Sávio Luís Oliveira da Silva, Rafael Carvalho da Silva Mocarzel, Bruna Medeiros Neves
INTRODUÇÃO: A prática regular de atividade física é fundamental para a promoção da saúde, autonomia e qualidade de vida de pessoas com deficiência física. Entretanto, barreiras arquitetônicas ainda limitam o acesso e a permanência desse público em espaços destinados à prática de exercícios físicos, como academias de ginástica.
OBJETIVO: Analisar as condições de acessibilidade arquitetônica para pessoas que utilizam cadeira de rodas em academias do município de Maricá, Rio de Janeiro.
MÉTODOS: Trata-se de um estudo observacional, exploratório e transversal, realizado em 23 academias do município, por meio da aplicação de um checklist adaptado da NBR 9050. A análise dos dados foi de natureza descritiva, com apresentação de frequências e percentuais, complementada por análise qualitativa interpretativa.
RESULTADOS: Os resultados indicaram que nenhuma das academias avaliadas apresentou acessibilidade arquitetônica plena. Observou-se ausência ou inadequação de rotas acessíveis, sinalização, mobiliário, equipamentos e sanitários adaptados, em desacordo com os parâmetros normativos vigentes.
CONCLUSÃO: Conclui-se que as academias investigadas apresentam importantes limitações estruturais, evidenciando a necessidade de adequações arquitetônicas e de ações sistemáticas de fiscalização para garantir o direito de acesso à prática de atividade física por pessoas que utilizam cadeira de rodas.
OSMnx is a Python package for downloading, modeling, analyzing, and visualizing urban networks and any other geospatial features from OpenStreetMap data. A large and growing body of literature uses it to conduct scientific studies across the disciplines of geography, urban planning, transport engineering, computer science, and others. The OSMnx project has recently developed and implemented many new features, modeling capabilities, and analytical methods. The package now encompasses substantially more functionality than was previously documented in the literature. This article introduces OSMnx's modern capabilities, usage, and design -- in addition to the scientific theory and logic underlying them. It shares lessons learned in geospatial software development and reflects on open science's implications for urban modeling and analysis.
Saurabh Kumar, Sourav Bansal, Neeraj Agrawal
et al.
Customer care is an essential pillar of the e-commerce shopping experience with companies spending millions of dollars each year, employing automation and human agents, across geographies (like US, Canada, Mexico, Chile), channels (like Chat, Interactive Voice Response (IVR)), and languages (like English, Spanish). SOTA pre-trained models like multilingual-BERT, fine-tuned on annotated data have shown good performance in downstream tasks relevant to Customer Care. However, model performance is largely subject to the availability of sufficient annotated domain-specific data. Cross-domain availability of data remains a bottleneck, thus building an intent classifier that generalizes across domains (defined by channel, geography, and language) with only a few annotations, is of great practical value. In this paper, we propose an embedder-cum-classifier model architecture which extends state-of-the-art domain-specific models to other domains with only a few labeled samples. We adopt a supervised fine-tuning approach with isotropic regularizers to train a domain-specific sentence embedder and a multilingual knowledge distillation strategy to generalize this embedder across multiple domains. The trained embedder, further augmented with a simple linear classifier can be deployed for new domains. Experiments on Canada and Mexico e-commerce Customer Care dataset with few-shot intent detection show an increase in accuracy by 20-23% against the existing state-of-the-art pre-trained models.
We present a data-driven machine-learning approach for modeling space-time socioeconomic dynamics. Through coarse-graining fine-scale observations, our modeling framework simplifies these complex systems to a set of tractable mechanistic relationships -- in the form of ordinary differential equations -- while preserving critical system behaviors. This approach allows for expedited 'what if' studies and sensitivity analyses, essential for informed policy-making. Our findings, from a case study of Baltimore, MD, indicate that this machine learning-augmented coarse-grained model serves as a powerful instrument for deciphering the complex interactions between social factors, geography, and exogenous stressors, offering a valuable asset for system forecasting and resilience planning.
Salim I. Amoukou, Tom Bewley, Saumitra Mishra
et al.
