Search Results (15)

Cyber-physical systems such as Supervisory Control and Data Acquisition (SCADA) have been applied in industrial automation and infrastructure management for decades. They are hybrid tools for administration, monitoring, and continuous control of real physical systems through their computational representation. SCADA systems have evolved along with computing technology, from their beginnings with low-performance computers, monochrome monitors and communication networks with a range of a few hundred meters, to high-performance systems with advanced 3D graphics and wired and wireless computer networks. This article presents a methodology for the design of a SCADA system with a 3D Visualization for Drinking Water Supply, and its implementation in the Lerma Basin System of Mexico City as a case study. The monitoring of water consumption from the wells is presented, as well as the pressure levels throughout the system. The 3D visualization is generated from the GIS information and the communication is carried out using a hybrid radio frequency transmission system, satellite, and telephone network. The pumps that extract water from each well are teleoperated and monitored in real time. The developed system can be scaled to generate a simulator of water behavior of the Lerma Basin System and perform contingency planning. Full article

Purpose/Objectives: Salvage radiotherapy (SRT) after a radical prostatectomy is a curative approach for patients with biochemical recurrence (BR). However, outcomes are often less favorable when imaging reveals macroscopic local recurrence. In such cases, dose escalation through stereotactic salvage radiotherapy (SSRT) may offer improved disease control. The STARR trial (NCT05455736) is a prospective, multicenter study evaluating the efficacy and safety of SSRT in patients with macroscopic prostate bed recurrence. This interim analysis reports early findings from the initial patient cohort. Materials and Methods: Patients with BR (PSA > 0.2 ng/mL) post-prostatectomy and PET-confirmed macroscopic recurrence (PSMA or Choline PET, confirmed by MRI) were eligible. Treatment involved CyberKnife^®-based SSRT delivering 35 Gy in five fractions to the visible lesion. Androgen deprivation therapy (ADT) was not permitted. Complete biochemical response (CBR) was defined as PSA < 0.2 ng/mL, and biochemical response (BR) as a ≥50% PSA reduction. Additional outcomes included biochemical, radiological, and ADT-free survival (bPFS, rPFS, aPFS). Results: As of analysis, 51 patients were enrolled, with a median follow-up of 16 months (95% CI: 16–22). CBR and BR were achieved in 45.1% and 80.4% of patients, respectively. Events affecting bPFS, rPFS, and aPFS occurred in 12, 5, and 6 patients, with median values not yet reached. Toxicity was minimal, with two cases each of acute grade 2 GI and GU events, and one late grade 2 GI event. No grade ≥ 3 toxicities were reported. Conclusion: Early data support SSRT as a safe and a promising option for macroscopic local recurrence, with encouraging response rates and minimal toxicity. Full article

Quality management during construction is critical to ensuring compliance with technical specifications and quality standards. Traditional practices often rely on manual, paper-based documentation, leading to inefficiencies, data fragmentation, and poor traceability. This study presents QualiSite, a novel digital workflow that integrates Building Information Modeling (BIM) and Geographic Information Systems (GIS), aligned with ISO 9001:2015 requirements, to enhance quality management in building projects. The research is framed under the Design Science Research Method (DSRM), guiding the iterative development and validation of the tool. QualiSite was tested in a real-world case study involving the construction of reinforced concrete walls. The results demonstrated functional improvements in inspection traceability, consistency of quality records, and coordination between field data and BIM elements. Using structured digital forms contributed to more consistent data capture and greater efficiency in recording, organizing, and visualizing quality control statuses within the 3D environment. These outcomes enabled transparent inspection processes and clear visualization of quality status across construction elements. The digital workflow also facilitated the identification of nonconformities and streamlined communication between field inspectors and model managers. This approach advances traditional quality management by embedding inspection records into a Cyber-Physical Systems (CPS) framework, contributing to the digital transformation of the Architecture, Engineering, and Construction (AEC) industry and supporting the vision of Smart Industry. Full article

