Geolocation Data Research Articles

IoT-Enabled Fire Detection and Alert System Leveraging HSV Thresholding

Fire detection plays an important role in minimizing damage caused by forest fires and enhancing response times. In this proposed study, a real-time fire detection system using a USB camera and Raspberry Pi, has been developed utilizing the concepts of IoT and image processing. The system continuously monitors the environment for the presence of fire by analysing video frames captured by the camera. The HSV colour space model is utilized to detect fire-like colours based on predefined thresholds, ensuring accurate identification of fire regions. Upon detection, the system triggers an alarm, retrieves geolocation data, and sends real-time alerts to a designated webpage, providing the fire’s exact location and a notification for immediate action. This approach eliminates the need for manual intervention, making it suitable for deployment in high-risk areas. The system’s modular design allows for integration with additional technologies such as automated fire extinguishing systems or GSM-based notifications, expanding its potential applications in real-world scenarios.

An automated spam detection and location-based monitoring system

Spam is often considered as the most troublesome aspect in digital era with security and privacy concern. It is essential to develop effective solutions for spam issues. This project Automated spam detection and location-based monitoring system, provides a new detection system of spam attack in calls, messages and emails. It also provides location-based monitoring of these attacks. It relies on machine learning algorithms trained with updated datasets to accurately classify calls, messages and emails into spam. Through various sophisticated natural language processing techniques, it detects message and email spam content and patterns in call logs for identifying known spam numbers. It provides an interface through which user can enter a phone number or email address to detect spam. As soon as it detects a spam, the details are displayed with timestamp, and an alert is sent to user through email or SMS. The system also integrates location-based tracking to determine the geographic source of spam details. Leveraging geolocation data gathered from calls and account information, this feature aims for fraud prevention and situational awareness. Map visualization keep the insights actionable and transparent. The spam and location together form a single solution for real-time data processing / spam detection /location tracking that can be both seen as an application on its own or integrated into other platforms, making a very powerful weapon in fighting digital fraud while enhancing secured communication. This project provides practical applications for all users to facilitate a safer communication environment with greater accountability.

Abstract 4139115: Genomic, Phenomic, and Geographic Relationships with Leukocyte Telomere Length in the U.S. Yields New Insights for Neoplasms and Cardiometabolic Disease

Background: Leukocyte telomere length (LTL) is an age-related marker strongly associated with neoplasms and cardiometabolic disease. LTL is also intimately linked with clonal hematopoiesis of indeterminate potential (CHIP), a recently described risk factor for cardiovascular disease (CVD). However, population-scale investigations of LTL in diverse backgrounds are limited, and geolocational variance has not been studied. Method: LTL and CHIP were derived using blood-derived whole genome sequencing data from 244,819 diverse U.S. residents in the NIH All of Us Research Program (AoU). We performed association analyses of LTL with CHIP and other available health-related traits, Phecode, as well as geolocation data by ZIP code. Genome-wide association study (GWAS) of LTL in AoU and meta-analysis with UK Biobank were performed. All association tests were adjusted for age, sex, sequencing site, and the first 10 genetic principal components with multiple test corrections. Mediation analysis was adjusted for additional risk factors for CVD. Result: Among 244,819 AoU participants, 146,717 (59.9%) were female with mean (standard deviation) age 51.8 (±16) years. Estimated LTL correlated strongly with age, sex, and genetic ancestry in AoU. Socioeconomic status, lifestyle variables, anthropometry, blood biomarkers (e.g., lipids and glucose), and vital signs were associated with LTL in AoU. Phecode analysis showed LTL is associated with neoplasms and age-related conditions, including CVDs. LTL mediated some of the observed increased coronary artery disease risk among individuals with hematologic malignancies ( P < 2 × 10 -16 ). Significantly longer LTL clustered in the West Coast and Central Midwest ( P = 0.001 and 0.033, respectively), while significantly shorter LTL clustered in the Southeast ( P = 0.001) (Figure). GWAS meta-analyses identified 197 loci, of which 36 (18%) were novel, including ancestry-specific loci. Rare variant aggregation test found 9 novel associated genes. Conclusions: LTL is a marker of biological age that is associated and interacts with neoplasms, CVDs, and its various risk factors, with notable geographic variability within the U.S. Germline genetic analyses of LTL yield new insights into drivers of LTL.

