced through social relations. Communication in the social media ecosystem happens not only because of human interactions, but is also fueled by algorithmically controlled bot agents. Most studies have not looked at the social cyber geography of bots because they focus on bot activity within a single country. Since creating a bot uses universal programming technology, bots, how prevalent are these bots throughout the world? To quantify bot activity worldwide, we perform a multilingual and geospatial analysis on a large dataset of social data collected from X during the Coronavirus pandemic in 2021. This pandemic affected most of the world, and thus is a common topic of discussion. Our dataset consists of ~100 mil posts generated by ~31mil users. Most bot studies focus only on English-speaking countries, because most bot detection algorithms are built for the English language. However, only 47\% of the bots write in the English language. To accommodate multiple languages in our bot detection algorithm, we built Multilingual BotBuster, a multi-language bot detection algorithm to identify the bots in this diverse dataset. We also create a Geographical Location Identifier to swiftly identify the countries a user affiliates with in his description. Our results show that bots can appear to move from one country to another, but the language they write in remains relatively constant. Bots distribute narratives on distinct topics related to their self-declared country affiliation. Finally, despite the diverse distribution of bot locations around the world, the proportion of bots per country is about 20%. Our work stresses the importance of a united analysis of the cyber and physical realms, where we combine both spheres to inventorize the language and location of social media bots and understand communication strategies.
Jacopo Lenti, Lorenzo Costantini, Ariadna Fosch
et al.
It is increasingly important to generate synthetic populations with explicit coordinates rather than coarse geographic areas, yet no established methods exist to achieve this. One reason is that latitude and longitude differ from other continuous variables, exhibiting large empty spaces and highly uneven densities. To address this, we propose a population synthesis algorithm that first maps spatial coordinates into a more regular latent space using Normalizing Flows (NF), and then combines them with other features in a Variational Autoencoder (VAE) to generate synthetic populations. This approach also learns the joint distribution between spatial and non-spatial features, exploiting spatial autocorrelations. We demonstrate the method by generating synthetic homes with the same statistical properties of real homes in 121 datasets, corresponding to diverse geographies. We further propose an evaluation framework that measures both spatial accuracy and practical utility, while ensuring privacy preservation. Our results show that the NF+VAE architecture outperforms popular benchmarks, including copula-based methods and uniform allocation within geographic areas. The ability to generate geolocated synthetic populations at fine spatial resolution opens the door to applications requiring detailed geography, from household responses to floods, to epidemic spread, evacuation planning, and transport modeling.
This study examines the fly ash from Soc Son municipal waste power plant (SMPP) and suggests ways to repurpose it to reduce its environmental impact. Fly ash from the Soc Son waste power plant has a gray color, spherical particles with a 5–103 μ m diameter, and a high carbon and heavy metal content. Bermorite crystals can absorb and release heavy metals, making monitoring secondary pollutants during incineration crucial. The EDX analysis of fly ash from the Soc Son waste power plant revealed that it was predominantly contaminated with metal elements, with the highest percentage of calcium. The EDX was able to detect heavy metals in incinerator fly ash. The concentration of Zn in the fly ash exceeded QCVN 07:2009/BTNMT standards, indicating the high amounts of some elements that may be hazardous to the environment and human health. Using the SEM/EDX and XRF, the fly ash from the Soc Son landfill power plant was analyzed and discovered that it exceeds permissible limits for dangerous heavy elements. The most common inorganic elements are Ca, followed by Zn, Pb, Cd, and Ag. Fly ash is classed as hazardous waste due to its high concentration of heavy metals, which results from the combustion of municipal solid waste that has not been separated. Vietnam fights municipal solid waste incinerator fly ash production. Some nations stabilize fly ash to remove harmful components and use it in buildings. Stabilized fly ash makes unfired construction bricks and cement manufacturing components and combining fly ash with inorganic trash protects the environment.
