Search Results (457)

Technological advancements in low-cost devices, the Internet of Things (IoT), numerical models, big data infrastructures, and high-performance computing are revolutionizing urban management, particularly air quality governance. This study examines the application of smart technologies to address urban air quality challenges using integrated sensor networks and predictive models. The decision support system (DSS), AERQ, incorporates the AERMOD modeling tool, achieving a 10 m spatial and 1 h temporal resolution for air quality predictions. It processes hourly climate and traffic data via a high-performance computing (HPC) platform, significantly enhancing prediction accuracy and decision-making efficiency. AERMOD has been calibrated and validated for NO₂, showing a good performance against observations. Tested in Cagliari, Sardinia, Italy, AERQ demonstrated a 99% reduction in computation time compared to modern desktop systems, delivering detailed 5-year scenarios in under 15 h. AERQ equips stakeholders with air quality indices, scenario analyses, and mitigation strategies, combining advanced visualization tools with actionable insights. By enabling data-driven decisions, the system empowers policymakers, urban planners, and citizens to improve air quality and public health. This study underscores the transformative potential of integrating advanced technologies into urban management, providing a scalable model for efficient, informed, and responsive air quality governance. Full article

Waste is a complex challenge that requires collaboration between multiple stakeholders to achieve a circular economy. In this context, there is a growing demand for digital solutions that integrate physical and digital infrastructure to create digital waste governance systems. Analog management, without accurate data, is becoming increasingly unfeasible in light of the UN Sustainable Development Goals. Tools such as online geographic information systems (WebGIS) allow the collection and integration of large volumes of physical and human data and the establishment of a digital governance structure that brings together different technologies, tools and methods in the same environment. This article aims to present the State of the Art on the topics of zero-waste cities, WebGIS, and disruptive innovation. The article starts from the hypothesis that only a process of disruptive and systemic innovation in the value chain and urban solid waste management (MSWMS), supported by the principle of zero-waste cities, circular economy and webGIS, can effectively help to solve this problem. The research uses an exploratory literature review on the concepts of zero-waste cities, systemic innovation and webGIS applied to waste management, linking them to the theoretical framework of sustainability as a science and to Brazilian public policies, such as the National Solid Waste Policy (Law 12.305/2010), the National Circular Economy Policy (Law 1.874/2022) and the National Digital Government Strategy of Brazil 2024–2027 (ENGD). As a result, scientific publications on zero-waste cities increased from 2018 to 2023 and several countries have adopted zero-waste guidelines in waste management policies. WebGIS, remote sensing, geoprocessing and different technologies are increasingly being incorporated into waste management, generating significant impacts on the diversion of resources from landfills, mitigating climate change, and generating and/or adding value to the useful life of waste and garbage resources, in addition to the optimization and efficiency of collection operators and citizen engagement in public policies. Disruptive innovation has proven to be a concrete process to enable the transition from obsolete sociotechnical systems (such as the linear economy), where sustainable finance and environmental entities play a fundamental role in orchestrating and coordinating the convergence of private, public and civil society actors towards this new sustainable development paradigm. The case study proved to be fruitful in proposing and encouraging the adoption of such methods and principles in municipal waste management, allowing us to outline a first conception of a digital government structure and digitalization of public services for zero-waste cities, as well as pointing out the difficulties of implementing and transforming these systems. This digital governance structure demonstrates the possibility of being replicable and scalable to other cities around the world, which can materialize an important tool for the implementation, articulation and development of a long-term sustainable development paradigm, based on the vision of the circular economy and zero-waste cities. Full article

In recent years, secondary schools and university departments related to geography have begun to teach various topics using Geographic Information Systems (GIS). In particular, WebGIS, available online without installing additional software, is recognized as a powerful educational tool for educators and students. However, research on how students perceive and utilize digital maps to understand geographical objects and investigate the complexity of such learning is insufficient. Therefore, we initiated a study to clarify these research questions by implementing Disaster Risk Reduction (DRR) education using a digital hazard map developed with WebGIS technology, focusing on young people in Japan, including secondary school and university students. The results indicate that DRR education using a simple digital map is helpful for a wide range of students regardless of age. Still, some perceive difficulty in learning to use a digital hazard map. Map representation strongly affects students’ interpretation of vulnerable areas. The maps’ layers and functions are more useful when added gradually, corresponding to students’ ability and familiarity with GIS in the initial stage of geography education using maps to prevent students’ negative impressions caused by complex issues and technical problems. Full article