The lasting impact of historical residential security maps on experienced segregation

AbstractWe study the impact of 1930s Residential Security maps on experienced segregation as measured by geolocation data. We compare adjacent neighborhoods with different creditworthiness grades. Using a sample of neighborhood boundaries, which, based on estimated propensity scores, were likely drawn for idiosyncratic reasons, residents of neighborhoods on the lower graded side of the boundary are more likely to visit other historically lower graded destination neighborhoods and neighborhoods that currently tend to have lower household income and lower educational attainment. Disparities in visits are not driven by work commutes, very local visits, or differences in income.

Real-time Vehicle Monitoring and Route Prediction using Geolocation through LSTM

Vehicle travel, such as by bus, automobile, etc., is a common way to use public transportation since it is convenient, economical, easily accessible, and eco-friendly. However, there are moments when its challenging to keep an eye on everyone while traveling, avoid traffic bottlenecks, and provide enough service, which makes the passengers bored. In this paper, we offer a real-time geolocation Android application that gathers real-time geolocation data (longitude and latitude), date, time, and speed, and integrates it with Google Firebase to assist predictive analytics for the operational efficiency of a vehicle firm. After being exported from the database as a JSON file, the data must be transformed into a CSV file. The company will be able to anticipate a vehicles future location on a predetermined route based on date, time, and speed by using this database to train an LSTM (Long short-term memory) model, a sort of sequential neural network. Determining the vehicles expected arrival time, allocating passengers, and predicting traffic conditions will therefore be useful in preventing congestion. Utilizing this technology allows a car company to improve scheduling, boost monitoring, and provide better overall service.

Temporal and geogrpahic trends in female representation within European cardiovascular imaging among 13,436 individuals associated with EACVI

Abstract Introduction Women are under-represented in cardiology trial enrolment, specialty training, faculty, scientific authorship and board membership in cardiology journals and societies. An EACVI Task Force on Women in Cardiovascular Imaging aims to promote women’s representation and leadership, but data is lacking in this area. Purpose To characterise the representation of women in cardiovascular imaging within Europe and examine temporal and geographic trends. Methods We scraped publicly available name data from over 100 online sources including EACVI taskforces, committees, research project members, consensus and position paper authors, board membership and past presidents alongside members of affiliated cardiovascular imaging national societies and working groups. We also included names from EACVI or national accreditation records for cardiac imaging. We recorded data on country location and year of affiliation. We defined gender in a binary manner as either male or female. The previously validated artificial intelligence (AI) system Namsor assigned gender based on name morphology and geolocation data. We pre-set a confidence of gender-name matching of 95%. Results The AI was able to assign gender at 95% confidence to 12,643/13,436 (94.1%) names. The overall number of names assigned female gender was 5903/12643 (46.7%), rejecting the null hypothesis that gender was equally distributed (i.e. 50% female) [p<0.0001]. Among the 9712 individuals with year of affiliation we observed female representation to increase over time: 1989-2000 4/18 (22.2%), 2001-2004 107/287 (37.8%) 2005-2009 77/170 (45.3%), 2010-2014 109/203 (53.7%), 2015-2019 1063/2022 (51.3%) and 2020-2024 3352/3064 (52.2%) (Cochran-Armitage trend test 24.196 [p<0.0001]) (Figure 1). Female representation was lower in countries outside of Europe, compared to within Europe; 740/2654 (27.9%) vs. 5163/9989 (51.7%) [p<0.0001]. The lowest rate of female representation was in Saudia Arabia (15.1%), the highest in Lithuania (83.3%). Figure 2 displays a choropleth of gender representation (%) between European countries with n>20. Conclusion(s) There are significant limitations in this form of analysis, including that we have arbritarily defined gender in a binary format, thereby excluding individuals who do not identify with such a social construct. It is also likely that the excluded names based on the 95% confidence of the AI software disproportionately represents individuals from ethnic minorities. However, our data provides a basic understanding of the representation of women in European cardiovascular imaging over time and between countries. We observed a trend of increasing female participation but with substantial variation between countries both inside and outside of Europe. This data may be useful to refine and direct efforts to improve female representation in areas in which it is needed most.