We introduce a novel approach for detecting distribution shifts that negatively impact the performance of machine learning models in continuous production environments, which requires no access to ground truth data labels. It builds upon the work of Podkopaev and Ramdas [2022], who address scenarios where labels are available for tracking model errors over time. Our solution extends this framework to work in the absence of labels, by employing a proxy for the true error. This proxy is derived using the predictions of a trained error estimator. Experiments show that our method has high power and false alarm control under various distribution shifts, including covariate and label shifts and natural shifts over geography and time.
Study region: Typical basin in humid areas in the Huaihe River Study focus: Accurate flood forecasting is essential for making timely decisions regarding flood control and disaster reduction. The theory of watershed runoff generation and convergence serves as a crucial foundation for flood forecasting, while the calculation of runoff is necessary to simulate flood discharge. Identifying watershed runoff generation mechanisms has been a challenging task, particularly under complex underlying surface conditions. To improve the accuracy of flood simulation, this study examines the underlying surface information in the watershed, such as particle composition and content, soil bulk density, geological slope, land use, and other spatial attributes, aiming to analyze the mechanisms of runoff generation. In the study of sub-watersheds, various combinations of runoff generation mechanisms are identified to determine the patterns of runoff. Subsequently, a semi-distributed hydrological model is developed, which incorporates both saturation-excess and infiltration-excess runoff, utilizing the information obtained from the underlying surface. The model is validated using rainfall-runoff data from 14 events at the Xiagushan watershed. New hydrological insights for the region: The analysis of the fundamental physical conditions of the underlying surface of the watershed revealed that 69.70% of the area is prone to saturation-excess runoff, with an additional 30.30% of the area being susceptible to infiltration-excess runoff. The model considers the spatial distribution of runoff patterns by incorporating complex underlying surface information and demonstrates high accuracy in simulating flood events (NSE= 0.87, Epeak = 12.08%, Wpeak = 13.16%, Tpeak = 0.14 h, R2 = 0.90). The model is straightforward, practical, and exhibits promising potential in terms of timeliness and applicability, thus lending itself well to further application in other watersheds, contributing to the scientific foundation of flood warning and forecasting efforts.
The genus Talbotia Bernardi, 1958 is a member of the subfamily Pierinae within the family Pieridae. It consists of a single species, Talbotia naganum (Moore, 1884), commonly known as Naga White, which is a highly uncommon species in India. In order to examine its morphological characteristics, including its genital attributes, a male specimen of Talbotia naganum (Moore) was analyzed from the collections held at the National Museum of Lepidoptera, Zoological Survey of India, Kolkata. The various male genital attributes of this species have been thoroughly studied, illustrated, and compared in detail for the first time with the commonly found pierid butterfly P. brassicae (Linnaeus).
Norma González Ruda, Ibette Alfonso Pérez, Raquel Bermúdez Morris
La orientación educativa en el proceso de enseñanza-aprendizaje de la educación superior puede coadyuvar al cumplimiento de las exigencias en la formación de profesionales en el siglo XXI. No obstante, este espacio no constituye aún, un ámbito privilegiado para programar acciones de orientación educativa, los profesores no cuentan con una guía para realizar esta labor. El objetivo del trabajo se centra en reflexionar sobre los fundamentos teórico-metodológicos para realizar la orientación educativa en el PEA de la educación superior. Para estudiar este particular se desarrolló una investigación que permitió la revisión, interpretación y contrastación de diversas fuentes bibliográficas mediante los métodos histórico-lógico, analítico-sintético y el inductivo-deductivo. Estos métodos permitieron el análisis de la información obtenida y la elaboración de síntesis conclusivas en el plano teórico. Los resultados obtenidos en el análisis de los modelos estudiados evidencian que la orientación educativa debe ser realizada por un personal especializado o que ha sido preparado para cumplir con esta labor. Aunque se reconoce en los modelos más actuales al profesor como agente orientador, no quedan esclarecidos los fundamentos teórico-metodológicos para la orientación que debe realizar este agente educativo en el PEA de la educación superior. Se hace necesario entonces, integrar los fundamentos que aportan los modelos de orientación educativa con los fundamentos de la Didáctica, de manera que se ofrezca una base conceptual y metodológica para la orientación educativa en el PEA de la educación superior.