Ensuring secure data transmission in agrotechnical monitoring systems using unmanned aerial vehicles (UAVs) is critical due to increasing cyber threats, particularly with the advent of quantum computing. This study proposes the integration of Quantum Key Distribution (QKD), based on the BB84 protocol, as a secure key management mechanism to enhance data security in UAV-based geographic information systems (GIS) for monitoring agricultural fields and forest fires. QKD is not an encryption algorithm but a secure key distribution protocol that provides information-theoretic security by leveraging the principles of quantum mechanics. Rather than replacing traditional encryption methods, QKD complements them by ensuring the secure generation and distribution of encryption keys, while AES-128 is employed for efficient data encryption. The QKD framework is optimized for real-time operations through adaptive key generation and energy-efficient hardware, alongside Lempel–Ziv–Welch (LZW) compression to improve the bandwidth efficiency. The simulation results demonstrate that the proposed system achieves secure key generation rates up to 50 Mbps with minimal computational overhead, maintaining reliability even under adverse environmental conditions. This hybrid approach significantly improves data resilience against both quantum and classical cyber-attacks, offering a comprehensive and robust solution for secure agrotechnical data transmission. Full article

During historical and contemporary crises in the U.S., Blacks and other marginalized groups experience an increased risk for adverse health, social, and economic outcomes. These outcomes are driven by structural factors, such as poverty, racial residential segregation, and racial discrimination. These factors affect communities’ exposure to risk and ability to recover from disasters, such as pandemics. This study examines whether areas where descendants of enslaved Africans and other Blacks lived in Chicago were vulnerable to excess death during the 1918 influenza pandemic and whether these disparities persisted in the same areas during the COVID-19 pandemic. To examine disparities, demographic data and influenza and pneumonia deaths were digitized from historic weekly paper maps from the week ending on 5 October 1918 to the week ending on 16 November 1918. Census tracts were labeled predominantly Black or white if the population threshold for the group in a census tract was 40% or higher for only one group. Historic neighborhood boundaries were used to aggregate census tract data. The 1918 spatial distribution of influenza and pneumonia mortality rates and cases in Chicago was then compared to the spatial distribution of COVID-19 mortality rates and cases using publicly available datasets. The results show that during the 1918 pandemic, mortality rates in white, immigrant and Black neighborhoods near industrial areas were highest. Pneumonia mortality rates in both Black and immigrant white neighborhoods near industrial areas were approximately double the rates of neighborhoods with predominantly US-born whites. Pneumonia mortality in Black and immigrant white neighborhoods, far away from industrial areas, was also higher (40% more) than in US-born white neighborhoods. Around 100 years later, COVID-19 mortality was high in areas with high concentrations of Blacks based on zip code analysis, even though the proportion of the Black population with COVID was similar or lower than other racial and immigrant groups. These findings highlight the continued cost of racial disparities in American society in the form of avoidable high rates of Black death during pandemics. Full article

The growing fear of virus transmission during the 2019 coronavirus disease (COVID-19) pandemic has called for many scientists to look into the various vehicles of infection, including the potential to travel through aerosols. Few have looked into the issue that gastrointestinal (GI) procedures may produce an abundance of aerosols. The current process of risk management for clinics is to follow a clinic-specific HVAC formula, which is typically calculated once a year and assumes perfect mixing of the air within the space, to determine how many minutes each procedural room refreshes $99\%$ of its air between procedures when doors are closed. This formula is not designed to fit the complex dynamic of small airborne particle transport and deposition that can potentially carry the virus in clinical conditions. It results in reduced procedure throughput as well as an excess of idle time in clinics that process a large number of short procedures such as outpatient GI centers. We present and tested a new cyber-physical system that continuously monitors airborne particle counts in procedural rooms and also at the same time automatically monitors the procedural rooms’ state and flexible endoscope status without interfering with the clinic’s workflow. We use our data gathered from over 1500 GI cases in one clinical suite to understand the correlation between air quality and standard procedure types as well as identify the risks involved with any HVAC system in a clinical suite environment. Thanks to this system, we demonstrate that standard GI procedures generate large quantities of aerosols, which can potentially promote viral airborne transmission among patients and healthcare staff. We provide a solution for the clinic to improve procedure turnover times and throughput, as well as to mitigate the risk of airborne transmission of the virus. Full article