Travel to malaria-endemic areas: using digital geo-location to assess potential exposure risks and health behaviours.

Travellers frequently visit popular destinations like Brazil, India, Peru, Thailand, and Tanzania, each presenting varying malaria risks. The extent to which travellers enter high-risk malaria-endemic areas in destinations with heterogeneous malaria risk remains unclear. We used geo-location via smartphone application to (i) describe where travellers go within countries with heterogeneous malaria risk (Brazil, India, Peru, Thailand), and (ii) compare mosquito bite prevention behaviours between these destinations and Tanzania, considered entirely high-risk for malaria. This analysis is a sub-study of the TOURIST2 cohort, which prospectively recruited 1000 travellers (≥ 18years, travelling ≤ 4weeks) from Swiss travel clinics (Zurich and Basel) between 09/2017-04/2019. We included 734 travellers to Brazil, India, Peru, Thailand, and Tanzania who provided geo-location data. Daily health and geo-location data were collected using a smartphone application. Malaria risk was categorised using 2022 malaria maps from the Swiss Expert Committee for Travel Medicine. Of the 734 travellers, 525 travelled to Brazil, India, Peru, and Thailand, and 225 to Tanzania. In Brazil, India, Peru, and Thailand, only 2% (n = 13) visited high-risk malaria areas. In Peru, 4% (n = 4) visited a high-risk area; in Brazil, 3% (n = 6); in Thailand, 2% when crossing the border into Myanmar (n = 3); and in India, 0%. Travellers to high-risk areas were more often male (62%), slightly older (median age 42.0), and planned longer trips (median 23.0days) than other travellers. No participants were diagnosed with malaria. Travellers to Brazil, India, Peru and Thailand used mosquito bite prevention measures less frequently than travellers to Tanzania. Those in Tanzania had higher, but still suboptimal, use of insect spray (65% of travel-days). Travellers to Brazil, India, Peru, and Thailand rarely visited high-risk malaria areas, and their adherence to mosquito bite prevention measures was generally low. In Tanzania, adherence was higher but still suboptimal.

Exploring the risks of fragmentation in health care markets – An analysis of inpatient care in Georgia

Private providers play an important role in health systems in low-and middle-income countries. In many such contexts, markets are characterized by a high number of relatively small private facilities. The potential risks from highly concentrated healthcare markets are well-researched, and feature in the “Theories of Harm” investigated by competition regulators. However, there is limited evidence on markets that exhibit substantial harms as a result of very low concentration. This paper explores the risks associated with such market fragmentation, drawing on the example of Georgia, which has a largely privatized provider network.We used a mixed-method study design to analyze the inpatient market in Georgia. Market structure was described using administrative data on bed capacity and discharge numbers and geo-location data on travel time between facilities. The implications of the market structure were explored through in-depth interviews (n = 35) with policymakers, healthcare managers, and local experts and an anonymous online survey of similar groups (n = 97).Georgia's inpatient sector is characterized by a high number of small hospitals in terms of bed numbers and inpatient volumes, mitigated to a limited degree by the presence of provider networks. Travel time to the 3rd nearest competitor was extremely short, ranging from 3 to 5 min in big cities to 10 min in small towns and 33 min in remote locations. The fragmented nature of the market, together with inadequate regulatory and purchasing mechanisms, was argued to exacerbate challenges in the availability and competence of clinical staff, while the financial challenges caused by intense competition encouraged wasteful marketing, harmful cost-cutting measures, and demand inducement.We present “Theories of Harm” from market fragmentation, and argue that an effective policy response requires market-shaping activities using regulatory, financing, and purchasing mechanisms to encourage appropriate levels of market consolidation and so enhance quality, efficiency, and effective governance.