Large pre-trained models, also known as foundation models (FMs), are trained in a task-agnostic manner on large-scale data and can be adapted to a wide range of downstream tasks by fine-tuning, few-shot, or even zero-shot learning. Despite their successes in language and vision tasks, we have yet seen an attempt to develop foundation models for geospatial artificial intelligence (GeoAI). In this work, we explore the promises and challenges of developing multimodal foundation models for GeoAI. We first investigate the potential of many existing FMs by testing their performances on seven tasks across multiple geospatial subdomains including Geospatial Semantics, Health Geography, Urban Geography, and Remote Sensing. Our results indicate that on several geospatial tasks that only involve text modality such as toponym recognition, location description recognition, and US state-level/county-level dementia time series forecasting, these task-agnostic LLMs can outperform task-specific fully-supervised models in a zero-shot or few-shot learning setting. However, on other geospatial tasks, especially tasks that involve multiple data modalities (e.g., POI-based urban function classification, street view image-based urban noise intensity classification, and remote sensing image scene classification), existing foundation models still underperform task-specific models. Based on these observations, we propose that one of the major challenges of developing a FM for GeoAI is to address the multimodality nature of geospatial tasks. After discussing the distinct challenges of each geospatial data modality, we suggest the possibility of a multimodal foundation model which can reason over various types of geospatial data through geospatial alignments. We conclude this paper by discussing the unique risks and challenges to develop such a model for GeoAI.
By improving its total factor productivity, China may attain higher quality and more sustainable economic growth. As a key market-based incentive for environmental regulation, does environmental protection tax increase total factor productivity and provide a win-win situation for both economic and environmental performance? It is a debate-worthy topic. Based on data of Chinese listed companies, this paper uses the triple difference method to analyze China’s environmental protection tax reform as a natural experiment. The results show that the environmental protection tax can significantly boost the firm’s total factor productivity by encouraging technological innovation and enhancing resource allocation. Based on analysis of heterogeneity, it appears that state-owned enterprises, larger corporations, and regions with more strict environmental enforcement are more responsive to environmental protection tax policies. This report provides critical empirical evidence for upgrading China’s tax framework to protect the environment.
Forest disturbance monitoring can provide scientific data for the decision making and management of nature reserves. LandTrendr algorithm has been applied to identify forest disturbances on a long-time scale through appropriate segmentation and linear fitting. In this study, 23 nature reserves were detected using LandTrendr during 1987–2020, and the vegetation loss was quantified by years and pixel numbers. The results illustrated that (1) most disturbances occurred in the 1990s and early 21st century. (2) From the spatial distribution of forest loss, the area of forest vegetation disturbance in the coastal zone was larger than the protected area in the internal Hainan Island, the area disturbed in the coastal zone protected area was 97.12 km2, and the area disturbed in the internal area of Hainan Island protected area was 63.02 km2. (3) In terms of different levels of nature reserves, the disturbed area of national nature reserves was 28.39 km2 and the total disturbed area of provincial nature reserves was 131.75 km2. (4) In terms of different types of nature reserves, forest ecological nature reserves had the largest disturbed area of 102.96 km2, followed by marine coastal nature reserves with a disturbed area of 36.99 km2, wildlife nature reserves with a disturbed area of 10.22 km2, and wild plant nature reserves with the smallest disturbed area of 9.96 km2. The results are hoped to provide scientific support and data for the management and planning of nature reserves in Hainan Island.
Abstract Whilst virtual reality (VR) has a long history, recent technological advancements, increased accessibility and affordability have seen its usage become widespread within western consumer society. Despite the relevance of VR to Geography, these more recent developments have escaped scholarly attention. This paper takes a critical perspective on the development of VR and its varied applications, and how emerging theoretical debates within cultural and digital geography can critically attend to the social and cultural implications of VR technologies. The paper begins by considering how VR spaces are imagined and communicated to publics in ways that promote popular understandings of, and desires for, virtual spaces. Next, the paper critically addresses the cultural politics of VR content, particularly drawing attention to the socio‐spatial differences evoked through VR. The paper goes on to argue for the need to consider VR through the concept of interface as a way of critically attending to the broader techno‐socio relations and the embodied spatial encounters they produce. Finally, some methodological implications for thinking with and through VR are outlined.
To describe international trade flows, we propose the coulomb force formulation, in which the magnitude of the charge represents gross domestic product (GDP) and the distance between countries is the bilateral distance, the product of spatial distance and "dielectric constant," rather than the spatial distance as used in the gravitation model, allowing it to be time dependent. The "dielectric constant" is influenced by factors such as warfare, transportation disruptions, trade agreements, social, geography, politics, culture, and others. The GDP and distance power parameters were estimated using data from high-GDP countries' export-import transactions. We also developed a trade strength distribution equation that fits World Bank data reasonably well over a decade.
