Hasil untuk "Geography"

Geoff Boeing, Yuquan Zhou

Travel time prediction is central to transport geography and planning's accessibility analyses, sustainable transportation infrastructure provision, and active transportation interventions. However, calculating accurate travel times, especially for driving, requires either extensive technical capacity and bespoke data, or resources like the Google Maps API that quickly become prohibitively expensive to analyze thousands or millions of trips necessary for metropolitan-scale analyses. Such obstacles particularly challenge less-resourced researchers, practitioners, and community advocates. This article argues that a middle-ground is needed to provide reasonably accurate travel time predictions without extensive data or computing requirements. It introduces a free, open-source minimally-congested driving time prediction model with minimal cost, data, and computational requirements. It trains and tests this model using the Los Angeles, California urban area as a case study by calculating naive travel times from open data then developing a random forest model to predict travel times as a function of those naive times plus open data on turns and traffic controls. Validation shows that this interpretable machine learning method offers a superior middle-ground technique that balances reasonable accuracy with minimal resource requirements.

Attila Dobi, Aravindh Manickavasagam, Benjamin Thompson et al.

Content safety teams need metrics that reflect what users actually experience, not only what is reported. We study prevalence: the fraction of user views (impressions) that went to content violating a given policy on a given day. Accurate prevalence measurement is challenging because violations are often rare and human labeling is costly, making frequent, platform-representative studies slow. We present a design-based measurement system that (i) draws daily probability samples from the impression stream using ML-assisted weights to concentrate label budget on high-exposure and high-risk content while preserving unbiasedness, (ii) labels sampled items with a multimodal LLM governed by policy prompts and gold-set validation, and (iii) produces design-consistent prevalence estimates with confidence intervals and dashboard drilldowns. A key design goal is one global sample with many pivots: the same daily sample supports prevalence by surface, viewer geography, content age, and other segments through post-stratified estimation. We describe the statistical estimators, variance and confidence interval construction, label-quality monitoring, and an engineering workflow that makes the system configurable across policies.

Eshaan Tanwar, Anwoy Chatterjee, Michael Saxon et al.

Most multilingual question-answering benchmarks, while covering a diverse pool of languages, do not factor in regional diversity in the information they capture and tend to be Western-centric. This introduces a significant gap in fairly evaluating multilingual models' comprehension of factual information from diverse geographical locations. To address this, we introduce XNationQA for investigating the cultural literacy of multilingual LLMs. XNationQA encompasses a total of 49,280 questions on the geography, culture, and history of nine countries, presented in seven languages. We benchmark eight standard multilingual LLMs on XNationQA and evaluate them using two novel transference metrics. Our analyses uncover a considerable discrepancy in the models' accessibility to culturally specific facts across languages. Notably, we often find that a model demonstrates greater knowledge of cultural information in English than in the dominant language of the respective culture. The models exhibit better performance in Western languages, although this does not necessarily translate to being more literate for Western countries, which is counterintuitive. Furthermore, we observe that models have a very limited ability to transfer knowledge across languages, particularly evident in open-source models.

Yunshuang Yuan, Monika Sester

Historical maps are essential resources that provide insights into the geographical landscapes of the past. They serve as valuable tools for researchers across disciplines such as history, geography, and urban studies, facilitating the reconstruction of historical environments and the analysis of spatial transformations over time. However, when constrained to analogue or scanned formats, their interpretation is limited to humans and therefore not scalable. Recent advancements in machine learning, particularly in computer vision and large language models (LLMs), have opened new avenues for automating the recognition and classification of features and objects in historical maps. In this paper, we propose a novel distillation method that leverages LLMs and attention mechanisms for the automatic annotation of historical maps. LLMs are employed to generate coarse classification labels for low-resolution historical image patches, while attention mechanisms are utilized to refine these labels to higher resolutions. Experimental results demonstrate that the refined labels achieve a high recall of more than 90%. Additionally, the intersection over union (IoU) scores--84.2% for Wood and 72.0% for Settlement--along with precision scores of 87.1% and 79.5%, respectively, indicate that most labels are well-aligned with ground-truth annotations. Notably, these results were achieved without the use of fine-grained manual labels during training, underscoring the potential of our approach for efficient and scalable historical map analysis.

Zekun Cai, Yiheng Yao, Guangji Bai et al.