Advancing AI-Driven Linguistic Analysis: Developing and Annotating Comprehensive Arabic Dialect Corpora for Gulf Countries and Saudi Arabia

This study harnesses the linguistic diversity of Arabic dialects to create two expansive corpora from X (formerly Twitter). The Gulf Arabic Corpus (GAC-6) includes around 1.7 million tweets from six Gulf countries—Saudi Arabia, UAE, Qatar, Oman, Kuwait, and Bahrain—capturing a wide range of linguistic variations. The Saudi Dialect Corpus (SDC-5) comprises 790,000 tweets, offering in-depth insights into five major regional dialects of Saudi Arabia: Hijazi, Najdi, Southern, Northern, and Eastern, reflecting the complex linguistic landscape of the region. Both corpora are thoroughly annotated with dialect-specific seed words and geolocation data, achieving high levels of accuracy, as indicated by Cohen’s Kappa scores of 0.78 for GAC-6 and 0.90 for SDC-5. The annotation process leverages AI-driven techniques, including machine learning algorithms for automated dialect recognition and feature extraction, to enhance the granularity and precision of the data. These resources significantly contribute to the field of Arabic dialectology and facilitate the development of AI algorithms for linguistic data analysis, enhancing AI system design and efficiency. The data provided by this research are crucial for advancing AI methodologies, supporting diverse applications in the realm of next-generation AI technologies.

Increasing Street Credibility: Cartography-Aware Forensic Analysis of GNSS Trace Validity

Geolocation data recorded by consumer electronics is usually considered very helpful in criminal investigations: Every few steps, every few seconds, the location of a suspect, victim, witness, or others can be pinpointed as it was automatically recorded in the background. Compared to the commonly used cell tower location data, device-local data from a global navigation satellite system (GNSS) has far higher precision (both spatial and temporal), but suffers from a lack of trust, because data provenance is under potential control of the user. In this article, we propose two approaches of validating and analyzing such data with high confidence. The first approach formulates and checks internal consistency criteria of GNSS data, while the second approach also takes external data sources about the surrounding environment in the form of OpenStreetMap data into account. In both approaches, we formalize the concept of a data anomaly and argue that an absence of anomalies implies more trustworthy data and thus higher evidential value. This way, the vast information contained in high-density location data may actually lead to more detailed insights instead of only increasing data noise in investigations.

Quantifying the effect of striking with picketing on grocery store foot traffic

Unionized workers often use striking and picketing to raise attention to their grievances, dissuade customers from patronizing a business, and pressure employers in negotiations. Despite its wide use and recognition in popular culture, the effects of picketing and striking on retail business are not well understood. Adjacent literature has used cell phone tracking and other digital geo-tagging techniques to measure the effects of factory closures, COVID-19 restrictions, and stimulus payments on store patronage and economic activity. This article provides a case study using mobile geolocation data to quantify the loss of store foot traffic due to striking with picketing by analyzing the 2022 King Soopers strike in Colorado, USA. Using the historic foot traffic data of the past two years for 118 King Soopers locations, 78 of which went on strike, two SARIMA models were trained, and their predicted foot traffic values were compared to the actual values during the strike period. This technique indicates an average 47% decrease in foot traffic for striking stores and a 14% decrease in foot traffic for nonstriking locations.