The present research activity carried out demonstrated how simulation tools developed through WebGIS 3D digital platforms are capable of producing approximate forecasts of the effects of potentially catastrophic meteorological phenomena that may affect riverbeds in the territories observed. This work presents an analysis and simulation platform with graphic representation of the results in the form of three-dimensional animation. This methodology may represent a useful tool for all bodies and organizations that need to create hypothetical scenarios for the management of emergencies related to flooding events in watercourses, especially in areas of maximum hydrogeological vulnerability in the Italian territory. These scenarios are particularly useful in cases where watercourses are located near inhabited centers, industrial areas or strategic infrastructures, where the risk of material damage and danger to the population is greater. The simulation is based on the morphology of the land adjacent to the bed of an affected watercourse, taking elevation into account to determine the direction of the expansion of the water mass. An important aspect of the platform is the extreme speed of simulation resolution, which allows the tool to be used even in real time. This real-time forecasting approach is crucial for making quick and informed decisions, thus reducing reaction times and improving emergency management on the ground, with a potential positive impact on the safety of the population and the protection of infrastructure. Full article

Precision beekeeping focuses on ICT approaches to collect data through various IoT solutions and systems, providing detailed information about individual bee colonies and apiaries at a local scale. Since the flight radius of honeybees is equal to several kilometers, it is essential to explore the specific conditions of the selected area. To address this, the aim of this study was to explore the potential of using crowdsourced data combined with geographic information system (GIS) solutions to support beekeepers’ decision-making on a larger scale. This study investigated possible methods for processing open geospatial data from the OpenStreetMap (OSM) database for the environmental analysis and assessment of the suitability of selected areas. The research included developing methods for obtaining, classifying, and analyzing OSM data. As a result, the structure of OSM data and data retrieval methods were studied. Subsequently, an experimental spatial data classifier was developed and applied to evaluate the suitability of territories for beekeeping. For demonstration purposes, an experimental prototype of a web-based GIS application was developed to showcase the results and illustrate the general concept of this solution. In conclusion, the main goals for further research development were identified, along with potential scenarios for applying this approach in real-world conditions. Full article

In this paper, we propose a recommendation method for food intake order based on the glycemic index (GI) using deep learning to reduce rapid blood sugar spikes during meals. The foods in a captured image are classified through a food detection network. The GIs for the detected foods are found by matching their names or categories with the information stored in the database. If the detected food name or category is not found in the database, the food information is found from a public API. The food is classified into one of the food categories based on nutrients, and the median GI of the corresponding category is assigned to the food. The food intake order is recommended from the lowest to the highest GI. We implemented a web service that visualizes the food analysis results and the recommended food intake order. In experimental results, the average inference time and accuracy were 57.1 ms and 98.99% for Mask R-CNN, respectively, and 24.4 ms and 91.72% for YOLOv11, respectively. Full article

Cesium is an open-source 3D virtual earth engine based on WebGL and one of the most widely used 3D GIS engines. The 3DTiles rendering parameters in Cesium improve rendering efficiency but involve numerous data types and complex interdependencies, making simultaneous optimization challenging. In this paper, we proposed a multi-strategy probabilistic discrete differential evolution algorithm (MSPDDE) for finding the optimal values of the rendering parameters of Cesium 3DTiles model, which increases the search space and improves the convergence speed by introducing multiple mutation strategies. These strategies effectively reduce the probability of falling into local optimality due to too many parameters and deal with discrete variables in the rendering parameters by applying a probabilistic discretization strategy to the discrete variables so that MSPDDE is able to find all the rendering parameter optima of Cesium 3DTiles. Under three different sizes of 3DTiles model cases, the rendering model time using the optimal parameter configurations found by the algorithm is reduced by 28.84%, 27.89%, and 13.32%, respectively, compared with the default parameter configurations of Cesium, which shortens the time of rendering 3DTiles models by Cesium. Full article