Presented are algorithms for enforcing function diagram commutativity and anti-commutativity database constraints, using the database software application constraint-driven design and development methodology, in the realm of the (Elementary) Mathematical Data Model ((E)MDM). MatBase, an intelligent data and knowledge management system prototype mainly based on the (E)MDM, uses these algorithms to automatically generate corresponding code in both its versions (i.e., the MS Access and the .NET and SQL Server ones). Of course, any software developer may also use these algorithms manually. The paper also discusses the code generated to enforce two such constraints from a Geography database.
The article is an attempt to contribute to explorations of a common origin for language and planned-collaborative action. It gives `semantics of change' the central stage in the synthesis, from its history and recordkeeping to its development, its syntax, delivery and reception, including substratal aspects. It is suggested that to arrive at a common core, linguistic semantics must be understood as studying through syntax mobile agent's representing, tracking and coping with change and no change. Semantics of actions can be conceived the same way, but through plans instead of syntax. The key point is the following: Sequencing itself, of words and action sequences, brings in more structural interpretation to the sequence than which is immediately evident from the sequents themselves. Mobile sequencers can be understood as subjects structuring reporting, understanding and keeping track of change and no change. The idea invites rethinking of the notion of category, both in language and in planning. Understanding understanding change by mobile agents is suggested to be about human extended practice, not extended-human practice. That's why linguistics is as important as computer science in the synthesis. It must rely on representational history of acts, thoughts and expressions, personal and public, crosscutting overtness and covertness of these phenomena. It has implication for anthropology in the extended practice, which is covered briefly.
Zeyuan Hu, Akshay Subramaniam, Zhiming Kuang
et al.
Modern climate projections often suffer from inadequate spatial and temporal resolution due to computational limitations, resulting in inaccurate representations of sub-grid processes. A promising technique to address this is the Multiscale Modeling Framework (MMF), which embeds a kilometer-resolution cloud-resolving model within each atmospheric column of a host climate model to replace traditional convection and cloud parameterizations. Machine learning (ML) offers a unique opportunity to make MMF more accessible by emulating the embedded cloud-resolving model and reducing its substantial computational cost. Although many studies have demonstrated proof-of-concept success of achieving stable hybrid simulations, it remains a challenge to achieve near operational-level success with real geography and comprehensive variable emulation that includes, for example, explicit cloud condensate coupling. In this study, we present a stable hybrid model capable of integrating for at least 5 years with near operational-level complexity, including coarse-grid geography, seasonality, explicit cloud condensate and wind predictions, and land coupling. Our model demonstrates skillful online performance, achieving a 5-year zonal mean tropospheric temperature bias within 2K, water vapor bias within 1 g/kg, and a precipitation RMSE of 0.96 mm/day. Key factors contributing to our online performance include an expressive U-Net architecture and physical thermodynamic constraints for microphysics. With microphysical constraints mitigating unrealistic cloud formation, our work is the first to demonstrate realistic multi-year cloud condensate climatology under the MMF framework. Despite these advances, online diagnostics reveal persistent biases in certain regions, highlighting the need for innovative strategies to further optimize online performance.
The preservation and revitalization of endangered and extinct languages is a meaningful endeavor, conserving cultural heritage while enriching fields like linguistics and anthropology. However, these languages are typically low-resource, making their reconstruction labor-intensive and costly. This challenge is exemplified by Nushu, a rare script historically used by Yao women in China for self-expression within a patriarchal society. To address this challenge, we introduce NushuRescue, an AI-driven framework designed to train large language models (LLMs) on endangered languages with minimal data. NushuRescue automates evaluation and expands target corpora to accelerate linguistic revitalization. As a foundational component, we developed NCGold, a 500-sentence Nushu-Chinese parallel corpus, the first publicly available dataset of its kind. Leveraging GPT-4-Turbo, with no prior exposure to Nushu and only 35 short examples from NCGold, NushuRescue achieved 48.69% translation accuracy on 50 withheld sentences and generated NCSilver, a set of 98 newly translated modern Chinese sentences of varying lengths. A sample of both NCGold and NCSilver is included in the Supplementary Materials. Additionally, we developed FastText-based and Seq2Seq models to further support research on Nushu. NushuRescue provides a versatile and scalable tool for the revitalization of endangered languages, minimizing the need for extensive human input.