Central America’s agro-ecological suitability for cultivating coca, Erythroxylum spp

Abstract We assess how much of Central America is likely to be agriculturally suitable for cultivating coca (Erythroxylum spp), the main ingredient in cocaine. Since 2017, organized criminal groups (not smallholders) have been establishing coca plantations in Central America for cocaine production. This has broken South America’s long monopoly on coca leaf production for the global cocaine trade and raised concerns about future expansion in the isthmus. Yet it is not clear how much of Central America has suitable biophysical characteristics for a crop domesticated in, and long associated with the Andean region. We combine geo-located data from coca cultivation locations in Colombia with reported coca sites in Central America to model the soil, climate, and topography of Central American landscapes that might be suitable for coca production under standard management practices. We find that 47% of northern Central America (Honduras, Guatemala, and Belize) has biophysical characteristics that appear highly suitable for coca-growing, while most of southern Central America does not. Biophysical factors, then, are unlikely to constrain coca’s spread in northern Central America. Whether or not the crop is more widely planted will depend on complex and multi-scalar social, economic, and political factors. Among them is whether Central American countries and their allies will continue to prioritize militarized approaches to the drug trade through coca eradication and drug interdiction, which are likely to induce further expansion, not contain it. Novel approaches to the drug trade will be required to avert this outcome.

The Construction of Data Usability

ABSTRACT Technical communication aids in helping people envision the usability of technology. A similar need exists for constructing the usability of data sets, but the rhetorical work is less well documented. This article reports on how writers attribute meaning and usability to a geolocation data set and its derivative objects. This rhetorical work connects the data to existing and potential social realities, which provide a framework for thinking of that data set’s usability.

Towards development of a roadway flood severity index

Flooding is among the costliest and deadliest weather disasters. Moreover, different types of flooding have significant impacts on the transportation network and infrastructure including flash, riverine, urban, coastal, and storm surge. The variety of flooding scenarios makes it challenging to quantify the impacts of flooding on transportation across spatial scales; however, such metrics would be beneficial both prior to and after the event. Pre-flood metrics can promote enhanced impact-based decision-support guidance and hazard communication, while post-flood metrics may include larger regional disruptions located away from the most inundated areas and their associated secondary societal impacts. This study developed a retrospective Roadway Flood-Severity Index (RFSI) from 1982 to 2020 capable of integrating geo-located hydrometeorological data and transportation mobility information across localized and multi-state, sub-national regions to (1) categorize larger-scale, flood-related transportation disruptions, (2) understand the origins of those disruptions, and (3) identify severity risk levels of individual road segments and broader regions of transportation disruption during flood events. The fundamental question is, as flooding events unfold, can past hydrometeorological inundation information be coupled with transportation system network and mobility data to identify the most vulnerable roadway segments and regions? The overall mobility impacts of flooding on transportation were highly variable and relatively uncommon throughout the study period. Given this variability in other mobility data (e.g., vehicle speeds), hydrometeorological parameters were used exclusively as model inputs and crowdsourced Waze flood reports were used as the target response variable. A logistic regression based RFSI was found to best align with the dataset providing a “no flood” or “flood” classification. Eventually, this retrospective analysis will be extended to provide predictive capability as well. The RFSI is intended to provide transportation agencies with a quantitative metric to classify, categorize, and communicate the potential impacts of flood events throughout the transportation network.

From Optimism to Concern: Unveiling Sentiments and Perceptions Surrounding ChatGPT on Twitter

Artificial intelligence (AI) technologies, as a product of processes aimed at imitating human intelligence through computers and software, affect our daily lives in various aspects, including cultural, technological, and economic. The advent of ChatGPT, developed by OpenAI, signifies a pivotal AI advancement in human language comprehension and generation, heralding profound implications across societal, economic, and cultural dimensions. However, a notable gap exists in scholarly literature concerning the examination of perceptions and discussions surrounding ChatGPT. This study aims to address this gap by analyzing Twitter conversations, comprising 1.1 million tweets containing “ChatGPT” or “#ChatGPT” between December 1 2022 and June 1 2023, with geolocation data. Employing a text as data approach encompassing text, sentiment, and semantic network analyses, sentiment polarity and lexical patterns were explained, while semantic network analysis revealed central expressions and prominent themes in Twitter discussions. The study highlights the social effects of AI technologies from the perspective of Twitter users and reveals the sentimental tendency and themes in tweets with geographical and economic dimensions. The findings reflect the prevalence of positive sentiment and hype toward ChatGPT while there are also concerns regarding privacy, cybersecurity, and misuse of AI tools. More importantly, content generation, technical aspects, business applications, educational use, and competitors are among the main themes of ChatGPT-related discussions on Twitter.