Real-scene 3D digital campuses are essential for improving the accuracy and effectiveness of spatial data representation, facilitating informed decision-making for university administrators, optimizing resource management, and enriching user engagement for students and faculty. However, current approaches to constructing these digital environments face several challenges. They often rely on costly commercial platforms, struggle with integrating heterogeneous datasets, and require complex workflows to achieve both high precision and comprehensive campus coverage. This paper addresses these issues by proposing a systematic multi-source data fusion approach that employs open-source technologies to generate a real-scene 3D digital campus. A case study of Lanzhou Jiaotong University is presented to demonstrate the feasibility of this approach. Firstly, oblique photography based on unmanned aerial vehicles (UAVs) is used to capture large-scale, high-resolution images of the campus area, which are then processed using open-source software to generate an initial 3D model. Afterward, a high-resolution model of the campus buildings is then created by integrating the UAV data, while 3D Digital Elevation Model (DEM) and OpenStreetMap (OSM) building data provide a 3D overview of the surrounding campus area, resulting in a comprehensive 3D model for a real-scene digital campus. Finally, the 3D model is visualized on the web using Cesium, which enables functionalities such as real-time data loading, perspective switching, and spatial data querying. Results indicate that the proposed approach can effectively get rid of reliance on expensive proprietary systems, while rapidly and accurately reconstructing a real-scene digital campus. This framework not only streamlines data harmonization but also offers an open-source, practical, cost-effective solution for real-scene 3D digital campus construction, promoting further research and applications in twin city, Virtual Reality (VR), and Geographic Information Systems (GIS). Full article

Dysbiosis in the gut microbiota plays a significant role in GI cancer development by influencing immune function and disrupting metabolic functions. Dysbiosis can drive carcinogenesis through pathways like immune dysregulation and the release of carcinogenic metabolites, and altered metabolism, genetic instability, and pro-inflammatory signalling, contributing to GI cancer initiation and progression. Helicobacter pylori infection and genotoxins released from dysbiosis, lifestyle and dietary habits are other factors that contribute to GI cancer development. Emerging diagnostic and therapeutic approaches show promise in colorectal cancer treatment, including the multitarget faecal immunochemical test (mtFIT), standard FIT, and faecal microbiota transplantation (FMT) combined with PD-1 inhibitors. We used search engine databases like PubMed, Scopus, and Web of Science. This review discusses the role of dysbiosis in GI cancer onset and explores strategies such as FMT, probiotics, and prebiotics to enhance the immune response and improve cancer therapy outcomes. Full article

Surveillance video has emerged as a crucial data source for web Geographic Information Systems (GIS), playing a vital role in traffic management, facility monitoring, and anti-terrorism inspections. However, previous methods encountered significant challenges in achieving effective large-scale multi-video overlapping visualization and efficiency, particularly when organizing and visualizing large-scale video-augmented geographic scenes. Therefore, we propose a parallel-optimized visualization method specifically for large-scale multi-video augmented geographic scenes on Cesium. Firstly, our method employs an improved octree-based model for the unified management of large-scale overlapping videos. Then, we introduce a novel scheduling algorithm based on Cesium, which leverages a Web Graphics Library (WebGL) parallel-optimized and dynamic Level-of-Detail (LOD) strategy. This algorithm is designed to enhance the visualization effects and efficiency of large-scale video-integrated geographic scenes. Finally, we perform comparative experiments to demonstrate that our proposed method significantly optimizes the visualization of video overlapping areas and achieves a rendering efficiency increase of up to 95%. Our method can provide a solid technical foundation for large-scale surveillance video scene management and multi-video joint monitoring. Full article

Evolving from an ethical consideration to a strategic imperative, green innovation (GI) compels businesses to continually enhance their processes to achieve sustainable growth. Based on bibliometric analysis of 594 Web of Science (WOS)-sourced articles from 2000 to 2023 using VOSviewer-1.6.20 and Bibliometrix-4.3.0, this study sheds light on the existing trends of green innovation, its contributors, and potential future directions in today’s business landscape. Our findings unveil significant insights from GI literature; an upward growth trajectory in publications; limited collaboration among researchers and institutions (notable collaborative networks among countries include China, Spain, and the United Kingdom); and trending GI terms and themes include green intellectual capital, GI efficiency, green product innovation, green absorptive capacity, green knowledge acquisition, big data, etc. These insights serve as a comprehensive guide for practitioners and scholars navigating the study of GI within the business and management sphere. Full article