G. S. H. Cruttwell, Jean-Simon Pacaud Lemay, Elias Vandenberg
There is an abstract notion of connection in any tangent category. In this paper, we show that when applied to the tangent category of affine schemes, this recreates the classical notion of a connection on a module (and similarly, in the tangent category of schemes, this recreates the notion of connection on a quasi-coherent sheaf of modules). By contrast, we also show that in the tangent category of algebras, there are no non-trivial connections.
Cancer is more than just a cluster of diseases. Beyond patho-physiological variations, it has been mobilized by experts and laypeople to make sense of a wide variety of phenomena. The regional variability and socio-historical situatedness of experiences of cancer have been widely published in recent decades (Bennet et al. forthcoming; Dein 2006; Livingston 2012; Manderson et al. 2005; Mathews et al. 2015; McMullin and Weiner 2009). Many scholars in medical anthropology have championed a research approach that foregrounds the voices of and practices carried out by people affected by cancer, enquiring into how diverse populations (do not) seek diagnosis, (do not) undergo treatments, and attempt to carry on with their lives with and despite cancer (Hunt 1998; LoraWainwright 2013; Manderson 2005; Mulemi 2010; Porroche-Escudero 2014; Stacey 2013; Vindrola Padros 2011). A focus on the dynamics structuring clinical cancer care has revealed the porous boundaries between clinics and their socio-political environments (Van Der Geest and Finkler 2004). This has included unpacking the ways in which wider sociocultural arrangements inform the narrative structure of clinical experiences (del Vecchio Good et al. 1994, 1994; Mattingly 1998); the negotiation that takes place within the doctor–patient relationships (Bell, 2009; Fainzang 2016; Høybye and TjørnhøjThomsen 2014); the translation of knowledge practices into therapeutic technologies (Gibbon 2007; Gibbon et al. 2014; Keating and Cambrosio 2011); and the impact of the political economy of health that affords different possibilities of care (Day 2015; Iriart and Gibbon forthcoming; Livingston 2012; Mulemi 2008; Sanz 2017). The promise of technoscientific developments is slowly but continuously informing ethnographies of cancer care. Novel therapies enabled by the discovery of candidate biomarkers (Arteaga 2021), and through the blurring between clinical drug trials and therapy, are starting to modify the temporality of cancer treatments and the clinical pathways that patients go through (Cambrosio et al. 2018; Keating and Cambrosio 2011). Such dynamics are transforming the ways in which cancer is experienced in different milieus, reconfiguring forms of pain and suffering, and shaping the efforts people make to understand what is happening to them and what they are expected to do in and through surviving cancer (Day et al. 2017; Jain 2013; Kerr and Cunningham-Burley 2015; Stacey 2013; Steinberg 2015). Nevertheless, this promise of improved health outcomes is intertwined with precariousness. Tensions arise when considering what treatments are made available and for whom (Jain and Kaufman 2011). Transnational research networks that circulate biomedical resources to sites where hospital infrastructure is missing create impermanent solutions (Caduff et al. 2018; Gibbon et al. 2014; Mika 2016; Petryna 2013), and many cancer types and geographies are left behind in terms of funding allocation for research and access to therapeutics (Bell 2014; Caduff and Hollen 2019). Furthermore, some people affected by cancer may not want to seek biomedical treatment at all or prefer to stop it altogether due to its economic or emotional costs and/or its iatrogenic effects. This tension is what Benson Mulemi understands as a core “treatment ambiguity” in Kenya through which anti-cancer treatment increases rather than alleviates suffering (Mulemi 2010), and Julie Livingston provocatively poses with the question “Leg or Life”? (Livingston 2012:91). The moral dilemma that this question captures is deciding whether to compromise someone’s ability to earn income, work, and fulfil everyday responsibilities for a couple more months or years of life. To cut off a leg is to transform the intimate social body in which the patient is embedded, demonstrating not only that some cancer treatment alternatives might be as harsh as the disease itself, but also that cancer and the consequences