Enhanced Cohort Methods for HIV Research and Epidemiology (ENCORE): Protocol for a Nationwide Hybrid Cohort for Transgender Women in the United States.

In the United States, transgender women are disproportionately impacted by HIV and prioritized in the national strategy to end the epidemic. Individual, interpersonal, and structural vulnerabilities underlie HIV acquisition among transgender women and fuel syndemic conditions, yet no nationwide cohort monitors their HIV and other health outcomes. Our objective is to develop a nationwide cohort to estimate HIV incidence, identify risk factors, and investigate syndemic conditions co-occurring with HIV vulnerability or acquisition among US transgender women. The study is informed by the Syndemics Framework and the Social Ecological Model, positing that stigma-related conditions are synergistically driven by shared multilevel vulnerabilities. To address logistical and cost challenges while minimizing technology barriers and research distrust, we aim to establish a novel, hybrid community hub-supported digital cohort (N=3000). The digital cohort is the backbone of the study and is enhanced by hubs strategically located across the United States for increased engagement and in-person support. Study participants are English or Spanish speakers, are aged ≥18 years, identify as transgender women or along the transfeminine spectrum, reside in 1 of the 50 states or Puerto Rico, and do not have HIV (laboratory confirmed). Participants are followed for 24 months, with semiannual assessments. These include a questionnaire and laboratory-based HIV testing using self-collected specimens. Using residential zip codes, person-level data will be merged with contextual geolocated data, including population health measures and economic, housing, and other social and structural factors. Analyses will (1) evaluate the contribution of hub support to the digital cohort using descriptive statistics; (2) estimate and characterize syndemic patterns among transgender women using latent class analysis; (3) examine the role of contextual factors in driving syndemics and HIV prevention over time using multilevel regression models; (4) estimate HIV incidence in transgender women and examine the effect of syndemics and contextual factors on HIV incidence using Poisson regression models; and (5) develop dynamic, compartmental models of multilevel combination HIV prevention interventions among transgender women to simulate their impact on HIV incidence through 2030. Enrollment launched on March 15, 2023, with data collection phases occurring in spring and fall. As of February 24, 2024, a total of 3084 individuals were screened, and 996 (32.3%) met the inclusion criteria and enrolled into the cohort: 2.3% (23/996) enrolled at a hub, and 53.6% (534/996) enrolled through a community hub-supported strategy. Recruitment through purely digital methods contributed 61.5% (1895/3084) of those screened and 42.7% (425/996) of those enrolled in the cohort. Study findings will inform the development of evidence-based interventions to reduce HIV acquisition and syndemic conditions among US transgender women and advance efforts to end the US HIV epidemic. Methodological findings will also have critical implications for the design of future innovative approaches to HIV research. DERR1-10.2196/59846.

Comparative analysis of leukemia and risk estimation in working age population between provinces of Ecuador.

Leukemia is associated with exposure to radiation, benzene derivatives, and pesticides. Previous research has documented an increase in work-related leukemia in the Latin American Andean region. To date, there are only few studies in Ecuador on the impact of oil exploitation on adjacent indigenous communities. Our study aims to show the impact of leukemia on the working-age population. For the calculation of morbidity and mortality rates, we used hospital discharge and death records from the National Institute of Statistics of Ecuador. These data were collected and adjusted to the corresponding province's population for further analysis. Large differences were observed between provinces in adjusted rates of leukemia mortality and morbidity in the working-age population. The variations in altitude among different areas in Ecuador give the provinces a distinct geographic identity. Likewise, the provinces with the highest morbidity and mortality rankings, such as Azuay, Loja, Imbabura, and Tungurahua, have an average altitude above 2000 meters. As a result, there are variations in the average temperature, exposure to solar and cosmic radiation, and mining and farming methods. The observed differences warrant the future collection of geolocation data for affected individuals. This could help to better understand how leukemia cases have demogrpahic hotspots in the country, identify possible risk factors associated with the disease in each region, and design more effective prevention and control strategies.

