Understanding the distance between human languages is central to linguistics, anthropology, and tracing human evolutionary history. Yet, while linguistics has long provided rich qualitative accounts of cross-linguistic variation, a unified and scalable quantitative approach to measuring language distance remains lacking. In this paper, we introduce a method that leverages pretrained multilingual language models as systematic instruments for linguistic measurement. Specifically, we show that the spontaneously emerged attention mechanisms of these models provide a robust, tokenization-agnostic measure of cross-linguistic distance, termed Attention Transport Distance (ATD). By treating attention matrices as probability distributions and measuring their geometric divergence via optimal transport, we quantify the representational distance between languages during translation. Applying ATD to a large and diverse set of languages, we demonstrate that the resulting distances recover established linguistic groupings with high fidelity and reveal patterns aligned with geographic and contact-induced relationships. Furthermore, incorporating ATD as a regularizer improves transfer performance in low-resource machine translation. Our results establish a principled foundation for testing linguistic hypotheses using artificial neural networks. This framework transforms multilingual models into powerful tools for quantitative linguistic discovery, facilitating more equitable multilingual AI.
This study compared health-conscious behaviors, dietary patterns, supplement use, and body image dissatisfaction among amateur female fitness competitors in lower-demand categories and recreational gym users. A total of 199 participants completed a self-constructed and validated questionnaire (BSQ-34). Fitness competitors trained more frequently and followed seasonally variable diets, with stricter adherence during competition periods. Although both groups showed health awareness, competitors prioritized performance, while recreational athletes emphasized long-term well-being. Supplement use was significantly higher among competitors, who favored effectiveness over natural composition. Recreational athletes preferred natural ingredients and were more concerned about side effects. Despite healthier routines, recreational athletes reported greater body dissatisfaction, likely influenced by aesthetic goals and social media. Findings reveal that even in lower-demand categories, competition preparation involves health-compromising practices similar to bodybuilding. In contrast, recreational athletes maintain consistent health-oriented behaviors. These results highlight the need for education and support to promote sustainable health practices in fitness sport environments.
The growth of cities has traditionally been studied from a population perspective, while urban expansion-its spatial growth-has often been approached qualitatively. However, characterizing and modeling this spatial expansion is crucial, particularly given its parallels with surface growth extensively studied in physics. Despite these similarities, approaches to urban expansion modeling are fragmented and scattered across various disciplines and contexts. In this review, we provide a comprehensive overview of the mathematical modeling of this complex phenomenon. We discuss the key challenges hindering progress and examine models inspired by statistical physics, economics and geography, and theoretical ecology. Finally, we highlight critical directions for future research in this interdisciplinary field.
Filippo Marchesani, Francesca Masciarelli, Andrea Bikfalvi
The rise of smart cities represents a significant trend in urban development. However, only in recent years has attention shifted toward the international promotion of these cities. Despite ongoing academic discussions on the impact of smart city development on urban environments, the global recognition of smart cities remains uncertain due to their multidisciplinary nature. To address this, we conducted a systematic literature review of articles published in top-tier peer-reviewed journals from 2008 to December 2021, offering a comprehensive analysis of the existing literature.
Fair access to healthcare facilities is fundamental to achieving social equity. Traditional travel time-based accessibility measures often overlook the dynamic nature of travel times resulting from different departure times, which compromises the accuracy of these measures in reflecting the true accessibility experienced by individuals. This study examines public transport-based accessibility to healthcare facilities across England from the perspective of travel time variability (TTV). Using comprehensive bus timetable data from the Bus Open Data Service (BODS), we calculated hourly travel times from each Lower Layer Super Output Area (LSOA) to the nearest hospitals and general practices and developed a TTV metric for each LSOA and analysed its geographical inequalities across various spatial scales. Our analysis reveals notable spatial-temporal patterns of TTV and average travel times, including an urban-rural divide, clustering of high and low TTV regions, and distinct outliers. Furthermore, we explored the relationship between TTV and deprivation, categorising LSOAs into four groups based on their unique characteristics, which provides valuable insights for designing targeted interventions. Our study also highlights the limitations of using theoretical TTV derived from timetable data and emphasises the potential of using real-time operational data to capture more realistic accessibility measures. By offering a more dynamic perspective on accessibility, our findings complement existing travel time-based metrics and pave way for future research on TTV-based accessibility using real-time data. This evidence-based approach can inform efforts to "level up" public transport services, addressing geographical inequalities and promoting equitable access to essential healthcare services.
In recent years, techniques from Topological Data Analysis (TDA) have proven effective at capturing spatial features of multidimensional data. However, applying TDA to spatiotemporal data remains relatively underexplored. In this work, we extend previous studies of disease spread by using the Mapper algorithm to analyze the Ohio drug overdose epidemic from 2007 to 2024. We introduce a novel method for constructing covers in Mapper graphs of spatiotemporal data that respects geographic structure and highlights the time-dependent variables. Finally, we generate a Mapper visualization of regional demographics to examine how these factors relate to overdose deaths. Our approach effectively reveals temporal trends, overdose hotspots, and time-lagged patterns in relation to both geography and community demographics.
This study introduces a metric designed to measure urban structures through the economic complexity lens, building on the foundational theories of urban spatial structure, the Central Place Theory (CPT) (Christaller, 1933). Despite the significant contribution in the field of urban studies and geography, CPT has limited in suggesting an index that captures its key ideas. By analyzing various urban big data of Seoul, we demonstrate that PCI and ECI effectively identify the key ideas of CPT, capturing the spatial structure of a city that associated with the distribution of economic activities, infrastructure, and market orientation in line with the CPT. These metrics for urban centrality offer a modern approach to understanding the Central Place Theory and tool for urban planning and regional economic strategies without privacy issues.