Real-world data distributions often shift continuously across multiple latent factors such as time, geography, and socioeconomic contexts. However, existing domain generalization approaches typically treat domains as discrete or as evolving along a single axis (e.g., time). This oversimplification fails to capture the complex, multidimensional nature of real-world variation. This paper introduces the task of Continuous Domain Generalization (CDG), which aims to generalize predictive models to unseen domains defined by arbitrary combinations of continuous variations. We present a principled framework grounded in geometric and algebraic theories, showing that optimal model parameters across domains lie on a low-dimensional manifold. To model this structure, we propose a Neural Lie Transport Operator (NeuralLio), which enables structure-preserving parameter transitions by enforcing geometric continuity and algebraic consistency. To handle noisy or incomplete domain variation descriptors, we introduce a gating mechanism to suppress irrelevant dimensions and a local chart-based strategy for robust generalization. Extensive experiments on synthetic and real-world datasets, including remote sensing, scientific documents, and traffic forecasting, demonstrate that our method significantly outperforms existing baselines in both generalization accuracy and robustness.

Dorian Peters, Fernanda Espinoza, Marco da Re et al.

There is justifiable interest in leveraging conversational AI (CAI) for health across the majority world, but to be effective, CAI must respond appropriately within culturally and linguistically diverse contexts. Therefore, we need ways to address the fact that current LLMs exclude many lived experiences globally. Various advances are underway which focus on top-down approaches and increasing training data. In this paper, we aim to complement these with a bottom-up locally-grounded approach based on qualitative data collected during participatory workshops in Latin America. Our goal is to construct a rich and human-centred understanding of: a) potential areas of cultural misalignment in digital health; b) regional perspectives on chatbots for health and c)strategies for creating culturally-appropriate CAI; with a focus on the understudied Latin American context. Our findings show that academic boundaries on notions of culture lose meaning at the ground level and technologies will need to engage with a broader framework; one that encapsulates the way economics, politics, geography and local logistics are entangled in cultural experience. To this end, we introduce a framework for 'Pluriversal Conversational AI for Health' which allows for the possibility that more relationality and tolerance, rather than just more data, may be called for.

Alexandre Chaussard, Anna Bonnet, Elisabeth Gassiat et al.

When studying ecosystems, hierarchical trees are often used to organize entities based on proximity criteria, such as the taxonomy in microbiology, social classes in geography, or product types in retail businesses, offering valuable insights into entity relationships. Despite their significance, current count-data models do not leverage this structured information. In particular, the widely used Poisson log-normal (PLN) model, known for its ability to model interactions between entities from count data, lacks the possibility to incorporate such hierarchical tree structures, limiting its applicability in domains characterized by such complexities. To address this matter, we introduce the PLN-Tree model as an extension of the PLN model, specifically designed for modeling hierarchical count data. By integrating structured variational inference techniques, we propose an adapted training procedure and establish identifiability results, enhancing both theoretical foundations and practical interpretability. Experiments on synthetic datasets and human gut microbiome data highlight generative improvements when using PLN-Tree, demonstrating the practical interest of knowledge graphs like the taxonomy in microbiome modeling. Additionally, we present a proof-of-concept implication of the identifiability results by illustrating the practical benefits of using identifiable features for classification tasks, showcasing the versatility of the framework.

Yikun Yang, Fenghui Ren, Minjie Zhang

Fog computing has become an attractive research topic in recent years. As an extension of the cloud, fog computing provides computing resources for Internet of Things (IoT) applications through communicative fog nodes located at the network edge. Fog nodes assist cloud services in handling real-time and mobile applications by bringing the processing capability to where the data is generated. However, the introduction of fog nodes can increase scheduling openness and uncertainty. The scheduling issues in fog computing need to consider the geography, load balancing, and network latency between IoT devices, fog nodes, as well as the parent cloud. Besides, the scheduling methods also need to deal with the occurrence of uncertain events in real-time so as to ensure service reliability. This paper proposes an agent-based framework with a decentralized structure to construct the architecture of fog computing, while three agent-based algorithms are proposed to implement the scheduling, load balance, and rescheduling processes. The proposed framework is implemented by JADE and evaluated on the iFogSim toolkit. Experimental results show that the proposed scheduling framework can adaptively schedule tasks and resources for different service requests in fog computing and can also improve the task success rate when uncertain events occur.

Andrés Alegría, Elvira Poloczanska, Sina Loeschke et al.

Abstract Climate risk assessments are crucial in quantifying and communicating risks in a clear and concise manner. In light of the rapidly proceeding climatic changes, there is a growing need for a more comprehensive integration and a more effective overview of available and relevant data that go into these assessments, particularly on the temporal and spatial dynamics of risk. In this paper, we describe the advantages, challenges and opportunities for increasing the accessibility of temporal and spatial data needed to support climate risk assessments through the development of an Intergovernmental Panel on Climate Change (IPCC) Atlas, integrated across IPCC Working Groups. We propose that using a climate risk framework to organise this Atlas will result in a more practical resource for understanding and informing risk assessments undertaken by the IPCC, and also make methodologies and results more accessible to a wider audience.