The precise monitoring of forest pest and disease outbreaks is a crucial prerequisite for efficient prevention and control. With the extensive application of remote sensing monitoring technology in the forest, a large amount of data on pest and disease outbreaks has been collected. It is highly necessary to practically apply these data and improve the efficiency of forest pest and disease monitoring and management. In this study, a Digital Forest Protection (DFP) system based on the geographic information system (GIS) was designed and developed for pine wilt disease (PWD) monitoring and management, a devastating forest disease caused by the pine wood nematode, Bursaphelenchus xylophilus. The DFP system consists of a mobile app for data collection and a web-based data analysis platform. Meanwhile, artificial intelligence and deep-learning methods had been conducted to integrate a real-time unmanned aerial vehicle (UAV) remote sensing monitoring with PWD detection. This system was implemented in PWD monitoring and management in Zhejiang Province, China, and has been applied in data collection under certain circumstances, including the manual epidemic survey, the UAV epidemic survey, and eradication monitoring, as well as trunk injection. Based on DFP system, the effective monitoring of PWD outbreaks could be achieved, and corresponding efficient management strategies could be formulated in a timely manner. This allows for the possibility to optimize the integrated management strategy of PWD on a large geographic scale. Full article

Background: In the last few decades, it has been emphasized that dopamine, a well-known neurotransmitter with multiple roles in central nervous system, is also implicated in the activity of peripheral tissues and organs, more specifically influencing the gastrointestinal system (GI). Methods: We registered a protocol under the CRD42024547935 identifier in the Prospero register of systematic reviews. Furthermore, using the Population, Intervention, Comparison, Outcome, and Study Design strategy to guide our study rationale, and under the Preferred Reporting Items for Systematic reviews and Meta-Analyses recommendations, we conducted a qualitative systematic literature search based on the PubMed, Scopus, and Web of Science databases using the “gastric cancers AND dopamine” search criteria. We obtained 68 articles from PubMed, 142 articles from Scopus, and 99 articles from the Web of Science database. Results: Within gastric cancer biology, dopamine has notable effects on STAT-3 and DARPP-32. STAT-3, a transcription factor involved in cellular proliferation and invasion, plays a significant role in cancer progression. Conclusions: Understanding the roles of dopamine in cancer, beyond aspects such as cancer cell invasion, immune response modulation, or tumor growth, could guide the development of new cancer therapies by modulating its pathways, especially the DARPP-32/CXCR4/CXCL-12 complex axis, in order to improve the morbidity and mortality caused by this type of cancer. Full article

Real-time GIS enables multiple geographically dislocated users to collaboratively edit geospatial data. However, being based on the strong consistency model, traditional real-time GIS implementations cannot provide fully automatic conflict resolution. In highly dynamic situations with increased probability for conflicts, this will hinder user experience. Conflict-free replicated data types (CRDTs), a technology based on a more relaxed concurrency control model called strong eventual consistency, can resolve all conflicts in real time, letting the users work on their local copies of the data without any restrictions. The application of CRDTs to real-time geospatial geometry co-editing has, to the best of our knowledge, not been investigated. Within this research, we therefore developed a simple web-based real-time geospatial geometry co-editing system using an existing CRDT implementation in Javascript coupled with OpenLayers. When applied to the co-editing of geospatial geometry in its native form, standard CRDT conflict resolution mechanics exhibit some issues. As an attempt to address these issues, we developed an advanced operation generation technique named “tentative operations”. This technique allows for the operations to be generated over the most recent session-wide state of the data, which in effect highly reduces concurrency and provides “geometry aware” conflict resolution. The tests we conducted using the developed system showed that in low-latency network conditions, the negative effects of standard CRDT conflict resolution mechanics do get minimized even under increased system loads. Full article

Over almost 20 years, a data storage, analysis, and project administration engine (TMFdw) has been continuously developed in a series of several consecutive interdisciplinary research projects on functional biodiversity of the southern Andes of Ecuador. Starting as a “working database”, the system now includes program management modules and literature databases, which are all accessible via a web interface. Originally designed to manage data in the ecological Research Unit 816 (SE Ecuador), the open software is now being used in several other environmental research programs, demonstrating its broad applicability. While the system was mainly developed for abiotic and biotic tabular data in the beginning, the new research program demands full capabilities to work with area-wide and high-resolution big models and remote sensing raster data. Thus, a raster engine was recently implemented based on the Geo Engine technology. The great variety of pre-implemented desktop GIS-like analysis options for raster point and vector data is an important incentive for researchers to use the system. A second incentive is to implement use cases prioritized by the researchers. As an example, we present machine learning models to generate high-resolution (30 m) microclimate raster layers for the study area in different temporal aggregation levels for the most important variables of air temperature, humidity, precipitation, and solar radiation. The models implemented as use cases outperform similar models developed in other research programs. Full article