Thiago Trafane Oliveira Santos, Daniel Oliveira Cajueiro
Zipf's law states that the probability of a variable being larger than $s$ is roughly inversely proportional to $s$. In this paper, we evaluate Zipf's law for the distribution of firm size by the number of employees in Brazil. We use publicly available binned annual data from the Central Register of Enterprises (CEMPRE), which is held by the Brazilian Institute of Geography and Statistics (IBGE) and covers all formal organizations. Remarkably, we find that Zipf's law provides a very good, although not perfect, approximation to data for each year between 1996 and 2020 at the economy-wide level and also for agriculture, industry, and services alone. However, a lognormal distribution also performs well and even outperforms Zipf's law in certain cases.
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.
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.
Hendra Hendra, Widodo Setiyo Pranowo, Choirul Umam
et al.
The Malacca Strait is a strategic waterway for Indonesia as it serves as an international shipping route connecting East Asia with the Middle East and Europe. In addition, the Malacca Strait also has great potential for natural resources, such as oil and gas as well as fish and other marine products. This study aims to describe the Thermocline Layer in the Malacca Strait based on Marine Copernicus Data in 2020 with a depth of up to 1000 meters. The temperature data was visualized using ODV 5.5.2 software. The results of processing the Marine Copernicus Temperature Data in 2020 in the Malacca Strait with a depth of up to 1000 meters show that the thermocline boundary varies each season. In the western season, the thermocline boundary is at a depth between 11 meters to 131 meters, in the first transitional season, it is at a depth between 22 meters to 131 meters, in the eastern season, it is at a depth between 56 meters to 156 meters, and in the second transitional season, it is at a depth between 78 meters to 131 meters.
Bagian Selat Malaka adalah perairan yang strategis bagi Indonesia karena menjadi jalur pelayaran internasional yang menghubungkan Asia Timur dengan Timur Tengah dan Eropa. Selain itu, Selat Malaka juga memiliki potensi sumber daya alam yang besar, seperti minyak dan gas bumi serta ikan dan hasil laut lainnya. Penelitian ini bertujuan untuk mengidentifikasi dan menganalisis Lapisan Termoklin di Perairan Selat Malaka berdasarkan Data Marine Copernicus tahun 2020 dengan kedalaman sampai 1000 meter. Data Temperatur diolah dan dianalisis menggunakan software ODV 5.5.2. Hasil pengolahan Data Temperatur Marine Copernicus tahun 2020 di Perairan Selat Malaka dengan kedalaman mencapai 1000 meter dengan batas termoklin setiap musim dimana pada Musim barat batas termoklin berada pada kedalaman antara 11 meter sampai dengan 131 meter, Musim Peralihan I batas termoklin berada pada kedalaman antara 22 meter sampai dengan 131 meter, Musim Timur batas termoklin berada peda kedalaman antara 56 meter sampai dengan 156 meter dan Musim Peralihan II batas termoklin berada pada kedalaman antara 78 meter sampai dengan 131 meter.
Anh Pham Thi Minh, Abhishek Kumar Singh, Soumya Snigdha Kundu
This work aims to explore the community structure of Santiago de Chile by analyzing the movement patterns of its residents. We use a dataset containing the approximate locations of home and work places for a subset of anonymized residents to construct a network that represents the movement patterns within the city. Through the analysis of this network, we aim to identify the communities or sub-cities that exist within Santiago de Chile and gain insights into the factors that drive the spatial organization of the city. We employ modularity optimization algorithms and clustering techniques to identify the communities within the network. Our results present that the novelty of combining community detection algorithms with segregation tools provides new insights to further the understanding of the complex geography of segregation during working hours.
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.
Oceanography, River, lake, and water-supply engineering (General)
How does our society appreciate the uniqueness of cultural products? This fundamental puzzle has intrigued scholars in many fields, including psychology, sociology, anthropology, and marketing. It has been theorized that cultural products that balance familiarity and novelty are more likely to become popular. However, a cultural product's novelty is typically multifaceted. This paper uses songs as a case study to study the multiple facets of uniqueness and their relationship with success. We first unpack the multiple facets of a song's novelty or uniqueness and, next, measure its impact on a song's popularity. We employ a series of statistical models to study the relationship between a song's popularity and novelty associated with its lyrics, chord progressions, or audio properties. Our analyses performed on a dataset of over fifty thousand songs find a consistently negative association between all types of song novelty and popularity. Overall we found a song's lyrics uniqueness to have the most significant association with its popularity. However, audio uniqueness was the strongest predictor of a song's popularity, conditional on the song's genre. We further found the theme and repetitiveness of a song's lyrics to mediate the relationship between the song's popularity and novelty. Broadly, our results contradict the "optimal distinctiveness theory" (balance between novelty and familiarity) and call for an investigation into the multiple dimensions along which a cultural product's uniqueness could manifest.
Kelsey Linnell, Mikaela Fudolig, Aaron Schwartz
et al.
The COVID-19 pandemic disrupted the mobility patterns of a majority of Americans beginning in March 2020. Despite the beneficial, socially distanced activity offered by outdoor recreation, confusing and contradictory public health messaging complicated access to natural spaces. Working with a dataset comprising the locations of roughly 50 million distinct mobile devices in 2019 and 2020, we analyze weekly visitation patterns for 8,135 parks across the United States. Using Bayesian inference, we identify regions that experienced a substantial change in visitation in the first few weeks of the pandemic. We find that regions that did not exhibit a change were likely to have smaller populations, and to have voted more republican than democrat in the 2020 elections. Our study contributes to a growing body of literature using passive observations to explore who benefits from access to nature.
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.