Ahmed M. Asfahani

Abstract This study elucidates the transformative influence of data integration on talent management in the context of evolving technological paradigms, with a specific focus on sustainable practices in human resources. Historically anchored in societal norms and organizational culture, talent management has transitioned from traditional methodologies to harnessing diverse data sources, a shift that enhances sustainable HR strategies. By employing a narrative literature review, the research traces the trajectory of HR data sources, emphasizing the juxtaposition of structured and unstructured data. The digital transformation of HR is explored, not only highlighting the evolution of Human Resource Information Systems (HRIS) but also underscoring their role in promoting sustainable workforce management. The integration of advanced technologies such as machine learning and natural language processing is examined, reflecting on their impact on the efficiency and ecological aspects of HR practices. This paper not only underscores the imperative of balancing data-driven strategies with the quintessential human element of HR but also provides concrete examples demonstrating this balance in action for practitioners and scholars in sustainable human resources.

Shekhar Sharan Goyal, Raviraj Dave, Rohini Kumar et al.

Abstract Intensive agricultural practices have powered green revolutions, helping nations attain self-sufficiency. However, these fertilizer-intensive methods and exploitative trade systems have created unsustainable agricultural systems. To probe the environmental consequences on production hubs, we map the fate of Nitrogen and Phosphorus in India’s interstate staple crop trade over the recent decade. The nation’s food bowls, while meeting national food demand, are becoming pollution-rich, sustaining around 50% of the total surplus from trade transfer, accounting for 710 gigagrams of nitrogen per year and 200 gigagrams of phosphorus per year. In combination with water balance analysis, surplus nutrient conversion to a graywater footprint further highlights an aggravated situation in major producer regions facing long-term water deficits. Given India’s role in global food security, identifying the nation’s environmental vulnerability can help in designing appropriate policy interventions for sustainable development.

Grishin Igor, Selivanov Victor, Rudenko Marina et al.

It is generally accepted that UAVs - unmanned aerial vehicles, otherwise known as drones, are used only for military purposes. This is a misconception: since the 60s of the last century, Russian and American specialists have been building unmanned UAVs not only for the armies of their countries, but also for peaceful purposes. The purpose of the article is to study the possibilities and progress in the development of drones for civil and needs. In preparing and writing the article, such research methods as general scientific methods of historical and logical, abstract and concrete, analysis and synthesis, comparisons and analogies were used. The main result of the study is the conclusion that unmanned aerial vehicles can be successfully used for civilian purposes, and not just for military purposes. Drones are now actively used for agricultural and environmental purposes. They are called “eco-drones”. They are no different from ordinary ones; the prefix is designed to emphasize their purely peaceful, scientific purpose.

Zongliang Zhang, Xueming Wu, Enshang Xiao et al.

Abstract Based on the emergency rescue, the subsequent disposal, and the development and utilization projects of the Hongshiyan Landside Dam in Ludian, Yunnan, China, research has been conducted on key technical issues facing the development and utilization of landside dams, including the possibilty evaluation of development and utilization, structure analysis of wide gradation material, performance evaluation, investigation and design, dam seepage control, construction technology and equipment, and safe operation assessment. And innovative results has made in all seven aspects mentioned above, writing the history in this field. The achievements were directly applied to the development planning, investigation and design, construction, and operation and maintenance of the Hongshiyan Landside Dam, a comprehensive water conservancy project that integrates flood control, water supply, irrigation, and power generation, with significant comprehensive benefits.

Nuria Navarro, Inmaculada Rodríguez-Santalla

Coastal wetlands are valuable and sensitive environments that are among the most productive yet highly threatened systems in the world [...]