Technologies around the world produce and interact with geospatial data instantaneously, from mobile web applications to satellite imagery that is collected and processed across the globe daily. Big raster data allow researchers to integrate and uncover new knowledge about geospatial patterns and processes. However, we are at a critical moment, as we have an ever-growing number of big data platforms that are being co-opted to support spatial analysis. A gap in the literature is the lack of a robust assessment comparing the efficiency of raster data analysis on big data platforms. This research begins to address this issue by establishing a raster data benchmark that employs freely accessible datasets to provide a comprehensive performance evaluation and comparison of raster operations on big data platforms. The benchmark is critical for evaluating the performance of spatial operations on big data platforms. The benchmarking datasets and operations are applied to three big data platforms. We report computing times and performance bottlenecks so that GIScientists can make informed choices regarding the performance of each platform. Each platform is evaluated for five raster operations: pixel count, reclassification, raster add, focal averaging, and zonal statistics using three raster different datasets. Full article

The convergence of big data and geospatial computing has brought challenges and opportunities to GIScience with regards to geospatial data management, processing, analysis, modeling, and visualization. This special issue highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates the opportunities for using big data for geospatial applications. Crucial to the advancements highlighted here is the integration of computational thinking and spatial thinking and the transformation of abstract ideas and models to concrete data structures and algorithms. This editorial first introduces the background and motivation of this special issue followed by an overview of the ten included articles. Conclusion and future research directions are provided in the last section. Full article

Smart technologies are advancing, and smart cities can be made smarter by increasing the connectivity and interactions of humans, the environment, and smart devices. This paper discusses selective technologies that can potentially contribute to developing an intelligent environment and smarter cities. While the connectivity and efficiency of smart cities is important, the analysis of the impact of construction development and large projects in the city is crucial to decision and policy makers, before the project is approved. This raises the question of assessing the impact of a new infrastructure project on the community prior to its commencement—what type of technologies can potentially be used for creating a virtual representation of the city? How can a smart city be improved by utilizing these technologies? There are a wide range of technologies and applications available but understanding their function, interoperability, and compatibility with the community requires more discussion around system designs and architecture. These questions can be the basis of developing an agenda for further investigations. In particular, the need for advanced tools such as mobile scanners, Geospatial Artificial Intelligence, Unmanned Aerial Vehicles, Geospatial Augmented Reality apps, Light Detection, and Ranging in smart cities is discussed. In line with smart city technology development, this Special Issue includes eight accepted articles covering trending topics, which are briefly reviewed. Full article

One longstanding complication with Earth data discovery involves understanding a user’s search intent from the input query. Most of the geospatial data portals use keyword-based match to search data. Little attention has focused on the spatial and temporal information from a query or understanding the query with ontology. No research in the geospatial domain has investigated user queries in a systematic way. Here, we propose a query understanding framework and apply it to fill the gap by better interpreting a user’s search intent for Earth data search engines and adopting knowledge that was mined from metadata and user query logs. The proposed query understanding tool contains four components: spatial and temporal parsing; concept recognition; Named Entity Recognition (NER); and, semantic query expansion. Spatial and temporal parsing detects the spatial bounding box and temporal range from a query. Concept recognition isolates clauses from free text and provides the search engine phrases instead of a list of words. Name entity recognition detects entities from the query, which inform the search engine to query the entities detected. The semantic query expansion module expands the original query by adding synonyms and acronyms to phrases in the query that was discovered from Web usage data and metadata. The four modules interact to parse a user’s query from multiple perspectives, with the goal of understanding the consumer’s quest intent for data. As a proof-of-concept, the framework is applied to oceanographic data discovery. It is demonstrated that the proposed framework accurately captures a user’s intent. Full article