Surveillance of global, travel-related illness using a novel app: a multivariable, cross-sectional study

IntroductionCurrent traveller health surveillance is ‘top-down’. Mobile-based surveillance could capture infection symptoms in real time. We aimed to evaluate the spectrum of illness in travellers using a mobile app-based system.MethodsThis...

Generative artificial intelligence of things systems, multisensory immersive extended reality technologies, and algorithmic big data simulation and modelling tools in digital twin industrial metaverse

Research background: Multi-modal synthetic data fusion and analysis, simulation and modelling technologies, and virtual environmental and location sensors shape the industrial metaverse. Visual digital twins, smart manufacturing and sensory data mining techniques, 3D digital twin simulation modelling and predictive maintenance tools, big data and mobile location analytics, and cloud-connected and spatial computing devices further immersive virtual spaces, decentralized 3D digital worlds, synthetic reality spaces, and the industrial metaverse. Purpose of the article: We aim to show that big data computing and extended cognitive systems, 3D computer vision-based production and cognitive neuro-engineering technologies, and synthetic data interoperability improve artificial intelligence-based digital twin industrial metaverse and hyper-immersive simulated environments. Geolocation data mining and tracking tools, image processing computational and robot motion algorithms, and digital twin and virtual immersive technologies shape the economic and business management of extended reality environments and the industrial metaverse. Methods: Quality tools: AMSTAR, BIBOT, CASP, Catchii, R package and Shiny app citationchaser, DistillerSR, JBI SUMARI, Litstream, Nested Knowledge, Rayyan, and Systematic Review Accelerator. Search period: April 2024. Search terms: “digital twin industrial metaverse” + “artificial Intelligence of Things systems”, “multisensory immersive extended reality technologies”, and “algorithmic big data simulation and modelling tools”. Selected sources: 114 out of 336. Published research inspected: 2022–2024. PRISMA was the reporting quality assessment tool. Dimensions and VOSviewer were deployed as data visualization tools. Findings & value added: Simulated augmented reality and multi-sensory tracking technologies, explainable artificial intelligence-based decision support and cloud-based robotic cooperation systems, and ambient intelligence and deep learning-based predictive analytics modelling tools are instrumental in augmented reality environments and in the industrial metaverse. The economic and business management of the industrial metaverse necessitates connected enterprise production and big data computing systems, simulation and modelling technologies, and virtual reality-embedded digital twins.

Validation of cheap axial passive sampler and procedure suitable for atmospheric NO2 community-based monitoring

Nitrogen dioxide (NO2) is an air pollutant highly impacting on human health, and its measurement is crucial for air quality assessment. Use of passive samplers for long-term large-scale monitoring is a reasonably reliable and economic alternative to more sophisticated and expensive equipment employed in active air sampling by environmental control authorities. In recent years the Citizen Science approach, based on low-cost devices, is spreading more and more in environmental control. Passive samplers available on the market (like the consolidated “Palmes” tubes) are often used in community-based monitoring campaigns. We describe validation of a new cheap axial diffusion tube for NO2 monitoring, used in combination with a new user-friendly App for smartphone that represents an innovation to speed up recording of geo-localization and exposition period data. Affordability and availability of materials, simple construction protocol and easy App procedure, allow possible self-production by school students and non-expert users, making the proposed tube a potential tool to realize extended Citizen Science monitoring campaigns. Accuracy within 25% and precision within 20%, evidenced in validation, show comparability of the tube performance with Palmes-type tubes and agreement with the official monitoring station results. Two small-scale trial monitoring campaigns, involving high school students, were performed to test the efficacy of the proposed “tube-App” system in combining educational impact and community value of air quality monitoring.