Behice Varışoğlu, Serdar Başutku

Today, one of the countries where Turkish language teaching is common is the Republic of North Macedonia, located in the Balkan peninsula. Many ethnic communities live in North Macedonia. Macedonians, Albanians, Turks, Bosnians, Serbs, Vlachs and Romani constitute the different ethnic national structure of North Macedonia. As in many countries of the Balkans, Turks living in North Macedonia have preserved their mother tongue, culture and traditions. In addition, ethnic groups in North Macedonia were given the right to education in their mother tongue, and thus education and training in Turkish continued. Since Turkish was the language of education, science, art and commerce in this region during the Ottoman period, it has been able to maintain its influence and existence until today. The speakers of Turkish as a heritage language in this region are not only Turks. Other ethnic groups (Romani, Bosnian, Serbian, Vlach, Torbesh, Bulgarian), especially Albanians, are both carriers and protectors of Turkish as a heritage language. In the Balkan geography, where linguistic and cultural heritage is a richness that creates mutual understanding, Skopje is one of the places where individuals from different cultures and ethnic origins come together. The aim of this study is to reveal the socio-cultural role of Turkish in intercultural interaction in Skopje. Today, the existence of Turkish as a heritage language in the Balkans, especially in Skopje, is influenced not only by the Turks in the region, but also by the existence of other ethnic groups. In the study, non-interactive qualitative research design was used. In this study, it is aimed to define the existence of Turkish in Skopje and to evaluate it within the scope of heritage language. In this context, it is thought that the study will contribute to the field by adding new and different dimensions to the concept of heritage language due to the presence of Turkish in the Balkans.

Alice Militello

I concorsi dell’Accademia dei Virtuosi al Pantheon scandiscono la vita della stessa istituzione a partire dal 1837. La documentazione presente in archivio e le opere lasciate dai concorrenti rappresentano, nonostante le lacune e la qualità discontinua, un nucleo significativo e sostanzialmente inedito del patrimonio accademico. Recentemente l’accademia ha inventariato e catalogato il patrimonio documentale e grafico della sezione di architettura: questo studio, seguendo e provando a implementare i criteri metodologici già utilizzati, si focalizza sulla parte documentale dell’area scultura.

Grace Chang, Galen Egan, Joseph D. McNeil et al.

Abstract Novel analysis of in situ acoustic and optical data collected in a shallow, wave‐ and current‐driven environment enabled determination of (1) particle characteristics that were most affected by near‐bed physical forcing over seasonal scales and (2) characteristic shear stress, τchar, at which the rate of change to particle characteristics was most pronounced. Near‐bed forcing and particle responses varied by season. Results indicated that moderate τchar values of 0.125 Pa drove changes in particle composition during summer. In winter, particle concentration effects were most affected at τchar of 0.05 Pa, suggesting dominance of fluff layer resuspension. Changes to particle size were most relevant during a biologically productive springtime period, with initiation of particle disaggregation occurring most commonly at τchar of 0.25 Pa. These results suggest that it may be more important to parameterize τchar, as opposed to critical shear stress for erosion, for sediment transport models.

Sara Reed, Ann Melissa Campbell, Barrett W. Thomas

Parking is a necessary component of traditional last-mile delivery practices, but finding parking can be difficult. Yet, the routing literature largely does not account for the need to find parking. In this paper, we address this challenge of finding parking through the Capacitated Delivery Problem with Parking (CDPP). Unlike other models in the literature, the CDPP accounts for the search time for parking in the objective and minimizes the completion time of the delivery tour. When we restrict the customer geography to a complete grid, we identify conditions for when a Traveling Salesman Problem (TSP) solution that parks at each customer is an optimal solution to the CDPP. We then determine when the search time for parking is large enough for the CDPP optimal solution to differ from this TSP solution. We also identify model improvements that allow reasonably-sized instances of the CDPP to be solved exactly. We introduce a heuristic for the CDPP that quickly finds high quality solutions to large instances. Computational experiments show that parking matters in last-mile delivery optimization. The CDPP outperforms industry practice and models in the literature showing the greatest advantage when the search time for parking is high. This analysis provides immediate ways to improve routing in last-mile delivery.

Fabian Stephany, Otto Kässi, Uma Rani et al.

The Online Labour Index (OLI) was launched in 2016 to measure the global utilisation of online freelance work at scale. Five years after its creation, the OLI has become a point of reference for scholars and policy experts investigating the online gig economy. As the market for online freelancing work matures, a high volume of data and new analytical tools allow us to revisit half a decade of online freelance monitoring and extend the index's scope to more dimensions of the global online freelancing market. In addition to measuring the utilisation of online labour across countries and occupations by tracking the number of projects and tasks posted on major English-language platforms, the new Online Labour Index 2020 (OLI 2020) also tracks Spanish- and Russian-language platforms, reveals changes over time in the geography of labour supply, and estimates female participation in the online gig economy. The rising popularity of software and tech work and the concentration of freelancers on the Indian subcontinent are examples of the insights that the OLI 2020 provides. The OLI 2020 delivers a more detailed picture of the world of online freelancing via an interactive online visualisation updated daily. It provides easy access to downloadable open data for policymakers, labour market researchers, and the general public (www.onlinelabourobservatory.org).