Blockchain Technology (BCT) is a growing digital technology that in recent years has gained widespread traction in various industries in the public and private sectors. BCT is a decentralized ledger that records every transaction made in the network, known as a ‘block’, the body of which is comprised of encrypted data of the entire transaction history. BCT was introduced as the working mechanism that forms the operational basis of Bitcoin, the first digital cryptocurrency to gain mainstream appeal. The introduction of decentralized data exchange technology in any industry would require strengthened security, enforce accountability, and could potentially accelerate a shift in workflow dynamics from current centralized architectures to a decentralized, cooperative chain of command and affect a cultural and societal change by encouraging trust and transparency. BCT aims at creating a system that would offer a robust self-regulating, self-monitoring, and cyber-resilient data transaction operation, assuring the facilitation and protection of a truly efficient data exchange system. In the state of Florida, climate change and unpredicted weather disasters have put pressure on state and local decision-makers to adapt quick and efficient post-disaster recovery systems. Part of the recovery efforts is the reconstruction of buildings and infrastructure. The introduction of new technologies in the Architecture, Engineering, and Construction (AEC) industry can contribute to addressing recovery and rebuilding after the event of a natural disaster. With parallel technological advancement in geospatial data and Geographic Information System (GIS), as well as worsening climatic conditions, concerns can be suitably addressed by employing an integrated system of both Building Information Modeling (BIM) and BCT. While several potential applications of BIM must provide solutions to disaster-related issues, few have seen practical applications in recent years that indicate the potential benefits of such implementations. The feasibility of BIM-based applications still rests on the reliability of connectivity and cyber-security, indicating a strong use case for using BCT in conjunction with BIM for post-disaster recovery. This research depicts a survey of BCT and its applications in the Architecture, Engineering, and Construction (AEC) industries and examines the potential incorporation within the BIM process to address post-disaster rebuilding problems. Moreover, the study investigates the potential application of BCT in improving the framework for automating the building permitting process using Smart Contract (SC) technologies and Hyperledger Fabric (HLF), as well as discussing future research areas. The study proposes a new conceptualized framework resulting from the integration of BCT and BIM processes to improve the efficiency of building permit processes in post-disaster events. Full article

Understanding human mobility patterns is of great importance for urban planning, traffic management, and even marketing campaign. However, the capability of capturing detailed human movements with fine-grained spatial and temporal granularity is still limited. In this study, we extracted high-resolution mobility data from a collection of over 1.3 billion geo-located Twitter messages. Regarding the concerns of infringement on individual privacy, such as the mobile phone call records with restricted access, the dataset is collected from publicly accessible Twitter data streams. In this paper, we employed a visual-analytics approach to studying multi-scale spatiotemporal Twitter user mobility patterns in the contiguous United States during the year 2014. Our approach included a scalable visual-analytics framework to deliver efficiency and scalability in filtering large volume of geo-located tweets, modeling and extracting Twitter user movements, generating space-time user trajectories, and summarizing multi-scale spatiotemporal user mobility patterns. We performed a set of statistical analysis to understand Twitter user mobility patterns across multi-level spatial scales and temporal ranges. In particular, Twitter user mobility patterns measured by the displacements and radius of gyrations of individuals revealed multi-scale or multi-modal Twitter user mobility patterns. By further studying such mobility patterns in different temporal ranges, we identified both consistency and seasonal fluctuations regarding the distance decay effects in the corresponding mobility patterns. At the same time, our approach provides a geo-visualization unit with an interactive 3D virtual globe web mapping interface for exploratory geo-visual analytics of the multi-level spatiotemporal Twitter user movements. Full article

This paper describes an efficient, decentralized algorithm to monitor qualitative spatial events in a dynamic scalar field. The events of interest involve changes to the critical points (i.e., peak, pits and passes) and edges of the surface network derived from the field. Four fundamental types of event (appearance, disappearance, movement and switch) are defined. Our algorithm is designed to rely purely on qualitative information about the neighborhoods of nodes in the sensor network and does not require information about nodes’ coordinate positions. Experimental investigations confirm that our algorithm is efficient, with O(n) overall communication complexity (where n is the number of nodes in the sensor network), an even load balance and low operational latency. The accuracy of event detection is comparable to established centralized algorithms for the identification of critical points of a surface network. Our algorithm is relevant to a broad range of environmental monitoring applications of sensor networks. Full article

This article reviews the range of delivery platforms that have been developed for the PySAL open source Python library for spatial analysis. This includes traditional desktop software (with a graphical user interface, command line or embedded in a computational notebook), open spatial analytics middleware, and web, cloud and distributed open geospatial analytics for decision support. A common thread throughout the discussion is the emphasis on openness, interoperability, and provenance management in a scientific workflow. The code base of the PySAL library provides the common computing framework underlying all delivery mechanisms. Full article