Benjamin Caine, Rebecca Roelofs, Vijay Vasudevan
et al.
To safely deploy autonomous vehicles, onboard perception systems must work reliably at high accuracy across a diverse set of environments and geographies. One of the most common techniques to improve the efficacy of such systems in new domains involves collecting large labeled datasets, but such datasets can be extremely costly to obtain, especially if each new deployment geography requires additional data with expensive 3D bounding box annotations. We demonstrate that pseudo-labeling for 3D object detection is an effective way to exploit less expensive and more widely available unlabeled data, and can lead to performance gains across various architectures, data augmentation strategies, and sizes of the labeled dataset. Overall, we show that better teacher models lead to better student models, and that we can distill expensive teachers into efficient, simple students. Specifically, we demonstrate that pseudo-label-trained student models can outperform supervised models trained on 3-10 times the amount of labeled examples. Using PointPillars [24], a two-year-old architecture, as our student model, we are able to achieve state of the art accuracy simply by leveraging large quantities of pseudo-labeled data. Lastly, we show that these student models generalize better than supervised models to a new domain in which we only have unlabeled data, making pseudo-label training an effective form of unsupervised domain adaptation.
Victor Koryahin, Zinoviy Mykytyuk, Yaryna Turchyn
et al.
The study objective. Substantiation and implementation of the heart rate monitoring tool, developed on the basis of information systems for the rapid registration of cardiac rhythm during exercise.
Material and Methods. The study was implemented at the theoretical and empirical level. The basis of the study is the use of a set of theoretical methods: scientific analysis and synthesis, comparison, systematization, induction and deduction, generalization. The following methods of empirical research were used: description, empirical comparison, technical modeling, sphygmographic method of registration of pulsograms.
Results. According to the results of the search activity, a device designed to monitor heart rate in real time was presented. To implement the new electronic method and heart rate monitor of the functional state of the cardiovascular system, an optical block that eliminates the subjective determination of control results associated with the probability of errors was created. The use of an optical unit provides a fast dynamic picture of heart rate measurement, since the unit uses an optical sensor. Spectral characteristics of blood, which change under the influence of physical activity, were used for heart rate registration. Positive characteristics that ensure the quality of real-time HR monitoring procedures using the developed device in addition to high technical parameters are: high level of sensitivity, wide dynamic range, harmonized frequency response, linearity of conversion, note also non-invasiveness, security in application, low energy consumption signal, and transformations that do not affect or distort the control results.
Conclusions. The use of information systems in heart rate control ensures the accuracy of the measurement information and the correspondence between the degree of scientific reliability and practical value of the obtained results.
Abstract Renewable‐electricity‐driven N2 reduction is an attractive approach for ambient NH3 synthesis, but active electrocatalysts are needed to enable the N2 reduction reaction. Monolithic electrodes with active components anchored on conductive supports provide many advantages like structural stability, large surface area, and low electrical resistance. Here, a novel “oxidation‐etching” strategy is proposed to carve the surface of Cu foam into structures of particles, cubes, and sheets for N2 reduction electrocatalysis. The optimal catalyst achieves a Faradic efficiency as high as 18% at −0.35 V vs reversible hydrogen electrode (RHE) and a large NH3 yield of 2.45 × 10−10 mol s−1 cm−1 at −0.40 V vs RHE in 0.1 M HCl. Notably, it also shows superior long‐term electrochemical durability, with the preservation of electro‐activity for at least 20 hours.
Antonios Garas, Sophie Guthmuller, Athanasios Lapatinas
Using the economic complexity methodology on data for disease prevalence in 195 countries during the period of 1990-2016, we propose two new metrics for quantifying the relatedness between diseases, or the `disease space' of countries. With these metrics, we analyze the geography of diseases and empirically investigate the effect of economic development on the health complexity of countries. We show that a higher income per capita increases the complexity of countries' diseases. Furthermore, we build a disease-level index that links a disease to the average level of GDP per capita of the countries that have prevalent cases of the disease. With this index, we highlight the link between economic development and the complexity of diseases and illustrate, at the disease-level, how increases in income per capita are associated with more complex diseases